SOIL FERTILITY AND PRODUCTIVITY DECLINE

RESULTING FROM TWENTY-TWO YEARS OF

INTENSIVE TARO CULTIVATION IN TAVEUNI, FIJI

by

Ami Chand Sharma

A thesis submitted in fulfilment of the

requirements for the Degree of

Master of Agriculture

Copyright © 2016 by Ami Chand Sharma

School of Agriculture and Food Technology

Faculty of Business and Economics

The University of the South Pacific

March, 2016

iii

ACKNOWLEDGEMENT

I would like to express my sincere gratitude to the Government of Australia through the

Australian Centre for International Agricultural Research (ACIAR) for sponsoring my

study for a Master of Agriculture degree. I am grateful to my employer, the Government

of Fiji, for providing me this opportunity to pursue higher studies.

I want to express my gratefulness to my supervisor Dr. D. Guinto for his excellent

guidance. Without his direct assistance, this thesis would not have been possible. Also, I

would like to express my special thanks to Ms. Miliakere Nawaikula, Director of

Research Division, and Ministry of Agriculture for the encouragement and assistance

throughout my study period especially during the data collection.

I would like to appreciate Mr. Rohit Lal, Agriculture Officer, Ministry of Agriculture,

Taveuni for providing the taro production and export reject data of the research sites.

Thanks to my fellow colleagues at the Koronivia Research station for assisting in

retrieving soil fertility data from archival files. I would also like to thank Director of

Meteorological Services for his consent and approval to access climatic data of Taveuni.

I would like to extend my sincere gratitude to Mr. Sanjay Anand who always had time

for me and had the advice ready. Thank you very much for your valuable assistance in

the statistical analysis of my research data. It was always a pleasure to discuss with him

a draft of content of this thesis. I enjoyed the way he raised questions that always

allowed me to dig more into the scientific content of my research.

iv

This study would not have been possible without constant and valuable support from my

family, particularly my lovely wife, Kusum Sharma who was behind my shoulders

encouraging me and feeding my hopes to get successful results. Thanks to my sons

Antriksh and Kritesh, for their constant stimulation and for showing me the sense of our

life. To my family members and friends who have been constantly interested in my

progress with the studies - May God bless you all.

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ABSTRACT

Soil degradation is the loss of actual or potential productivity and utility of the soil and it

implies a decline in the soil’s inherent capacity to produce economic goods and perform

environmental regulatory functions. With short-term observations, the transient

phenomena can be missed or misinterpreted. In general, observations made over a long

period allow more rigorous conclusions with regards to decline in soil fertility.

Soil data for “22-year period” was retrieved from the archival files at the Koronivia

Research Station while other important information was gathered through survey

questionnaire and ministry officials based on the Island. The effects of 22 years

continuous cropping of taro on selected soil chemical properties and yields were studied

on the island of Taveuni, Fiji. The high native fertility levels and production potential of

Taveuni Andosols declined rapidly when the forest cover was replaced by the annual

crop of taro. This was particularly evident from the trend analyses of the nutrient

elements which, altogether with soil pH and taro yields, revealed significant declines,

with the exception of exchangeable K. Significant associations between and dependence

of taro yields on soil pH, Olsen P, exchangeable Ca and exchangeable Mg were also

observed. In addition, significant changes in these four chemical parameters were

observed when the pre and the post cultivation levels were compared. Olsen P and

exchangeable Mg were identified to be the most limiting nutrients for the taro soils of

Taveuni. The archival database provides an important tool for looking at soil test trends

over time on taro commercial sites.

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TABLE OF CONTENTS

Chapter 1 Introduction 1

1.1 Research problem 3

1.2 Research objectives 4

1.3 Research questions 4

1.4 Research approach 5

Chapter 2 Literature Review 6

2.1 Background of Fiji 6

2.1.1 Fiji’s taro industry 6

2.1.2 Taveuni soils 7

2.1.3 Detailed description and fertility status of Taveuni soils 7

2.1.3.1 The Andosols 8

2.1.3.1.1 Vitric Andosols 8

2.1.3.1.2 Humic Andosols 8

2.1.3.2 The Ferralsols 9

2.1.3.2.1 Ferralic Cambisols 9

2.1.3.2.2 The Humic Ferralsols 9

2.2 Soil chemical properties 10

2.2.1 Soil reaction- pH 11

2.2.2 Total Nitrogen 11

2.2.3 Available P 12

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2.2.4 Exchangeable potassium 13

2.2.5 Exchangeable calcium and magnesium 13

2.3 Historical land use and land cover change of Taveuni 14

2.4 Agricultural intensification 14

2.5 Soil fertility degradation 15

2.6 Soil fertility degradation in relation to land use and land cover

change

17

2.7 Soil fertility trends under different landuse 19

2.8 Soil fertility decline and spatial and temporal boundaries 21

2.9 Data types to assess soil fertility decline 21

2.9.1 Expert knowledge 22

2.9.2 Type I data 22

2.9.3 Type II data 22

2.9.4 Semi quantitative data 23

2.10 Minimum data set 23

Chapter 3 Materials and Methods 25

3.1 Scope of study 25

3.2 Origin of Taveuni 25

3.3 Soil Sampling sites 26

3.4 Data collection 28

3.4.1 Soil chemical fertility indices 28

3.4.2 Taro production data 28

3.4.3 Meteorological data 28

viii

3.4.4 Crop management data 29

3.5 Statistical Analysis 29

Chapter 4 Results and Discussion 31

4.1 Meteorological parameters 31

4.1.1 Rainfall 31

4.1.2 Temperature 32

4.2 Soil chemical indices 33

4.2.1 Soil pH 33

4.2.2 Total soil nitrogen 34

4.2.3 Olsen available phosphorus 35

4.2.4 Exchangeable K 36

4.2.5 Exchangeable Ca 37

4.2.6 Exchangeable Mg 38

4.2.7 Ca:Mg ratio 39

4.3 Taro production and export rejects 40

4.3.1 Taro yields 40

4.3.2 Taro export rejects 41

4.4 Correlation analysis between the selected meteorological variables,

taro yields and soil chemical indices

42

4.4.1 Dry production strata 42

4.4.2 Intermediate production strata 43

4.4.3 Wet production strata 44

ix

4.5 Relationship of selected chemical indices to taro corm yield 46

4.6 Comparison of soil chemical properties between pre and post 22

year cultivation period

48

4.7 Changes in selected soil management practices over 22 year

cultivation period

54

4.8 Production constraints as identified by taro growers 57

Chapter 5 Summary and Conclusions 59

References 61

Appendices 74

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LIST OF TABLES

Table 3.1 Research sites under each strata 26

Table 4.1 (a) Correlation matrix of selected meteorological and soil chemical

indices of taro soils from the dry production strata of Taveuni

43

(b) Correlation matrix of selected meteorological and soil chemical

indices of taro soils from the intermediate production strata of Taveuni

44

(c) Correlation matrix of selected meteorological and soil chemical

indices of taro soils from the wet production strata of Taveuni

45

Table 4.2 Estimates of parameters for the multiple linear regression analysis of

yield on soil pH, Olsen P, exchangeable Ca and exchangeable Mg

47

Table 4.3 (a) Paired sample t-test for the chemical indicators between pre and post

period of intensive cultivation

50

(b) Soil chemical fertility decline resulting from 22 year intensive

cultivation

51

Table 4.4 Comparisons of end of research period levels against critical levels

and suggested ameliorative measures

52

Table 4.5 (a) Distribution of land tenure systems for the surveyed farms 55

(b) Distribution of farm size under taro cultivation 56

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LIST OF FIGURES

Figure 3.1 Location of the study area 27

Figure 3.2 Soil sampling sites 29

Figure 4.1 (a) & (b) Rainfall pattern and 22 year mean annual seasonal distribution

for the island of Taveuni

33

Figure 4.2 (a) & (b) Mean annual and 22 year monthly mean temperature 34

Figure 4.3 (a) Soil pH trends for the three taro production strata 35

(b) 22 year mean pH trend for Taveuni

Figure 4.4 (a) Total N trends for the three taro production strata 36

(b) 22 year mean Total N trend for Taveuni

Figure 4.5 (a) Olsen available P trends for the three taro production strata 38

(b) 22 year mean Olsen available P trend for Taveuni

Figure 4.6 (a) Exchangeable K trends for the three taro production strata 39

(b) 22 year mean Exchangeable K trend for Taveuni

Figure 4.7 (a) Exchangeable Ca trends for the three taro production strata 37

(b) 22 year mean Exchangeable Ca trend for Taveuni

Figure 4.8 (a) Exchangeable Mg trends for the three taro production strata 38

(b) 22 year mean Exchangeable Mg trend for Taveuni

Figure 4.9 (a) Ca:Mg Ratio trends for the three taro production strata 39

(b) 22 year mean Ca:Mg ratio trend for Taveuni

(c) Relative removal of Ca and Mg

Figure 4.10 (a) Taro yield trends for the three taro production strata 40

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(b) 22 year mean yield trend for Taveuni

