EXAMINING THE CHEMICAL FERTILITY OF

SOILS IN TAVEUNI, FIJI: THE PROBLEM OF

SOIL HEALTH AND FOOD SECURITY

SOFINA NISHA

EXAMINING THE CHEMICAL FERTILITY OF SOILS IN

TAVEUNI, FIJI: THE PROBLEM OF SOIL HEALTH AND

FOOD SECURITY

by

Sofina Nisha

A thesis submitted

In fulfillment of the requirements for the degree of

Masters of Science

Copyright © 2014 by Sofina Nisha

School of Biological and Chemical Sciences

Faculty of Science, Technology and Environment

The University of the South Pacific

January 2014

i

ACKNOWLEDGMENTS

This thesis project would not have been possible without the continuous love and

support of many people. But above all, I would like to thank the Almighty God for

having his grace upon me throughout my studies. I thank Him for giving me the strength

and knowledge to start and complete my studies.

My sincere gratitude goes to my principal supervisor Associate Professor Surendra

Prasad for his abundant and invaluable assistance, support, guidance and continuous

follow up throughout the entire process of my research. My heartiest gratitude also goes

to my co-supervisor Dr. Jagdish Bhati, Senior Lecturer in Agricultural Economics, for

the success of this research which would not have been possible without his knowledge

and assistance.

I would like to convey my appreciation to the Australian Centre for International

Agricultural Research (ACIAR) for awarding Graduate Assistantship and sponsoring my

MSc. study at the University of the South Pacific. I am grateful to the entire ACIAR soil

health team for providing me assistance during my study visits to Taveuni, Fiji

especially Dr. Siosiua Halavatau and Mr. Tony Gunua of the Secretariat of the Pacific

Community (SPC), Mr. Geoff Dean of Tei Tei Taveuni group, Mr. Malakai, Mr. Ami

Sharma and Mr. Waisea of Koronivia Research Station. I am also thankful to the

farmers in Taveuni who provided their support by participating in the questionnaire

survey.

Special thanks goes to the technical staff of the School of Biological and Chemical

Sciences especially to our chief technician Mr. Steven Sutcliffe and senior technician

Mr. Shelvin Prasad for providing necessary glassware, chemicals and assistance with

instruments.

My deepest gratitude goes to my family for their continuous support and encouragement

throughout my studies and for believing in me. I would also take this opportunity to

ii

thank Mr. Sheraaz Buksh for being the guarantor of my scholarship. I thank him for

being patient with me during challenging times and for his continuous reassurance.

Last but not the least, I would to like thank all my friends and colleagues who had kept

me motivated throughout my studies and provided support in any way.

iii

ABSTRACT

Studies have shown that the soil health in Taveuni, Fiji is deteriorating over time due to

continuous plantation and indiscriminate and imbalanced fertiliser use. The declining

trend in soil fertility on the island of Taveuni, Fiji has threatened taro producers. There

are visible impacts of deteriorating soil health such as stagnating/plateauing crop yields

and slow agricultural growth. This situation has resulted in high rates of deforestation on

the island as farmers are forced to search for new fertile soil.

Despite the crucial role of agriculture in Fiji’s economy and the increasingly serious

issue of soil degradation in Taveuni, Fiji, a perusal of literature reveals that no chemical

analysis of the soils in the taro growing areas of Taveuni has been carried out. This

project was therefore undertaken to study the problem of soil health affecting food

security by examining the chemical fertility of soils in Taveuni, Fiji.

Four farms were selected as the study sites. Soil samples were collected four times

during the cultivation period of taro: before planting of mucuna beans, before planting

of taro, three months after planting of taro and at the time of harvest. The soil samples

were analyzed for macronutrients (Ca, Mg, K, P and NO3--N), micronutrients (Cu, Fe,

Mn and Zn), soil organic matter (SOM) and soil pH. Structured questionnaire survey

was also conducted on 50 farmers from areas near the sampling sites to get a feedback

on the trend of their crop production over the years, fertiliser usage and some of the

constraints the farmers faced in following proper soil fertility management practices.

The adequacy of applied methodology for soil nutrient analysis was confirmed by

measures of accuracy, reproducibility and recovery. Calibration was performed by the

method of standard additions and quality control. Inter-laboratory comparison of results

was carried out to confirm the accuracy of the method used. The detection limit for the

essential nutrients in the soil matrix was also determined as a quality control measure.

Measurements of each sample were carried out in three replicates. Mean, standard

iv

deviation and relative standard deviation were calculated for interpretation and

comparison of data.

At the time of harvest, Matei site showed low levels of SOM, NO3

--N, available P,

exchangeable Ca and extractable Zn. The site however, had ideal levels of exchangeable

Mg and K, as well as extractable soil Cu, Fe and Mn. During the same period of time,

Mua site showed low levels of SOM, NO3--N, available P and extractable Zn.

Extractable soil Cu, Fe and Mn were at ideal levels while exchangeable Ca, Mg and K

were at critical levels. For Vione site, SOM and extractable Cu, Fe and Mn were at

sufficient levels, while NO3--N, available P and extractable Zn were at low levels. The

site also showed Ca, Mg and K deficiency. Similarly, Delaivuna site had low levels of

exchangeable Ca, Mg and K as well as SOM, NO3--N and extractable Mn and Zn. There

was however sufficient levels of available P and extractable Cu and Fe.

Statistical analysis of the soil test data confirmed the declining trend of the soil fertility

on the island of Taveuni, Fiji. Crop output obtained through questionnaire survey also

revealed the declining trend of taro production over the years. There was significant

reduction (ρ < 0.05) in the soil macronutrients, micronutrients and SOM in all the four

study sites and showed below critical levels, critical to medium levels of these nutrients

in the soil at the end of the cultivation period of taro.

v

ABBREVIATIONS

[H+] – Hydrogen ion concentration

AAS – Atomic absorption spectrophotometry

ACIAR – Australian Centre for International Agricultural Research

Ag+ – Silver ion

Ag2SO4 – Silver sulfate

AgCl – Silver chloride

Al – Aluminum

Al3+ – Aluminum ion

NH4OAc – Ammonium acetate

C – Carbon

Ca – Calcium

CaCO3 – Calcium carbonate

Ca2+ – Calcium ion

CaCl2 – Calcium chloride

CEC – Cation Exchange Capacity

Cl- – Chloride ion

CO3-3 – Carbonate ion

CRC – Cadmium reductor coil

Cu – Copper

vi

Cu2+ – Copper(II) ion

DAC – Department of Agriculture and Cooperation

DTPA – Dethylenetriaminepentaacetic acid

Eds. – Editors

EDTA – Ethylenediaminetetraacetic acid,

FAO – Food and Agriculture Organization

FDA – Fiji Department of Agriculture

Fe – Iron

Fe3+ – Iron(III) ion

Fe2+ – Iron(II) ion

FIA – Flow Injection Analysis

gkg-1 – Grams per kilogram

h – Hours

ha – Hectare

H+ – Hydrogen ion

Ha – Alternative hypothesis

Ho – Null hypothesis

H2BO3- – Borate ion

H2PO4- – dihydrogen phosphate ion

H2SO4 – Sulfuric acid

H3BO3 – Boric acid

vii

H3O+ – Hydronium ion

H3PO4 – Phosphoric acid

HCl – Hydrogen chloride

HCO3- – Hydrogen carbonate ion

HPO42- – Hydrogen phosphate ion

ICP – Inductively Coupled Plasma

ICP-OES – Inductively Coupled Plasma Optical Emission Spectrometry

K – Potassium

K+ – Potassium ion

K2Cr2O7 – Potassium dichromate

K2O – Potassium oxide

K2SO4 – Potassium sulfate

KARI – Kenya Agricultural Research Institute

KCl – Potassium chloride

KH2PO4 – Potassium dihydrogen phosphate

Kgha-1 – Kilograms per hectare

kj – Kilojoules

KMnO4 – Potassium permanganate

KNO3 – Potassium nitrate

M – Molar

Mg – Magnesium

viii

Mg2+ – Magnesium ion

mg – Milligrams

mgkg-1 – Milligrams per kilogram

mgL-1 – Milligrams per liter

min – Minutes

mL – Milliliter

Mn – Manganese

Mn2+ – Manganese(II) ion

MoO42- – Molybdate ion

N – Nitrogen

n – Sample size

Na2S2O3 – Sodium thiosulfate

NaCN – Sodium cyanide

NaF – Sodium fluoride

NaHCO3 – Sodium hydrogen carbonate

NaHCO3 – Sodium bicarbonate

NFV – Nitrogen Fertiliser Value

NGO – Non-government Organization

NH4+ – Ammonium ion

NH4F – Ammonium fluoride

NH4OAc – Ammonium acetate

ix

nm – Nanometer

NO2- – Nitrite ion

NO3- – Nitrate ion

NO3--N – Nitrate nitrogen

OH- – Hydroxide ion

OM – Organic Matter

P – Phosphorous

KNO3 – Potassium nitrate

P2O5 – Phosphorous pentoxide

ppm – Parts per million

rpm – Revolution per minute

RSD – Relative Standard Deviation

SCEP – Fiji Soil and Crop Evaluation Project

SFA – Segmented Flow Analysis

SO42- – Sulfate ion

SOM – Soil Organic Matter

t C/ha – Tonnnes carbon per hectare

TDF – Total dietary fibre

TEA – Triethanolamine

TOC – Total Organic Carbon

UNCCD – United Nations Convention to Combat Desertification

x

UV – Ultra-violet

yr – Year

Zn – Zinc

Zn2+ – Zinc(II) ion

μgg-1 – Micrograms per gram

% – Percentage

xi

TABLE OF CONTENTS

PAGES

ACKNOWLEDGMENTS .................................................................................................. i

ABSTRACT……………………………………………………………………………..iii

ABBREVIATIONS ........................................................................................................... v

TABLE OF CONTENTS ................................................................................................. xi

LIST OF FIGURES ........................................................................................................ xvi

LIST OF TABLES ....................................................................................................... xviii

LIST OF PLATES ......................................................................................................... xxii

CHAPTER 1 INTRODUCTION ................................................................................... 1

Taro Farming in Taveuni, Fiji ......................................................................... 1

Soil Essential Elements ................................................................................... 4

The Linkage of Soil Health and Food Security ............................................... 5

Soil Health Problems in Taveuni, Fiji ............................................................. 7

Soil Testing ..................................................................................................... 8

Historical Background of Soil Testing Service and Classification of Taveuni

Soil .................................................................................................................. 9

Objectives and Scope of this Study ............................................................... 10

CHAPTER 2 LITERATRE REVIEW ......................................................................... 12

Soil Chemical Fertility .................................................................................. 12

Soil Chemical Properties ............................................................................... 15

2.2.1 Soil pH..................................................................................................... 15

2.2.2 Soil Organic Matter (SOM)..................................................................... 16

2.2.3 Nitrate Nitrogen (NO3--N) ....................................................................... 17

xii

2.2.4 Macronutrients – Calcium (Ca), Magnesium (Mg) and Potassium (K) .. 17

2.2.5 Extractable Phosphorus (P) ..................................................................... 18

2.2.6 Micronutrients – Iron (Fe), Manganese (Mn), Zinc (Zn) and Copper (Cu)

…………………………………………………………………………19

Nutrient Analysis........................................................................................... 20

2.3.1 Analytical Procedures for Soil Organic Matter (SOM) .......................... 20

2.3.2 Analytical Procedures for Soil Nitrate Nitrogen (NO3--N) ..................... 21

Analytical Procedures for Soil Phosphorus (P) ....................................... 23

Analytical Procedures for Potassium (K), Calcium (Ca) and Magnesium

(Mg) ......................................................................................................... 24

Analytical Procedures for Micronutrients: Zinc (Zn), Iron (Fe),

Manganese (Mn) and Copper (Cu).......................................................... 26

Soil Fertility Measures .................................................................................. 27

2.4.1 Common Organic Nutrient Sources ........................................................ 27

2.4.1.1 Compost ............................................................................................ 28

2.4.1.2 Animal Manure and Green Manure .................................................. 29

2.4.1.3 Legumes, Cover Crops and Crop Residues ...................................... 30

2.4.1.4 Mulching ........................................................................................... 31

2.4.2 Chemical (Mineral/Inorganic) Nutrient Sources ..................................... 32

2.4.3 Other Soil Fertility Measures .................................................................. 33

2.4.3.1 Crop Rotations and Fallowing .......................................................... 33

2.4.3.2 Agroforestry ..................................................................................... 33

Nutrient Requirements of Taro ..................................................................... 34

Food Value of Taro ....................................................................................... 38

xiii

CHAPTER 3 METHODOLOGY ................................................................................ 40

Research Location ......................................................................................... 40

Field Methods ................................................................................................ 41

3.2.1 Experimental Treatments ........................................................................ 41

3.2.2 Experiment Randomization & Layout .................................................... 41

3.2.3 Actual Site Plans ..................................................................................... 42

3.2.4 Soil Sampling and Storage ...................................................................... 44

Laboratory Methods ...................................................................................... 46

3.3.1 Instrumentation........................................................................................ 46

3.3.2 Standards and Reagents ........................................................................... 49

3.3.3 Chemical Characterization ...................................................................... 49

3.3.3.1 Determination of Soil pH ................................................................. 49

3.3.3.2 Determination of Soil Organic Matter (SOM) ................................. 49

3.3.3.3 Determination of Soil Exchangeable Base (Ca, Mg, K)................... 52

3.3.3.4 Determination of Soil Micronutrient (Cu, Fe, Mn, Zn) .................... 53

3.3.3.5 Determination of Soil Available Phosphorous (P) ........................... 55

3.3.3.6 Determination of Soil Nitrate-Nitrogen (NO3--N) ............................ 56

Questionnaire Survey .................................................................................... 59

Statistical Analysis ........................................................................................ 59

CHAPTER 4 RESULTS AND DISCUSSION ........................................................... 60

4.1 Quality Control .............................................................................................. 60

4.1.1 Limit of Detection ................................................................................... 65

4.2 Comparison of Different Treatments ............................................................ 65

4.3 Changes in Soil pH and Soil Organic Matter (SOM) over the Cropping Cycle

of Taro ........................................................................................................... 66

xiv

4.3.1 Soil pH..................................................................................................... 66

4.3.2 Soil Organic Matter (SOM)..................................................................... 68

4.4 Changes in Macronutrient Contents in the Soil over the Cropping Cycle of

Taro ............................................................................................................... 71

4.4.1 Soil Nitrate Nitrogen (NO3--N) ............................................................... 71

4.4.2 Soil Available Phosphorous (P) .............................................................. 74

4.4.3 Soil Exchangeable Bases (K, Ca and Mg) .............................................. 76

4.4.3.1 Soil Exchangeable Potassium (K) .................................................... 76

4.4.3.2 Soil Exchangeable Calcium (Ca) ...................................................... 79

4.4.3.3 Soil Exchangeable Magnesium (Mg) ............................................... 81

4.5 Changes in Micronutrient Content in the Soil over the Cropping Cycle of

Taro ............................................................................................................... 83

4.5.1 Soil Extractable Copper (Cu) .................................................................. 83

4.5.2 Soil Extractable Iron (Fe) ........................................................................ 86

4.5.3 Soil Extractable Manganese (Mn) ........................................................... 88

4.5.4 Soil Extractable Zinc (Zn) ....................................................................... 90

4.6 Results from the Farm Survey (Questionnaire Analysis).............................. 93

4.6.1 Crop Output and Chemical Fertiliser Usage for the Years 2010-2012 ... 93

4.6.2 Routine Soil Analysis and Fertiliser Application .................................... 96

4.6.3 Proper Soil Fertility Management Constraints ........................................ 96

4.6.4 Organic Sources of Nutrients .................................................................. 96

4.6.5 Chemical Nutrient Sources...................................................................... 98

4.6.6 Other Soil Fertility Measures ................................................................ 100

xv

CHAPTER 5 CONCLUSION AND RECOMMENDATION .................................. 103

5.1 Conclusions ................................................................................................. 103

5.2 Recommendations ....................................................................................... 105

REFERENCES .......................................................................................................... 107

APPENDICES ........................................................................................................... 127

Appendix I: Standard Calibration Curves .............................................................. 127

Appendix II: Mann-Whitney test (ρ levels) for Comparison between Treatments 129

Appendix III: Sample Questionnaire ..................................................................... 132

Appendix IV: Crop Output Comparison for 2010-2012 using Wilcoxon Signed

Ranks Test ................................................................................................... 136

Appendix V: NPK Usage Comparison for 2010-2012 using Wilcoxon Signed Ranks

Test ............................................................................................................. 136

Appendix VI: Publication ...................................................................................... 137

xvi

LIST OF FIGURES PAGES

Figure 1.1: Taro export for Fiji for the years 2008-2012. ................................................ 3

Figure 1.2: Taro export earnings for Fiji for the years 2008-2012. ................................. 3

Figure 3.1: Distribution of the four farms across Taveuni, Fiji Islands. ........................ 40

Figure 3.2: Matei site plan. ............................................................................................ 42

Figure 3.3: Delaivuna site plan. ..................................................................................... 42

Figure 3.4: Mua site plan. .............................................................................................. 43

Figure 3.5: Vione site plan. ............................................................................................ 43

Figure 3.6: Process flow diagram of soil sampling and storage. ................................... 45

Figure 3.7: Colour change during titration for SOM analysis. ...................................... 51

Figure 3.8: FIA peaks profiles for the calibration standards. ......................................... 58

Figure 3.9: Calibration curve of the measured peak areas (volts.sec.) vs. NO3--N

concentrations (μM). ....................................................................................................... 58

Figure 4.1: Variation in pH in the soil over the different periods in the cropping cycle of

taro.. ................................................................................................................................. 67

Figure 4.2: Variation in SOM (%) over the different periods in the cropping cycle of

taro.. ................................................................................................................................. 69

Figure 4.3: Variation in soil NO3--N (mgkg-1) over the different periods in the cropping

cycle of taro……………………………………………………………………………..72

Figure 4.4: Variation in soil P (mgkg-1) over the different periods in the cropping cycle

of taro.. ............................................................................................................................ 75

xvii

Figure 4.5: Variation in soil K (mgkg-1) over the different periods in the cropping cycle

of taro.. ............................................................................................................................ 77

Figure 4.6: Variation in soil Ca (mgkg-1) over the different periods in the cropping cycle

of taro.. ............................................................................................................................ 81

Figure 4.7: Variation in soil Mg (mgkg-1) over the different periods in the cropping

cycle of taro. .................................................................................................................... 82

Figure 4.8: Variation in soil Cu (mgkg-1) over the different periods in the cropping

cycle of taro.. ................................................................................................................... 84

Figure 4.9: Variation in soil Fe (mgkg-1) over the different periods in the cropping cycle

of taro. 1. ......................................................................................................................... 87

Figure 4.10: Variation in soil Mn (mgkg-1) over the different periods in the cropping

cycle of taro. .................................................................................................................... 89

Figure 4.11: Variation in soil Zn (mgkg-1) over the different periods in the cropping

cycle of taro.. ................................................................................................................... 91

Figure 4.12: Trends in taro production for farms with clay soil (Questionnaire survey,

n= 50). ............................................................................................................................. 94

Figure 4.13: Trends in taro production for farms with clay loam soil (Questionnaire

survey, n= 50). ................................................................................................................. 95

Figure 4.14: Trends in taro production for farms with loam soil (Questionnaire survey,

n= 50). ............................................................................................................................. 95

Figure 4.15: Trends in taro production for farms with sandy loam soil (Questionnaire

survey, n= 50). ................................................................................................................. 95

xviii

LIST OF TABLES

Table 1.1: Essential elements and their form in which they are available to plants ……5

Table 2.1: Biomass production and dry matter content of fertilised and unfertilised taro

......................................................................................................................................... 35

Table 2.2: Nutrient content in unfertilised and fertilised taro roots and corms.............. 36

Table 2.3: Nutrient uptake (kgha-1) form the soil by roots, corms and leaves of

unfertilized and fertilized taro. ........................................................................................ 37

Table 2.4: Levels of nutrients present in taro tubers at harvest. ..................................... 38

Table 2.5: Food values of taro (100 g) as per Pacific Food composition table. ............. 39

Table 3.1: Randomized experimental design of the replication block with the different

treatments. ....................................................................................................................... 41

Table 3.2: Standard atomic absorption conditions for the determination of micro and

macronutrients ................................................................................................................. 53

Table 4.1: Results of % difference between duplicate extractions in the determination of

the different nutrients. ..................................................................................................... 61

Table 4.2: Results of spiked recoveries in the determination of the different nutrients. 64

Table 4.3: Method Detection Limit (MDL) for different nutrients. ............................... 65

Table 4.4: Soil pH levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 67

Table 4.5: Mann-Whitney test (ρ levels) for the changes in soil pH levels during

different periods at various experimental sites. ............................................................... 68

Table 4.6: Soil organic matter (SOM) levels at different stages of the crop cycle at

various experimental sites. .............................................................................................. 69

xix

Table 4.7: Mann-Whitney test (ρ levels) for the changes in SOM levels during the

different periods at various sites...................................................................................... 71

Table 4.8: Soil NO3--N levels at different stages of the crop cycle at various

experimental sites. ........................................................................................................... 72

Table 4.9: Mann-Whitney test (ρ levels) for changes in soil NO3--N levels during the

different periods at various experimental sites. ............................................................... 73

Table 4.10: Soil P levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 74

Table 4.11: Mann-Whitney test (ρ levels) for changes in soil P levels during the

different periods at various experimental sites. ............................................................... 76

Table 4.12: Soil K levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 77

Table 4.13: Mann-Whitney test (ρ levels) for changes in soil K levels during the

different periods at various experimental sites. ............................................................... 78

Table 4.14: Soil Ca levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 79

Table 4.15: Mann-Whitney test (ρ levels) for changes in soil Ca levels during the

different periods at various experimental sites. ............................................................... 80

Table 4.16: Soil Mg levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 82

Table 4.17: Mann-Whitney test (ρ levels) for changes in soil Mg levels during the

different periods at various experimental sites. ............................................................... 83

Table 4.18: Soil Cu levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 84

xx

Table 4.19: Mann-Whitney test (ρ levels) for changes in soil Cu levels during the

different periods at various experimental sites. ............................................................... 85

Table 4.20: Soil Fe levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 86

Table 4.21: Mann-Whitney test (ρ levels) for changes in soil Fe levels during the

different periods at various experimental sites. ............................................................... 88

Table 4.22: Soil Mn levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 89

Table 4.23: Mann-Whitney test (ρ levels) for changes in soil Mn levels during the

different periods at various experimental sites. ............................................................... 90

Table 4.24: Soil Zn levels at different stages of the crop cycle at various experimental

sites. ................................................................................................................................. 91

Table 4.25: Mann-Whitney test (ρ levels) for changes in soil Zn levels during the

different periods at various experimental sites. ............................................................... 92

Table 4.26: Trends of the average taro yields (kg/acre) in different soil types on farms

from 2010-2012. .............................................................................................................. 94

Table 4.27: Number of farmers using the different organic fertilisers on their farms

(Data presented as frequency of total survey population). .............................................. 97

Table 4.28: Average quantity of different organic fertilisers used on farms. ................ 98

Table 4.29: Reasons for insufficient use of organic sources of nutrients. ..................... 98

Table 4.30: Number of farmers using the different chemical fertilisers on their farms

(Data presented as frequency of total survey population). .............................................. 99

Table 4.31: Average quantity of different chemical fertilisers used on farms. .............. 99

xxi

Table 4.32: Number of farmers using the NPK on their farms (Data presented as

frequency of total survey population). ............................................................................ 99

Table 4.33: Average quantity of NPK used on farms. ................................................. 100

Table 4.34: Number of farmers using the different fertility measures on their farms

(Data presented as frequency of total survey population). ............................................ 101

Table 4.35: Number of farmers using fallowing technique on their farms (Data

presented as frequency of total survey population). ...................................................... 101

xxii

LIST OF PLATES

Plate 3.1: Lambda 25 (PerkinElmer) UV-Visible spectrophotometer. .......................... 46

Plate 3.2: QuickCkem FIA+ 8000 series (Lachat Instrument) Flow Injection Analyzer.

......................................................................................................................................... 47

Plate 3.3: Atomic Absorption Spectrometer 3110 (PerkinElmer). ................................. 47

Plate 3.4: Hanna Instrument pH Meter. .......................................................................... 48

Plate 3.5: Mechanical Shaker. ........................................................................................ 48

Plate 3.6: Colour development of P standards and soil extracts. ................................... 56

1

CHAPTER 1

INTRODUCTION

This chapter represents an overview of the of taro farming on Taveuni island in Fiji. It

highlights on soil nutrients and the essential elements that are needed for plant growth.

The chapter then looks into the linkage between soil health and food security and

discusses the issue of soil health in Taveuni. Furthermore, the chapter describes the

importance of periodically examining the soil health and the need for balanced and

sufficient fertiliser use on taro farms. It also looks into the historical background of soil

testing in Fiji. Finally, the chapter outlines the objective and the scope of this research.