Figure 4.11 (a) Taro export reject trends for the three taro production strata 41

(b) 22 year mean export reject trend for Taveuni

Figure 4.12 (a) Regression of taro yield on soil pH 46

(b) Regression of taro yield on Total N 46

(c) Regression of taro yield on Olsen P 46

(d) Regression of taro yield on Exchangeable K 46

(e) Regression of taro yield on Exchangeable Ca 46

(f) Regression of taro yield on Exchangeable Mg 46

Figure 4.13 Farmer adoption of various management practices to support

intensive cultivation

54

Figure 4.14 Identification of production constraints by farmers 57

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LIST OF APPENDICES

Appendix 1 Soil and land use capability maps of Taveuni 74

Appendix 2 Export specification for taro 75

Appendix 3 1990 – 2012 Data on: A) Soil Fertility, B) Temperature, C) Rainfall

and D) Taro production (1994 – 2013) 76

Appendix 4 Analysis of variance for between rainfall-zones (strata) comparison 82

Appendix 5 Paired sample t-test for comparisons of soil chemical indices pre and

post 22-year cultivation period 90

Appendix 6 Correlation analyses for association between indices for the dry zone

(strata) of Taveuni 105

Appendix 7 Correlation analyses for association between indices for the

intermediate zone (strata) of Taveuni 105

Appendix 8 Correlation analyses for association between indices for the wet zone

(strata) of Taveuni 117

Appendix 9 22- year trend regression analyses of variance 129

Appendix 10 Linear regression analyses of variance of taro yield on individual

chemical indices 134

Appendix 11 Multiple linear regression analyses of variance of taro yield on

significant individual chemical indices 137

Appendix 12 Farmer survey questionnaire 138

1

CHAPTER 1

INTRODUCTION

Soil is a fundamental resource on which human populations are dependent for food, fuel

and fibre. Land use shifts and their sustainability are an important part of global change,

and it is through the response of the plant-soil system that climate change will have its

main impact on humankind. Furthermore, it is in the tropics that the demands of

developing human populations are most tightly linked to climate- and soil-determined

limits. Paradoxically, it is in this zone and on these topics that our capacity to respond

scientifically is weakest (Swift, 1984).

Successful agriculture requires the sustainable use of soil resource, because soils can

easily lose their quality and quantity within a short period of time for many reasons.

Agricultural practice therefore, requires basic knowledge of sustainable use of the land.

Success in soil management to maintain soil quality depends on the understanding of

how the soil responds to agricultural practices over time (Negassa, 2001). Revising these

trends lies in the enhancement of sustainable development of the agricultural sector.

However, the basis of this sustainable agricultural development is good quality of soil,

since maintenance of soil quality is an integral part of sustainable agriculture.

Although soils in the tropical regions are highly diverse, with some soils having a high

production potential, there are many areas where the soil resources suffer from serious

limitations hindering agricultural production and development. Some tropical soils have

a very low chemical fertility, are extremely acidic and contain toxic substances (Young,

1999).

Changes in land use and land cover are central to the study global environmental change

including soil fertility, degradation, and reflect the rapid population growth in tropics.

As a result of increasing demand for food and fibre, natural land covers, particularly

tropical forests are being degraded or converted to cropland at an alarming rate (Islam

2

and Weil, 2000). Humans as a soil forming factor has been a difficult issue in pedology

(Hartemink, 2003), whereas many soils in the world have been drastically altered or

degraded as a result of human interference (Wu and Tiessen, 2002).

Soil fertility degradation by nutrient depletion, mostly caused by erosion but also by

removal of nutrients in crops, is one of the threats that taro production systems in

Taveuni are facing (Kumwenda et al., 1996). Soil erosion is obviously the most visible

and sometimes most destructive form and has received considerable attention in Fiji’s

land use policy.

Taro is Fiji’s largest agricultural export after sugar (FAO, 2012a). Fiji’s annual taro

export for the last few years has been around 10,000 tonnes, earning about FJD 19–20

million annually with about 65% going to New Zealand and the balance to Australia and

the USA (McGregor, 2011). Taveuni accounts for 70% of Fiji’s taro exports (Sun Fiji

Newsroom, 2009).

Despite taro (Colocasia esculenta) being the staple diet for Fijians for centuries, its

cultivation as a highly significant export crop began only in 1993 when the taro leaf

blight disease decimated the Samoan taro industry (McGregor, 2011). Fiji took

advantage of the opportunity and captured the market for the same variety of taro

internationally, especially Australia, New Zealand and United States. The taro exports

increased from 3,000 tons in 1994 to 10,000 tons in 2009 (Ministry of Primary

Industries-Taveuni Annual Report, 2010). However, the island’s taro exports stagnated

during recent years due to declining productivity and increasing production costs

(McGregor, 2011).

The productive capacity of a soil depends on soil fertility. Achieving and maintaining

appropriate levels of soil fertility is of utmost importance if agricultural land is to remain

capable of nourishing crop production. After 22 years of intense taro cultivation and

with little or no fallow practice, due to scarcity of land resources and other economic

3

factors, the fertility and the productivity of the Taveuni taro soils will predictably

decline due to cultivation, soil erosion and nutrient uptake.

Soil fertility evaluation is largely based on old data and development of several generic

crop models (Bouma, 1989). There is a great need for updated soil survey and soil

fertility information to monitor the effects of current and past land management on soil

properties.

1.1 Research problem

Soil fertility degradation has become a major problem for agricultural management in

Taveuni. The main agent causing change in controlling processes is human activity,

mainly agriculture, and a complete explanation of fertility components cannot be

achieved without an understanding of human-induced soil change at landscape level

(Pennock and Veldkamp, 2006).

Land use changes, especially cultivation of deforested land may rapidly diminish soil

quality. However, the decline of soil fertility in the complex lithology of Taveuni taro

soils is currently poorly understood. In order to design and implement the national

policy in conservation and restoration of soil fertility, policy makers need a clear view of

nutrient removal and how much needs to be restored. As with accurate information on

soil fertility, soil change information is needed by today’s decision makers for a variety

of management goals, including short and long-term productivity, economics,

sustainability and environmental quality.

The Taveuni taro study area provides an ideal ‘laboratory’ for assessing soil fertility

change, since: (1) it was largely forested until commercial taro production commenced

in the year 1993; (2) there was a baseline soil survey done prior to deforestation; and (3)

the area has been deforested and due to agricultural and settlement activities, it has faced

dramatic erosion and changes in soil management, in particular intensive cropping of

taro.

4

1.2 Research objectives

This study is aimed to investigate, quantify and establish temporal trends of selected soil

chemical indices for Taveuni taro soils resulting from land use change and related

management.

The specific objectives of this research are:

1. To determine the temporal trends of selected soil chemical indicators, climatic

variables and taro yields over a period of 22 years for the island of Taveuni.

2. To investigate existence of any temporal association between selected soil chemical

indicators, climatic variables and taro yields over a period of 22 years for the island

of Taveuni.

3. To compare spatial distribution of changes in selected soil chemical indicators,

climatic variables and taro yields over a period of 22 years across stratified climatic

zones on the island of Taveuni.

4. To determine the temporal changes in the adoption by farmers of selected soil

management practices relevant to the maintenance of soil fertility in Taveuni.

1.3 Research questions

1. Is there a significant change in soil fertility over the last 22 years? If so, what is it,

and where are the changes most pronounced?

2. How does the change in individual indicators of soil fertility reflect on the final

yield?

3. To what extent, does land use change (agricultural intensification) contributes to

soil fertility change at island level?

4. Is there any association that exists between changes in climatic variables and

changes in soil fertility?

1.4 Research Approach

A study on how land-use and land cover change affects the soil fertility must involve the

response of the soil fertility indicators. In fact, all the soil properties are not equally

affected by the land-use and land cover change in space and time. For example, previous

5

studies have shown that most of the physical properties are usually much less variable

over short distances than chemical properties (Yemefack, 2005). Cost is also one of the

principal factors that lead to minimise the sample size and parameters in many

researches. The database will provide a tool for investigating temporal trends with

regards to selected soil chemical parameters of the study sites and provide an insight into

assessing the sustainability of soil fertility management practices of commercial taro

production.

6

CHAPTER 2

LITERATURE REVIEW

The review of literature has been divided into ten subsections. The first two sections

give an overview of Fiji, taro industry, and detailed description of Taveuni soils. The

next section gives the brief on soil fertility indices and its importance. The fourth section

provides the overview of land use and land cover change of Taveuni. While the rest of

the sections discussed on agricultural intensification and its consequences, and the final

section of the review discussed on data types used to assess soil fertility decline and

advantages.

2.1 Background of Fiji

The Fiji group lies in the southern hemisphere between latitudes 15 to 22 degrees south

and longitudes of 174 degrees east and 17 degrees west (Wikipedia, 2001). Fiji islands

consist of 332 islands spread across 1.3 million square kilometres of Economic

Exclusive Zone and its total land mass is 18,333 square kilometres (Berdah, 2005). The

two major islands are Viti Levu with 10,429 square kilometres and Vanua Levu 5,556

square kilometres. Taveuni is the third largest island in the group with 470 square km of

land mass (Fiji Government Online Portal, 2009). The climate is of the typical oceanic

type with the southeast trade winds prevailing. The hot, wet months are from November

to April. The annual rainfall of the island ranges from 2,400-4,500 mm (All Fiji, 2011).

2.1.1 Fiji’s taro industry

Taro is Fiji’s largest agricultural export after sugar (FAO, 2012a). Fiji’s annual taro

export for the last few years has been around 10,000 tonnes, earning about FJD 19–20

million annually with about 65% going to New Zealand and the balance to Australia and

the USA (McGregor, 2011).

Taveuni accounts for 70% of Fiji’s taro exports (Sun Fiji Newsroom, 2009). The variety

grown in Taveuni is the same as the variety that was grown in Samoa before the taro leaf

7

blight and is called ‘Tausala ni Samoa’ (Wikipedia, 2012). The taro exports increased

from 3,000 tons in 1994 to 10,000 tons in 2009 (Ministry of Primary Industries-Taveuni

Annual Report, 2010). However, the island’s taro exports stagnated during recent years

due to declining productivity and increasing production costs (McGregor, 2011).

2.1.2 Taveuni soils

Soils of Taveuni are highly variable in the physical and chemical properties. Twenty-

three soil series have been surveyed and described on the island. Many of the soils have

been derived from volcanic ash (Wikipedia, 2011). The soils belong to the orders

Inceptisols and Andosols, having low bulk density with the exchange complex

dominated by amorphous materials (Morrison et al., 1986). According to Leslie (1997),

the Taveuni soils have the following properties:

a. Acid oxalate extractable aluminum is 2% or more

b. Bulk density of the fine earth, measured in the field moist state, is less than

0.9g/cm³.

c. Phosphate retention is more than 85%.

2.1.3 Detailed description and its fertility status

The soils of Taveuni are all of recent origin, being from recent volcanic deposits.

Twyford and Wright (1965) classed the whole as ‘latosolic soils’, and regarded them as

an essential homogeneous complex. However, they have been subjected to the

weathering effects of humid tropical climate and pedological development is very rapid

under these conditions. Detailed studies of soils in the north and south of the island

reveal that the soils of the two areas have evolved quite differently. The north of the

island is characterised by very mature soils (Ferralsols), rich in sesquioxides of alumina

and iron; in the south, on the other hand, the soils are very much youthful (Andosols)

and the mineral complex remains only weakly crystallised. It seems most probable that

the different state of development of soils in the two regions is linked to the age of

volcanic material from which they are formed.

8

2.1.3.1 The Andosols

Andosols of Taveuni are characterised by very weak profile differentiation, high

porosity accompanying low bulk density, and a dominance of allophanes among the clay

minerals (FAO-UNESCO, 1974). Two types are encountered: Vitric Andosols rich in

unaltered volcanic material and sandy in texture while Humic Andosols which are more

deeply weathered, rich in organic material and with humiferous horizons of average base

saturation levels (Appendix 1).

2.1.3.1.1 Vitric Andosols

Vitric Andosols are developed in southern Taveuni on volcanic cones and their lower

slopes. Soils on these slopes are shallow; contain large numbers of lapilli, and many

blocks of vesicular lava. At the foot of the cones soils are deeper and of finer texture.

These latter are rich in organic matter and nitrogen. The pH levels are weakly acid; the

soils have a high cation exchange capacity (CEC) and weak base saturation. Potassium

levels are high. Total analysis reveals that the youth of the soils by high levels of

insoluble material and of alkaline and soil-alkali cations. Phosphorus reserves are

important, and the assimilable fraction, extracted by Olsen reagent, is high. These soils

thus have very high fertility, and their agronomic potential is limited by conditions of

slope (FAO-UNESCO, 1974).

2.1.3.1.2 Humic Andosols

Humic Andosols found only in the south of the island, particularly on gentler slopes.

The effect of recent eruptions is weaker, and the soils are more finely textured, with

higher clay content. Three sub-types are distinguished: soils with a gravelly horizon at

shallow depth (petric phase); soils with the surface littered by blocks of basalt (stony

phase); deep soils (deep phase). It is very difficult to delimit the distribution of these

three phases for mapping purposes, as they have no sharp boundaries. Chemical analysis

of the Humic Andosols shows them to be rich in organic matter closely bound to the

mineral elements. Nitrogen levels are high. The pH is weakly acid; cation exchange

capacity is high and base saturation levels average. Elements such as calcium and

magnesium are abundant, but exchangeable potassium is rather lean except in the

9

humiferous horizons. Phosphorus is very abundant, and the assimilable fraction of this

element is high. These soils thus have a very high mineral fertility and their agronomic

value is limited chiefly by slope and by soil texture (FAO-UNESCO, 1974).

2.1.3.2 The Ferralsols

The Ferralsols and associated soils are found in the north of the island. These soils have

developed a distinct ferralitic character. Among the clay minerals, allophane has

practically disappeared, and has been replaced by kaolinites and by sesquioxides of

aluminium and iron. Two main types are distinguished: Ferralic Cambisols in which

ferralitic characteristics are not yet strong, with a high level of halloysites and

metahalloysites and the other one as Humic Ferralsols in which sesquioxides of

aluminium and iron predominate in the mineral fraction.

2.1.3.2.1 The Ferralic Cambisols

The Ferralic Cambisols are fairly shallow, the weathered horizon being seldom deeper

than 60 cm. To some extent these soils are poorer in organic matter content than the

Andosols. Nitrogen levels are high, pH levels are weakly acid, cation exchange capacity

is high and average base saturation levels. However, there is a slight potassium

deficiency. Phosphorus levels are high, comparable with those of the Andosols, but the

assimilable fraction is much lower than the latter group of soils. Mineral fertility is thus

only average, but the soils have good agronomic possibilities being found mainly in

areas of gentle slope in the extreme north and northeast (FAO-UNESCO, 1974).