Taro Farming in Taveuni, Fiji

Agriculture is the mainstay of Fiji’s economy as it employs around 65% of total

population (FDA, 2010). Taro is a staple food for many people in developing countries

especially those in the Pacific (Goenaga and Chardon, 1995; Tumuhimbise et al., 2009).

It is known to be an ancient and culturally central crop which is particularly important

for food security and sustainable livelihoods in the Pacific, South East Asia, West Africa

and the Caribbean. It is ranked at the 14th position worldwide among the staple crops

(Guarino et al., 2003; Vilikesa et al., 2002).

Taro is considered as “true food” in Fiji where meals and social functions are considered

incomplete without it (Kuhlken and Crosby, 1999). It also has traditional implications in

the chiefly system of marriages, funerals and religious gatherings. The corm of taro

plant is a good source of calcium while the taro leaves of certain varieties are

particularly valued as a green leafy vegetable as it has a significant source of vitamins,

especially folic acid (Guarino et al., 2003; Osorio, et al. 2003).

Taro is not only an important traditional food crop; it is also a substantial export

commodity in a number of countries, including Fiji. Fiji’s largest agricultural export

after sugar is taro (Duncan, 2010; McGregor, 2011). The main export markets for taro

are New Zealand, Australia and the West Coast of the United States of America due to

2

the high population density of Polynesian Pacific Islanders who are the biggest

consumers. Figure 1.1 and Figure 1.2 give a glimpse of year wise taro export (tonnes)

and export earnings ($) in Fiji respectively. Taro sold in local markets is always in great

demand especially during festive seasons, irrespective of its high prices (Guarino et al.,

2003). Therefore, it is a continuous source of income to the farmers in Fiji.

Taveuni, which is third largest island in Fiji, is well-known for producing the largest

quantity of grade one taro and supplies approximately 70-80 % of taro production in the

country bringing in millions of dollars in export revenue (FDA, 2012; Mala, 2009). The

common taro variety in Taveuni is the Tausala ni Samoa, which is pink in colour

(McGregor, 2011).

Taveuni is a large shield volcano complex (437 km2), located at the northern end of the

Koro Sea in a unique position astride the 180o Meridian with its axial ridge dotted with

over 150 distinctive scoria cones, craters and fissure zones. The island has been famous

for its fairly fertile dark brown volcanic soil (Cronin, 1999). Since many of the soils of

Taveuni have been derived from young volcanic ash, they have properties that lead to

their classification as Andepts (Morrison et al., 1986).

There are approximately 3700 farmers in Taveuni, including farmers on the island of

Qamea. Of this an estimated 2000 farmers lie in the semi-subsistence category (5000-

15000 plants) while about 750 taro farmers fall under fully commercial category

(>15,000 plants). About 17000 people are either directly or indirectly dependent on taro

as a source of income (McGregor, 2011). While Taveuni has grown as the core for

commercial taro production, its taro exports are deteriorating due to factors such as

decline in production, increase in production costs and exporting issues to Australia and

New Zealand (McGregor, 2011).

3

Figure 1.1: Taro export for Fiji for the years 2008-2012. Source: Data provided by Economic, Planning and Statistics, Ministry of Primary Industries, Fiji.

Figure 1.2: Taro export earnings for Fiji for the years 2008-2012.

Source: Data provided by Economic, Planning and Statistics, Ministry of Primary Industries, Fiji.

02,000,0004,000,0006,000,0008,000,000

10,000,00012,000,00014,000,000

2008 2009 2010 2011 2012

Ton

nes

Year

Taro Export in Fiji,2008 - 2012

Export (tonnes)

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

2008 2009 2010 2011 2012

Earn

ings

($)

Taro Export Earnings in Fiji,2008 - 2012

Export Earnings ($)

4

Soil Essential Elements

The greatest natural resource of any country is soil. Soil is a thin layer of earth’s crust

where plants grow. In the soil, interaction between plant and other organism take place

and nutrients, oxygen and water for plants are reserved (Brady, 1984; DAC, 2011). Soil

is the medium from which majority of the food is produced either directly or indirectly.

Approximately 45 % of the soil comprises of mineral matter and 1-5% of the soil is

made up of OM. The remaining 50 % consists of air and water. The mineral composition

of the soil and the OM define the chemical properties of soil (Ahn, 1993; Brady, 1984).

Hence, soil quality defines quantity and quality (nutritional value and safety) of the

foods grown (Rosegrant and Cline, 2003; Yong-Guan, 2009). Since food, which plays

an important life-sustaining role, is generated from the soil, it is extremely essential to

maintain the capacity of the soil to sustain productivity, environmental quality and the

human health (Bennett et al., 1999).

While plants may absorb a large number of elements, all of these elements are not

essential for the growth of crops. The elements are only absorbed because they happen

to be in the soil solution. Those taking active part in the growth and developmental

processes are called the essential ones, some of which are required in large amounts and

some in traces (Ahn, 1993; Archer, 1985; DAC, 2011; Thiagalingam, 2000; Havlin et

al., 2005).

There are at least 13 elements that are found to be essential for proper plant growth and

development (Archer, 1985). These essential elements are classified into two groups as

macronutrients and micronutrients. Macronutrients (e.g. Ca, Mg, K, N, P and S) are

those that are needed in large amounts and micronutrients (e.g. B, Cl, Cu, Fe, Mn, Mo

and Zn) are required in relatively small quantities. However, all are equally essential for

plant growth. The essential elements and their forms which are absorbed by the plants

are shown in Table 1.1. Deficiencies of these essential elements in the soil will limit the

growth of plant as these elements serve as raw materials for growth and development of

plants and formation of fruits and seeds (Ahn, 1993; Archer, 1985).

5

Table 1.1: Essential elements and their form in which they are available to plants

(Archer, 1985).

Essential Elements for plant growth Form in which it is absorbed by plants

Nitrogen (N) NH4+, NO3

-

Phosphorous (P) HPO42-, H2PO4

-

Potassium (K) K+

Calcium (Ca) Ca2+

Sulphur (S) SO42-

Magnesium (Mg) Mg2+

Iron (Fe) Fe2+, Chelates

Manganese (Mn) Mn2+

Boron (B) H3BO3 and H2BO3-

Copper (Cu) Cu2+

Zinc (Zn) Zn2+

Molybdenum (Mo) MoO42-

Chlorine (Cl) Cl- Although most of the essential elements are found in large quantities in the mineral

soils, these may not be available to the plants, as they are tied up in mineral and

chemical compounds which the roots cannot absorb and transport to growing plants.

Thus, it becomes essential to assess the plant available amounts of nutrients in the soil

and meeting deficiency by application of manures and fertilisers to such soils for

optimum crop production (DAC, 2011; Thiagalingam, 2000; Havlin et al., 2005).

The Linkage of Soil Health and Food Security

The performance of the agricultural sector plays a vital role in the economic

development of a country and the input of this sector is dependent on how the natural

resources of the country are managed. Regrettably, the quantity and quality of natural

resources in many developing countries are declining (Mengstie, 2009). Soil is usually

the primary natural resource for many rural households and so the management of its

fertility is essential to ensure the lasting prosperity of such households. However,

6

increased agricultural intensity has resulted in dramatic and durable modifications to this

resource (Colin et al., 2005). The intensive agriculture system which aims at increasing

yield for increasing population is showing negative effects on soil fertility management

strategies. Besides, there has been greater demand to increase the production of food in

the recent decades. This had resulted in a decrease in the amount of land vacant for the

production of food (Bankó, 2008; Rezig et al., 2013a).

The deprivation of soil fertility is a major environment and economic issue in

developing countries like Fiji. Poor soil management practices and intensive cultivation

of land causing soil degradation has become a global concern as it is a major

contributing factor to soil erosion, soil acidification and soil organic matter (SOM) and

soil nutrient losses. On the whole, it decreases the agricultural production capacity, food

security, and livelihoods. The problem of degrading soil is aggravated by the need to

reduce poverty and unsustainable farming practices (Sanchez et al., 1997).

Food security has been a poverty mitigation technique in less developed countries and

the long-term sustainability of the world at large. Studies such as the 2020 Vision and

the World Food Summit have confirmed that food security is one of the main global

concerns (Sanchez, et al., 1997). The United Nations Food and Agriculture Organization

(FAO), defines food security as “when all people, at all times, have physical and

economic access to sufficient, safe and nutritious food to meet their dietary needs and

food preferences for an active and healthy life” (Yong-Guan, 2009). Hence, food

security should be a multi-faceted goal, and requires holistic approaches towards

harmonization among land use, environmental sustainability, economic benefits and

human health (Yong-Guan, 2009). Concerted efforts of scientists to improve agricultural

and food productivity, technological and nutrition are imperative to facilitate appropriate

strategies for defeating hunger and malnutrition around the globe i.e. food security (Wu

et al., 2014). While food security can be improved through technological advancements,

soil remains the primary subject of the issue.

7

Since agriculture provides a major source of employment, income, export earnings and

ensures food security in many developing countries, it is necessary to improve

agricultural sustainability through the most favourable management practices of soil

fertility. Therefore, protecting the soil’s physical, chemical and biological integrity is

crucial in safeguarding global food security (Stocking, 2003; Rosegrant and Cline, 2003;

Yong-Guan, 2009).

Soil Health Problems in Taveuni, Fiji

Evidence suggests that land degradation problem in Fiji is not improving in spite of the

numerous environmental issues awareness. In Fiji, the primary form of land degradation

is soil degradation (Asafu-Adjaye, 2008) and loss of soil chemical fertility due to

nutrient depletion is an increasingly serious problem in Fiji (Prasad, 2006). Fiji’s soils

including those of Taveuni have been reported to be deficient of many essential

nutrients. Reports by Duncan, suggest that the problem of declining soil fertility is

threatening taro producers in Fiji. Hence, soil testing and fertiliser trials were suggested

to determine whether the nutrition problem was due to lack of macronutrients or

micronutrients (Duncan, 2010).

Declining soil fertility in Taveuni, Fiji has posed threat to food and income security for

the country due to poor crop yields and low profit margins (Panapasa, 2012). The study

undertaken by the Australian Centre for International Agricultural Research (ACIAR)

has confirmed the declining trend in soil fertility on the island of Taveuni (Smith, 2011).

The study showed that in some of the taro production areas on Taveuni, there are visible

impacts of deteriorating soil health in Fiji. Crop yields are stagnating/plateauing. Thus,

agricultural growth has slowed down, causing unsustainable taro production (Smith,

2011). This situation has challenged the farmers in trying to reach their planned

harvesting goal. The situation has also led to constant search for new fertile soil hence,

the need to clear extensive areas of land. As a result, Taveuni has encountered the

highest rate of deforestation in Fiji in recent years (Panapasa, 2012). Clearing of forested

lands excessively for farming has always been an important environmental problem.

8

This is surely hazardous not only to the agriculture sector but will also be a major factor

for environmental, social and economic problems for future generations (Prasad, 2006).

Moreover, due to the increase in food demand, the traditional shifting cultivation

technique is also slowly replaced by intensive taro culture in Fiji. Hence, as a result of

this frequent cultivation of land, nutrients are lost and sufficient time is not given for

nutrient replenishment to be achieved. Increase in demand of taro for export has also

caused changes in farmers’ cultivation practices from the traditional shifting cultivation

to the more intensive mono cropping system. Also, there is more usage of herbicides to

clear land than using the technique of “slash and burn” (Panapasa, 2012). Thus intensive

cultivation has been the main cause of soil fertility depletion in Taveuni island of Fiji.

For good quality taro with good yield, taro needs to be grown in rotation and grown on a

land that has been left fallow for quite some time (Robin and Pilgrim, 2003 as cited by

McGregor, 2011). Studies have shown that this is not the case in commercial taro

production areas of Taveuni. Due to land constraints in some parts of Taveuni, taro has

been cultivated on the same piece of land for up to 15 years or at most left a piece of

land fallow for two years. Fertility measures such as green manure, longer fallow period

and organic fertilisers as well as good quality planting materials are now under trial to

improve the production and quality of taro (Robin, 2006; McGregor, 2011).

Soil Testing

Plant tissues are composed of carbon and certain essential nutrients which cause these

nutrients to be removed from the soil at harvest (Yong-Guan, 2009). Therefore, when

land is intensively used with no appropriate replenishment of plant nutrients, the soil

nutrients get exhausted and thus the soil fertility declines. Therefore, to ensure

sustainable soil environment and crop production, nutrients removed must be equal to

nutrient replenished (Jahiruddin and Satter, 2010). Nutrients in the soil should be

replaced with sources of nutrients such as synthetic fertilisers, manures, municipal

wastes, etc. (Havlin et al., 2005; Mbah and Onweremadu, 2009). It is important to note

that if nutrients are supplied below the nutrient needs of the plant, crop yield will be

affected, causing decline in long term productivity of the land. On the other hand, if

9

nutrients are applied in excess of the amount needed, it increases the threat to agronomic

problems such as crop lodging (in the case of N), nutrient imbalances or toxicities and

environmental problems such as nitrate leaching to groundwater, P runoff to surface

water and release of greenhouse gases to the atmosphere (Havlin et al., 2005; Mbah and

Onweremadu, 2009).

A reliable and inexpensive management tool for accurately determining the available

nutrient condition of the soil is the soil nutrient (soil fertility) analysis. In agriculture,

this is a common practice and is fundamental for soil nutrient management, good plant

health and maximum crop productivity. Therefore, regularly analyzing soil nutrients is

important in order to be updated with the current fertility status of the soil and to

identify nutrient deficiencies and toxicities. It is also useful for efficient fertiliser

recommendations (Júnior et al, 2008; Hak-Jin et al., 2009; Praveen, 2009).

Measuring the total nutrient level in the soil is not a useful indicator of whether there are

sufficient levels of nutrients for the growth of the plant. This is because only a certain

level of nutrient from the soil is available to plants (Horneck, et al., 2011; Yost and

Uchida, 2000). Thus, soil tests are done to measure soil nutrients that are likely to

become plant-available.

While SOM is an essential indicator of soil quality and health, SOM fluctuates with soil

type and alters too slowly to reflect recent management. Hence, the best fertility

indicator is crop yields which is also highly based on weather conditions and crop

management practices (Hoffmann and Lepossa, 2013).

Historical Background of Soil Testing Service and Classification of Taveuni Soil

Fiji’s soil pattern shows that 65% of soils develop on steep slopes (>21°), 20% on

rolling and hilly land (4–21°) and 15% on flat land (<4°) (UNCCD National Focal Point,

2007). Since the 1950’s, detailed soil surveys and investigations were carried out on

many parts Fiji including Taveuni, by the New Zealand Soil Bureau. Soil chemical

analysis was also carried out together with production of soil maps at various scales. In

the 1980’s, a project titled “Fiji Soil and Crop Evaluation Project (SCEP)” which was

10

funded by Australian and New Zealand Aids was initiated in Fiji. The main aim of the

project was to develop predictive soil and crop management models for application on

farmers’ fields in order to increase self-sufficiency in food crops as well as export

earnings for Fiji. Field trials were carried out in the project to assess soil fertility,

nutrient and agronomic requirements for nationally important cash and subsistence crops

(Leslie, 2010).

The soil of Taveuni has been classified as humic andosols which show slight nutrient

deficiencies and is acidic and non-saline in nature. Generally, the soils show very low

phosphorous, potassium and nitrogen below 15 cm. The slope microrelief of Taveuni

has been described as rolling to strongly rolling (12-20o) and generally of uneven

microrelief. The land has been found to be non-susceptible to flooding and waterlogs

and well drained (Leslie and Seru, 1998; Leslie, 2012).

To reverse the declining of soil fertility and taro production in Taveuni, “ACIAR Soil

Health Project” is going on in collaboration with the Department of Agriculture and the

NGO Teitei Taveuni to address the issue of declining soil fertility on the island. The

present study was the part of the “ACIAR Soil Health Project” with the objectives

shown in the section 1.7.

Objectives and Scope of this Study

The overall objective of the study was to examine the variation in nutrient levels in the

soil during the cropping season of taro. The specific objectives of the study were to

examine:

1. The gap between the soil nutrient demand and supply on taro farms of Taveuni, Fiji.

2. The amount of fertiliser usage and reasons for imbalanced fertiliser use by farmers.

3. Reasons for insufficient use of organic sources of soil nutrients.

4. Soil health issues affecting food productions which have impact on food security and

provide proper recommendations to it.

11

Soil samples were collected four times during the cropping cycle: before planting of

mucuna beans on the trial sites, before planting of taro, three months after planting and

at the time of harvest. Questionnaires were also distributed to farmers to get feedback on

the trend of their crop production over the years and their fertiliser usage.

The results of soil analysis and questionnaire survey in selected farming areas in

Taveuni were intended to provide the agricultural producer with an estimate of the

amount of fertiliser nutrients needed to supplement those in the soil and to educate

farmers on health concepts and practices to sustainably improve the productivity of

crops.

12

CHAPTER 2

LITERATRE REVIEW

This chapter provides a detailed review of the literature related to the topic of the study.

It aims to familiarize the readers with global issue of declining soil fertility. It highlights

on the various chemical properties and essential nutrients of the soil and the analytical

procedures for analyzing these nutrients. The chapter also focuses on the numerous

sources of soil nutrients and fertility measures that could be utilized for sustainable crop

production. Finally, the chapter gives an overview on the nutrient requirements and food

value of taro.

Soil Chemical Fertility

Colin et al. (2005) defines soil fertility as the total physical, chemical and biological

aspects of soil that contribute to it structure, porosity and availability of soil nutrients.

Ahn (1993) described it as the soils ability to supply nutrients to the plants. These

factors influence crop growth and impose limitations on crop yield. The chemical

fertility of the soil is the ability of a soil to supply all the nutrients essential to the crop

influencing crop growth and yield (Jate, 2012; Tilahun, 2007). It can be improved by

various forms of chemical and organic fertilisers (Tilman et al., 2002).

The cultivation of agricultural land alters these properties of soil. According to Johnson

(1992) and Mann (1986), cultivation usually decreases the OM content of the soil.

Moreover, long-term application of fertilisers can result in accumulation of their

residues in soils (Marrs, 1993). For example, Compton and Boone (2000), Koerner et al.

(1997) and Richter et al. (2000), found increased N content in the soil. However, Kalisz

(1986) found that P content was lowered. Moreover, increased K (Goovaerts et al.,

1990), Ca (Kalisz, 1986) and Mg (Goovaerts et al., 1990) content and higher pH

(Goovaerts et al., 1990; Kalisz, 1986; Koerner et al., 1997) was also reported. Hence,

these results consistently suggest that former agricultural use of soil has a long-term

effect on soil properties (Antti and Hytonen, 2005).

13

According to a research conducted by Bista et al. (2010), it was found that Nepal faced

serious issues of declining soil fertility. They came across several limitations in soil

fertility management such as deforestation, non-agricultural uses of fertile land, land

fragmentation and cultivation in marginalized areas, cultivation on the slopes,

imbalanced use of agrochemicals, declining use of organic manure, etc. The problem

was worsened due to lack of knowledge of farmers on soil fertility management

techniques (Bista et al., 2010). Farmers in Kenya have also faced the problem of

declining soil fertility and productivity of crops. Because of limited land space for

agricultural practice, crops are continuously planted with application of little or no

fertiliser leading to soil fertility issues (Nyambati et al., 2009).

Hartemink and Johnston (1998) have reported that most small-scale farmers in Papua

New Guinea grow taro for only one season due to problems such as depletion of soil

fertility. Hence, the study suggested that to sustain and improve the yield, inorganic

fertiliser is a practicable option as taro responded well to fertilisers. In another study

carried out by Mora et al. (2012), it has been stated that 45 % of the world’s humid

tropics which are located in tropical Latin American have acidic soils and are deficient

in phosphorous (90 %), have aluminum toxicity (73 %), and have low nutrient reserves

(50 %).

Poor management of agro-ecosystems caused by adopting agricultural practices to

improve livestock management, in order to meet the demand for food of growing

population has resulted in changes in soil properties. Damages to soil nutrients and plant

growth have occurred, hence, becoming a barrier to sustainable productivity. This could

eventually lead to irreversible soil degradation. According to Mora, et al. (2012), the

lands in the native rangelands of Mexican humid tropics, are not fertilized or sown with

productive legumes to add nitrogen (N) to the system. As a result of not using N

fertilisation for decades, degradation of soil and grassland fertility has occurred (Mora et

al., 2012).

14

A survey carried out by Adesanwo et al. (2009) reflected that in Southwestern Nigeria,

due to the way soil fertility is managed, sustainable rice production is hindered and

poses a great challenge to farmers. The production of rice has been found to be very

poor due to decline in soil fertility especially due to phosphorous deficiency. However,

due to financial constraints, it is not possible to import or establish more fertiliser plants.

Their research findings have also shown that due to lack of technical knowledge on

fertilisers, farmers tend to use excess of fertilisers which proves to be damaging to the

crops. Overall, results from questionnaire survey showed that the soil fertility techniques

that were practiced by rice farmers in Southwestern Nigeria can neither sustain nor

improve soil fertility (Adesanwo et al., 2009).

Similarly, in Ethiopia, agricultural land is a crucial resource for the country as the

economy is based largely on agriculture for employment. Such dependence on

agricultural land has increased its susceptibility to problems such as land degradation in

the form of soil erosion, nutrient depletion, deforestation, etc. As a result, the farmers in

Ethiopia are unable to increase agricultural production and diminish the problem of

poverty and food insecurity. The Government of Ethiopia has identified land

degradation as a serious environmental and socio-economic concern and has made

numerous involvements to combat the problem. However, the problem has reached such

a point that it has become difficult to reverse it. Ethiopian soil is still losing its fertility at

an alarming rate (Mengstie, 2009).

In smallholder farms of sub-Saharan Africa, the main cause for declining per capita food

production is the soil fertility depletion. In a period of 30 years, an estimated 660 kg N

ha-1, 75 kg P ha-1 and 450 kg K ha-1 has been lost from about 200 million ha of

cultivated land in 37 African countries (Sanchez et al., 1997). In sub-Saharan Africa

where most communities depend on agriculture for their livelihood, the declining soil

fertility is becoming a great threat to food security for those people. The major cause of

this decline in soil fertility has been found to be the inappropriate human activities

which causes nutrient imbalance. These activities include, intensive continuous cropping

without replacing the nutrients back into the soil, deforestation and the clearing of

vegetation on sandy soils (Rezig et al., 2013b).

15

The Abakaliki agro-ecological zone of Southeastern Nigeria has also not been saved

form the depleting soil fertility issue. Due to intensive use of land with no definite

management system, soil productivity has been greatly affected. Hence, trials were

carried out to see effect of organic and chemical fertiliser on the on crop yield (Mbah

and Onweremadu, 2009).

Soil Chemical Properties

2.2.1 Soil pH

Soil pH determination is a simple and informative analytical technique for diagnosing a

nutrient deficiency or toxicity problem. It indicates the acidity of the soil by measuring

the hydronium ion activity in the soil (Carter and Gregorich, 2006). It is used to explain

many aspects of soil chemistry and crop production such as solubility and availability of

nutrients, toxic substances, activity of microbial populations, etc. Availability of

micronutrients is high at low pH compared to high pH where the binding capacity of

micronutrients is high and exists mostly in solid mineral. Conversely, exchangeable

cations are mostly low in concentration at low soil pH as they are more weakly bound to

the soil, hence, can leach out of the surface soil. At extremely acidic or alkaline soil,

plant life tends to decline.

Good soils for crop production are often reasonably acid (Ahn, 1993; Archer, 1985;

Jones and Jacobsen, 2001; Watson and Brown, 2011 and Amacher et al., 2007).

Moreover, soil pH value associated with optimum plant growth, varies with crop species

and can be influenced by soil type (Tilahun, 2007 and Carter and Gregorich, 2006). In

most soils, the soil pH is buffered by several components of the solid phase, including

hydroxyl aluminum monomers and polymers, SOM and undissolved carbonates in soils

(Gelderman and Beegle, 2011).

16

2.2.2 Soil Organic Matter (SOM)

Soil organic matter (SOM) is made up of organic species from living or dead plant and

animal materials in the soil. It is part of many soil processes and is considered to be a

crucial characteristic of soil quality (Ahn, 1993; Archer, 1985; Carter et al., 1997;

Ilumae et al., 2009; Tilahun, 2007). SOM has a constructive influence on crop yield and

soil physical properties (Kismanyoky and Toth, 2013). Since SOM is a reservoir of

metabolic energy, it improves soil structure, buffers soil from changing pH, etc., so it is

a key factor in maintaining long-term soil fertility (Ahn, 1993; Jones and Jacobsen,

2001; Jahiruddin and Satter, 2010; Watson and Brown, 2011). Thus, the determination

of OM is a critical part of any site classification since its presence or absence can

distinctly influence how chemicals will react in the soil or sediment (Mbah and

Onweremadu, 2009; Schumacher, 2002; Simon et al., 2013).

A good soil should have at least 2.5 % OC (Jahiruddin and Satter, 2010) while most

crops grow in soils that contain between 2 to 5 % OC (Sánchez and Richard, 2009).

However, it is accepted that potential C storage can vary with soil type and climate

conditions (Carter et al., 1997). Uncultivated soils have higher SOM compared to those

soils that have been cultivated for years (Jahiruddin and Satter, 2010; Sánchez and

Richard, 2009; Tilahun, 2007). However, reasonably cultivating and using proper plant

production strategy can avoid further loss of OM. Mineral fertilisation and organic

manuring have different impact on SOM. The quantity of OM can be maintained with

mineral fertilisers while with organic manure, it can be increased (Bankó, 2008; Mbah

and Onweremadu, 2009).

According to Ali (2003), legume crops not only provide a bonus yield but also benefit

the succeeding or companion crop. Mandal (2008) reported that the OM and total soil N

concentrations were found to be higher under green manuring treated plots while

Rahman (2003) found that soil aggregation, structure, permeability, fertility and

infiltration rate were improved with the inclusion of legumes in the cropping system.

17

2.2.3 Nitrate Nitrogen (NO3--N)

Nitrogen (N) is found to be the main limiting factor for most field crops (Osorio et al.,

2003). Nitrate (NO3-) is a naturally occurring form of N absorbed by crop plants. It is

formed from fertilisers, decaying plants, manure and other organic residues (Uwah, et

al., 2009). Soil N is also contained in the soil OM whose availability to plants is

dependent on OM breakdown (Kaneko et al., 2010; Walworth, 2011). N can be lost

from the soil through many different pathways i.e. by the action of gravitational water

leaching or by being transformed into gaseous form and diffused into the atmosphere.

Since both nitrate (NO3-) and nitrite (NO2

-) ions have a negative charge, they are able to

move easily with the water in the soil as they are attracted to the positively charged soil

particle surfaces. N used by plants varies from one species to another and it varies with

genotype and environment (Ahn, 1993; Archer, 1985; Asghar et al., 1986).

Available N and N mineralization potential of soils are indicators of soil fertility. It is

however difficult to test available N, as the soil has many pathways to gain or lose this

nutrient. Since NO3--N is the form of N directly available to plants, it is usually

analysed to provide an indication of short term N availability. This form can however,

be quickly lost from the soil, either leached past the rooting zone or lost to the

atmosphere in gaseous forms. Therefore, if the test results are used for making fertiliser

recommendations, soil samples should be collected either shortly before planting time or

during the growing season (Kaneko et al., 2010; Walworth, 2011).

2.2.4 Macronutrients – Calcium (Ca), Magnesium (Mg) and Potassium (K)

Potassium (K) is the third most important essential element (macronutrient) that limits

plant productivity after N and P. Its availability and behaviour in the soil is influenced

by soil cation exchange properties, mineral weathering, soil management practice,

intensity of cultivation and climatic conditions (Ahn, 1993; DAC, 2011; Tilahun, 2007).

K is essential for photosynthetic activity and assists in ionic balance. It also activates

more than 80 enzyme systems involved in the regulation of the major plant growth

reactions (DAC, 2011; Ryan, 2010; Tilahun, 2007). K is not hazardous to the

environment when it leaves the soil. However, loss of K through leaching is found to be

18

serious on soils with low-activity clays than soils with high- activity clays (DAC, 2011

and Tilahun, 2007).

Calcium (Ca) and magnesium (Mg) are macronutrients that are determined quite easily.

They are held in plant available forms on the cation exchange complex of the soil

(Asghar et al., 1986). Soil exchangeable Ca is usually present in high levels in soils with

high pH. Ca is vital for the formation of cell-walls and new tissues, root and leaf

development, activation of some plant enzyme systems as well as in balancing organic

acids in the plant (DAC, 2011; Ryan, 2010). Moreover, it increases the availability of

other nutrients, like P, N and Mo when applied to acidic soils. However, excess Ca in

calcareous soils lowers the uptake of K and Mg (Archer, 1985; DAC, 2011). Mg is

essential for the photosynthesis process, phosphate metabolism, plant respiration and for

the activation of many enzyme systems (Ryan, 2010). Deficiency of Ca and Mg occurs

in soils that are intensively cultivated and contain high exchangeable and extractable Al

and have low pH (Tilahun, 2007).

2.2.5 Extractable Phosphorus (P)

Soil phosphorous (P) is another common plant growth-limiting macronutrient in both the

cultivated and uncultivated tropical soils. It is for sustainable agricultural production

(Ahn, 1993; Tilahun, 2007). It is needed for cell division, development of meristematic

tissues of plants, photosynthesis, respiration, energy transfer and storage and cellular

processes (Amba et al., 2011; Bolland et al, 2003; DAC, 2011; Ryan, 2010).

Total soil P exists in inorganic as well as organic forms within the soil. The content of P

varies from soil to soil depending on the soil parent material, soil texture and how the

soil is managed. According to Howard et al. (2006), agricultural soils contain mostly

inorganic forms of P, mostly 50 to 75 % of total P. However, approximately 25 % of the

P applied to soil is available to the plant roots, while the rest is adsorbed by the soil and

is released for plant usage over time. The soil P tests provide an indication of the level

of soil P that is available to the plant and not the total concentration of P in the soil. The

ability of crops to use P is enhanced when there is a balance of nutrients in the soil,

especially N. N improves P uptake by increasing the growth rate, solubility and

19

availability of P. The uptake of P by plants also increases with the increase in soil

temperature, moisture, aeration, etc. P availability is improved at a pH range of 6-6.5

(Archer, 1985; Howard et al., 2006; Schulte and Kelling, 1996; Watson and Mullen,

2007). However, excess of P in the soil can reduce micronutrient solubility and impair

crop production. In comparison to other major plant nutrients, P is poorly utilised by

crops. Due to the sorption of P by soils and the slow mobilisation of orthophosphate ions

into solution, the effectiveness of fertiliser that is applied to soils is reduced. As a result,

P fertilisers are applied to increase soil test P above a “critical” value in order to

optimize crop production (Pittman et al., 2005; Schulte and Kelling, 1996).

2.2.6 Micronutrients – Iron (Fe), Manganese (Mn), Zinc (Zn) and Copper (Cu)

Micronutrients are elements needed by plants in very small quantities (Ahn, 1993; DAC,

2011). Still these elements are essential to the life of the plant as macronutrients (Asghar

et al., 1986). For estimation of micronutrients in the soil, the plant available form is

critical and not the total content (Archer, 1985; DAC, 2011). These micronutrients exist

in the soil in the following ways:

� In solution

� As exchangeable ions on soil particles

� Complexed with OM

� As constituent of soil minerals (Widdowson and Hart, 1986).

These micronutrients exhibit their deficiency symptoms at very low levels, particularly

in calcareous and sandy soils. They are removed from the soil by the process of

adsorption (Amacher et al., 2007; Ahn, 1993; DAC, 2011).

Cu is vital in the formation of chlorophyll in plants and acts as a catalyst for other plant

reactions. Fe acts as a catalyst in the formation of chlorophyll as well as an oxygen

carrier in the nodules of legumes. It is also needed in the formation of certain respiratory

enzyme systems. Mn on the other hand mainly functions as a part of plant enzyme

systems, activates several metabolic reactions and also aids in chlorophyll synthesis. Zn

assists in the synthesis of plant growth substances and enzyme systems. It is also

20

essential in the promotion of certain metabolic reactions. In addition, Zn is necessary for

the production of chlorophyll and carbohydrates (Ryan, 2010).

These nutrients are present in various forms in the soil (Table 1.1). Zn is present as

divalent cation Zn2+ while Fe is present mostly in sparingly soluble ferric oxide form

and is mainly taken up by plants as Fe2+ ions. However, the availability of Fe is affected

by soil redox potential and pH and its uptake by plants is repressed by phosphate levels

which forms insoluble iron phosphate. Mn which originates primarily from the

decomposition of ferromagnesian rocks and exists in many oxidation states is taken up

by the plants as Mn2+ ions. Mn and phosphate are mutually antagonistic. Moreover, Cu

which exists in soils mainly as Cu2+ is usually adsorbed by the clay minerals or

associated with OM. However, high phosphate fertilization can bring about Cu

deficiency (DAC, 2011).

Nutrient Analysis

2.3.1 Analytical Procedures for Soil Organic Matter (SOM)

Once the extraction phase is completed SOM can be determined using various

techniques such as simple titration, automated titration using potentiometric

determination, calorimetry, gravimetric determination, volumetric measurement, etc.

(Brian, 2002). The methods for OM quantification are based upon the indiscriminate

removal of all OM followed by gravimetric determination of sample weight loss.

The loss-on-ignition method is a common and widely used technique for the

determination of OM by its heat destruction in the soil or sediment (Brian, 2002; Veres,

2002). In this method, the temperature is maintained below 440oC to prevent the

destruction of inorganic carbonates that may be present in the sample. The difference in

weight between the dry sample and the ash gives the amount of OM present in the

sample. It is a quick and cheap method compared to all other methods employed for OM

(Veres, 2002). However, it has also been found that the procedure has many biases. The

ignition procedure not only volatilizes OM but other soil components. This leads to

21

losses of various volatile salts, structural water and inorganic carbon depending on

ignition temperature (Heiri et al., 2001; Hoskins, 2012).

Hydrogen peroxide (H2O2) digestion method is another method used for the OM

determination where the sample is destroyed through oxidation by continuous addition

of concentrated hydrogen peroxide (30% or 50%) to a known weight of soil or sediment

until sample frothing ceases. Temperature of sample is brought to 90oC to speed up the

reaction. The OM is then determined gravimetrically. Limitations of the procedure

include incomplete oxidation of the OM, variation in the extent of oxidation from one

soil or sediment to another and loss of volatile organic compounds if samples are air or

oven-dried prior to digestion (Brian, 2002).

Dichromate oxidation method is another common method for SOM determination. It is

also known as Walkley-Black method. It is a wet combustion technique used for the

estimation of organic carbon. The method relies on the heat of solution of the sulphuric

acid and water for the reaction. In this method, to convert organic carbon (C) value to

OM, a correction factor is used. Since the Walkley-Black procedure is simple, rapid and

requires minimal equipment, it is widely used in spite of the fact that it leads to

incomplete oxidation of organic C (Burt, 2004; Combs and Nathan, 2011; Walkley and

Black, 1934). This technique was also implemented in the present study.

2.3.2 Analytical Procedures for Soil Nitrate Nitrogen (NO3--N)

Nitrate (NO3-) is a measure of the available N in agricultural soils. Since it is water-

soluble, a number of solutions including water have been used as extractants. However,

the most common extractant for NO3- is 2.0 M KCl. Water has its disadvantage as it is of

low ionic strength which can cause dispersion and result in cloudy filtrates. Chloride

(Cl-) extractants can also cause problems if NO3--N is measured by ion chromatography

or ion selective electrode because Cl- interferes in the analysis of NO3--N by these

methods (Carter and Gregorich, 2006; Gary et al., 2009). NO3- in KCl extracts should be

analysed within 24 h of extraction (Carter and Gregorich, 2006).

22

The methods of determination for NO3- range from ion selective electrode to

colorimetric techniques, micro diffusion, steam distillation and continuous flow

analysis. Some laboratories also use test kits to measure soil NO3- in the field.

Automated colorimetric techniques using continuous flow analyzers are preferred for

routine analysis. Segmented flow analysis (SFA) and flow injection analysis (FIA) are

continuous flow systems that are rapid, free from interferences and very sensitive

methods for the determination of soil NO3- (Carter and Gregorich, 2006; Gary et al.,

2009).

While the ion selective electrode method is fast, simple and relatively cheap, the method

is subject to numerous interferences and subtle variations in sample handling, electrode

calibration, reference electrodes, electrode preparation, etc. Moreover, KCl extractants

cannot be used due to Cl- interference in this method and the low sensitivity of the

electrode requires relatively small soil: extractant ratios (e.g. 1:2.5) to measure NO3--N

in many soils (Gary et al., 2009).

SFA/FIA involves an automated or manual spectrophotometric method, where nitrate

(NO3-) is reduced to nitrite (NO2

-) using copperized cadmium reductor coil (CRC).

Reduced NO2- is determined using a modified Griess- Ilosvay method which is based on

the principle that NO2- reacts with aromatic amines (diazotizing agents) in acidic

solutions to give diazo salts. These salts couple with aromatic agents, N-1-

naphthylethylenediamine dihydrochloride to form reddish purple azo compounds or

dyes. A spectrophotometer is then used to determine the colour intensity of this

compound (Carter and Gregorich, 2006; Gelderman and Beegle, 2011; Lopez et al.,

2007). The Griess-Ilosvay procedure is very sensitive, rapid and free from interferences

from OM and soil cations. 40 to 100 samples can be analysed per hour using automated

procedures. However, automated instrumentation is comparatively expensive (Carter

and Gregorich, 2006; Gelderman and Beegle, 2011; Gary et al., 2009; Lopez, et al.,

2007). This procedure was adopted for the present study.

Phenoldisulfonic acid method is another colorimetric method for estimating NO3- in

soils. The drawback of this method is the difficulty in trying to get a clear colourless

23

extract with low contents of organic and inorganic substances which interferes with the

method. Chloride (Cl-) is the main anion which interferes with colour development. If

the Cl- concentration in the soil is more than 15 μgg-1, it needs to be removed to adopt

this method. Cl- can be removed by the use of Ag2SO4 to precipitate Cl- as AgCl. It is

also essential to remove the excess Ag+ before NO3- analysis in the extract because it

also interferes in this method (DAC, 2011).

Analytical Procedures for Soil Phosphorus (P)

The Bray and Kurtz P-1 soil test is a P analysing method that was developed by Roger

Bray and Touby Kurtz of the Illinois Agricultural Experiment Station in 1945 (Bray and

Kurtz, 1945; Kovar and Pierzynski, 2009). Bray and Kurtz P-1 extractant is a dilute

hydrochloric acid and ammonium fluoride solution. This test has been approved by the

United States Department of Agricultural North Central Regional Soil Testing Research

Committee for the acid/neutral soils and is now widely used in the Midwestern and

North Central United States (Bray and Kurtz, 1945; Kovar and Pierzynski, 2009;

Watson and Mullen, 2007). The extractant works for neutral and acid soils of pH < 7.0,

but not for alkaline soils of pH > 7.0. The fluoride in the Bray and Kurtz extractant

enhances P release from aluminum phosphates for acidic soils (Watson and Mullen,

2007; Kovar and Pierzynski, 2009). The technique was used for the present study as all

the soil samples collected had pH < 7.

The Olsen P or sodium bicarbonate is another soil P test method that was developed by

Olsen et al. (1954) to predict crop response to fertiliser P inputs on calcareous soils. The

method is best suited for calcareous soils, particularly those with > 2% calcium

carbonate (CaCO3). It has also been shown that it can be an effective method for

determination of P in acidic soils. The method uses HCO3-, CO3

2- and OH- in the pH 8.5

and 0.5M NaHCO3 solution to decrease the solution concentrations of soluble Ca2+ by

precipitation as CaCO3 and soluble Al3+ and Fe3+ by formation of Al and Fe

oxyhydroxides and thus increasing P solubility (Frank et al., 2011; Kovar and

Pierzynski, 2009; Matejovic and Durackova, 1994).

24

Mehlich 3 extractant has also been used for determining P in soil. The extracting

solution consists of solutions such as acetic acid, ammonium nitrate, ammonium

fluoride, nitric acid and the chelating agent EDTA (Watson and Mullen, 2007; Kovar

and Pierzynski, 2009). This method demonstrates a good correlation with the sodium

bicarbonate (Olsen) method on calcareous and non-calcareous soils and on non-

calcareous soils with Bray and Kurtz P-1 (Frank et al., 2011). The method is an

improved multi-element extractant for P, K, Ca, Mn, Cu, Fe, Mn and Zn and is well

suited to a wide range of soils; both acidic and basic (Watson and Mullen, 2007; Kovar

and Pierzynski, 2009).

Colorimetric procedures have also been used for P analysis in soils. The procedures are

sensitive, reproducible and suitable for automated analysis. On the other hand,

inductively coupled plasma (ICP) spectrophotometry is also commonly used for this

purpose. The ICP method has become quite popular due to its ability to analyze multi-

element soil extractants (Kovar and Pierzynski, 2009; Nathan et al., 2002).

Analytical Procedures for Potassium (K), Calcium (Ca) and Magnesium (Mg)

Exchangeable nutrients in the soil can be extracted by several extracting solutions. Soil

extracting solution does not remove all the available nutrients from the soil and different

extractants extract different amounts of these nutrients. Ammonium acetate (NH4OAc) 1

M solution is the most common extractant used for exchangeable cations (Cooksey and

Barnett, 1979). Water soluble and exchangeable forms of K, Ca and Mg are used to

estimate the amount of K, Ca and Mg in the soil (Warncke and Brown, 2011).

Mehlich 3 has also been found a suitable extractant for K, Ca and Mg in non-calcareous

soils. Research also shows that either 1 M NH4OAc or Mehlich 3 may be used to extract

K. However, Mehlich 3 cannot be used as an alternative for 1 M NH4OAc as an

extractant for Ca and Mg for calcareous soils (Warncke and Brown, 2011). To obtain

exchangeable Ca and Mg using NH4OAc, the water soluble cations are estimated in the

1:2 soil water extract (DAC, 2011).

25

The accurate, simple and fast ethylenediaminetetraacetate (EDTA) titration method for

the determination of exchangeable bases, after the extraction process, as developed by

Cheng and Bray (1951) was used in this study. The method is based on the principle that

Ca2+, Mg2+ and a number of other cations form stable complexes with versenate i.e.

ethylendiaminetetraacetic acid disodium salt at different pH. The interferences by metals

such as Cu, Zn, Fe and Mn are prevented by the use of 2 % NaCN solution or

carbamate. However, the concentration of these interfering metal ions is usually

insignificant in soil extracts and thus could be neglected. In this method, a known

volume of extracted solution is titrated with standard versenate 0.01M solution using

murexide indicator. The colour changes from orange red to purple at pH 12. This is

when the whole of cation forms a complex with EDTA (DAC, 2011).

Atomic absorption spectrophotometry (AAS) is the most commonly used analytical

techniques for soil exchangeable base cation quantification. A method was developed by

Zhang et al. (2012) to analyse ammonium acetate soil extract for exchangeable base

cations Ca2+, Mg2+, K+ and Na+ using AAS. The accuracy of the soil test results was in

line with national standard reference soil data. This technique was found to be reliable

and reflection of the actual situation of soil cation exchange properties on agricultural

lands. Praveen’s (2009) study also indicated that AAS method for element analysis in

soil samples is quite reliable. The method was found to be accurate based on the analysis

of reference materials and excellent spike recovery results. The method was also used

for the present investigation.

The multi-element analytical technique known as inductively coupled plasma optical

emission spectrometry (ICP-OES), offers fast sample throughput and high sensitivity for

a wide range of analysis. Soil element analysis with ICP-OES technique is well

documented (Isaac and Johnson, 1983; Nham, 2010). The technique is applicable to

around 73 elements (including macro and micro-nutrients), providing fast multi-element

analysis with superior detection limits compared to AAS for many elements (Knowles,

2010).

26

Analytical Procedures for Micronutrients: Zinc (Zn), Iron (Fe), Manganese (Mn) and Copper (Cu)

Microelements in soil extract can be estimated by various methods, such as volumetric

analysis, UV-Visible spectrophotometry, AAS, ICP-OES, ICP-atomic emission

spectroscopy, etc. Zn and Mn can also be estimated using EDTA titration while KMnO4

titration can be used to analyse Fe. On the other hand, Cu can be estimated by titration

with Na2S2O3. Spectrophotometric methods such as dithiozone method for estimation of

Zn, orthophenonthroline method for Fe, potassium periodate method for Mn, carbamate

method for Cu are well known methods which leads to specific colour development. In

these methods interference due to other elements has to be eliminated. These methods

are however time consuming. Therefore, the most commonly used method is AAS. The

interference by other elements in AAS method is almost negligible as estimation of an

element is carried out at a specific emission spectraline (DAC, 2011).

Dethylenetriaminepentaacetic acid (DTPA) method developed by Lindsay and Norvell

(1978) is a common extractant widely used for simultaneous extraction of elements, like

Zn, Cu, Fe and Mn. It is the most efficient method to assess the availability of

micronutrients such as Cu, Fe, Mn and Zn although, many other methods of extraction

has been mentioned in the literature (Júnior, et al, 2008). This universal extractant saves

costs of chemicals and time over a specific extractant for each element (DAC, 2011).

DTPA- triethanolamine (TEA) extractant uses DTPA as the chelating agent to extract

available Fe, Cu, Mn, and Zn from soils by combining with free metal ions in the

solution to form soluble complexes of these elements (DAC, 2011; Walworth, 2011).

Study conducted by Lindsay and Norvell (1978) revealed that factors such as pH, soil-

to-solution ratio, chelating agent concentration, shaking time and extraction temperature

affect the amount of micronutrients extracted by the DTPA method. Other works have

shown that extraction intensity and sample preparation also affect the results (Whitney,

2011). This method was used in the present study for micronutrient analysis.

27

Soil Fertility Measures

2.4.1 Common Organic Nutrient Sources

Organic nutrient sources are often seen as substitutions to inorganic fertilisers (chemical

fertiliser). However, the organic inputs such as compost, crop residues, animal manure,

lime, etc. cannot fulfill crop nutrient demand over large areas due to limited quantities of

organic input available, their low nutrient content and the high labor demands needed

for processing and application (Jate, 2012; Palm, 1997). Conversely, abundant supply of

inorganic fertilisers in some countries has changed farmers' perception and appreciation

of organic sources of nutrients such as manure. It is looked upon as `waste' in some

regions. Although it is easier to manage inorganic fertilisers compared to manure, given

proper consideration on the composition of manure nitrogen fertiliser value (NFV) can

be improved. This will enable a reduction in the use of chemical fertiliser, nutrient

surpluses and environmental pollution (Schroder, 2005).

Studies have shown the positive effect of organic wastes on soil productivity such as

increased total soil N and high OM content. Agbedea et al. (2013) conducted a field

experiment at Owo, Southwest Nigeria to find best organic fertiliser treatments suited to

sustain high soil fertility and yam productivity on a nutrient-depleted tropical Alfisol.

They found that organic fertilisers had significantly increased tuber weight and growth

of yam. It had also increased the level of N, P, K, Ca and Mg, soil pH and organic C in

the soil when compared to the control experiment where no organic fertiliser was

applied (Agbedea et al., 2013).

It has also been reported that a combined input of both organic and inorganic fertilisers

have shown increased intensity and enhanced synchronization of nutrient release and

uptake by crop causing improved yields (Mbah and Onweremadu, 2009; Kunzova,

2013; Rezig et al., 2013a). Jate (2012) in his study to find the impact of mineral fertiliser

with farmyard manure on the yield of crops and soil fertility found that the mineral and

organic fertiliser combination improved the availability and corrected the balance of

nutrients to accomplish healthy growth and development of crops. Likewise, research

carried out by Baniuniene and Zekaite (2008) to find the effect of mineral and organic

28

fertilisers on potato tuber yield and quality showed that the potato tuber yield increased

by 35 - 82 % with farmyard manure combined with various combinations of mineral

fertiliser. Mutegi et al. (2012) found similar results when finding the effect of organic

and mineral fertilisers on maize production. Their results showed that either using only

organic or a combined organic/mineral N produced better crop yield than using only

mineral fertiliser. Therefore, the challenge is to combine the right proportion and type of

different qualities of organic nutrient inputs with inorganic fertilisers (chemical

fertiliser) to optimize nutrient availability to plants (Jate, 2012; Palm, 1997).

2.4.1.1 Compost

Although composts are an important part of nutrient management, it does not contain

nutrients in balanced forms as needed by plants as most other organic sources of

nutrients. However, while adding nutrients to the soil, it also improves long-term soil

quality. Composts show best results when used with other soil fertility management

strategies (Sánchez and Richard, 2009). According to DeLuca and DeLuca (1997), the

technique of on-farm composting is a useful practice to protect surface and groundwater

from nutrient loading. They stated many advantages of compost such as its ability to

stabilize nutrients, kill pathogens and weed seed and improving physical properties of

manure. In general, compost manure improves the quality of soil as well as utilizes less

energy when compared with commercial fertiliser (DeLuca and DeLuca, 1997).

Research was carried out by Pinamonti (1998) on two composts to test them as

mulching materials in a vineyard. The first compost was sewage sludge and bark

compost with a low heavy metal content while the second one was the municipal solid

waste compost with a higher concentration of heavy metals. It was found that both

compost mulches had increased the OM content, available P and exchangeable K of soil.