2.1.3.2.2 The Humic Ferralsols

The Humic Ferralsols are deep soils with a maturely evolved clay mineral fraction;

however, they often contain large quantities of gravel and almost unweathered blocks of

basalt. They are rich in organic matter content, but the carbon/nitrogen ratio is often

high. In some localities they are quite highly acidic. The cation exchange capacity is

weak, and the base saturation levels high. Exchangeable cations are of average values in

the humiferous horizon, but very low in the mineral horizons. Phosphorus reserves are

good, but the assimilable fraction of this element, as in the Cambisols, is low (FAO-

UNESCO, 1974).

10

Two sub-types may be distinguished within the Humic Ferralsols. Rocky soils are

developed in the steep and the very steep areas in the northern end of the volcanic chain,

and around isolated cones. Deeper soils, sometimes with patches of stone in the profiles,

are encountered on the undulating terrain away from the main volcanic chain. Humic

Ferralsols where little rockiness or on steep and gentle slopes have good agronomic

qualities. However, it would seem likely that these soils are much more fragile and less

likely to retain their qualities under prolonged cultivation, than the Andosols (FAO-

UNESCO, 1974).

Twyford and Wright (1965) classed Taveuni soils as ‘fertile’. They are probably the

most fertile soils in the whole Fijian archipelago. However, there are quite important

differences within the island, and these have agronomic significance. Taro yields, as

determined by Haynes (1976), are generally higher in the north than in the south, and the

highest yield obtained was from a steep site on the petric phase of the Humic Ferralsols

while the lowest yield obtained was from a site on gently undulating land with Humic

Andosols in the south. However, the former was a first crop; and the latter from land

used continuously for more than a decade so they are not truly comparable. The

allophanes present in large quantity in the Andosols have the potential of retaining

nutritive elements and thus depriving the plants of sustenance, whereas nutrients are

more readily released from the Ferralsols in the north. It is probably because the fertility

of the Ferralsols is more quickly exhausted (FAO-UNESCO, 1974).

2.2 Soil Chemical Properties

Soil chemical properties are the most important among the factors that determine the

nutrient supplying power of the soil to the plants and soil microbes. The chemical

reactions that occur in the soil affect processes leading to soil development, soil fertility

build up and soil biology. Minerals inherited from the soil parent materials overtime

release chemical elements that undergo various changes and transformations within the

soil.

11

2.2.1 Soil Reaction - pH

Soil reaction or pH affects nutrient availability and toxicity, microbial activity, and root

growth. Thus, it is one of the most important chemical characteristics of the soil solution

because both higher plants and microorganisms respond so markedly to their chemical

environment. Descriptive terms commonly associated with certain ranges in pH are

extremely acidic (pH < 4.5), very strongly acidic (pH 4.5-5.0), strongly acidic (pH 5.1 -

5.5), moderately acidic (pH 5.6 - 6.0), slightly acid (pH 6.1 - 6.5), neutral (pH 6.6 - 7.3),

slightly alkaline (pH 7.4 - 7.8), moderately alkaline (pH 7.9-8.4), strongly alkaline (pH

8.5 - 9.0), and very strongly alkaline (pH > 9.1) (Foth and Ellis, 1997). The degree and

nature of soil reaction influenced by different anthropogenic and natural activities

including leaching of exchangeable bases, acid rains, decomposition of organic

materials, application of commercial fertilisers and other farming practices (Rowell,

1994; Miller and Donahue, 1995; Tisdale et al.,1995; Brady and Weil, 2002). In strongly

acidic soils, Al3+ becomes soluble and increase soil acidity while in alkaline soils,

exchangeable basic cations tend to occupy the exchange sites of the soils by replacing

exchangeable H and Al ions (Miller and Donahue, 1995; Eylachew, 1999; Brady and

Weil, 2002).

2.2.2 Total nitrogen

Nitrogen (N) is the fourth plant nutrient taken up by plants in greatest quantity next to

carbon, oxygen and hydrogen, but it is one of the most deficient elements in the tropics

for crop production (Sanchez, 1976; Mengeland Kirkby, 1987; Mesfin, 1998). The total

N content of soil is directly associated with its organic carbon (OC) content and its

amount on cultivated soils is between 0.03% and 0.04% by weight (Mengel and Kirkby,

1987; Tisdale et al., 1995) but could be high even on tropical soils not subjected to

intensive cultivation (e.g. Samoan soils). The N content is lower in continuously and

intensively cultivated and highly weathered soils due to leaching and low organic matter

(OM) content (Tisdale et al., 1995). Wakene (2001) reported that there was a 30% and

76% depletion of total N from agricultural fields cultivated for 40 years and abandoned

land, respectively, compared to the virgin land in Bako area, Ethiopia. Average total N

increased from cultivated to grazing and forest land soils, which again declined with

12

increasing depth from surface to subsurface soils (Nega, 2006). The considerable

reduction of total N in the continuously cultivated fields could be attributed to the rapid

turnover (mineralisation) of the organic substrates derived from crop residue (root

biomass) whenever added, following intensive cultivation (McDonagh et al., 2001).

Moreover, the decline in soil OC and total N, although commonly expected following

deforestation and conversion to farm fields, might have been exacerbated by the

insufficient inputs of organic substrates from the farming system (Mulugeta, 2004). The

same author also stated that the levels of soil OC and total N in the surface soil (0-10

cm) were significantly lower, and declined increasingly with cultivation time in the farm

fields, compared to the soil under the natural forest

2.2.3 Available phosphorus

Phosphorus (P) is known as the master key to agriculture because lack of available P in

the soils limits the growth of both cultivated and uncultivated plants (Foth and Ellis,

1997). Following N, P has more widespread influence on both natural and agricultural

ecosystems than any of the other essential elements. In most natural ecosystems, such as

forests and grasslands, P uptake by plants is constrained by both the low total quantity of

the element in the soil and very low solubility of the scarce quantity that is present

(Brady and Weil, 2002). It is the most commonly plant growth-limiting nutrient in the

tropical soils next to water and N (Mesfin, 1996). Erosion tends to transport largely the

clay and OM fractions of the soil, which are relatively rich in P fractions. Thus,

compared to the original soil, eroded sediments are often enriched in P by a ratio of two

or more (Brady and Weil 2002). According to Foth and Ellis (1997), natural soil will

contain from 50 to over 1,000 mg of total P/kg of soil. Of this quantity, about 30 to 50%

may be in inorganic form in mineral soils (Foth and Ellis, 1997). The main sources of

plant available P are the weathering of soil minerals, the decomposition and

mineralisation of soil OM and commercial mineral fertilisers. Most of the soils in

tropical, particularly Andosols and other acid soils are known to have low P contents,

not only due to the inherently low available P content, but also due to the high P fixation

capacity of the soils due to the allophane component.

13

2.2.4 Exchangeable Potassium

Soil parent materials contain potassium (K) mainly in feldspars and micas. As these

minerals weather, the K ions released become either exchangeable or exist as adsorbed

or as soluble in the solution (Foth and Ellis, 1997). Potassium is the third most important

essential element next to N and P that limit plant productivity. Its behaviour in the soil is

influenced primarily by soil cation exchange properties and mineral weathering rather

than by microbiological processes. Unlike N and P, K causes no off-site environmental

problems when it leaves the soil system. It is not toxic and does not cause eutrophication

in aquatic systems (Brady and Weil, 2002). Wakene (2001) reported that the variation in

the distribution of K depends on the mineral present, particles size distribution, degree of

weathering, soil management practices, climatic conditions, degree of soil development,

the intensity of cultivation and the parent material from which the soil is formed. The

greater the proportion of clay mineral high in K, the greater will be the potential K

availability in soils (Tisdale et al., 1995). Soil K is mostly in mineral form and the daily

K needs of plants are little affected by organic associated K, except for exchangeable K

adsorbed on OM. Mesfin (1996) described low presence of exchangeable K under acidic

soils while Alemayehu (1990) observed low K under intensive cultivation.

2.2.5 Exchangeable calcium and magnesium

Soils in areas of moisture scarcity have less potential to be affected by leaching of

cations than soils under wet conditions (Jordan, 1993). Soils under continuous

cultivation, application of acid forming inorganic fertilisers, high exchangeable and

extractable Al and low pH are characterised by low contents of Ca and Mg mineral

nutrients resulting in Ca and Mg deficiency due to excessive leaching (Dudal and

Decaers, 1993). Exchangeable Mg commonly saturates only 5 to 20% of the effective

CEC, as compared to the 60 to 90% typical for Ca in neutral to somewhat acid soils

(Brady and Weil, 2002). The response to calcium fertilisers is ideal for most crops when

the exchangeable Ca is less than 0.2cmol (+)/kg of soils, while 0.5cmol (+)/kg soil is

reported to be the deficiency threshold level for Mg in the tropics (Landon, 1991).

14

2.3 Historical land use and land cover change of Taveuni

The change in land use on the island of Taveuni is a result of rapid expansion of taro

cultivation following severe taro leaf blight incidence in Samoa, which devastated taro

production and resulted in loss of Samoan taro export market in the year 1993. Prior to

this ‘great taro revolution’, agricultural lands on the island were only farmed in a

traditional manner for subsistence purposes. Farmers reasonably fallowed their land

under the practice of shifting cultivation and this somewhat maintained soil fertility.

However, with the prospect of lucrative export markets, new areas were opened up for

commercial taro production. This export production demand coupled with an increase in

human population, exerted great pressure on the island’s remaining fragile natural

ecosystems, particularly natural rainforests. Fallow durations were reduced and

dependency on chemical fertilisers increased until it became no longer sustainable.

Attaining optimum taro yield and meeting export requirements for specifications became

difficult. Consequently, rejects from the export markets were high, causing huge loss of

farmer income.

2.4 Agricultural Intensification

Agricultural intensification is a production system conventionally characterised by a low

fallow ratio and an intensive use of inputs, such as capital, labour, pesticides, and

chemical fertilisers, to raise agricultural yields, thereby increasing farmers’ income level

and reducing poverty. Previous studies demonstrated that intensive agricultural

production has led to increased erosion, lower soil fertility, and reduced biodiversity

(Matson et al., 1997).

Expansion of cultivation in many parts of East Africa has changed land cover to more

agro-ecosystems and less cover of natural vegetation. These changes are fuelled by a

growing demand for agricultural products that are necessary to improve food security

and generate income not only for the rural subsistence farmers but also for the large-

scale investors in commercial farming sector. Food production in Kenya, for example, is

reported to have increased steadily between 1980 and 1990, but with increase with

15

population, the food supply in calories per head fell slightly during that same period.

Historically, humans have increased agricultural outputs mainly by bringing more land

into production (Lambin et al., 2003). Indeed, land conversion to agriculture in East

Africa has outpaced the proportional human population growth in recent decades.

Natural vegetation cover has given way not only to cropland but also to native or planted

pasture (Lambin et al., 2003).

Globally, concerns about the changes in land use/cover emerged due to realisation that

land surface processes influence climate and that change in these processes impact on

ecosystem goods and services (Lambin et al., 2003). The impacts that have been of

primary concern are the effects of land use change on biological diversity, soil

degradation and the ability of biological systems to support human needs. Crop yields

have declined, forcing people to cultivate more land to meet their needs (Kaihura and

Stocking, 2003). Grazing areas have become scarce and less productive resulting from

over stocking of livestock.

2.5 Soil fertility degradation

Global Assessment of Soil Degradation has shown that the soil chemical degradation is

believed to be important in many parts of the tropics. The major factors contributing

towards declining soil fertility are: insufficient usage of fertilisers, reduction in soil OM,

and inadequate consideration to crop nutrient needs (Kumwenda et al., 1996). The

increase in fertiliser prices has forced farmers to limit its use (Ministry of Agriculture,

2010). In addition, continuous mono-cropping and poor husbandry practices have

decreased yields and profitability margins (Silatoga, 2012).

Soil fertility depletion is one of the major environmental and economic issues in

developing countries like Fiji. Evidence suggests that the land degradation problem in

Fiji is not improving in spite awareness of the numerous environmental issues (MPI,

2010). The primary form of land degradation in most productive soils in Fiji is the soil

chemical fertility degradation (Asafu-Adjaye, 2008). The loss of the soil chemical

fertility in most agricultural soils in Fiji is due to nutrient depletion which is becoming

16

an increasingly serious problem (Prasad, 2006). In Fiji, Taveuni soils have been reported

to be deficient of many essential plant available nutrients due to intensive cultivation

system. The problem of declining soil fertility is threatening taro producers in Fiji,

specifically in Taveuni (Duncan, 2010).

The physical, biological, and chemical characteristics of a soil such as its organic matter

content, acidity, texture, depth, and water-retention capacity all influence fertility

(Gruhn et al., 2000). According to Bationo and Mokwunye(1991); Bado et al.(1997) and

Bationo (2008), continuous cropping soil with inadequate application of fertilisers and

soil amendments have weak soil buffering capacity due to low soil organic carbon

(SOC) and clay content, low cation exchange capacity (CEC) and P deficiency are the

main limiting factors to agricultural productivity of the upland soils of West Africa. Data

from many long-term experiments in upland soils show yield declines over time as a

consequence of a decrease in SOC, soil acidification and a decrease of nutrient use

efficiency.

The quality of soil is essential in determining the sustainability and yield of the above

ground components (Doran et al., 1994). When crop residues are removed from the

intensively cultivated fields, organic matter is significantly reduced leading to declining

yields (Minten and Ralison, 2003). Soil degradation is not a new problem and many of

the ancient cultures broke down and disintegrated because of soil degradation problems

such as erosion and salinisation (Hillel, 1991).According to Lal (1997), degradation

occurs when soil cannot perform one of the several principal functions:

1. Sustain biomass production and biodiversity including preservation and

enhancement of the gene pool.

2. Regulate water and air quality by filtering, buffering, detoxification and regulate

geo chemical cycles.

3. Support socio-economic structure, culture and aesthetic values and provide

engineering foundation.

17

Soil degradation is the loss of actual or potential productivity and utility, and it implies a

decline in the soil’s inherent capacity to produce economic goods and perform

environmental regulatory functions (Lal, 1997). Soil degradation is not the same as land

degradation, which embraces the degradation of the overall capacity of the land to

produce economic goods and to perform environment regulating functions. Soil erosion,

salinisation, acidification and nutrient depletion are some important forms of soil

degradation. In addition, degraded soils become either acidic or saline. Leaching of

bases by percolating water causes soil acidity (Fenton, 2003). In addition, extended use

of most ammonia-based fertilisers will also lower soil pH (Lal, 1997).

According to Hartemink (2003), some of the guidelines that can be used in assessing soil

degradation are:

1. Clear signs of soil degradation that can be observed in the field. These could be

erosion, slaking of the soil surface, salt accumulation at the surface or compacted

and dense soil layers.

2. Trends in soil properties like declining pH, N, P, K and other nutrients.

3. Trends in crop yields.

2.6 Soil fertility degradation in relation to land use and land cover change

Land use and land cover change play a crucial role in soil fertility dynamics when

compared to natural factors, and can have impact upon soil quality particularly under

tropical conditions. The majority of land cover changes are related to agricultural use

of the land, including pastures. Agricultural activities change the soil chemical, physical,

or biological properties. Such activities include cultivation (mechanised or by hand),

tillage, weeding, terracing, sub-soiling, deep ploughing, manure, compost and fertiliser

applications, liming, draining, irrigation, and imploding (Bridges and de Bakker, 1997)

but also biocide applications on cultivated crops may affect soil properties. Many

degraded soils have been improved since people started cultivation and soil

improvements program continue to enhance the knowledge of farmers through training

and awareness programme in many agricultural areas. Adequate levels of agro-inputs are

applied when needed by the crops, losses are minimised and environmental awareness

18

and legislation have created agricultural practices that are ecologically and economically

more sustainable and profitable.