Both had also improved the porosity and water retention capacity of the soil and reduced

soil temperature fluctuations and evaporation of soil water. It was also noted the

composts had some effect on the nutrient content in leaf samples. Overall, it was found

that both the compost mulch had significant advantage on the soil management

29

practices. It reduced chemical weed control and could be used in place of chemical

fertilisers (Pinamonti, 1998).

Another study was conducted by Singer et al. (2004), to determine the effect of tillage

and compost manure on the yield of corn, soybean and wheat as well its effect on the

OM, P and K content in the soil. It was found that the compost-amended soil had

increased in the concentration of OM. Hence, it was concluded that the yield of crops

could be enhanced with the application of compost manure. However, Singer, et al.

(2004) mentioned that precaution must be taken to prevent accumulation of soil P by

balancing OM enhancement with P input.

2.4.1.2 Animal Manure and Green Manure

The use of animal manure to maintain soil fertility is common in areas where livestock

farming and arable farming is carried out in the same area. Many agronomists believe

that the beneficial effects of animal manure are due to the stimulation of bacterial

activity and the build-up of OM in the soil. Nutrients from animal sources such as

manure have excess of P and K compared to the plant requirement for N. This can lead

to accumulation of nutrients in the soil upon repeated application (Sánchez and Richard,

2009).

Green manure comes from crops that are turned into soil while young and tender. Some

researchers have defined it as an herbaceous, shrubby or woody plant material that is

grown either in situ or ex situ. This is done usually through the process of ploughing.

Green manures add N to the soil, suppress weeds, search for nutrients left in the soil and

increase the SOM content (Ahn, 1993; Sánchez and Richard, 2009).

Edmeades (2003) reviewed the long-term effects of fertilisers and manures on crop

production and soil properties. The review concluded that nutrient input in the soil either

as chemical fertiliser or manure had tremendous effect on soil productivity in terms of

crop yields. In addition, it was found that manured soils had higher levels of OM, P, K,

Ca and Mg in topsoils and NO3--N, Ca and Mg in subsoils and more number of

microfauna than soil that was treated with chemical fertiliser. Moreover, compared to

30

fertilised soil, manured soil had lower bulk density, higher porosity, hydraulic

conductivity and aggregate stability. However, in the long term effects of manure and

fertiliser, it was found that there was no significant difference on the crop production

(Edmeades, 2003).

2.4.1.3 Legumes, Cover Crops and Crop Residues

Legume crops are well identified as soil building plants and can add nitrogen to the soil.

They improve soil quality by adding OM, improve soil structure and water filtration

property. Legumes are able to establish relationships with soil borne bacteria that are

capable of extracting N gas from the atmosphere and converting it into a form that the

plant can use. The tissues of leguminous crops have a lot of N relative to the amount of

C, which results in their decomposing rather quickly when turned into the soil. This

results in a relatively quick release of N as the plants decompose, but the amount of OM

added to the soil is limited over the long term. Since legumes return N and other

nutrients to the soil, it contributes significantly to the yields of crops. Leguminous crops

however, differ in the amount of N they can add to the soil (Amba et al., 2011; Mora et

al. 2012; Ritchie and Tilman, 1995; Sánchez and Richard, 2009).

Green manure as cover crops and legumes have widely been used in many countries and

many researchers’ have written about the use of legume as cover crop as a soil

amendment technique or as a measure to increase soil fertility (Liu et al., 2012). It has

also been found that the % C increased under legume cover while exchangeable Ca, Mg

and K were generally maintained at a higher level.

In a research conducted to examine the effect of P fertiliser on the N-nitrogen fixing

efficiency of grain legumes and its effect on some soil chemical properties it was found

that the chemical properties of the soil collected after harvesting improved. This could

have been the result of crop residues of legumes left which might have added OM to the

soil. Hence, increasing the organic C, total N, available P and soil pH (Amba et al.,

2011).

31

It has been reported that crop residues improve nutrient cycling and reduce the need for

fertiliser usage (Onyango and Clegg, 1993). Other researches have also shown that

removing crop residues reduce grain yields and addition increase the yields as well as

nutrient content in the soil (Hinga, 1979; Qureshi, 1996; Nair et al., 1979).

Mucuna or velvet bean is a legume that has been found to have high but unexploited

potential for soil fertility improvement. Research carried out by the Legume Research

network project of the Kenya Agricultural Research Institute (KARI) have proven the

benefits of mucuna on soil properties and on the yields of cereal crops. It has also been

found that mucuna works well in a wide range of environmental conditions including the

semi-arid areas (Mureithi et al., 2003). Other researches have also shown that the

legume increases SOM content as well as improves the chemical soil conditions of the

soil. In the tropical regions of Latin America, some farmers have adopted to the use of

mucuna beans as sustainable and inexpensive way for crop production. It has been

incorporated into agriculture by using mucuna beans for either crop rotation or

intercropping (Nyambati et al, 2009). Mucuna beans have now been introduced to taro

farmers in Taveuni, Fiji to improve the soil fertility on the island which is under threat.

The legume has been introduced with the assistance from the Secretariat of the Pacific

Community (SPC), Fiji (Delaivoni, 2010).

2.4.1.4 Mulching

Mulching is a soil fertility measure where loose covering or sheets of organic material

are placed on the soil surface. This technique helps to preserve soil moisture, represses

weed, improves soil consistency and structure and guards roots from severe temperature

(Moore-Gordon et al., 1997; Zamir et al., 2013). Zamir et al. (2013) carried out a study

to see the effect of organic mulches and tillage practices on growth and yield of maize.

Their results showed that mulching is a useful practice to increase the yield of crops.

Moore-Gordon et al. (1997) applied thick composted pine bark mulch to investigate it as

an approach to increase yield and to ease the small fruit problem in avocado fruit.

Results showed prolonged and extensive root growth in the mulched trees. Average fruit

yield and mass was increased by 22.6 % and 6.6 % respectively over three seasons.

32

There was also increase in fruit number and exportable grade fruit by 45 % and 20 %

respectively.

All these studies concluded that mulching technique improved the soil fertility (Moore-

Gordon et al., 1997; Mupangwaa et al., 2007; Zamir et al., 2013).

2.4.2 Chemical (Mineral/Inorganic) Nutrient Sources

Chemical (mineral/inorganic) fertilisers are a mixture of various mineral rocks which are

stable and non-leaching (Dyne, 2011). They are either natural or manufactured materials

which contain essential nutrients (N, P and K) for the growth and development of plants,

supplied in required amounts (Isherwood, 2000). The production of sufficient food at

this rate would have been impossible without the use of chemical (mineral/inorganic)

fertilisers. There would have been more conversion of natural ecologies to agricultural

land to support food security (Tilman et al., 2002).

Chemical nutrient sources are added to enhance the soil’s natural nutrient resource to

meet the nutrient requirements of plants ensuring high and economically feasible yields.

They are also added to replenish the nutrients that are lost either by the removal of crops

or by leaching. Generally, fertilisers are added to preserve good soil conditions for crop

production (Isherwood, 2000). Studies have shown that the nutrients available in mineral

fertilisers are more effective than the same amount of these nutrients available in

farmyard manure (Baniuniene and Zekaite, 2008).

In the research to find the effect of P fertiliser on some soil chemical properties and N

fixation of legumes, it was concluded that P fertiliser application not only increases the

nutrient status of the soil but also boosts the N fixation capability of the legumes

resulting in an increase in soil fertility (Amba et al., 2011). Overall, the main objective

with fertiliser usage should be to optimize agricultural output with the application of the

required amounts of fertiliser in order to meet the agricultural requirements of the world

(Isherwood, 2000). However, fertiliser application should be based on soil tests for

estimated nutrient requirements at each location due to differences in nutrient content

within different agricultural sites (Kim et al., 2009).

33

2.4.3 Other Soil Fertility Measures

2.4.3.1 Crop Rotations and Fallowing

Fallowing is a practice where a land is left idle for some cropping seasons to enable the

soil to restore its fertility (Franzel, 1999) while crop rotation involves the regular

changing of the type of crop grown in one particular area. They play an essential role in

improving soil productivity, increasing yield as well as in increasing ecosystem services

(Bullock, 1992; Liu et al., 2012). Other benefits of crop rotation include improving soil

properties, enhancing soil nutrient and water levels as well as controlling weeds and

diseases (Kunzova, 2013).

Jama et al. (1998), in their study stated that rotating Sesbania sesban (L.) Merr., which

is a fast-growing N2-fixing tree, with maize (Zea maysL.) has the prospective of

increasing the fertility of tropical soils. This is useful in places where the availability of

fertiliser is limited due to financial constraints. They also found that increasing the

period of fallow can improve the nutrient supply to crops. Franzel (1999) studied the

feasibility, profitability, and acceptability of improved tree fallows. Coomes et al. (2000)

reported on the forest fallow management practices among traditional farmers of the

Peruvian Amazon by collecting data through in-depth household interviews. The study

revealed that the length of fallow period is greatly influenced by the type of crop grown

previously on the land, the size of the farm and labour access. Farmers with smaller land

size had significantly shorter fallow periods compared to those with larger farm size.

However, this technique is used to improve the fertility of soil by rotating trees or scrubs

with crops.

2.4.3.2 Agroforestry

Agroforestry is a soil fertility measure where trees, scrubs, etc. are grown in connotation

with herbaceous plants (Young, 1997). It has been based on the principle that trees

sustain soil fertility. This is established on observations that crop yields are high when

it’s grown near trees or planting where trees had previously been grown (Palm, 1995;

Schroeder, 1994). Agroforestry is found essential as a C sequestration strategy due to its

34

potential to store C from its surrounding of plants and from the soil. However, this

potential has neither been well documented nor exploited (Montagnini and Nair, 2004).

In a successful agroforestry system, the competition for light, water and nutrient

between trees and crops is well managed. With the replenishment of lost nutrients via

fertiliser input, etc., agroforestry systems can be sustainable, controlling erosion,

improving biodiversity, reducing land clearing and preserving carbon (Sanchez, 1995;

Schroeder, 1994). Duguma et al. (2001) found that cacao cultivation system as an

agroforestry practice causes minimum damage to soil resources.

Nutrient Requirements of Taro

Generally, taro has medium to high requirements for N, medium to low requirements for

P2O5 and medium requirements for K2O (Lamberts and Olson, 2012). The most limiting

mineral nutrient for taro growth is mostly N. In an experiment carried out by Osorio et

al. (2003) to find out the effect of different N levels and NO3- : NH4

+ ratios on the

growth of taro, it was found that in a total N level of 3 mM, taro plants best grew when

the predominant N form in the nutrient solution was NO3-. Moreover, it was found that

very high levels of N in the form of NH4+ becomes toxic to plants and leads to the

decrease in plant growth. NH4+ toxicity was also found to cause a decrease in pH levels

(Osorio et al., 2003).

Hartemink and Johnston (1998) carried out an experiment to quantify root biomass

production and nutrient uptake of taro in the lowlands of Papua New Guinea. Tables 2.1

to 2.3 present a summary of their research outcome.

35

Table 2.1: Biomass production and dry matter content of fertilised and unfertilised taro

(Hartemink and Johnston, 1998).

Plant Part

Mid-Season At Harvest

Unfertilised Fertilised Unfertilised Fertilised

Dry Weight

(mgha-1)

Roots 0.26 0.52 0.51 0.50

Corms 0.82 1.21 2.53 6.99

Leaves 0.67 2.13 2.00 3.64

Total 1.75 3.86 5.04 11.13

Dry Matter

Content

(%)

Roots DNA 5 12 11

Corms DNA 19 30 30

Leaves DNA 7 16 16 *DNA: Data not available

Results in Table 2.1 showed that there was significant difference in total biomass in the

fertilized and unfertilized taro. Fertilized taro had more total biomass than the

unfertilized taro. Moreover, the dry matter content had also increased from the mid-

season till the harvesting period. However, it was unaffected by fertiliser.

According to the results presented in Table 2.2, there was no significant difference in the

concentration of nutrients absorbed by the roots of fertilized and unfertilized taro for

samples taken at the time of harvest. However, on a whole-plant basis, more Ca, Mg and

S were absorbed by the fertilized taro than the unfertilized taro.

36

Table 2.2: Nutrient content in unfertilised and fertilised taro roots and corms

(Hartemink and Johnston, 1998).

Nutrient

Unit

Unfertilised

Roots Corms

Mid-Season At Harvest Mid-Season At Harvest

N gkg-1 13.0 11.4 8.3 5.8

P gkg-1 2.1 2.2 2.6 2.1

K gkg-1 52.7 48.9 20.9 17.0

Ca gkg-1 14.0 10.7 5.8 3.6

Mg gkg-1 5.9 5.9 1.4 0.8

S gkg-1 1.2 1.5 0.5 0.4

B mgkg-1 12.0 30.0 4.0 16.0

Mn mgkg-1 77.0 84.0 40.0 24.0

Zn mgkg-1 91.0 105.0 68.0 37.0

Cu mgkg-1 42.0 39.0 18.0 11.0

Fertilised

Roots Corms

Nutrient Unit Mid-Season At Harvest Mid-Season At Harvest

N gkg-1 14.1 10.6 14.5 4.5

P gkg-1 1.8 2.2 1.9 1.8

K gkg-1 47.7 46.3 19.0 13.0

Ca gkg-1 14.4 11.0 4.9 3.1

Mg gkg-1 7.1 6.0 1.3 0.8

S gkg-1 2.3 2.0 0.8 0.4

B mgkg-1 25.0 25.0 13.0 17.0

Mn mgkg-1 120.0 94.0 43.0 21.0

Zn mgkg-1 63.0 114.0 28.0 27.0

Cu mgkg-1 62.0 29.0 19.0 9.0

37

In general, based on Hartemink and Johnston’s (1998) experiment, it was found that at

harvest, roots of taro take up approximately 5 kgha-1 N, 1 kgha-1 P, 25 kgha-1 K, 5 kgha-

1 Ca, and 3 kgha-1 Mg as presented in Table 2.3.

Table 2.3: Nutrient uptake (kgha-1) from the soil by roots, corms and leaves of

unfertilized and fertilized taro (Hartemink and Johnston, 1998).

Sampling

Period

Plant

Part

Unfertilised Taro

Fertilised Taro

N P K Ca Mg S N P K Ca Mg S

Mid-

Season

Roots 3 <1 13 4 2 <1 8 1 25 8 4 1

Corms 5 2 17 4 1 <1 14 2 22 6 2 1

Leaves 19 5 46 8 1 1 63 9 119 29 5 4

Total 27 9 76 16 4 2 85 13 166 42 10 5

At

Harvest

Roots 6 1 25 5 3 1 5 1 23 5 3 1

Corms 13 5 42 8 2 1 31 12 86 23 5 3

Leaves 34 10 80 21 3 2 55 18 106 46 6 4

Total 53 16 147 35 8 4 91 31 215 74 15 8

Similar work was carried out by Goenaga and Chardon (1995), where they studied two

taro cultivars, to determine the growth, nutrient uptake and yield performance. The

results showed that the nutrient uptake by the two cultivars was significantly different

except for Mg. However, during the first 159 days, the nutrient uptake between these

cultivars was generally quite similar. Cultivar ‘Blanca’ had a maximum of 208 kgha-1 N,

70 kgha-1 P, 376 kgha-1 K, 106 kgha-1 Ca, 24 kgha-1 Mg and 0.88 kgha-1 Zn. Cultivar

‘Lila’ on the other hand had a maximum uptake of 154 kgha-1 N, 48 kgha-1 P, 254 kgha-

1 K, 62 kgha-1 Ca, 25 kgha-1 Mg and 0.71 kgha-1 Zn. The results also showed that like

most root crops, taro has a higher requirement for K compared to N. This was also

shown by Thiagalingam (2000) where the levels of nutrients present in taro tubers at

harvest were analysed, as presented in Table 2.4.

38

Table 2.4: Levels of nutrients present in taro tubers at harvest (Thiagalingam, 2000).

Nutrient Unit Concentration Protein

Nitrogen % 0.58 3.60

Phosphorous % 0.24

Potassium % 1.33

Calcium % 0.20

Magnesium % 0.09

Sulphur % 0.03

Zinc mg/kg 45.00

Boron mg/kg 6.00

Iron mg/kg 83.00

Manganese mg/kg 8.00

Copper mg/kg 7.00

Food Value of Taro

In taro, the corm and the cormel are the main parts with nutritional value. However, the

young leaves and petioles, which are also occasionally used for food, contain

approximately 23 % protein on a dry weight basis. Taro in general is rich in Ca, P, Fe,

vitamin C, thiamine, riboflavin and niacin (Tumuhimbise et al., 2009). The data reported

on the food value of taro in the Pacific food composition table are presented in Table

2.5.

39

Table 2.5: Food values of taro (100 g) as per Pacific Food composition table (Dignan et

al., 2004).

Parameters

Measured

Taro corm

(raw)

Taro leaves

(Raw)

Taro stalk

(Raw)

H2O (g) 75 83 95

Energy (kj) 407 207 66

Protein (g) 2.2 5.8 0.7

Total fat (g) 0.4 1.3 0.3

CHO (g) 20.9 0.9 1.6

TDF (g) 1.3 5.7 2.1

Na (mg) 28 5 12

Mg (mg) 54 47 28

K (mg) 328 748 393

Ca (mg) 34 276 46

Fe (mg) 1.2 2.8 1.2

Zn (mg) 0.4 0.6 0.3

β-carotene 26 6090 201

Total Vitamin A

(mg)

2.0 508 17

Vitamin C (mg) 8.0 81.0 4.0

Vitamin E (mg) 2.0 2.3 0.1

40

CHAPTER 3

METHODOLOGY

In this chapter the methods used to achieve the objectives of the study conducted in

respect of Taveuni island of Fiji are described. It focuses on the research location and

various techniques used for sampling and storing the soil samples needed for the study.

The chapter describes the analytical procedures used for extraction and analysis of the

soil nutrients. It also gives an overview of the questionnaire survey and the statistical

tests that were used for the analysis and interpretation of the soil test and questionnaire

survey data.

Research Location

Since the study was a part of the larger Soil Health project of the Australian Centre for

International Agricultural Research (ACIAR) which is underway in Taveuni island of

Fiji, the soil chemistry investigation for the research was undertaken on the soil samples

collected in Taveuni. The four locations (sites) across Taveuni island were randomly

selected for the survey: Matei, Mua, Vione and Delaivuna as shown in Figure 3.1.

Figure 3.1: Distribution of the four farms across Taveuni, Fiji Islands. Source: http://www.gpsvisualizer.com

41

Soil samples were collected four times during the cropping cycle: before planting of

mucuna beans (3rd March 2012), before planting of taro (11th - 14th September 2012),

three months into planting of taro (10th - 12th December 2012) and at harvest (12th - 15th

March 2013).

Field Methods

The ACIAR soil health team came up with four different treatments for the taro trials

based on previously conducted trials. The team also selected a randomized experimental

design and layout. The design had five replicas with the four treatments in each replica

as shown in Table 3.1. Mucuna beans were planted on each trial site six months prior to

taro planting and 35g of fertiliser/taro hole was applied during the planting of taro. The

total area per site was 960m2 and each treatment in each replica had a 1m x 1m plot area.

3.2.1 Experimental Treatments

The experimental treatment of the study sites were:

A = Lime/Mucuna

B = Lime/Mucuna/Fish manure/Rock P

C = Mucuna/Fertiliser-NPK (13:13: 21)/Bio brew

D = Mucuna

3.2.2 Experiment Randomization & Layout

The following Table 3.1 shows the randomized experimental design and layout of the sampling sites.

Table 3.1: Randomized experimental design of the replication block with the different

treatments.

Replication Blocks Treatment of Plots

Rep I A D C B Rep II B D C A Rep III C A D B Rep IV D A B C Rep V C B A D

42

3.2.3 Actual Site Plans

The actual experimental (sampling) layout of each of the four sites is shown in Figures

3.2 to 3.5.

Figure 3.2: Matei site plan.

Figure 3.3: Delaivuna site plan.

43

Figure 3.4: Mua site plan.

Figure 3.5: Vione site plan.

44

3.2.4 Soil Sampling and Storage

Using the site plans, soil samples under the four different treatments were sampled. For

each treatment a composite sample was taken by collecting three samples randomly

from each plot of the same treatment. The sampling depth for all nutrients except

nitrates was approximately 20 cm since most plant roots grow to about that depth and

nutrients are mixed into the soil to about 20 cm deep as well during tillage. For soil

NO3-, the sampling depth was approximately 30 cm because NO3

- moves more easily

with soil water compared to other nutrients (Reid, 2006).

For collecting the samples, a steel spade and clean plastic pail was used. A V-shaped

hole was dug with the required depth. A slice was then cut of approximately 3cm

thickness. From the slice, both sides were trimmed leaving about a 3cm thin strip. This

strip of soil was placed in the plastic bucket. This process was repeated until three

samples were taken randomly from each plot for a particular treatment. Sampled soil

cores were then homogenized in the bucket until no evidence of soil cores existed before

a sub-sample was collected. Each sub-sample was approximately 400 g of soil. The

samples were stored in labelled zip-lock plastic bags until it was transferred to

laboratory (DAC, 2011; Brady, 1984; Hue et al., 1997; White, 1987).

Once transported to the laboratory, soil samples were air-dried and crushed with a

porcelain mortar and pestle. The crushed samples were then screened through a stainless

steel 10-mesh sieve (2mm sieve) to remove larger clods and unwanted debris. The

sieved soil samples were stored in zip-lock plastic bags till required for analysis (Brady,

1984; Hue et al., 1997). The process flow diagram of the soil sampling and storage is

shown in Figure 3.6.

45

Figure 3.6: Process flow diagram of soil sampling and storage.

Step 1: Soil Sampling Step 2:

Homogenising soil

Step 4: Air-drying soil samples

Step 3: Storing samples in zip-lock bags

Step 5: Crushing soil samples with a porcelain mortar and pestle

Step 6: Screening through 10-mesh sieve

Step 7: Sieved soil samples

46

Laboratory Methods

3.3.1 Instrumentation

A Lambda 25 (PerkinElmer) UV-Visible Spectrophotometer with 1 cm matched quartz

cells were used for the absorbance measurements of available P. QuickCkem FIA+ 8000

series (Lachat Instrument) Flow Injection Analyzer (FIA) was used for measuring the

peak area of NO3- and NO2

- and an Atomic Absorption Spectrometer (AAS) 3110

(PerkinElmer) was used for the measurement of absorbance of exchangeable bases and

trace metals in the soil extracts. A Hanna Instruments model 209 pH meter was used for

measuring the soil pH as well as setting pH of buffer solutions. A mechanical shaker

was used in the experiment as and when required.

Plate 3.1: Lambda 25 (PerkinElmer) UV-Visible spectrophotometer.

47

Plate 3.2: QuickCkem FIA+ 8000 series (Lachat Instrument) Flow Injection Analyzer.

Plate 3.3: Atomic Absorption Spectrometer 3110 (PerkinElmer).

48

Plate 3.4: Hanna Instrument pH Meter.

Plate 3.5: Mechanical Shaker.

49

3.3.2 Standards and Reagents

All chemicals used were of either ultra-pure or analytical reagent grade. Distilled

deionised water was used in the preparation of all solutions in the experiments. For NO3-

analysis deionised milli-q water was used for the preparation of all reagents. Working

standards were prepared fresh on the day of the use. All glassware was soaked in 10%

nitric acid bath for 12-18 h and was washed with distilled water before use.

3.3.3 Chemical Characterization

3.3.3.1 Determination of Soil pH

The pH meter was calibrated using pH 7 and pH 4 standard buffer solutions. To 1.00g

of air-dried, sieved (< 2 mm) soil sample in a centrifuge tube, 2.5 mL of 0.01M CaCl2

was added. The solution was stirred vigorously and allowed to stand for 30 min, with

occasional shaking. The pH electrode was immersed in the partially settled suspension

and the pH was recorded when the reading had stabilized (Heiri et al., 2001; Mckeague,

1978; Kumar, 2010; Schofield and Taylor, 1955; Watson and Brown, 2011).

3.3.3.2 Determination of Soil Organic Matter (SOM)

To 0.50 g or 1.00g of air-dried, sieved (< 2 mm) soil samples and a sample blank for

every batch of samples, 10 mL of 0.167 M K2Cr2O7 and 20 mL of concentrated H2SO4

was added. This was swirled gently and allowed to stand for 30 min. The suspension

was then diluted with 200 mL of deionised water to provide a clear suspension for

observing the endpoint. To this 10 mL of 85 % H3PO4 and 0.2 g of NaF was added. This

was added to complex the Fe3+ which would otherwise interfere with the titration

endpoint. After addition of 10 drops of ferroin indicator, the suspension was titrated with

0.5 M Fe2+ solution to a burgundy endpoint as shown in Figure 3.7 (Heiri et al., 2001;

Mckeague, 1978; Walkley and Black, 1934; Walkley, 1935, Walkley, 1947; Watson and

Brown, 2011). SOM % was calculated using the following equations.