Most of the concerns about soil degradation are justifiable, however, lack of hard data

on the severity, extent and impact are little which makes soil degradation a debated issue

– particularly in tropical regions (Hartemink, 2006). A major factor in soil degradation is

the soil chemical fertility and then in particular its decline as a result of the lack of

nutrient inputs. This has been a major concern since sedentary agriculture started and is

the main reason why farmers clear more land when farming in forested areas: the soil is

depleted of plant nutrients (FAO- Staff, 1957; Nye and Greenland, 1960). Since the late

1980s, declining soil fertility has been recognised as an important cause for low

agricultural production in tropical regions (Pieri, 1989; Stoorvogel and Smaling, 1990;

van der Pol, 1992; Henao and Baanante, 1999; Sanchez, 2002).

Deforestation is a drastic land cover change and the clearing and burning of the natural

forest has a large impact on soils (Lal, 1986). All deforestation studies found

considerable changes in soil physical and chemical properties (Sanchez and Salinas,

1981; Lal, 1986; Ghuman and Lal, 1991; Veldkamp, 1994; Juo and Manu, 1996). Most

studies indicate that the abrupt transition from natural climax vegetation to a managed

system by man has several short-term effects on soil properties. The most important on-

site effect is the loss of organic matter causing a reduction in nutrient reserve, CEC, and

structure stability. The increase in soil organic C oxidation is due to higher soil surface

temperatures in arable soils as compared to soils under forests. Another effect that

occurs in deforested sloping areas is erosion (Lal, 1986). This is often mentioned as the

main cause of soil degradation (Willet, 1994). Burning of biomass and debris reduces N

and S stocks, while deforestation with heavy machinery may cause soil compaction and

erosion (Dias and Nortcliff, 1985; Hulugalle, 1994). Compaction effects are particularly

severe on volcanic ash soils (Andosols) (Spaans et al., 1989).

A sharp decline in soil organic C and increase in bulk densities in Ultisols was found

under various cropping systems up to 4 years after deforestation (Ghuman et al., 1991;

19

Ghuman and Lal, 1991). Conversion from forest to pasture or new forest has smaller

dramatic effects on soil organic C and bulk density compared to conversion from forest

to cropland (Veldkamp, 1994). A decline in soil organic C (corrected for compaction)

was found followed by a stabilisation after 5 years. The original forest soil organic C

continued to decline up to 20 years after deforestation.

The conversion of forest to perennial crops usually results in lower levels in the rates

soil fertility decline because – to some extent - these systems mimic the forest cover

(Hartemink, 2005b). Nonetheless, both erosion and soil chemical changes can be

significant in the early stages of crop development when the canopy is not closed and the

soil not covered. Soil erosion as well as leaching (both leading to a decline in soil

fertility) can be high due to the lack of nutrient uptake and soil exposure to the weather.

2.7 Soil fertility trends under different landuses

A case study in Zunhua County, northern China from 1980 to 1999 indicated that the

areas of farmland, grassland, and paddy decreased and were replaced by forest and

residential land. Soils under forest in 1999 transformed from farmland in 1980 increased

in organic matter by 21%, total N by 18%, available N by 65%, available P by 17% and

available K by 17%. Similarly, in the area which was converted from farmland in 1980

to grassland in 1999, soil organic matter, total N, available N, available P, and available

K all increased. Changes from farmland to forest and grassland not only changed land

cover but also improved soil fertility (Fu et al., 2001).A long-term (14 year period) trend

in soil fertility was established in New Zealand on pasture lands of different soil groups

and regions. The study revealed that Olsen P values were, on average, higher on dairy

farms than sheep/beef farms and significantly lower on sedimentary soils than other

soils(Wheeler et al., 2004), and this is attributable to continuous fertilisation of pastures

with P fertiliser which in deficient in many New Zealand soils. Soil test values for pH,

Ca and K were relatively constant over time while Mg level decreased constantly under

different land use and regions (Wheeler et al., 2004).The nature of trends of soil quality

indices under different land use, soil types and region principally depends on amount

and type of fertiliser applications.

20

A technical report titled “Soil quality monitoring in the Waikato region 2011” was

published in Waikato, New Zealand in 2013, reported that soil quality indicators vary

with land use over time. Soil pH levels were, significantly higher at sites under annual

cropping systems, than at sites under dairy pastures. Sites under native (forest) and

forestry had significantly higher pH levels (Taylor, 2013). Total C concentration were,

on average, significantly lower at sites under annual cropping than at sites under native,

forestry, horticulture and dairy pasture, indicating loss of soil organic matter(Taylor,

2013). Soil management practices such as reduced tillage and increased return of plant

materials, to mention a few, is the way forward to address the carbon problems in the

soil under any land use system (Dick & Gregorich, 2004). Total nitrogen concentrations

were significantly lower at sites under annual cropping than sites under different land

use practices (Taylor, 2013).Soils with lower soil organic matter have a lesser ability to

hold on nitrogen. Olsen P measurements were significantly higher at sites under annual

cropping systems compared those of other landuse practices. The report also revealed

that extreme levels of Olsen P were found in some production sites due to high rate of

phosphate fertiliser application. Soils with extreme Olsen P concentration have high risk

of phosphorus being leached to ground or transported to surface water (McDowell,

2001).Similar study was conducted by Eni et al.(2010) in Calabar South farmland,

Nigeria, estimated annual depletions of soil fertility at 32 kg nitrogen, 5kg phosphorus

and 18kg potassium per hectare of land degraded. In 2002 about 85% of cultivated land

had nutrient mining rates at more than 30 kg nutrients (NPK)/hectare yearly and 40%

had rates greater than 60 kg/ha yearly. Long term data obtained from the field indicates

that intensive farming can cause yield reductions of 60% and more in some parts of

Calabar South environments. Even under best variety selections and management

practices, yields are stagnated (Eni et al., 2010).

Report published in 2014 by Environmental Monitoring and Investigations staff of

Greater Wellington Regional Council (Greater Wellington) revealed that most soil

macro-nutrients vary with land-use, management practices and soil types. Overall, there

were significant changes in most soil quality indicators under dairy farm between 2000

and 2009. The most significant changes were an increase in nutrients, both total nitrogen

21

and Olsen P, macroporosity and cadmium but no significant trends were evident in bulk

density or soil pH values across the three sampling events (Drewry, 2014).

2.8 Soil fertility decline and spatial and temporal boundaries

Growing agricultural crops implies that nutrients are removed from the soil through

agricultural produce and crop residues. Nutrient removal may result in a decline of the

soil fertility if not replenished with fertilisers (organic or inorganic) adequately. Soil

fertility decline is defined as the decline in chemical soil fertility, or decreases in the

level of soil organic carbon, CEC, pH and plant nutrients. Soil fertility decline thus

includes nutrient depletion, nutrient mining, acidification, the loss of soil organic matter

and an increase in toxic elements (e.g. Al, Mn) (SSSA, 1997). To assess soil fertility

decline, it is necessary to define the spatial and temporal boundaries of the systems

under study.

The total amount of nutrient in the soil declines when the output exceeds the input over a

given period of time, soil depth, and at a certain location. Spatial and temporal

boundaries need to be chosen to ascertain whether the nutrient level declined. A spatial

boundary is the plot or paddock, whereas the temporal boundary is the period the plot

was cultivated, or the number of growing seasons during which the crop is grown

(Hartemink, 2003). When such boundaries are chosen it is easy to differentiate the soil

fertility trends.

2.9 Data types to assess soil fertility decline.

Soil degradation features such as water erosion and salinisation may be observed and

assessed with remote sensing and aerial photograph. Such techniques cannot be used to

measure a decline in soil nutrient levels. There are three different data types are used to

assess soil changes caused by agriculture production systems:

1. Expert knowledge

2. Nutrient balance

3. Monitoring of soil chemical properties over time (Type I) or at different sites

(Type II)

22

Some of these data can be relatively easy to collect where as other require long-term

commitment and are costly to collect (Hartemink, 1996).