50

Easily oxidizable organic C % = B-S × M of Fe2+ × 12 × 100

g of soil × 4000

where:

B = mL of Fe2+ solution used to titrate blank

S = mL of Fe2+ solution used to titrate sample

M of Fe2+ = 0.5 M

12/4000 = milliequivalent weight of C in g

Total C % = Easily oxidizable organic C % ×1.30

SOM % = Total C % × 1.72

0.58

51

Figure 3.7: Colour change during titration for SOM analysis.

Color at the beginning of the titration

Color during titration

Color at the end-point

52

3.3.3.3 Determination of Soil Exchangeable Base (Ca, Mg, K)

(i) Extraction of Soil Exchangeable Bases (Ca, Mg, K)

2.5 g of air-dried, sieved (< 2 mm) soil sample was weighed out into a 50 mL screw cap

polypropylene centrifuge tube. For each batch of extraction, a method blank was also

included. To this 25 mL of 1M NH4OAc extracting solution of pH 7.0 was added. The

sample was then placed on a mechanical shaker and shaken for 15 min. The suspension

was centrifuged for 10 min at 2000 rpm and then filtered through a Whatman no. 42

filter paper to get a clear filtrate. The filtered solution was analyzed for Ca, Mg and K

using AAS (Hak-Jin et al., 2009; Kumar, 2010; Thomas, 1982; Warncke and Brown,

2011).

(ii) Analysis of Soil Exchangeable Bases (Ca, Mg, K) using AAS

Ca, Mg and K standard stock solutions (1000 ppm) were prepared using CaCO3, Mg

ribbon and KCl respectively. Working standards of Ca, Mg and K were prepared fresh

on the day of analysis from the 1000 ppm stock solvent. The working standards were

made using the extracting solvent i.e. 1M NH4OAc with pH 7.0. For Ca and Mg

working standards, the extracting solvent contained sufficient strontium to give a final

concentration of 1 % (wt./vol.). Strontium was used to prevent chemical interferences

(PerkinElmer, 1996).

The soil extracts were diluted using the extracting solvent so that absorbance could be

read within the linear range of the calibration standards. The extracts were analyzed

using the AAS parameters mentioned in Table 3.2. Exchangeable base concentrations of

dilute sample extracts were determined directly from the absorbance reading and

standard calibration curves (Hak-Jin et al., 2009; Kumar, 2010; PerkinElmer, 1996;

Warncke and Brown, 2011).

53

Table 3.2: Standard atomic absorption conditions for the determination of micro and

macronutrients (Ong, 2010; PerkinElmer, 1996).

Element Ca Mg K Cu Fe Mn Zn Wave-

length (nm)

422.7 285.2 766.5 324.8 248.3 279.5 213.9

Slit (nm) 0.7 0.7 0.7 0.7 0.2 0.2 0.7

Flame air-

acetylene

air-

acetylene

air-

acetylene

air-

acetylene

air-

acetylene

air-

acetylene

air-

acetylene

Lamp

Current

(mA)

5 4 10 15 20 15 15

Standards

(mgL-1)

1, 2, 3, 4,

5

0.05, 0.1,

0.2, 0.3,

0.4, 0.5

0.25, 0.5,

1, 1.5, 2

1, 2, 3, 4,

5

1, 2, 3, 4,

5

0.25, 0.5,

1, 1.5, 2

0.2, 0.4,

0.6, 0.8, 1

Replicates 3 3 3 3 3 3 3

Exchangeable base concentrations were calculated using the following equation.

Ca, Mg or K in soil (mgkg-1) = (a-b) × e × df

where:

a = Ca, Mg or K concentration in diluted sample solution (mgL-1)

b = Ca, Mg or K concentration in blank (mgL-1)

e = [extractant to soil ratio = extractant vol. (mL)/sample weight (g)] = 10

df = dilution factor

3.3.3.4 Determination of Soil Micronutrient (Cu, Fe, Mn, Zn)

(i) Extraction of Soil Micronutrients (Cu, Fe, Mn, Zn)

10 g of air-dried, sieved (< 2 mm) soil sample was weighed out into a 50 mL screw cap

polypropylene centrifuge tube. For each batch of extraction, a method blank was also

included. To this 20 mL of DTPA extracting solvent of pH 7.3 was added. The sample

was then placed on a mechanical shaker and shaken for 2 h. The suspension was

54

centrifuged for 10 min at 2000 rpm and then filtered through a Whatman no. 42 filter

paper to get a clear filtrate. The filtered solution was analyzed for Cu, Fe, Mn and Zn

using AAS (Kahn, 1979; Kumar, 2010; Whitney, 2011).

(ii) Analysis of Soil Micronutrients (Cu, Fe, Mn, Zn)

Cu, Fe, Mn and Zn standard stock solutions (1000 ppm) were prepared using Cu metal,

Fe wire, Mn metal and Zn metal respectively. Working standards of Cu, Fe, Mn and Zn

were prepared fresh on the day of analysis from the 1000 ppm stock solutions. The

working standards were made using the extracting solvent i.e. DTPA of pH 7.3

(PerkinElmer, 1996).

The soil extracts were diluted using the extracting solvent so that absorbance could be

read within the linear range of the calibration standards. The extracts were analyzed

using the AAS parameters mentioned in Table 3.2 within one day of extraction.

Micronutrient concentrations of dilute sample extracts were determined directly from

the absorbance reading and standard calibration curves (Júnior et al, 2008; Lindsay and

Norvell, 1978; Whitney, 2011). Micronutrient concentrations were calculated using the

following equation.

Cu, Fe, Mn or Zn in soil (mgkg-1) = (a-b) × e × df

where:

a = Cu, Fe, Mn or Zn concentration in diluted sample solution (mgL-1)

b = Cu, Fe, Mn or Zn concentration in blank (mgL-1)

e = [extractant to soil ratio = extractant vol. (mL)/sample weight (g)] = 2

df = dilution factor

55

3.3.3.5 Determination of Soil Available Phosphorous (P)

(i) Extraction of Soil Available Phosphorous (P)

1 g of air-dried, sieved (< 2 mm) soil sample was weighed out into a 50 mL screw cap

polypropylene centrifuge tube. For each batch of extraction, a method blank was also

included. To this, 10 mL of 0.025M HCl and 0.03M NH4F, referred to as Bray-1

extracting solution was added. The sample was then placed on a mechanical shaker and

shaken for 5 min. The suspension was centrifuged for 10 min at 2000 rpm and then

filtered through a Whatman no. 42 filter paper to get a clear filtrate. The filtered solution

was analyzed for available P on an UV-visible spectrophotometer (Frank, 2011; Hak-Jin

et al., 2009; Bray and Kurtz, 1945; Kovar and Pierzynski, 2009).

(ii) Analysis of Soil Available Phosphorous (P)

P standard stock solution (1000 ppm) was prepared using potassium dihydrogen

phosphate (KH2PO4). Working standards of P were prepared fresh on the day of analysis

from the 1000 ppm stock solutions. The working standards were prepared using the

extracting solvent i.e. Bray-1 extractant. 5 mL aliquots of the soil extracts were

transferred to a test tube. To it 0.25 mL of 0.02 M acid molybdate solution and 0.25 mL

of 0.67 M aminonaphthol-sulfonic acid were added. The soil extracts were diluted using

the extracting solvent so that absorbance could be read within the linear range of the

calibration standards. The colour was allowed to develop for 15 min before the

absorbance was taken on an UV-visible spectrophotometer at 660 nm. The reading was

taken within 45 min after adding the reducing agent (aminonaphthol-sulfonic acid) as it

was stable within that period. The working standards were prepared in the same manner,

where the acid molybdate solution and aminonaphthol-sulfonic acid were added and

colour was developed before absorbance was measured. Available P concentrations of

dilute sample extracts were determined directly from the absorbance reading and

standard calibration curves (Frank, 2011; Kovar and Pierzynski, 2009; Wolf and Baker,

1985).

56

Plate 3.6: Colour development of P standards and soil extracts.

Available P concentrations were calculated using the following equation.

P in soil (mgkg-1) = a-b) × e × df

where:

a = P concentration in diluted sample solution (mgL-1)

b = P concentration in blank (mgL-1)

e = [extractant to soil ratio = extractant vol. (mL)/sample weight (g)] = 10

df = dilution factor

3.3.3.6 Determination of Soil Nitrate-Nitrogen (NO3--N)

(i) Extraction of Soil Nitrate-Nitrogen (NO3--N)

5 g of air-dried, sieved (< 2 mm) soil sample was weighed out into a 50 mL screw cap

polypropylene centrifuge tube. For each batch of extraction, a method blank was also

included. To this 25 mL of 2 M KCl extracting solution was added. The sample was

then placed on a mechanical shaker and shaken for 30 min. The suspension was

centrifuged for 10 min at 2000 rpm and then filtered through a Whatman no. 42 filter

paper to get a clear filtrate. The filtered solution was analyzed for NO3- on FIA

instrument. The soil extracts were stored at 4 oC when not analyzed immediately

(Dorich and Nelson, 1984; Huffman and Barbarick, 1981; Keeney and Nelson, 1982).

57

(ii) Analysis of Soil Nitrate-Nitrogen (NO3--N)

NO3- standard stock solution (1000 ppm) was prepared using potassium nitrate (KNO3).

Working standards of NO3--N were prepared fresh on the day of analysis from the 1000

ppm stock solutions. The working standards were prepared using the extracting solvent

i.e. 2 M KCl. The soil extracts were diluted using the extracting solvent so that peak area

could be read within the linear range of the calibration standards. The extracts were

analyzed using FIA instrument at 520 nm using cadmium reduction method. In this

method NO3--N is determined by its reduction to NO2

- via a cadmium reductor coil,

diazotized with sulfanilamide and is coupled to N-(1-Napthyl)-ethylenediamine

dihydrochloride to form an azo chromophore (red-purple in color) which is then

measured spectrophotometrically (Bremmer and Keeney, 1965; Diamond, 2001; Wendt,

2000).

The quantification of nitrate in soil samples was carried out by employing an external

calibration standard method. Figure 3.8 shows a typical flow signals i.e. FIA peaks

profiles for the calibration standards of 0.0 – 5.0 μM NO3--N obtained through the FIA

under optimum conditions at 25ºC.

The NO3

--N peaks obtained were clear without any interference from the organic matter

in the soil matrices. The calibration graph was obtained for each run by injecting six

different concentrations (in triplicate) of NO3--N in the range 0.0 – 5.0 μM and plotting

peak area (volts.sec) against concentration (μM). A representative calibration curve of

the measured peak areas versus NO3--N concentrations (μM) is shown in Figure 3.9.

Similarly, the calibration curve for NO2--N was obtained and shown in Appendix I.

58

Figure 3.8: FIA peaks profiles for the calibration standards.

Figure 3.9: Calibration curve of the measured peak areas (volts.sec.) vs. NO3--N

concentrations (μM).

NO3--N concentrations of dilute sample extracts were determined directly from the peak

area and standard calibration curves. NO3--N concentrations were calculated using the

following equation.

NO3--N in soil (mgkg-1) = (a-b) × e × df

where:

a = NO3--N concentration in diluted sample solution (mgL-1)

b = NO3--N concentration in blank in (mgL-1)

e = [extractant to soil ratio = extractant vol. (mL)/sample weight (g)] = 5

df = dilution factor

Peak Area = 2.0444[NO3- + NO2

-]R² = 0.9992

-2.000

0.0002.000

4.000

6.0008.000

10.000

12.000

0 1 2 3 4 5 6

Peak

Are

a (v

olts

.sec.

)

[NO3-], µM

59

Questionnaire Survey

Fifty farmers were personally interviewed using a structured questionnaire. The farmers

were targeted from areas near the soil sampling sites i.e. Matei, Mua, Vione and

Delaivuna in Taveuni. Information gathered from these farmers included personal

details such as age, education level, farm size and location of their farm. Other

information included the crops they produced and its quantity over the years.

Information was also obtained about the various forms and quantity of fertilisers they

used as well as other soil fertility measures that they used on their farms. Ideas were also

gathered about problems that the farmers were facing in following proper soil fertility

management practices.

Statistical Analysis

The results were analyzed statistically using IBM SPSS Statistics 21 statistical package

and Microsoft Office Excel. Descriptive statistics such as mean and standard deviation

were reported. Normality of distribution of experimental results was assessed by

drawing a histogram using SPSS to see if it was bell-shaped, checking with a normal Q-

Q plot and using Kolmogorov-Smirnov test. On this basis, the statistical test was

selected, which was used to investigate the significance of differences between the

groups. Since the experimental results were not normally distributed, we chose a non-

parametric alternative to the t-test for independent populations. We used the Mann-

Whitney U test to compare the means of two independent populations. Results were

considered significantly different when ρ < 0.05 or ρ < 0.01 for 95 % confidence

interval and 99 % confidence interval respectively. The hypotheses tested were as

follows:

Ho: The two populations are not identical.

Ha: The two populations are identical.

60

CHAPTER 4

RESULTS AND DISCUSSION

This chapter presents the results of the chemical analysis of the soil samples obtained

from the four study sites in Taveuni and the farm survey data. The quality control

measures taken are discussed followed by the changes in soil pH, OM and macro and

micronutrients during the cropping cycle of taro. Based on the questionnaire survey, the

chapter also describes the trend in taro output and the level and types of fertilisers used

by farmers in taro farms in Taveuni.

4.1 Quality Control

A number of quality control measures were undertaken to ensure reliability of the

methodology. The calibration check standards were used to ensure that the instruments

were working properly (Appendix I). Standard and method blanks were analyzed to

monitor the purity of the reagent, deionized water and the cleanliness of glassware and

equipment. Samples were analyzed in replicates, together with duplicate extraction

which is presented in Table 4.1 and repeated analysis of selected samples to confirm

reproducibility and precision. Sample spiking was also performed to detect interferences

present in the sample and the results are presented in Table 4.2. However, certified

reference material (CRM) could not be included during analysis due to its late arrival.

61

Table 4.1: Results of % difference between duplicate extractions in the determination of the different nutrients.

Nutrients

Ca (mgkg-1)

K (mgkg-1)

Mg (mgkg-1)

Cu (mgkg-1)

Fe (mgkg-1)

Mn (mgkg-1)

Zn (mgkg-1)

P (mgkg-1)

SOM (%)

NO3--N

(mgkg-1) Before Planting Mucuna Beans

D1 4.64 0.72 3.02 0.58 25.37 16.84 0.80 30.54 5.03 - D2 4.68 0.79 3.02 0.51 26.55 15.87 0.80 31.11 4.97 -

Difference 0.77 8.79 0.00 11.76 4.55 5.93 0.00 1.87 1.16 -

D1 4.04 1.03 2.41 0.32 16.22 12.96 0.22 35.85 4.11 - D2 4.00 1.06 2.45 0.26 15.93 12.48 0.22 36.56 3.99 -

Difference 0.89 2.37 1.71 22.22 1.83 3.82 0.00 1.97 2.86 -

D1 0.46 0.31 0.57 1.13 11.21 1.62 0.51 25.71 7.00 - D2 0.41 0.30 0.57 1.13 11.50 1.62 0.66 26.10 7.08 -

Difference 13.11 5.41 0.00 0.00 2.60 0.00 24.88 1.49 1.23 -

D1 1.09 0.25 0.64 1.41 44.84 3.57 0.64 23.78 9.25 - D2 0.96 0.21 0.64 1.41 53.69 3.57 0.71 21.61 9.48 -

Difference 13.24 17.14 0.65 0.00 17.96 0.00 10.75 9.57 2.47 - Before Planting Taro

D1 4.65 0.71 2.48 0.58 24.62 21.82 0.74 22.19 6.71 18.34 D2 4.45 0.71 2.43 0.52 24.31 21.82 0.74 28.51 6.97 19.52

Difference 4.40 0.00 1.68 10.53 1.26 0.00 0.00 24.92 3.81 6.23

D1 0.34 0.27 0.42 0.86 19.08 1.86 0.80 19.41 8.73 16.51 D2 0.33 0.28 0.43 0.83 18.15 1.68 0.92 21.12 7.32 17.07

Difference 2.03 1.48 2.01 4.08 4.96 9.71 14.08 8.44 17.66 3.33

62

D1 0.92 0.21 0.53 0.98 52.89 3.06 0.36 26.21 11.36 27.94 D2 0.96 0.20 0.54 1.04 53.50 3.40 0.35 26.60 11.16 26.28

Difference 4.26 3.17 2.25 5.71 1.14 10.64 4.96 1.45 1.80 6.12

D1 1.71 0.10 1.09 0.58 46.15 0.25 0.56 34.90 23.54 19.71 D2 1.66 0.10 1.12 0.63 45.38 0.27 0.57 39.57 23.48 18.60

Difference 2.76 6.45 2.84 9.52 1.68 10.53 2.14 12.56 0.25 5.81 Three Months into Planting

D1 4.09 0.63 1.35 0.50 20.85 18.83 0.53 17.66 5.55 16.43 D2 4.05 0.64 1.31 0.50 19.81 19.95 0.51 16.41 5.64 16.09

Difference 0.98 0.78 2.69 0.00 5.08 5.82 4.25 7.33 1.55 2.07

D1 0.29 0.19 0.32 0.69 13.21 1.73 0.44 17.34 6.85 14.71 D2 0.28 0.17 0.32 0.64 14.04 1.53 0.45 16.37 6.77 16.25

Difference 4.20 9.84 1.32 7.72 6.06 11.86 2.46 5.75 1.27 9.91

D1 0.61 0.11 0.32 0.85 49.54 2.50 0.30 17.16 9.14 20.30 D2 0.63 0.12 0.33 0.86 49.12 2.70 0.31 18.68 9.19 21.39

Difference 3.23 5.22 1.48 1.86 0.84 7.44 2.89 8.49 0.63 5.24

D1 1.26 0.08 0.67 0.63 33.80 0.29 0.30 32.79 8.56 19.33 D2 1.21 0.08 0.67 0.63 32.77 0.20 0.32 33.12 8.27 17.61

Difference 3.87 3.68 0.36 0.00 3.10 36.27 7.08 1.01 3.44 9.35 At Harvest

D1 637.96 0.50 0.98 0.49 14.51 17.76 0.43 13.85 5.12 13.76 D2 656.70 0.50 0.95 0.45 13.95 19.26 0.48 13.51 5.12 14.01

Difference 2.89 0.87 2.83 8.08 3.92 8.09 9.37 2.53 0.00 1.83

63

D1 47.85 0.11 0.23 0.60 13.21 1.43 0.27 13.31 5.64 13.47 D2 47.05 0.12 0.24 0.57 13.39 1.25 0.24 13.03 5.49 13.38

Difference 1.68 3.80 4.35 4.08 1.40 13.75 12.40 2.18 2.60 0.61

D1 113.64 0.06 0.27 0.69 47.92 2.22 0.29 12.68 8.50 18.41 D2 109.65 0.06 0.26 0.76 47.02 2.45 0.30 12.88 8.56 18.69

Difference 3.57 4.65 1.81 9.84 1.88 9.88 2.92 1.56 0.68 1.52

D1 151.52 0.04 0.55 0.60 20.09 0.12 0.31 25.72 6.01 17.23 D2 149.52 0.04 0.55 0.55 21.21 0.16 0.29 25.94 5.78 17.12

Difference 1.32 4.60 0.13 8.33 5.41 26.49 7.12 0.85 3.92 0.66 D1- Duplicate 1 D2- Duplicate 2

64

Table 4.2: Results of spiked recoveries in the determination of the different nutrients.

1-Before planting mucuna beans 2-Before planting taro/after mucuna treatment 3-Three months into planting 4-At harvest

Sampling

Period

Nutrients spiked recovery + RSD (%)

Ca Mg K Cu Fe Mn Zn P NO3--N

1 94.19 + 4.11 97.84 + 1.74 95.90 + 3.30 95.13 + 2.29 96.18 + 2.57 97.53 + 0.62 97.38 + 0.64 94.44 + 0.86 -

2 95.50 + 2.01 99.15 + 0.24 94.75 + 2.18 96.75 + 0.52 97.65 + 0.72 97.93 + 0.39 97.08 + 1.10 98.23 + 1.08 94.63+ 3.27

3 93.20 + 3.33 96.93 + 0.64 93.30 + 5.01 97.30 + 3.79 94.28 + 0.62 94.60 + 0.82 94.23 + 2.68 88.58+ 4.72 92.80+ 2.39

4 90.18 + 3.73 91.85 + 1.02 90.70 + 1.77 93.70 + 2.66 95.50 + 0.87 92.15 + 2.37 92.95 + 2.82 88.50 + 1.46 93.00 + 6.13

65

4.1.1 Limit of Detection

Method detection limit was calculated using the method adopted by Keyvanfard and

Rezaei (2005) and Chand (2007). The measurements of seven blanks were taken and the

limit of detection was calculated using the equation YLOD = YBlank + 3SD, where

YLOD = Reading for limit of detection, YBlank = Average readings for the seven blanks

and SD = Standard deviation of the blank readings. The method detection limits for the

different nutrients calculated are shown in Table 4.3.

Table 4.3: Method Detection Limit (MDL) for different nutrients.

Nutrients MDL (mgkg-1)

Ca 0.24

Mg 0.01

K 0.04

Cu 0.08

Fe 0.20

Mn 0.05

Zn 0.02

P 0.54

NO3--N 0.01

4.2 Comparison of Different Treatments

Four different treatments were applied to the four study sites (Matei, Mua, Vione and

Delaivuna). No significant differences (ρ > 0.05) however could be observed between

the four treatments applied to the trial sites as at the end of the cropping season, there

was no significant difference in the nutrient levels in the plots applied with treatment A,

B, C and D (Appendix II). A nutrient budget analysis, where the nutrient taken up by the

roots, leaves and corm plus the nutrient levels in the soil at the time of harvest might

show the effects of the different treatments. However, this was beyond the scope of the

study and the current study was not sufficient to select the best treatment out of the four

treatments. Another factor that might have contributed to the insignificant difference on

66

the effect of the four treatments is the small amount of the fertilisers applied under each

treatment (35g/plant). A larger quantity might have shown the difference on the effect of

the treatments.

4.3 Changes in Soil pH and Soil Organic Matter (SOM) over the Cropping Cycle of

Taro

4.3.1 Soil pH

The soil pH levels at different stages of the crop cycle at various experimental sites are

given in Table 4.4. There was no significant difference in the soil pH levels from the

period before mucuna treatment till the time of taro planting for Matei, Mua and Vione

site (ρ > 0.05). Delaivuna site however, showed significant decline in the pH levels for

the same period of time (ρ < 0.05). From the time of taro planting till three months into

planting, no significant difference was observed in the pH levels in any of the trial sites

(ρ > 0.05). On the other hand, significant differences existed in the pH levels for Mua

and Deliavuna site from three months into planting till harvest at 5 % level of

significance (ρ < 0.05). For Mua site there was an increase in the soil pH levels while

Delaivuna site showed reduction in soil pH levels. The variations of soil pH at different

stages of the cropping cycle at various experimental sites are shown in Figure 4.1. In

general, from the time of taro planting till the time of harvest only Delaivuna site

showed significant decline in soil pH levels (ρ < 0.05). The other three sites (Matei, Mua

and Vione) had no significant changes in the soil pH throughout the cropping cycle of

taro.

67

Table 4.4: Soil pH levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of soil pH at experiment sites

Matei Mua Vione Delaivuna

1. Before planting of mucuna bean 5.41 + 0.07 4.42 + 0.02 4.71 + 0.04 5.54 + 0.11

2. Before taro planting 5.18 + 0.18 4.34 + 0.28 4.70 + 0.22 5.11 + 0.19

3. After three months into taro planting

5.02 + 0.20 4.21 + 0.11 4.66 + 0.10 5.12 + 0.19

4. After harvesting of taro 4.99 + 0.04 4.31 + 0.02 4.58 + 0.14 4.93 + 0.06

Figure 4.1: Variation in pH in the soil over the different periods in the cropping cycle of

taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months into

planting and 4. At harvest.

Mann-Whitney test (ρ levels) for the changes in soil pH levels during different periods

at various experimental sites are shown in Table 4.5. Significant difference existed in

soil pH levels between the study sites at the time of harvest (ρ < 0.05) except between

Matei and Delaivuna site. In these two sites, there was no significant difference in the

soil pH levels between the sites at the time of harvest (ρ > 0.05). Matei site had the

highest mean soil pH at the time of taro harvest (4.99 + 0.04) while Mua site had the

lowest level (4.31+ 0.02). According to the ratings by the Chemistry Laboratory,

Koronivia Research Station, Fiji, all sites showed lower than the ideal pH levels (5.6 –

0.00

1.00

2.00

3.00

4.00

5.00

6.00

1 2 3 4

Soil

pH

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

68

6.6) for taro planting at the time of harvest as well as throughout the cropping cycle of

taro (Table 4.4). Soil pH has an impact on the solubility of many macronutrients and

micronutrients. Therefore, at low pH (4.6-5.5) as in the four experimental sites in this

study, macronutrients such as Ca, Mg, K, N and P are expected to be present in low

levels in the soil. Similarly, at low or high pH, micronutrients such as Cu, Fe, Mn and

Zn are mostly present in lower levels in the soil. However, in moderately acid soils (5.6-

6.0) micronutrients are generally available in sufficient levels (Bohn et al., 1979; Coyne

and Thompson, 2006; Thiagalingam, 2000).

Table 4.5: Mann-Whitney test (ρ levels) for the changes in soil pH levels during

different periods at various experimental sites.

Changes in soil pH Experiment sites

Matei Mua Vione Delaivuna

1. From planting of mucuna bean till the

planting of taro

0.081 0.245 1.000 0.021*

2. From taro planting till three months into

taro planting

0.248 1.000 0.663 0.663

3. From three months into taro planting till

harvesting time

0.384 0.020* 0.149 0.021*

4. From taro planting till the harvesting of

taro

0.384 0.200 0.149 0.021*

*Significant at 5 % level (ρ < 0.05)

4.3.2 Soil Organic Matter (SOM)

The SOM levels at different stages of the crop cycle at various experimental sites are

given in Table 4.6. There was a significant increase in the SOM content (ρ < 0.05)

during the period from mucuna treatment till the time of taro planting for all the four

trial sites (Matei, Mua, Vione and Delaivuna). However, there was a significant decrease

in SOM levels from the time of taro planting till three months into planting and from

three months into planting till harvest (ρ < 0.05) for all the trial sites. The variations of

SOM at different stages of the cropping cycle at various experimental sites are shown in

69

Figure 4.2. In general, for all the four trial sites, there has been a significant decline (ρ <

0.05) in SOM content from the time of taro planting till the time of harvest with 5 %

level of significance.

Table 4.6: Soil organic matter (SOM) levels at different stages of the crop cycle at

various experimental sites.

Soil sampling time Levels of SOM (%) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean 4.66 + 0.03 7.32 + 0.01 9.26 + 0.06 10.55 + 0.10

2. Before taro planting 5.92 + 0.54 8.19 + 0.46 11.00 + 0.62 15.41 + 0.33

3. After three months into taro planting

5.51 + 0.21 7.06 + 0.27 9.28 + 0.19 8.11 + 0.64

4. After harvesting of taro 4.97 + 0.14 6.27 + 0.33 8.04 + 0.58 5.63 + 0.57

Figure 4.2: Variation in SOM (%) over the different periods in the cropping cycle of

taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months into

planting and 4. At harvest.

Mann-Whitney test (ρ levels) for the changes in SOM levels during different periods at

various experimental sites are shown in Table 4.7. Significant differences (ρ < 0.05)

were observed in the SOM level between the four study sites at the time of harvest

except between Mua and Delaivuna sites. In these two sites, there was no significant

difference in the SOM levels between the sites at the time of harvest (ρ > 0.05).

Delaivuna site showed the highest level of mean SOM content (10.55 + 0.10 %) in the

0.00

5.00

10.00

15.00

20.00

1 2 3 4

[SO

M],

%

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

70

soil while Matei site showed the lowest level of mean SOM content (4.66 + 0.03 %)

before mucuna bean was planted on the trial sites. All the sites showed an improvement

in the SOM levels after six months of mucuna bean treatment on the plots with

Delaivuna site showing quite a substantial increment. Delaivuna site also showed the

highest loss of mean SOM from the time of taro planting till three months into planting

and from three months into planting till harvest (Table 4.6). While legumes such as

mucuna bean have the ability to add organic matter, etc., and improve soil quality

(Amba et al., 2011; Mora et al. 2012; Ritchie and Tilman, 1995; Sánchez and Richard,

2009), the considerable increase in SOM content in Delaivuna site after mucuna

treatment could probably be due to the effect of lime application and chemical weed

control. Lime was applied to the trial sites by the “ACIAR soil health team” during the

time of mucuna bean planting and chemical weed controllers were sprayed during the

mucuna treatment period. It has been suggested that liming increases OM returns as well

as OM content in the soil (Haynes and Naidu, 1998). Liming could have increased the

degradation of crop residues from previous harvests and dead leaves and weeds

(Condron, 1993). Moreover, the soil texture in Delaivuna site was mostly heavy, clay

soil and the area was surrounded by rain trees, which provided a cool environment.

SOM tends to accumulate under cool and poorly-drained soil conditions (Kissel and

Sonon, 2008). The variation of SOM contents over the different periods of the cropping

cycle of taro is shown in Figure 4.2. Based on the ranking by the Chemistry Laboratory,

Koronivia Research Station, Fiji and Thiagalingam (2000), all sites had low levels (3.44

– 6.88 %) of SOM at the time of harvest of taro except for Vione site which had medium

levels of SOM as 8.04 + 0.58 %. The ideal level of SOM for taro cultivation as

classified by the Chemistry Laboratory, Koronivia Research Station, Fiji is 6.88-17.2 %.

71

Table 4.7: Mann-Whitney test (ρ levels) for the changes in SOM levels during the

different periods at various sites.

Changes in SOM Experiment sites Matei Mua Vione Delaivuna

1. From planting mucuna bean till the taro planting

0.000* 0.000* 0.000* 0.000*

2. From taro planting till three months into taro planting

0.021* 0.000* 0.000* 0.000*

3. From three months into taro planting till harvesting time

0.000* 0.000* 0.000* 0.000*

4. From taro planting till the harvesting of taro

0.000* 0.000* 0.000* 0.000*

*Significant at 5 % level (ρ < 0.05)

4.4 Changes in Macronutrient Contents in the Soil over the Cropping Cycle of

Taro

4.4.1 Soil Nitrate Nitrogen (NO3--N)

The soil NO3--N levels at different stages of the crop cycle at various sites are shown in

Table 4.8. The results showed a decline in NO3--N levels in the soil from the time of taro

planting till three months into planting for all four trial sites (Matei, Mua, Vione and

Delaivuna). However, while the decline was very significant for Vione site (ρ < 0.05), it

was marginally significant for Matei site (ρ = 0.043) and not statistically significant for

Mua and Delaivuna sites (ρ > 0.05). Similarly, a decline was noted in the NO3--N levels

in all four trial sites from three months into planting till harvest. For Matei, Mua and

Vione site, this decline was significant at 5 % probability level (ρ < 0.05), whereas for

Delaivuna site, it was marginally significant (ρ = 0.033). The variation in NO3--N

contents in the soil over different periods of the cropping cycle of taro is shown in

Figure 4.3. In general, there has been a significant decrease in NO3--N levels from the

time of taro planting till the time of harvest for all four trials. This was expected as

NO3--N is easily lost from the soil through various pathways (Kaneko et al., 2010;

Walworth, 2011).

72

Table 4.8: Soil NO3--N levels at different stages of the crop cycle at various

experimental sites.

Soil sampling time Levels of NO3--N (mgkg-1) at experiment sites

Matei Mua Vione Delaivuna 1. Before taro planting 18.29 + 2.80 16.91 + 1.59 25.71 + 1.98 16.84 + 2.20

2. After three months into taro planting

16.26 + 1.09 15.43 + 1.16 22.23 + 1.64 16.47 + 2.07

3. After harvesting of taro 13.86 + 0.95 12.82 + 0.42 20.37 + 1.33 14.36 + 2.84

Figure 4.3: Variation in soil NO3--N (mgkg-1) over the different periods in the cropping

cycle of taro. 1. Before planting taro, 2. Three months into planting and 3. At harvest.

Mann-Whitney test (ρ levels) for the changes in soil NO3--N levels during different

periods at various experimental sites are shown in Table 4.9. Significant differences

existed (ρ < 0.05) in the soil NO3--N level between the four study sites at the time of

harvest except between Matei and Delaivuna sites and between Mua and Delaivuna

sites (ρ > 0.05) where there was no significant difference in the NO3--N levels at the

time of harvest (Table 4.8). Vione site showed the highest level of mean NO3--N content

(20.37 + 1.33 mgkg-1) in the soil at the time of harvest while Mua site showed the lowest

level (12.82 + 0.42 mgkg-1). The ideal NO3--N level in the soil for taro cultivation is 25-

30 mgkg-1 (Heckman, 2002). Soil samples could not be analyzed for NO3--N before

mucuna treatment because the first sampling i.e. sampling prior to planting mucuna

beans on the trial sites was carried out by members of the “ACIAR soil health team” as

0.00

5.00

10.00

15.00

20.00

25.00

30.00

1 2 3

[NO

3- ], m

gkg-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

73

during the sampling period, the current project was not approved. Sampling during that

period was done at a depth of 20 cm. However, for soil NO3--N, the sampling depth

should be approximately 30 cm as NO3- moves more easily with soil water compared to

other nutrients (Reid, 2006). Hence, the effect of mucuna beans on soil NO3--N levels

could not be determined. According to the rating by Heckman (2002) and Thiagalingam

(2000), all sites had low levels (5-15 mgkg-1) of NO3--N at the time of harvest of taro

indicating a very likely chance of N deficiency, except for Vione site which had medium

levels of NO3--N (20.37 + 1.33 mgkg-1) . This could most likely be linked to the low

levels of SOM since N availability to plants is dependent on the breakdown of the SOM

(Ahn, 1993; Heckman, 2002; Kissel and Sonon, 2008). NO3--N, which is the principal

source of N for plants, is relatively unstable in the soil unlike other soil nutrients such as

P or K. The N is easily lost by the process of leaching. As a result soil NO3--N analysis

must be conducted and interpreted considering environmental factors such as rainfall

and temperature, which can rapidly change the availability of the nutrient. However,

these factors were not considered as they could not be determined under the scope of

this study.

Table 4.9: Mann-Whitney test (ρ levels) for changes in soil NO3--N levels during the

different periods at various experimental sites.

Changes in soil NO3--N Experiment sites

Matei Mua Vione Delaivuna 1. From taro planting till three months into taro planting

0.043* 0.166 0.001* 0.603

2. From three months into taro planting till harvesting time

0.000* 0.000* 0.013* 0.033*

3. From taro planting till the harvesting of taro

0.000* 0.000* 0.000* 0.038*

*Significant at 5 % (ρ < 0.05)

74

4.4.2 Soil Available Phosphorous (P)

The soil available P levels at the different stages of the crop cycle at various

experimental sites are shown in Table 4.10. The P levels in soil for the four trial sites

(Matei, Mua, Vione and Delaivuna) showed a decline at each of the following four

different stages of experiment:

� From the period before mucuna beans were planted till the period when taro was

planted i.e. after mucuna treatment for six months.

� From the time of taro planting till three months into planting.

� Three months into planting till harvest.

� From the time of planting till harvest.

Table 4.10: Soil P levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of P (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

33.20 + 0.19 24.10 + 0.36 25.39 + 0.53 45.97 + 0.42

2. Before taro planting

26.29 + 1.40 19.48 + 1.54 24.79 + 1.66 39.40 + 2.84

3. After three months into taro planting

19.58 + 2.33 20.60 + 1.40 18.26 + 1.01 26.97 + 2.62

4. After harvesting of taro

15.83 + 1.40 14.69 + 0.64 13.02 + 1.60 25.71 + 3.12

However, while the decline in P content for Matei, Mua and Delaivuna site was

statistically significant (ρ < 0.05), the decline of P content at Vione site was not

significant (ρ > 0.05) for the period before mucuna beans were planted till the period

when taro was planted i.e. after mucuna treatment for six months. From the time of taro

planting till three months into planting, the reduction in P levels for Mua site was not

statistically significant (ρ > 0.05). Conversely, for the same period of time, the decline in

Matei, Vione and Delaivuna site was significant (ρ < 0.05).

75

Moreover, from three months into planting of taro till harvest, the decline in P levels

was statistically significant for Matei, Mua and Vione site (ρ < 0.05) while it was not

significant for Delaivuna site (ρ > 0.05). The variation in P contents in the soil over the

different periods of the cropping cycle of taro is shown in Figure 4.4. However, in

general, the phosphorous level in the soil has had a significant decline (ρ < 0.05) from

the time of taro planting till harvest for all the four sites.

Figure 4.4: Variation in soil P (mgkg-1) over the different periods in the cropping cycle

of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months into

planting and 4. At harvest.

Mann-Whitney test (ρ levels) for the changes in soil P levels during different periods at

various experimental sites are shown in Table 4.11. There was evidence of significant

differences (ρ < 0.05) in the soil available P level between the four study sites at the

time of harvest except between Mua site and Vione site. The available P levels were

considerably the same for the two sites at the time of harvest (ρ > 0.05). Delaivuna site

showed the highest level of mean P content (25.72 + 3.12 mgkg-1) in the soil at the time

of harvest while the lowest level was recorded for Vione site (13.03 + 1.60 mgkg-1). The

ideal P level in the soil for taro cultivation is 20-40 mgkg-1 (Horneck, et al., 2011;

Mallarino et al., 2003 and Marx, et al., 1999). According to P soil test categories by

Horneck, et al. (2011), Mallarino et al. (2003) and Marx, et al. (1999) for Bray 1 test for

P, three sites (Matei, Mua and Vione) showed low levels (< 20 mgkg-1) of available P at

the time of harvest of taro except for one site (Delaivuna) which had medium levels (20-

40 mgkg-1) of P (Table 4.10). In farming areas in Nausori, Fiji, Kumar (2010) also found

0.00

10.00

20.00

30.00

40.00

50.00

1 2 3 4

[P],

mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

76

P deficiency in the soil. According to Ogwang (1988), tropical soils show signs of P

deficiency due to the soil mineralogical composition. Most of the P is bound to

compounds with low solubility such as calcium phosphate, which has very low

solubility and iron and aluminum phosphate which are insoluble forms (Millar, 2004). In

acidic soil, it is found in the insoluble iron and aluminum phosphate form (Foth, 1990).

Hence, the low available P in the study sites could be associated with the formation of

these relatively insoluble phosphates. This is also in agreement with the statement that at

low pH, soil available P is present in low levels (Bohn et al., 1979; Coyne and

Thompson, 2006). However, loss by leaching of P is insignificant in most soils due to its

low solubility and limited movement in the soil (Black, 1957).

Table 4.11: Mann-Whitney test (ρ levels) for changes in soil P levels during the

different periods at various experimental sites.

Changes in soil P levels Experiment sites Matei Mua Vione Delaivuna

1. From planting mucuna bean till the taro planting

0.000* 0.000* 0.245 0.000*

2. From taro planting till three months into taro planting

0.000* 0.057 0.000* 0.000*

3. From three months into taro planting till harvesting time

0.000* 0.000* 0.000* 0.119

4. From taro planting till the harvesting of taro

0.000* 0.000* 0.000* 0.000*

*Significant at 5 % level (ρ < 0.05)

4.4.3 Soil Exchangeable Bases (K, Ca and Mg)

4.4.3.1 Soil Exchangeable Potassium (K)

The levels of soil K at different stages of crop cycle at various sites are shown in Table

4.12. The results showed that there was a significant decrease in K levels at all four trial

sites (Matei, Mua, Vione, Delaivuna) prior to mucuna treatment till the time of taro

planting (ρ < 0.05). Similarly, ρ-values for Vione and Delaivuna sites indicated that the

decline in the K content in the soil was significant in the two sites from the period when

taro was planted till three months into planting. However, in the Matei and Mua sites,

the reduction in the K content in the soil in the same period was not significant (ρ >

77

0.05). Moreover, from three months into planting till harvest, the decline in K levels in

all the four sites was significant at 95 % confidence interval. The variation in K contents

in the soil over different periods of cropping cycle of taro is shown in Figure 4.5. In

general, the K level in the soil has had a significant decline (ρ < 0.05) from the time of

taro planting till harvest for all the four sites.

Table 4.12: Soil K levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Level of K (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

383.33 + 8.71 119.35 + 4.76 93.94 + 5.94 65.81 + 4.40

2. Before taro planting

265.35 + 19.10 83.12 + 15.25 77.90 + 0.05 45.72 + 9.08

3. After three months of taro planting

244.50 + 29.90 70.97 + 4.75 41.38 + 7.02 29.08 + 2.32

4. After harvesting of taro

209.82 + 25.44 39.54 + 4.61 22.96 + 4.81 15.85 + 1.65

Figure 4.5: Variation in soil K (mgkg-1) over the different periods in the cropping cycle

of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months into

planting and 4. At harvest.

-50.00

50.00

150.00

250.00

350.00

450.00

1 2 3 4

[K],

mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

78

Mann-Whitney test (ρ levels) for the changes in soil K levels during different periods at

various experimental sites are shown in Table 4.13. Significant differences (ρ < 0.05)

existed for K levels between the four study sites at the time of taro harvest. The highest

level of mean K content in the soil was recorded for Matei site (209.82 + 25.44 mgkg-1)

whilst the lowest level was recorded for Delaivuna site (15.85 + 1.65 mgkg-1) at the time

of harvest. According to ratings by the Chemistry Laboratory, at Koronivia Research

Station, Fiji and Thiagalingam (2000), Matei was the only site which showed ideal

levels of K (117-234 mgkg-1) at the time of harvest. Mua, Vione and Delaivuna site

showed below critical levels of soil K at the time of taro harvest (< 39 mgkg-1). Before

the cropping cycle of taro, Matei site showed high levels of soil K while Mua site

showed sufficient level of soil K (Table 4.12). Soil K is easily lost from soils with fine

texture such as clay and clay loam soils because of its availability in soluble and

exchangeable forms (Millar, 2004). Soils in the four study sites are of clay to clay loam

nature; hence K could have been easily leached through the soil, resulting in low and

declining levels of soil K in the trial sites. The low levels of K in Mua, Vione and

Delaivuna sites could also be linked to low soil pH, where K is generally available in

low levels at low pH (Bohn et al., 1979; Coyne and Thompson, 2006).

Table 4.13: Mann-Whitney test (ρ levels) for changes in soil K levels during the

different periods at various experimental sites.

Changes in soil K levels Experiment sites Matei Mua Vione Delaivuna

1. From planting mucuna bean till the taro planting

0.000* 0.000* 0.000* 0.011*

2. From taro planting till three months into taro planting

0.064 0.083 0.000* 0.000*

3. From three months into taro planting till harvesting time

0.011* 0.021* 0.000* 0.000*

4. From taro planting till the harvesting of taro

0.000* 0.021* 0.000* 0.000*

*Significant at 5 % level (ρ < 0.05)

79

4.4.3.2 Soil Exchangeable Calcium (Ca)

The levels of soil Ca at different stages of the crop cycle at various sites are shown in

Table 4.14. Significant differences in soil Ca were observed for the four trial sites

(Matei, Mua, Vione and Delaivuna). The results in Table 4.14 show a decline trend at

each stage of the four stages. However, while the decline in Ca content for the Vione

and Delaivuna sites was statistically significant (ρ < 0.05), the decline for Matei and

Mua sites was not significant (ρ > 0.05) for the period from planting of mucuna beans

till the period when taro was planted i.e. six months after mucuna treatment. The decline

in Ca levels in the soil from the time of taro planting till three months into planting was

statistically significant for Mua, Vione and Delaivuna sites (ρ < 0.05). It was however

not significant for Matei site (ρ > 0.05).

From three months into planting of taro till harvest, the decline in Ca levels was

statistically significant for all the four trial sites (ρ< 0.05). The variation in Ca contents

in the soil over different periods of cropping cycle of taro is shown in Figure 4.6. In

general, the Ca level in the soil has had a significant decline (ρ < 0.05) from the time of

taro planting till harvest for all the four sites. The decline resulted in Ca deficiency in

Mua, Vione and Delaivuna sites and if another season of taro cultivation takes place on

the same piece of land with no time for nutrient replenishment, the growth and

development of the plant will be affected (Ahn, 1993; Archer, 1985).

Table 4.14: Soil Ca levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of Ca (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

847.62 + 26.19 78.29 + 7.86 230.57 + 25.10 426.79 + 18.27

2. Before taro planting 817.50 + 104.91 62.20 + 18.07 187.00 + 33.12 375.79 + 48.26

3. After three months into taro planting

734.65 + 95.86 43.06 + 11.28 128.59 + 22.49 264.43 + 27.65

4. After harvesting of taro

439.44 + 53.13 27.59 + 6.67 77.77 + 14.60 167.89 + 19.88

80

Mann-Whitney test (ρ levels) for the changes in soil Ca levels during different periods at

various experimental sites are shown in Table 4.15. Significant differences (ρ < 0.05)

existed for Ca levels between the four study sites at the time of harvest. Matei site had

the highest level of mean Ca content (439.44 + 53.128 mgkg-1) in the soil at the time of

taro harvest while Mua site had the lowest (27.59 + 6.67 mgkg-1). According to the

ratings by the Chemistry Laboratory, Koronivia Research Station, Fiji and Thiagalingam

(2000) Mua, Vione and Delaivuna had Ca levels below critical levels of 200 mgkg-1 at

the time of harvest of taro. Mua site had below critical levels low levels of Ca even prior

to farm preparation for taro planting via mucuna treatment and showed signs of Ca

deficiency throughout the cropping cycle (Table 4.14). The ideal level of Ca in the soil

is 400-2000 mgkg-1 (Thiagalingam, 2000). Similar observation has been reported by

Kumar (2010), where the soil Ca levels in farms located in Nausori, Fiji was found

below the critical level. Acidic soils usually show evidence of Ca deficiencies (Horneck,

et al., 2011; Kumar, 2010; Marx et al., 1999). This happens because increased acidity in

soil decreases the availability of Ca due to the reduced cation exchange capacity and

exchangeable nutrient cation (Foth, 1990). Hence, high soil acidity may be a possible

factor contributing to the low soil Ca level in the study sites (Bohn et al., 1979; Coyne

and Thompson, 2006).

Table 4.15: Mann-Whitney test (ρ levels) for changes in soil Ca levels during the

different periods at various experimental sites.

Changes in soil Ca levels Experiment sites Matei Mua Vione Delaivuna

1.From planting mucuna bean till the taro planting

0.642 0.163 0.002* 0.005*

2. From taro planting till three months into taro planting

0.057 0.007* 0.000* 0.021*

3. From three months into taro planting till harvesting time

0.000* 0.000* 0.000* 0.021*

4. From taro planting till the harvesting of taro

0.000* 0.000* 0.000* 0.021*

*Significant at 5 % level (ρ < 0.05)

81

Figure 4.6: Variation in soil Ca (mgkg-1) over the different periods in the cropping cycle

of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months into

planting and 4. At harvest.

4.4.3.3 Soil Exchangeable Magnesium (Mg)

The levels of soil Mg at the different stages of crop cycle at various sites are shown in

Table 4.16. The decline in Mg content in the soil at Matei site from mucuna treatment

till taro planting was not statistically significant with 5 % level of significance (ρ >

0.05). On the other hand, for the same period, a significant decline in Mg existed for

Mua, Vione and Delaivuna sites (ρ < 0.05). Similarly, the decline in Mg levels in the

soil was found to be statistically significant (ρ < 0.05) in all the four sites from the time

of taro planting till three months into planting and from three months into planting till

harvest. The variation in Mg contents in the soil over the different periods of cropping

cycle of taro is shown in Figure 4.7. In general, the Mg level in the soil has had a

significant decline (ρ < 0.05) from the time of taro planting till harvest for all the four

sites.

-100.00

100.00

300.00

500.00

700.00

900.00

1 2 3 4

[Ca]

, mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

82

Table 4.16: Soil Mg levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of Mg (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

338.23 + 0.72 64.99 + 1.23 79.20 + 0.13 131.80 + 0.61

2. Before taro planting

316.10 + 32.05 47.07 + 8.32 62.92 + 3.13 116.67 + 11.85

3. After three months into taro planting

157.11 + 9.71 47.55 + 4.76 40.06 + 2.18 73.59 + 7.55

4. After harvesting of taro

121.48 + 7.00 34.61 + 2.85 35.25 + 3.37 63.50 + 2.80

Figure 4.7: Variation in soil Mg (mgkg-1) over the different periods in the cropping

cycle of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months

into planting and 4. At harvest.

Mann-Whitney test (ρ levels) for the changes in soil Mg levels during different periods

at the various experimental sites are shown in Table 4.17. Significant differences (ρ <

0.05) existed for Mg levels between the four study sites at the time of harvest. The

highest level of mean Mg content in the soil was noted for Matei site (121.48 + 7.00

mgkg-1) whilst the lowest level was seen in Mua site (34.61 + 2.85 mgkg-1) at the time

of taro harvest. According to ratings by the Chemistry Laboratory, Koronivia Research

Station, Fiji and Thiagalingam (2000), Matei site was the only site which had sufficient

levels of soil Mg (> 121 mgkg-1) at the time of taro harvest. The Mua and Vione had

critical levels of soil Mg (< 60 mgkg-1) while Delaivuna had low levels (60-120 mgkg-1).