2.9.1 Expert knowledge

The use of qualitative measurement of soil properties, such as soil colour and field

texture and soil mapping is regarded as expert knowledge. Farmers and other users of

the land have expert knowledge about their soils. The knowledge has been largely

ignored by soil science (Silitoe, 1998; Warkentin, 1999; WinklerPrins, 1999). A farmer

has empirical knowledge of his soils, which is not soil process but yield or management

oriented (Bouma, 1993). Yield decline as observed could, however, due to variety of

factors including soil fertility decline, adverse weather conditions, soil physical

deterioration or a combination of factors.

2.9.2 Type I Data

Soil dynamics can be monitored over time at the same site, which is called

chronosequential sampling (Tan, 1996) or type I data (Sanchez et al., 1985). This type of

data shows changes in a soil chemical property under a particular type of land use over

time. The original level is taken as the reference level to investigate the trends in

changes. Data from the previously analysed samples can be compared with the newly

collected and analysed samples. Type I data have been used to quantifying soil

degradation by comparing soil samples collected before the intensive agricultural period

with the recent samples taken from the same location (Lapenis et al., 2000). These data

are also useful in assessing the sustainability of land management practices in the tropics

(Greenland, 1994b).

2.9.3 Type II Data

The second approach, soils under adjacent different land use systems are sampled at the

same time and compared. This is called bio-sequential sampling (Tan, 1996). Moreover,

Type II data allows spatial and temporal change while Type I data allows only temporal

change analysis. The main underlying assumption is that the soils of the cultivated and

23

uncultivated lands are the same soil series, but the differences in soil properties can be

attributed to the differences in land use.

2.9.4 Semi-quantitative

A third way of studying soil fertility decline embraces a semi-quantitative approach,

which operates at a much coarser (smaller) scale. Existing soil data are combined with

pedo-transfer functions into GIS to estimate the decline in soil fertility at a given

location. Data of this nature with expert knowledge is ideal for modelling studies

(Hartemink, 2003).

2.10 Minimum dataset

Most data in the soil fertility decline studies were collected to supplement other

agronomic investigations in long term studies. Soil organic matter is one of the essential

components of soil fertility (Woomer et al., 1994), and a decline in its content must be

regarded as important factor affecting the productivity of the soil. Gregorich et al.

(1994) considered assessment of soil organic matter as a valuable step towards

identifying the overall quality of a soil. Soil pH, and together with other soil nutrients

such as total N, mineral nitrogen, available and total P, exchangeable K, Ca, and Mg.

These are important soil chemical properties that should be included in the minimum

data-set (Gregorich et al., 1994). The principal advantages of long-term experiments

according to Jenkinson (1991) are that they:

� Have continuous roles as living demonstrations for farmers and academics of the

effects of organic and inorganic manures;

� Enable the monitoring of trends in slow changing factors such as soil pH and other

soil fertility indices;

� Provide data for long-term studies of the relationship between crop yield and

meteorological variables;

� Provide data on the effects of atmospheric pollution; and,

� Can be used to validate computer simulation of field processes over time.

24

Furthermore, conducting long-term experiments is to document changing environmental

influences and system states before they become lost to the historical records (Pickett,

1991).Long- term experiments (LTE) provide the most convincing set of data as they

highlight trends and dynamics rather than the static snapshots of most other

measures(Southwood, 1994). LTEs serve as living laboratories providing opportunities

for experimentation in which the effects of manipulation may be separated from other

variables (Southwood, 1994). The increasing importance accorded to the development of

sustainable management practices for tropical landuse systems and the apprehension of

the potential impact of global climatic and environmental change has raised new interest

in the datasets from these experiments as well as the possibilities for new initiatives in

long-term monitoring and experimentation (Swift et al., 1994).

25

CHAPTER 3

MATERIALS AND METHODS

3.1 Scope of study

The fieldwork for this research was carried out on the island of Taveuni, located in the

north eastern Fiji group (Fig. 3.1)

Figure 3.1 Location of the study area. (Source: Wikipedia, 2007)

3.2 Origin of Taveuni

The island of Taveuni is an elongated shield volcano and its peak, Mount Uluigalau

reaches 1,241 meters above sea level. Volcanism on Taveuni began circa 780,000 years

ago, but most volcanic activity took place during the Holocene Epoch, which started

about 11,000 years ago (Wikipedia, 2007).

Since 9500 B.C., 167 volcanic vents have formed, mainly along the southern inland tip.

The youngest vent formed sometime between 4690 and 4900 B.C. Eruptions occurred at

an interval of about 70 years, but since 1200 B.C., there have been six periods of time

with frequent eruptions, each spanning between 200 and 400 years (Wikipedia, 2007).

26

3.3 Soil sampling sites

For ease of data collection, the area under investigation, that is, the whole island of

Taveuni, was divided into three rainfall zones that characterise the island. The three

rainfall zones are the dry zone in the north, the intermediate zone and the wet zone

towards the southern end of the island. This form of stratification was necessary to

assess soil fertility decline as it defines the spatial boundaries of the system under study

(Fig. 3.2).

The research involved a detailed examination and statistical analyses of archival data

from multi-location taro farms from each of the zone (strata) characterising the whole

island. A total of three main region shad been identified in each stratum for data

collection. However, small villages in the vicinity of the main regions were also

included to provide a better representation of the subject area. The site locations under

each stratum are given in Table 3.1 below.

Table 3.1 Research sites under each zone stratum

Rainfall zone Location on the island Site location

(village)

Mean annual rainfall

(mm)

Dry Northern end

Vunivasa

1500 – 2500 Qeleni

Matei

Intermediate Central

Lamini

2000 - 3500 Welagi

Qila

Wet Southern end

Waimaqere

2500 - 4000 Delaivuna

Vuna

27

Figure 3.2 Soil sampling sites

28

3.4 Data collection

3.4.1 Soil chemical fertility indices

Site-specific information on historical land use change and related management were

retrieved from archival sources for the last 22 years. The change in soil fertility for each

pre-determined stratum was assessed using chronosequential sampling. Data revealing

changes in soil chemical properties under continuous taro cultivation over time were

investigated. The original levels for soil chemical fertility indices prior to the

commercial cultivation of taro, that is, before 1993, were used as the reference level to

investigate any trends in such changes. The same approach was used to quantify the

change in soil fertility of the three different zone (strata) representing the three different

rainfall zones.

Nutrients in the exchangeable and soluble forms are readily plant-available. In this case,

topsoil properties were used as an indication of nutrient availability to plants because

most taro roots are concentrated in the A horizons (Lilienfien et al., 2003). Soil samples

collected over the archival period were from 0-20 cm depth. During the initial years of

the inception of taro program in Taveuni, about a total of 400 samples were received

with 40%, 30% and 30% from the dry, wet and intermediate zones, respectively.

However, as the area under cultivation increased and more intensive cultivation was

practised, problematic areas were identified and up to 1000 samples were analysed

annually with 34%, 36% and 30% from dry, wet and intermediate zones, respectively.

These samples were analysed at Koronivia Research Station for the following

determinations: pH (soil:water ratio of 1:5), organic carbon using the Walkley-Black

(1934) method, available P by Olsen et al. (1954) described by Blackmore et al.(1987)

and exchangeable cations by 1 M NH4OAcextraction at pH 7 (Daly et al., 1984 and

Blackmore et al., 1987).

The soil samples were collected from the same farms on a yearly basis to monitor the

changes in the soil chemical fertility. However, the analysis for soil organic carbon was

done only in the initial years of the monitoring and towards the end of the “22- year

intensive cultivation period”

29

imposing a severe limitation towards investigating the annual soil carbon stock trends.

This monitoring programme was initiated by the Ministry of Agriculture and farmers

association in the islands since the inception of commercial taro production under export

promotion programme.