-50.00

50.00

150.00

250.00

350.00

1 2 3 4

[Mg]

, mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

83

As in the case of soil Ca level, Mua and Vione site had low levels of Mg even before

mucuna bean was planted for land preparation before taro planting and showed signs of

Mg deficiency throughout the cropping cycle (Table 4.16). In humid areas, such as the

tropics, Mg, like Ca, is easily leached through the soil. Moreover, since Mg deficiencies

are frequently associated with low soil pH (Kissel and Sonon, 2008; Horneck, et al.,

2011), low soil pH in the trial sites could be a contributing factor to low Mg levels.

Table 4.17: Mann-Whitney test (ρ levels) for changes in soil Mg levels during the

different periods at various experimental sites.

Changes in soil Mg levels Experiment sites Matei Mua Vione Delaivuna

1. Before planting mucuna bean till the taro planting

0.163 0.011* 0.000* 0.011*

2. From taro planting till three months into taro planting

0.000* 0.000* 0.000* 0.000*

3. From three months into taro planting till harvesting time

0.000* 0.000* 0.004* 0.001*

4. From taro planting till the harvesting of taro

0.000* 0.000* 0.000* 0.000*

*Significant at 5 per cent level (ρ < 0.05)

4.5 Changes in Micronutrient Content in the Soil over the Cropping Cycle of

Taro

4.5.1 Soil Extractable Copper (Cu)

The Cu contents in the soil at different stages of the crop cycle at various environmental

sites are shown in Table 4.18. The results showed an increase in Cu content in the soil

for Matei site from the period mucuna treatment till the period when taro was planted

i.e. after mucuna treatment. This increase was however not statistically significant at 95

% confidence interval (ρ > 0.05). For the same period of time, there was a decrease in

the Cu levels in the soil for Mua, Vione and Delaivuna sites. The decline was very

significant for all the sites (ρ < 0.05). From the time of taro planting till three months

into planting, again there was an increase in Cu levels in the soil for Matei site. But this

increase was still not significant (ρ > 0.05). For Mua, Vione and Delaivuna sites, for the

same period of time, there was a significant decline in Cu content in the soil. From three

84

months into planting till harvest, Cu levels decreased in all the four trial sites. For Matei

and Mua sites, this decline was very significant (ρ < 0.05). It was also marginally

significant for Vione site (ρ = 0.037). However, the decline in Cu levels for Delaivuna

site was not significant (ρ > 0.05). The variation of Cu contents in the soil over different

periods of the cropping cycle of taro is shown in Figure 4.8. In general, the Cu level in

the soil has had a significant decline from the time of taro planting till harvest for Mua

and Vione sites (ρ< 0.05) while there was no significant difference in Cu levels for

Matei and Delaivuna site for the same period of time.

Table 4.18: Soil Cu levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of Cu (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

0.45 + 0.11 1.05 + 0.14 1.26 + 0.11 1.74 + 0.11

2. Before taro planting 0.47 + 0.13 0.63 + 0.15 0.87+ 0.15 0.74 + 0.15

3. After three months into taro planting

0.56 + 0.11 0.78 + 0.12 0.78 + 0.14 0.65 + 0.06

4. After harvesting of taro 0.45 + 0.08 0.57 + 0.10 0.66 + 0.10 0.63 + 0.10

Figure 4.8: Variation in soil Cu (mgkg-1) over the different periods in the cropping

cycle of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months

into planting and 4. At harvest.

0.00

0.50

1.00

1.50

2.00

1 2 3 4

[Cu]

, mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

85

Mann-Whitney test (ρ levels) for the changes in soil Cu levels during different periods at

various experimental sites are shown in Table 4.19. Significant differences (ρ < 0.05)

were observed in Cu levels between the four study sites at the time of taro harvest

except between Mua and Delaivuna sites and between Vione and Delaivuna sites. The

mean Cu content in the soil at the time of harvest was found to be highest for Vione

(0.66 + 0.10 mgkg-1) and lowest for Matei (0.45 + 0.08 mgkg-1). Based on the ratings by

the Chemistry Laboratory, Koronivia Research Station, Fiji and Thiagalingam (2000) all

sites showed ideal levels (> 0.2 mgkg-1) of Cu levels at the time of harvest (Table 4.18).

All sites also showed ideal levels of soil Cu throughout the cropping cycle of taro. This

is in agreement with the statement that at in acidic soils, micronutrients are generally

available in sufficient levels (Bohn et al., 1979; Coyne and Thompson, 2006). Cu forms

many kinds of soluble and insoluble complexes with organic substances. Therefore, the

binding capability of Cu to the soil and its solubility depends entirely on the types and

quantity of organic matter in the soil. As soil materials weather, Cu is released, after

which it is converted onto the exchangeable form to be taken up by plants or leached

from the soil (Kumar, 2010; Pendias and Pendias, 2001). Hence, the reduction in Cu

levels in the sites could be due to the presence of the soluble forms of Cu-complexes,

which could have been easily leached out from the soil.

Table 4.19: Mann-Whitney test (ρ levels) for changes in soil Cu levels during the

different periods at various experimental sites.

Change in soil Cu levels Experiment sites Matei Mua Vione Delaivuna

1. From planting mucuna bean till the taro planting

0.514 0.000* 0.000* 0.000*

2. From taro planting till three months into taro planting

0.082 0.563 0.133 0.133

3. From three months into taro planting till harvesting time

0.015* 0.002* 0.037* 0.451

4. From taro planting till the harvesting of taro

0.772 0.001* 0.003* 0.072

*Significant at 5 % level (ρ < 0.05)

86

4.5.2 Soil Extractable Iron (Fe)

The levels of soil Fe at the different stages of the crop cycle at various sites are shown in

Table 4.20. Significant increase in Fe levels in the soil were observed at Matei and Mua

site prior to mucuna treatment till the time of planting of taro i.e. after mucuna treatment

(ρ < 0.05). For the same period of time, there was a significant reduction in Fe levels in

Vione and Delaivuna site (ρ < 0.05). From the time of taro planting till three months into

planting there was a significant decline (ρ < 0.05) in Fe levels at all the four trial sites

(Matei, Mua, Vione and Delaivuna). Similarly, from three months into planting till

harvest, there was a significant decrease in Fe levels in the soil at Matei, Vione and

Daliavuna site (ρ < 0.05). The decline in Fe levels for the same period of time for Mua

site was marginally significant compared to the other sites (ρ = 0.042). The variation of

Fe contents in the soil over the different periods of the cropping cycle of taro is shown in

Figure 4.9. In general, the Fe level in the soil has had a significant decline (ρ < 0.05)

from the time of taro planting till harvest for all the four sites.

Table 4.20: Soil Fe levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of Fe (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

20.11 + 0.21 10.47 + 0.19 53.57 + 1.57 50.33 + 1.57

2. Before taro planting 25.50 + 3.03 19.69 + 2.02 47.80 + 6.48 43.56 + 3.00

3. After three months into taro planting

21.54 + 2.70 17.34 + 2.36 39.89 + 6.40 30.77 + 4.72

4. After harvesting of taro 16.76 + 1.84 14.69 + 0.92 34.52 + 7.95 20.97 + 1.77

87

Figure 4.9: Variation in soil Fe (mgkg-1) over the different periods in the cropping cycle

of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months into

planting and 4. At harvest.

Mann-Whitney test (ρ levels) for the changes in soil Fe levels during the different

periods at the various experimental sites are shown in Table 4.21. The ddifference in Fe

levels between the four study sites at the time of taro harvest was significant (ρ < 0.05).

Vione site had the highest level of mean Fe content at the time of taro harvest (34.52 +

7.95 mgkg-1) while Mua site had the lowest level (14.69 + 0.92 mgkg-1). According to

ratings by the Chemistry Laboratory, Koronivia Research Station, Fiji and Thiagalingam

(2000), all sites showed ideal levels of Fe (> 4.5 mgkg-1) at the time of harvest as well as

throughout the cropping cycle of taro (Table 4.20). This finding is consistent with those

of Aregheore et al. (2007) and Kumar (2010), where chances of Fe deficiency are less

due to its adequate levels in the tropical soils especially in the acidic nature of Fijian

soil. This is also in agreement with the statement that at in acidic soils, micronutrients

are generally available in sufficient levels (Bohn et al., 1979; Coyne and Thompson,

2006). However, high Fe levels associated with low soil pH could be a contributing

factor to low available soil P (Tejada et al., 1987).

0.00

10.00

20.00

30.00

40.00

50.00

60.00

1 2 3 4

[Fe]

, mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

88

Table 4.21: Mann-Whitney test (ρ levels) for changes in soil Fe levels during the

different periods at various experimental sites.

Change in soil Fe levels Experiment sites Matei Mua Vione Delaivuna

1. Before planting mucuna bean till the taro planting

0.000* 0.000* 0.011* 0.000*

2. From taro planting till three months into taro planting

0.004* 0.000* 0.008* 0.000*

3. From three months into taro planting till harvesting time

0.000* 0.042*

0.013* 0.000*

4. From taro planting till the harvesting of taro

0.000* 0.000* 0.002* 0.000*

*Significant at 5 % level (ρ < 0.05)

4.5.3 Soil Extractable Manganese (Mn)

The availability of Mn contents in the soil at different stages of the cropping cycle at

various experimental sites is shown in Table 4.22. A significant increase in Mn levels in

the soil existed for Matei site from the period before application of mucuna treatement

till the time of taro planting i.e. after mucuna treatment (ρ < 0.05). However, the

increase in Mn levels at Mua site and the decline in in Mn levels at Vione site is not

statistically significant (ρ > 0.05). On the contrary, the decrease in Mn levels in

Delaivuna site for the same period of time is statistically significant at 95 % confidence

interval. From the time of taro planting till three months into planting, the reduction in

Mn levels in the soil for Matei, Mua and Delaivuna site is not significant for ρ > 0.05.

Yet, for the same period of time, the decline is quite significant for Vione site (ρ =

0.028). Moreover, from three months into planting till harvest, a slight decline in Mn

levels in the soil was noted for all four trial sites. This decline however, was found not to

be statistically significant (ρ > 0.05). However, there has been a significant reduction in

Mn levels in the soil from the time of taro planting till harvest in the four study sites.

The variation of Mn in the soil over the different periods of the cropping cycle of taro is

shown in Figure 4.10.

89

Table 4.22: Soil Mn levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of Mn (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

14.50 + 0.34 1.67 + 0.08 3.73 + 0.07 0.32 + 0.03

2. Before taro planting 23.86+ 3.91 1.34 + 0.42 3.23 + 1.05 0.26 + 0.14

3. After three months into taro planting

21.18 + 2.28 1.28 + 0.62 2.32 + 0.58 0.19 + 0.06

4. After harvesting of taro 19.48 + 2.51 1.34 + 0.21 1.99 + 0.51 0.13 + 0.08

Figure 4.10: Variation in soil Mn (mgkg-1) over the different periods in the cropping

cycle of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months

into planting and 4. At harvest.

Mann-Whitney test (ρ levels) for the changes in soil Mn levels during the different

periods at various experimental sites are shown in Table 4.23. The difference in the

mean Mn levels between the four study sites at the time of harvest was significant (ρ <

0.05). Matei site had the highest level of mean Mn content at the time of taro harvest

(19.48 + 2.51 mgkg-1) while Delaivuna site had the lowest level (0.13 + 0.08 mgkg-1).

According to the ratings by Chemistry Laboratory, Koronivia Research Station, Fiji and

Thiagalingam (2000), Matei and Vione sites showed ideal levels (2-50 mgkg-1) of Mn at

the time of harvest of taro. Mua showed low levels (1-2 mgkg-1) while Delaivuna

showed critical levels (< 1 mgkg-1) of soil Mn at the time of harvest. Only Matei and

0.00

5.00

10.00

15.00

20.00

25.00

30.00

1 2 3 4

[Mn]

, mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

90

Vione had sufficient levels of Mn before the cropping cycle of taro (Table 4.22). Mn

deficiency is common when soil pH is 6 and above (Horneck et al., 2011; Kissel and

Sonon, 2008). However, all study sites had soil pH below 6. On the other hand, Mn

solubility is high in highly acidic soil (Bohn et al., 1979). This could explain the

reduction in soil Mn levels from the time of taro planting till harvest as soluble Mn

could have been subsequently lost through plant absorption or leaching.

Table 4.23: Mann-Whitney test (ρ levels) for changes in soil Mn levels during the

different periods at various experimental sites.

Change in soil Mn levels Experiment sites Matei Mua Vione Delaivuna

1. Before planting mucuna bean till the taro planting

0.000* 0.103 0.163 0.020*

2. From taro planting till three months into taro planting

0.057 0.056 0.028* 0.072

3. From three months into taro planting till harvesting time

0.149 0.642 0.165 0.049*

4. From taro planting till the harvesting of taro

0.006* 0.002* 0.003* 0.004*

*Significant at 5 % level (ρ < 0.05)

4.5.4 Soil Extractable Zinc (Zn)

The availability of Zn contents in the soil at different stages of the cropping cycle at

various experimental sites is shown in table Table 4.24. Results show that the increase in

Zn levels in the soil for Matei and Mua sites and the decline in Zn levels in the soil for

Vione and Delaivuna sites from mucuna treatment till taro planting was statistically

significant with 95 % confidence interval (ρ< 0.05). However, from the time of taro

planting till three months into planting, the reduction in Zn levels in the soil for all the

four experimental sites was found to be significant for ρ < 0.05. However, from three

months into planting till harvest, the decline in Zn levels at Matei and Mua site was

quite significant (ρ < 0.05) while, for the same period of time, the decline in Zn levels

for Vione and Delaivuna sites was not found to be significant (ρ > 0.05). The variation

in Zn contents in the soil over the different periods of the cropping cycle of taro is

shown in Figure 4.11. Nevertheless, in general, there was a significant reduction (ρ <

91

0.05) in Zn levels in the soil for all four trial sites from the time of taro planting till

harvest.

Table 4.24: Soil Zn levels at different stages of the crop cycle at various experimental

sites.

Soil sampling time Levels of Zn (mgkg-1) at experiment sites Matei Mua Vione Delaivuna

1. Before planting of mucuna bean

0.66 + 0.04 0.53 + 0.02 0.59 + 0.04 0.72 + 0.13

2. Before taro planting 0.74 + 0.08 1.28 + 0.24 0.40 + 0.10 0.55 + 0.04

3. After three months into taro planting

0.60 + 0.12 0.80 + 0.16 0.27 + 0.09 0.40 + 0.07

4. After harvesting of taro 0.51 + 0.10 0.60 + 0.15 0.27 + 0.05 0.36 + 0.05

Figure 4.11: Variation in soil Zn (mgkg-1) over the different periods in the cropping

cycle of taro. 1. Before planting mucuna beans, 2. Before planting taro, 3. Three months

into planting and 4. At harvest.

Mann-Whitney test (ρ levels) for the changes in soil Zn levels during the different

periods at the various experimental sites are shown in Table 4.25. Significant differences

existed in the mean Zn levels between the study sites at the time of taro harvest (ρ <

0.05) except between Matei and Mua sites and between Mua and Delaivuna sites where

the Zn levels were considerably the same at the time of taro harvest. Mua site had the

highest level of mean Zn content at the time of taro harvest (0.60 + 0.15 mgkg-1) while

0.000.200.400.600.801.001.201.40

1 2 3 4

[Zn]

, mgk

g-1

Periods in the cropping cycle

Matei

Mua

Vione

Delaivuna

92

Vione site had the lowest level (0.27 + 0.05 mgkg-1). Based on the rankings by the

Chemistry Laboratory, Koronivia Research Station, Fiji and Thiagalingam (2000), all

sites showed evidence of low soil Zn (0.3-0.8 mgkg-1) at the time of taro harvest as well

as throughout the cropping cycle of taro (Table 4.24). According to ratings by the

Chemistry Laboratory, Koronivia Research Station, Fiji the ideal levels of Zn in the soil

for taro production is > 1.2 mgkg-1. There was also evidence of Zn deficiency according

to the findings of Kumar (2010) in farming areas in Nausori, Fiji. Like other soil

micronutrients, low soil pH aids Zn solubility in humid areas and plant extraction of soil

Zn is high in acidic soil (Aubert and Pinta, 1977). This may explain the Zn deficiency as

soluble soil Zn may have been easily leached through the soil. Moreover, Zn deficiency

could have also been contributed by soil liming and addition of chemical phosphate

(Kissel and Sonon, 2008) which was part of the “ACIAR’s soil health” trial treatment

for the four sites.

Table 4.25: Mann-Whitney test (ρ levels) for changes in soil Zn levels during the

different periods at various experimental sites.

Change in soil Zn levels Experiment sites Matei Mua Vione Delaivuna

1. From planting mucuna bean till the taro planting

0.002* 0.000* 0.000* 0.000*

2. From taro planting till three months into taro planting

0.005* 0.001* 0.005* 0.000*

3. From three months into taro planting till harvesting time

0.043* 0.015* 0.561 0.272

4. From taro planting till the harvesting of taro

0.000* 0.000* 0.003* 0.000*

*Significant at 5 % level (ρ < 0.05)

93

4.6 Results from the Farm Survey (Questionnaire Analysis)

A total of 50 farmers were personally interviewed using a structured questionnaire

(Appendix III). The farmers were targeted from areas near the soil sampling sites i.e.

Matei, Mua, Vione and Delaivuna in Taveuni, Fiji. Information gathered from these

farmers included personal details such as age, education level, farm size and location of

their farm. Other information included the crops they produced and its quantity over the

past three years (2010-2012). Knowledge was also gained about the various forms and

quantity of fertiliser they used as well as other soil fertility measures that they have been

using on their farms for the past three years (2010-2012). Ideas were also gathered about

problems that they were facing in following proper soil fertility management practices.

The age of the farmers interviewed ranged from 20 to 71 years and the average farm size

was 2.15 acres ranging from 0.5 to 5 acres. The common crops grown on their farms

included taro, cassava, yams, yaqona and other cash crops such as bean, chilli, tomatoes,

etc. The farms had clay, clay loam, loam or sandy loam soil type. The main technique

used by farmers in Taveuni, Fiji to prepare their farms for planting is manual digging.

4.6.1 Crop Output and Chemical Fertiliser Usage for the Years 2010-2012

The trends of the taro production in different types of soils on farms from 2010-2012 is

shown in Table 4.26. The trends in taro production for farms having clay soil, clay loam

soil, loam soil and sandy soil are shown in Figures 4.12 to 4.15. Statistical analysis

carried out on the taro production data obtained from the farmers surveyed proves that

there has been a significant decline (ρ < 0.01) in the taro production from the year 2010

to 2012 (Appendix IV). However, results reveal a significant increase (ρ < 0.01) in the

amount of nitrogen (N), phosphorous (P) and potassium (K) appended into the soil by

these farmers in the form of chemical fertilisers (Appendix V). This trend, where despite

the increase in the input of fertilisers, the crop output decreases, could mean that the soil

fertility level in the study areas is quite depleted. Another contributing factor for this

trend could be that, the quantity of fertiliser used by these farmers is less than the

amount of nutrients lost during the previous cropping. The result is the lost nutrients,

94

which are not fully replaced, leading to continuous decline in soil fertility and reduced

taro production.

Table 4.26: Trends of the average taro yields (kg/acre) in different soil types on farms

from 2010-2012.

Soil type Year

2010 2011 2012

Clay soil 1000 700 600

Clay loam soil 1671 1197 830

Loam soil 2875 2184 1710

Sandy loam soil 1622 1295 920

Figure 4.12: Trends in taro production for farms with clay soil (Questionnaire survey,

n= 50).

0200400600800

10001200

1Taro

Out

put (

kg/a

cre)

No. of Farmers

2010

2011

2012

95

Figure 4.13: Trends in taro production for farms with clay loam soil (Questionnaire

survey, n= 50).

Figure 4.14: Trends in taro production for farms with loam soil (Questionnaire survey,

n= 50).

Figure 4.15: Trends in taro production for farms with sandy loam soil (Questionnaire

survey, n= 50).

0100020003000400050006000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

Taro

Out

put (

kg/a

cre)

No. of Farmers

2010

2011

2012

0100020003000400050006000

1 2 3 4 5 6 7 8 9 10 11

Taro

Out

put (

kg/a

cre)

No. of Farmers

2010

2011

2012

0

1000

2000

3000

4000

1 2 3 4

Taro

Out

put (

kg/a

cre)

No. of Farmers

2010

2011

2012

96

4.6.2 Routine Soil Analysis and Fertiliser Application

Knowing the fertility status of the soil is useful in designing a balanced fertiliser

application scheme (Bista et al., 2010). However, like many traditional farming systems,

many farmers in Taveuni, Fiji rely on the soils natural ability to provide essential

nutrients to crops and apply any form of fertiliser (organic or chemical) without

knowing the fertility status of the soil. The survey revealed that 94 % of the farmers

interviewed never had the soil on their farm tested while the remaining 6 %, who had

sent soil samples for analysis, did not get any feedback from the soil testing laboratories

on the nutrient status of their farm. Hence, fertiliser is applied either based on traditional

knowledge or on the availability of the fertiliser. The study showed that 4 % of the

farmers do not apply any form of fertiliser on their farm during any time in the cropping

cycle of a crop. The remaining 96 % apply (either organic, inorganic or a combination of

both) fertilisers either at the time of crop planting (6 %), in the middle of the cropping

cycle (82 %) or during both periods (8 %).

4.6.3 Proper Soil Fertility Management Constraints

Some of the factors that were mentioned by farmers that hindered them from following

proper soil fertility management practices included high cost of fertilisers, lack of

professional knowledge on fertilizer application, lack of knowledge on the soil fertility

status in their farms, insufficient supply of fertilisers and lack of government support.

Almost all the farmers (98 %) believed that government support in terms of fertilisers,

other farming equipment and educating farmers on soil management practices will help

resolve many of their soil management constraints.

4.6.4 Organic Sources of Nutrients

Mulching is the primary organic source of nutrient, serving as a major means for soil

fertility management in Taveuni, Fiji. It was used by 38 % of the famers interviewed on

their farms. The number of farmers using the different organic fertilisers and the average

quantity of these fertilisers used on their farms is shown in Table 4.27 and Table 4.28

respectively. The highest percentage is used on farms with clay loam soil type.

Mulching was however, not used by farmers with sandy loam soil type. From the 50

97

farmers interviewed 14 % of the farmers have been using fish meal as an organic source

of nutrient in their taro farms. Out of this, 12 % is applied to farms with clay loam soil

type and 2 % to farms with loam soil type. It was found that fish meal is not used on

farms with clay and sandy loam soil type. In general, an average of 75 g/taro plant of

fish meal have been used by these 14 % farmers (2010-2012). Similarly, compost has

been used by 16 % of the farmers who have clay loam soil type on their farm. The data

presented clearly shows that an average of 112.50 g/taro plant of compost are applied by

farmers who use this as a nutrient source. Moreover, 18 % of the farmers apply lime as a

source of nutrient on their farms of which 2 % is used on farms with loam soil type

while the remaining is used on farms with clay loam soil. For those using this as a

nutrient source on their farm, an average of 88.89 g/taro plant is used. Seaweed is also

used by a small group of farmers (4 %) on their farms with an average of 50 g/taro plant.

The farmers’ responses on the constraints for insufficient use of organic sources of

nutrients are shown in Table 4.29. According to this study, it is evident that a significant

number of farmers interviewed do not use organic sources of nutrient on their farm and

those that use, may not be using sufficient quantity. A number of constraining factors

was mentioned by the farmers that hindered them from using sufficient amounts of

organic fertilisers. The study revealed that farmers of Taveuni, Fiji are quite uninformed

about the various organic sources of nutrients and its benefits. Some farmers face

shortage of organic fertilisers while others do not have sufficient time to prepare them.

A major influencing factor was seen to be the lack of awareness on the fertility status of

the soil on their farm.

Table 4.27: Number of farmers using the different organic fertilisers on their farms

(Data presented as frequency of total survey population).

Soil Type Frequency, n = 50 (%)

Fish meal Compost Lime Seaweed Mulching

Loam soil 2 0 2 0 12 Clay loam soil 12 16 16 4 24 Clay soil 0 0 0 0 2 Sandy loam soil 0 0 0 0 0

98

Table 4.28: Average quantity of different organic fertilisers used on farms.

Soil Type Organic Fertilisers (g/plant)

Fish meal Compost Lime Seaweed

Loam soil 50.00 0.00 50.00 0.00 Clay loam soil 79.17 112.50 93.75 50.00 Clay soil 0.00 0.00 0.00 0.00 Sandy loam soil 0.00 0.00 0.00 0.00

Table 4.29: Reasons for insufficient use of organic sources of nutrients.

Constraints Farmer response, n = 50 (%)

Insufficient supply 32

Lack of knowledge on organic sources of nutrients 50

Lack of knowledge on the fertility status of the soil 80

Lack of knowledge on its benefits 60

Lack of time to prepare organic fertilisers 40

4.6.5 Chemical Nutrient Sources

Some farmers also used chemical fertilisers to supplement the nutrient content in the

soil. Common fertilisers used by these farmers include; urea, NPK, hydro-complex, and

alroc. The number of farmers using the different chemical fertilisers on their farms is

shown in Table 4.30 while the average quantity of different chemical fertilisers is shown

in Table 4.31. Furthermore, the number of farmers using NPK on their farms and the

average quantity of NPK used on farms are also shown in Table 4.32 and Table 4.33

respectively. Data evaluation shows that the highest percentages of farmers (50 %) that

used chemical fertilisers are those with clay loam soil type on their farm. Hence, they

are the highest group of farmers who input N, P and K in the soil through the chemical

fertilisers that they used. Although, farmers with clay loam soil type on their farm made

the most use of chemical fertilisers, a greater quantity of these fertilisers was used on

farms with loam soil. Hence, a greater quantity of N, P and K was supplemented into

loam soil. It is also seen that fertiliser B i.e. NPK (15:15:15) was opted by the largest

99

number of farmers while fertiliser F i.e. NPK (10:10:20) was used by a very small

percentage of the farmers.

Table 4.30: Number of farmers using the different chemical fertilisers on their farms

(Data presented as frequency of total survey population).

Soil Type Frequency, n = 50 (%)

A B C D E F

Loam soil 4 10 4 0 4 0 Clay loam soil 8 16 2 6 18 0 Clay soil 0 0 0 0 0 0 Sandy loam soil 2 2 0 6 0 2 A- Urea (45%), B- NPK (15:15:15), C- Hydro-complex (12N:11P:18K), D- Alroc (12N:14P:14K), E- NPK (13:13:21), F- NPK (10:10:20)

Table 4.31: Average quantity of different chemical fertilisers used on farms.

Soil Type Chemical Fertilisers (g/plant)

A B C D E F

Loam soil 235.00 112.00 235.00 0.00 42.50 0.00 Clay loam soil 47.50 38.75 30.00 53.33 41.11 0.00 Clay soil 0.00 0.00 0.00 0.00 0.00 0.00 Sandy loam soil 30.00 30.00 0.00 26.67 0.00 30.00 A- Urea (45%), B- NPK (15:15:15), C- Hydro-complex (12N:11P:18K), D- Alroc (12N:14P:14K), E- NPK (13:13:21), F- NPK (10:10:20)

Table 4.32: Number of farmers using the NPK on their farms (Data presented as

frequency of total survey population).

Soil Type Frequency, n = 50 (%)

N P K

Loam soil 14 14 14 Clay loam soil 20 38 38 Clay soil 0 0 0 Sandy loam soil 6 6 6

100

Table 4.33: Average quantity of NPK used on farms.

Soil Type NPK (g/plant)

N P K

Loam soil 51.85 20.96 23.21 Clay loam soil 10.15 6.33 8.00 Clay soil 0.00 0.00 0.00 Sandy loam soil 10.20 6.23 7.23

4.6.6 Other Soil Fertility Measures

A number of other soil fertility measures other than fertilisers have been adopted by

farmers in the study area. The number of farmers using the different fertility measures

on their farms is presented in Table 4.34. Legumes were used by 46 % of the farmers on

their farms. The common legumes used by these farmers include; mucuna beans,

tokatolu, and long bean. On taro farms in Taveuni, Fiji, legumes are mostly planted a

number of months prior to planting, after which the plants are sprayed and cleared

before taro is planted. Some farmers also implemented the crop rotation technique on

their farms. Common crops rotated with taro farming include yagona (26 %) and

vegetables (2 %). Agroforestry approach was another common soil fertility practice used

by the farmers in the study area. 26 % of farmers have planted rain tree on their taro

farms while 48 % of the farmers have planted drala tree. A small percent of the farmers

have also planted coconut tree (4 %) and pine tree (2 %) on their farms together with

taro plants.

101

Table 4.34: Number of farmers using the different fertility measures on their farms

(Data presented as frequency of total survey population).

Soil Type Frequency, n = 50 (%)

Legumes Crop Rotations Agroforestry

Yaqona Vegetables Raintree Drala Coconut

Tree Pine Loam soil 2 4 0 6 6 0 2 Clay loam soil 38 18 2 16 36 2 0 Clay soil 0 0 0 0 0 0 0 Sandy loam soil 6 4 0 4 6 2 0

Another essential soil fertility management practice is fallowing. The number of farmers

using fallowing technique on their farms is shown in Table 4.35. It was adept by 84 % of

the farmers interviewed. These farmers fallow their farming land for a certain period of

time to allow for soil nutrient replenishment before re-planting on that piece of land. 16

% of the farmers however do not practice this soil fertility measure. This is mainly

because the sizes of the farms owned by these farmers are relatively small; hence it

becomes costly to practice fallowing. On the contrary, 18 % of the farmers fallow their

taro farm for 3 to 6 months. 6 to 12 months of fallow period is practiced by 36 % of the

farmers while 30 % of the farmers leave their taro farms fallow for 1 to 2 years. Results

show that farms with clay loam soil type are left fallow for the longest periods of time.

Table 4.35: Number of farmers using fallowing technique on their farms (Data

presented as frequency of total survey population).

Soil Type Frequency, n = 50 (%)

No fallowing 3-6 Months 6-12 Months 1-2 Years

Loam soil 6 6 6 4 Clay loam soil 8 8 30 22 Clay soil 0 2 0 4 Sandy loams oil 2 2 0 0

102

In conclusion, the results from the soil test data on soil sampled at four different periods

during the cropping cycle of taro and data obtained from the questionnaire survey reveal

that there is substantial gap between the nutrient demand of the soils on the island of

Taveuni and supply of nutrients to the soil. It is also seen that there is an imbalanced and

insufficient use of chemical and organic sources of nutrients. The outcome of these

improper management practices has therefore been visible on the quality and quantity of

taro produced on the island (Duncan, 2010; McGregor, 2011; Panapasa, 2012; Smith,

2011).

However, it should be noted that the difference in the nutrient levels at each sampling

stage as shown by the soil test data is not entirely the nutrient concentration taken up the

plant or removed through harvest of taro. The difference in the nutrient levels at

different sites during the different periods in the cropping cycle of taro could also be

attributed to a number of environmental factors such as drought, rainfall, day light

intensity, soil temperature, soil type and slope. These factors were however beyond the

scope of this study.

Moreover, it should be taken into consideration that the third sampling (three months in

to planting) was done just a couple of hours after a heavy downpour. Hence, nutrients

could have been lost through leaching and soil erosion. Also, in the middle of the

cultivation period, cyclone Evan left disastrous effects on the island of Taveuni

destroying many plants on the trials site. This event could have also contributed to the

loss in the amount of plant-available nutrients in the soil.

Furthermore, the results do not show any significant difference in the outcome on the

different treatments applied by “ACIAR’s soil health team”. However, the effect of the

different treatments might be shown after continuous treatment and monitoring for a

number of years as the current result is only for one cropping season.

Finally, the possible bias from the selection technique of farmers for interview should

also be noted. The selection was based on the availability of farmers near the trial sites

as well as the willingness of farmers to participate.

103

CHAPTER 5

CONCLUSION AND RECOMMENDATION

5.1 Conclusions

The state of any environment and its food production ability is largely determined by the

agricultural practices (Tilman et al., 2002). Agricultural practices such as exceeding the

amount of nutrient removed through crop removal compared to the amount of nutrient

added cause soil fertility depletion. For a sustainable soil ecosystem, nutrient loss should

be equal to nutrient replacement (Havlin et al., 2005; Jahiruddin and Satter, 2010; Mbah

and Onweremadu, 2009). While the interest of any nation should be to maximize crop

yield and the interest of any farmer should be to achieve profitable yield, this should not

be at the cost of putting soil health at any risk. However, the study conducted on the

island of Taveuni, Fiji revealed that continuous planting has exhausted the soils nutrient

reserves, thereby causing the deterioration of soil health.

The results obtained from the soil test data showed that the four study sites had lower

than the ideal pH levels for taro planting. However, overall only Delaivuna site showed

a significant decline in the soil pH levels (ρ < 0.05) from the time of taro planting till

harvest.

Mucuna bean treatment which was applied six months prior to taro planting in the study

sites seemed to have a substantial impact on the soil organic matter levels. The OM

content in the soil increased significantly (ρ < 0.05) in all of the four trial sites (Matei,

Mua, Vione and Delaivuna). However, the results showed that at the end of the cropping

season of taro, there was a significant decline in SOM in all the sites resulting in the low

levels of OM in all the sites except for Vione site which had medium levels of SOM.

Similarly, at the end of the cropping season of taro, soil NO3--N levels had significantly

declined (ρ < 0.05) in the four study sites with low levels of NO3--N at the time of

harvest except for Vione site which had medium levels.

104

Likewise, all other macronutrient levels (P, K, Ca, and Mg) had significantly (ρ < 0.05)

decreased in the soil by the end of the cropping season of taro. Soil data analysis

revealed that except for Delaivuna site which had medium levels of soil P at the time of

taro harvesting, the other three sites had low levels of available P during the same period

of time. Moreover, only Matei site had sufficient levels of soil Ca, Mg and K while the

other three sites had either critical levels or below critical levels of these nutrients by the

end of the cropping cycle of the taro plants.

The micronutrient levels (Cu, Fe, Mn and Zn) in the study sites also had a significant

decrease (ρ < 0.05) from the time of taro planting till harvest except for Cu levels in

Matei and Delaivuna sites. Nevertheless, Cu and Fe levels were in sufficient levels in

the four study sites. Mn levels were also sufficient in all the sites except for Delaivuna

site, which showed Mn deficiency by the end of the cultivation period of taro.

Conversely, all sites had low levels of soil Zn at the time of taro harvest as well as

throughout the cultivation period.

Based on these soil test results and the taro output data obtained through the

questionnaire survey which showed a declining trend in taro output over the years, it can

be implicated that farmers on the island of Taveuni, Fiji are adding imbalanced or

insufficient nutrients to the soil to replenish those lost through leaching or removed by

cultivation. It is seen that due to the continuous cultivation, soil nutrients continue to

decline even though many farmers practiced some form of soil fertility management

techniques.

It is understood that due to the inadequate and unreliable soil testing facilities, many

farmers in Taveuni, Fiji were unaware about the nutrient availability of their soils. This

leads to the failure in correcting the soil health problems or in applying balanced and

sufficient quantities of fertilisers.

Moreover, the results of the survey showed that very few types of organic fertilisers

were used by these farmers and those that were used were selected by quite a small

percentage of the farmers. Farmers suggested a number of constraining factors that

hindered them from using sufficient quantities of organic fertilisers. Some of the factors

105

mentioned by the farmers included; lack of information the on organic nutrient sources,

insufficient quantities of organic fertilisers available as well as the time consuming

processes involved in preparing organic fertilisers. The foremost influencing factor was

perceived to be the lack of awareness on the fertility status of the soil on their farm.

Similarly, a small percentage of the farmers who were interviewed used chemical

fertilisers on their farms. Those that used chemical nutrient sources were also mostly

unaware about the balanced plant nutrition and the fertility status of the soil. However, a

substantial percentage of the farmers have incorporated other soil fertility measures on

their farms. Planting legumes, crop rotation, agroforestry and fallowing are a few

techniques practiced by these farmers.

In general, indiscriminate and imbalanced fertiliser usage and absence of proper

fertiliser recommendations were the key factors that caused the decline in crop

production for taro farmers on Taveuni, Fiji. As long as the gap between nutrient

demand and supply on farms are not filled and macro and micro-nutrient deficiencies are

not addressed, crop output will continue to decline and the threat to the country’s food

security will be inevitable.

5.2 Recommendations

Since soil fertility management is an important issue in combating the problem of soil

health and food security, emphasizing on the issue is extremely important. The

following four suggestions have been made in this connection.

Firstly, support is needed for more research to be conducted on soil fertility

management practices in specific areas. This should include a better understanding on

the combined effects of organic and chemical fertilisers on the soil. Findings of the

research should be imparted to farmers through regular trainings and workshops to

improve their skills and knowledge on the best farming practices.

Secondly, research and experiments carried out should be a combined effort for both

farmers and researchers so farmers could input their traditional knowledge and

experience in formularizing the best management practices.

106

Thirdly, to avoid the use of fertiliser indiscriminatingly, which not only increases the

cost of production but may also lead to damaging effects on the fertility of the soil. It is

important that one knows the fertility status of the soil on his/her farm. This can be

achieved by having the chemical properties of the soil analyzed to determine the plant

nutrient availability (Brady and Ray, 2002; Júnior, et al, 2008; Hak-Jin, 2009; Praveen,

2009; Horneck, et al., 2011). However, many farmers do not have the financial

capability to have their farm soil analyzed hence, an investment by government in this

area is essential. This will ensure that farmers are aware of the fertility status of their

farms at all levels and would be able to apply the appropriate type and amount of

fertiliser.

Fourthly, farmers should be advised on more sustainable soil management practices

such as combining both organic and chemical methods of fertilization. This will not only

ease the harmful effects of the fertiliser on the soil but will also enhance the physical

properties of the soil. Farmers should also be encouraged and advised on the benefits of

other soil fertility measures such as fallowing, crop rotation, intercropping, agroforestry

as well as planting leguminous plants. Farmers should be trained and informed in such a

way that they become experts on their own farms. Therefore, more effort should be

made by the respective government departments and policy makers in informing farmers

on the issue of soil health and its crucial role for sustainable resource management and

food security.

107

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APPENDICES

Appendix I: Standard Calibration Curves

Abs = 0.0296[Ca]R² = 0.9972

0.0000.0500.1000.1500.200

0 2 4 6

Abs

orba

nce

[Ca], mgL-1

Graph of [Ca] against Absorbance

Abs= 0.0864[K]R² = 0.996

0.0000.0500.1000.1500.200

0 1 2 3

Abs

orba

nce

[K], mgL-1

Graph of [K] against Absorbance

Abs = 0.138[Zn] + 0.0524R² = 0.9996

0.000

0.100

0.200

0.300

0 0.5 1 1.5

Abs

orba

nce

[Zn], mgL-1

Graph of [Zn] against Absorbance

Abs = 1.0077[Mg] + 0.0965R² = 0.995

0.0000.2000.4000.6000.800

0 0.2 0.4 0.6

Abs

orba

nce

[Mg], mgL-1

Graph of [Mg] against Absorbance

Abs = 0.0582[Mn] + 0.0592R² = 0.9996

0.000

0.100

0.200

0 1 2 3

Abs

orba

nce

[Mn], mgL-1

Graph of [Mn] against Absorbance

Abs = 0.0323[Fe]R² = 0.9954

0.000

0.100

0.200

0 2 4 6

Abs

orba

nce

[Fe], mgL-1

Graph of [Fe] against Absorbance

128

Abs = 0.0377[Cu]R² = 0.9983

0.0000.0500.1000.1500.200

0 2 4 6

Abs

orba

nce

[Cu], mgL-1

Graph of [Cu] against Absorbance

Abs = 0.1298[P]R² = 0.9972

0.0000.2000.4000.6000.800

0 2 4 6

Abs

orba

nce

[P], mgL-1

Graph of [P] against Absorbance

Peak Area = 1.9924[NO2-]

R² = 0.9995

-5.000

0.000

5.000

10.000

15.000

0 2 4 6

Peak

Are

a (v

olts

.sec.

)

[NO2-], µM

Graph of [NO2-] against Peak

Area

129

Appendix II: Mann-Whitney test (ρ levels) for Comparison between Treatments

Comparison between Treatment A and B

A = Lime/Mucuna B = Lime/Mucuna/Fish manure/Rock P

Comparison between Treatment A and C

A = Lime/Mucuna C = Mucuna /Fert-NPK (13:13: 21)/Bio brew

Site

ρ-Value

Ca Mg K Cu Fe Mn Zn P NO3--N OM

Matei 0.513 0.513 0.050 0.507 0.046 0.184 0.513 0.050 0.513 0.050

Mua 1.000 0.050 0.127 0.487 0.637 0.261 0.268 0.050 0.050 0.827

Vione 0.376 0.184 0.046 0.822 0.487 0.046 0.127 0.050 0.827 0.046

Delaivuna 0.184 0.050 0.050 0.376 0.050 0.376 0.178 0.050 0.050 0.046

Site

ρ-Value

Ca Mg K Cu Fe Mn Zn P NO3--N OM

Matei 0.827 0.050 0.050 0.268 0.046 0.184 0.275 0.046 0.050 0.050

Mua 1.000 0.077 0.184 1.000 0.369 1.000 0.817 0.050 0.275 0.507

Vione 0.513 0.050 0.121 0.376 0.046 0.050 0.376 1.000 0.050 0.050

Delaivuna 0.658 0.046 0.184 0.817 0.184 0.507 0.046 0.050 0.127 0.046

130

Comparison between Treatment A and D

A = Lime/Mucuna D = Mucuna Comparison between Treatment B and C

B = Lime/Mucuna/Fish manure/Rock P C = Mucuna /Fert-NPK (13:13: 21)/Bio brew

Site

ρ-Value

Ca Mg K Cu Fe Mn Zn P NO3--N OM

Matei 0.275 0.513 0.513 0.825 0.046 0.275 0.825 0.050 0.050 0.376

Mua 1.000 0.827 0.050 0.658 0.046 0.487 0.268 0.275 0.275 0.050

Vione 0.184 0.050 0.825 0.653 0.046 0.105 0.376 0.050 0.050 0.046

Delaivuna 0.050 0.077 0.050 0.637 1.000 1.000 0.050 0.050 0.050 0.046

Site

ρ-Value

Ca Mg K Cu Fe Mn Zn P NO3--N OM

Matei 0.827 0.077 0.050 0.043 0.658 0.827 0.513 0.046 0.050 0.050

Mua 1.000 0.050 0.827 0.487 0.376 0.658 0.127 0.050 0.050 0.268

Vione 0.275 0.127 0.050 0.376 0.050 0.825 0.376 0.050 0.050 0.046

Delaivuna 0.658 0.121 0.050 0.369 0.050 0.817 0.261 0.050 0.050 0.050

131

Comparison between Treatment B and D

B = Lime/Mucuna/Fish manure/Rock P D = Mucuna

Comparison between Treatment C and D

C = Mucuna /Fert-NPK (13:13: 21)/Bio brew D = Mucuna

Site

ρ-Value

Ca Mg K Cu Fe Mn Zn P NO3--N OM

Matei 0.658 0.827 0.077 0.197 0.658 0.513 0.507 0.275 0.050 0.184

Mua 1.000 0.050 0.184 0.817 0.050 0.817 0.050 0.050 0.050 0.050

Vione 0.513 0.050 0.050 0.346 0.050 0.043 0.077 0.050 0.050 0.043

Delaivuna 0.127 0.275 0.513 0.178 0.046 0.376 0.046 0.050 0.050 0.050

Site

ρ-Value

Ca Mg K Cu Fe Mn Zn P NO3--N OM

Matei 0.275 0.127 0.050 0.500 0.500 0.827 0.507 0.046 0.050 0.050

Mua 1.000 0.513 0.275 0.261 0.658 0.637 0.050 0.050 0.513 0.046

Vione 0.513 0.050 0.513 0.072 0.050 0.046 0.077 0.050 0.050 0.046

Delaivuna 0.127 0.046 0.050 0.500 0.268 0.369 0.046 0.050 0.513 0.050

132

Appendix III: Sample Questionnaire

The University of the South Pacific Faculty of Science, Technology and Environment

School of Biological and Chemical Sciences Private Mail Bag, Suva

Survey on “Examining Chemical Fertility of Soils in Taveuni, Fiji:

The Problem of Soil Health and Food Security”

Name of farmer: Age:

Education: Location:

Size of Farm: (Acres) Land tenure:Leasehold/communal/freehold

Techniques of Field Preparation: Plowing/Harrowing,

1. At the back of this page please make a rough map of your farm(s) and show different plots (numbering them 1, 2, 3, 4, etc.) on it and the crops generally grown on them. Also indicate the slope gradient of the farm and plots.

2. How much is the area of each plot on your farm, the crops generally grown on each

and their soil types.

Plot

No.

Area

(Acres)

Name of the

crop grown

Soil Type of each plots

[Please tick (��) the one which is applicable]

Clay Clay

loam

Silt Loam Sandyloam Sandy Peat

1

2

3

4

133

3. What were the average outputs of various crops grown on different plots of your farm during the past few years?

Quantity (Kg) of output produced on each

plot of your farm in the following years

Remarks/Reasons

Plot

No.

Name of

the crop

grown

2011 2010 2009 2008 2007

1

2

3

4

4. How many times do you apply fertilizers to your crops?

Before planting the crop � After planting the crop � Never �

5. What types of chemical fertilizers were used on different plots of your farm in the

past few years?

Plot

No.

Name of

the crop

grown

Name the types of fertilizers used on your farm, their quantities (kg)

and their Nitrogen, Phosphorus and Potash (N P K ) contents

2011 2010 2009 2008 2007

1

2

3

4

134

6. Name and quantify the organic sources of nutrients used on different plots of your farm.

Plot

No.

Name of the

crop grown

Livestock

manure

(kg)

Compost

(in Kg)

Green

manure

(kg)

Mulching Remarks

1

2

3

4

7. What soil fertility measures apart from fertilizers, do you use on your farm? Please tick the appropriate cell.

Plot

No.

Name of

legume crops

List of crops

for rotation

Name of the

cover crops

Agro-

forestry

(Name the

tree)

Fallow

(Specific

period)

Others

(Please

specify)

1

2

3

4

8. What are the factors that influence the quantity of fertilizers used in your farm?

Factors Response (Yes/No) Cost of fertilizers Insufficient supply of fertilizers Lack of knowledge on soil fertility status Others (Please specify)

135

9. How often do you get your farm soil(s) tested?

Never � Once in 5 years � Once in 2 years � Others (Please specify) �

10. If the farm soil was tested, what were the recommendations for fertilizer use and what are your uses of nutrients on your farm.

Plot

No.

Amount Recommended (kg) Amount Used (kg) Reasons

for low

use of

fertilizers

Nitrogen Phosphorus Potash Nitrogen Phosphorus Potash

1

2

3

4

11. What are the factors that influence in following proper soil test and fertility management recommendations?

Factors Response (Yes/No) Cost of soil testing Cost of fertilizers Insufficient supply of fertilizers Lack of government support Lack of professional knowledge Others (please specify)

136

Appendix IV: Crop Output Comparison for 2010-2012 using Wilcoxon Signed

Ranks Test

n Mean Rank

Sum of Ranks ρ-Value

Crop output 2010 to

Crop output 2011 50 26.19 1257.00 0.000

9.00 18.00

Crop output 2011 to

Crop output 2012 50 25.00 1225.00 0.000

0.00 0.00

Crop output 2010 to

Crop output 2012 50 26.00 1274.00 0.000

1.00 1.00

n= Sample Size

Appendix V: NPK Usage Comparison for 2010-2012 using Wilcoxon Signed Ranks

Test

(i) Nitrogen (N) Usage Comparison for 2010-2012 using Wilcoxon Signed Ranks Test

n Mean Rank

Sum of Ranks ρ-Value

Crop output 2010 to

Crop output 2011 50 0.00 0.00 0.000

15.00 435.00

Crop output 2011 to

Crop output 2012 50 1.00 1.00 0.002

7.50 90.00

Crop output 2010 to

Crop output 2012 50 0.00 0.00 0.000

15.50 465.00

n= Sample Size

137

(ii) Phosphorous (P) Usage Comparison for 2010-2012 using Wilcoxon Signed Ranks Test

n Mean Rank

Sum of Ranks ρ-Value

Crop output 2010 to

Crop output 2011 50 0.00 0.00 0.000

14.50 406.00

Crop output 2011 to

Crop output 2012 50 0.00 0.00 0.003

6.00 66.00

Crop output 2010 to

Crop output 2012 50 0.00 0.00 0.000

15.00 435.00

n= Sample Size

(iii) Potassium (K) Usage Comparison for 2010-2012 using Wilcoxon Signed Ranks Test

n Mean Rank

Sum of Ranks ρ-Value

Crop output 2010 to

Crop output 2011 50 3.00 3.00 0.000

14.93 403.00

Crop output 2011 to

Crop output 2012 50 0.00 0.00 0.003

6.00 66.00

Crop output 2010 to

Crop output 2012 50 0.00 0.00 0.000

15.00 435.00

n= Sample Size

Appendix VI: Publication

Sofina Nisha, Surendra Prasad and Jagdish Bhati; Assessment of Soil NO3--N, P and K

during Cultivation of Taro Crop: Enhancing the Soil Health and Food Security in

Taveuni, Fiji. Accepted for oral presentation during International Conference on

Applied Chemistry (ICONAC) to be held on 5-7 March 2014 at Fiji National University,

Suva, Fiji.