3.4.2 Taro production data

Taro production data consisting of exportable yield and rejects of the export variety

(Tausala) for a period of 20 years were collected from the Ministry of Primary Industry,

Taveuni office archival sources to assess the effect of change in soil fertility on the yield

of the crop. One of the limitations of the present study was that the nutrient uptake data

for “22 year intensive cultivation period” not collected.

3.4.3 Meteorological data

Mean monthly and annual rainfall and temperature data for the period of the research,

that is, “22 year intensive cultivation period” were retrieved from Fiji Meteorological

Office archival sources to assess the effect of climate change on the yield of the crop.

3.4.4 Crop management data

The changes in selected management practices over time were recorded through a

survey, to assess how attempts have been made to maintain soil fertility under

continuous cropping as opposed to shifting cultivation. This survey was conducted using

the questionnaires targeting a total of 90 progressive farmers (30 farmers per zone)

(Appendix 12). In addition, the inclusion of new management variables, such as

fallowing, commencement of fertiliser application and liming resulting from continuous

cultivation were also recorded. Results were expressed as percent of total farmers

surveyed.

3.5 Statistical Analysis

All the data collected were subjected to determine mean differences between the

production strata with respect to fertility indicators, meteorological variables and taro

yield data. Temporal heterogeneity in soil fertility indices, taro yield and rejects, and

30

meteorological data were carried out for the whole island of Taveuni using regression

trends. Correlation analyses were carried out to determine associations between soil

fertility and meteorological variables for each production stratum. Regression analyses

were carried out to ascertain any significant dependence of taro yield on individual soil

fertility and meteorological variables. Multiple linear regression analysis was used to

derive a predictive model using indices that were individually significant with the yield.

Only coefficients significant were retained in the model. Paired sample t-test was used to

compare the differences in the soil fertility variables as well as taro yield between the

start and the current levels. All the data were analysed using the Discovery Edition of

the Genstat statistical software package (VSN International Ltd., 2011).

31

0100200300400500600700800

Mea

n m

onth

ly ra

infa

ll (m

m)

Month

0

500

1000

1500

2000

2500

3000

3500

4000

1989 1994 1999 2004 2009 2014

Mea

n an

nual

rain

fall

(mm

)

Year

CHAPTER 4

RESULTS AND DISCUSSION

4.1 Meteorological parameters

4.1.1 Rainfall

The mean overall magnitude of rainfall, its annual seasonal distribution and intra-annual

variability for the entire island of Taveuni for the “22 year review period” are given in

Figure 4.1 (a) and (b) below.

(a) (b)

Figure 4.1 (a) Rainfall pattern; and, (b) 22 year mean annual seasonal distribution for the

island of Taveuni

The rainfall pattern given above has been very similar for the entire three production

zone with a mean annual range of 2,500-4,000 mm for the wet zone; 2,000-3,500 for the

intermediate zone; and, 1,500-2,500 for dry zone (Met. Fiji, 2014).By decomposing the

mean annual rainfall seasonality for the “22 year period” into its magnitude and timing

components, the intra-annual variability of seasonality over the island of Taveuni was

ascertained. This revealed a unimodal wet peak during the month of January and a

relatively weak drier season during the months of June and July.

32

23

24

25

26

27

28

Jan

Feb

Mar

Apr

May Jun

Jul

Aug Sep

Oct

Nov Dec

Mea

n m

onth

ly te

mp.

(o C)

Month

Y = 0.0023x2 - 9.1131x + 9116.1 R² = 0.4149

25.4

25.6

25.8

26

26.2

26.4

26.6

26.8

1989 1994 1999 2004 2009 2014

Mea

n an

nual

tem

pera

ture

(o C)

Year

4.1.2 Temperature

The mean overall annual and monthly temperature and intra-annual variability for the

entire island of Taveuni for the “22 year review period" is given in Figure 4.2 (a) and (b)

below.

(a) (b)

Figure 4.2 (a) Mean annual; and, (b) 22 year monthly mean temperature for the island

of Taveuni

The mean annual and monthly temperature given above has been very similar for all the

three production strata. Trend analysis revealed a significant increase (P=0.004) in the

mean annual temperature during the “22 year review period”. By decomposing the mean

annual temperature for the 22 year period into its magnitude and timing components, the

intra-annual variability for the island of Taveuni was ascertained. This revealed a

unimodal peak (hot season) during the month of March and a cool dry season during the

months of July and August.

33

Y = 0.0011x2 - 4.4439x + 4464.8 R² = 0.2514

5

5.5

6

6.5

1989 1994 1999 2004 2009 2014So

il pH

Year

5

5.5

6

6.5

1989 1994 1999 2004 2009 2014

Soil

pH

Year Dry Wet Intermediate

4.2 Soil chemical indices

4.2.1 Soil pH

The mean soil pH trends for the three taro production strata and the general trend for the

entire island of Taveuni for the 22 year review period is given in Figure 4.3 (a) and (b)

below.

(a) (b)

Figure 4.3 (a) Soil pH trends for the three taro production strata; (b) 22 year mean trend

for Taveuni

Trend analysis revealed a significant decline (R2=0.25; P<0.01) in the mean soil pH for

all the three production strata over the 22 year review period. The initial decline can be

attributed to the commencement of intensive cultivation of the newly cleared forest sites

while the latter fluctuations tend to reflect the use of chemical fertilisers for the taro

crop, and application of agricultural lime during the alternating fallow periods. The

survey data reveals that 100% of the farmers from all the strata did not carry out any

application of fertiliser or lime until year 2000, depending entirely on the natural levels

of soil fertility. However, nearly 90% of the total farmers surveyed depended on

fertiliser and lime applications to sustain yields thereafter. Liming did not result in an

apparent trend of increasing soil pH as any increase in soil pH could have been

counterbalanced by heavy application of mineral fertilisers, particularly urea and

blended complete fertilisers. Another reason could have been the low rates of spot

application of lime due to the predisposing economic climate that the farmers work

34

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1989 1994 1999 2004 2009 2014

Tota

l N (%

)

Year Dry Wet Intermediate

Y = 0.002x2 - 7.8318x + 7841.5 R² = 0.4711

00.10.20.30.40.50.60.70.80.9

1

1989 1994 1999 2004 2009 2014

% T

otal

N

Year

within. Furthermore, high rainfall could also have been the contributing factor for

inefficiency of lime in correcting the soil pH, as leaching losses tend to be higher with

high rainfall. There were significant differences in soil pH (P=0.014) between the three

production strata with the drier strata having lower pH than the intermediate and the wet

strata. This acidification can be partially attributed to the more intense and

comparatively earlier use of nitrogenous fertilisers in the dry strata to obtain optimum

yields following the depletion of native organic matter levels. Longu and Dynoodt

(2008) reported that long-term annual applications of urea resulted in significant

increase in soil acidification and decreased exchangeable bases in soil. Adams (1984)

confirms that the acidity produced by 1 kg N in urea is 71g H+, which is equivalent to

about 3.6 kg CaCO3.

4.2.2 Total soil nitrogen

The mean total soil nitrogen (%) trends for the three strata and the general trend for the

entire island of Taveuni for the 22 year review period are given in Figure 4.4 (a) and (b)

below.

(a) (b)

Figure 4.4 (a) Total N trends for the three taro production strata; (b) 22 year mean trend

for Taveuni

35

0

10

20

30

40

50

60

70

1989 1994 1999 2004 2009 2014

Ols

en a

vaila

ble

P (m

g/kg

)

Year Dry Wet Intermediate

Y = 0.1512x2 - 606.63x + 608539 R² = 0.8104

0

10

20

30

40

50

60

70

1989 1994 1999 2004 2009 2014

Ols

en A

vaila

ble

P (m

g/kg

)

Year

Trend analysis revealed a significant decline (R2=0.47; P<0.01) in the mean total N for

all the three production strata over the 22 year review period. The initial decline can be

attributed to the decline in the native reserves of organic matter following

commencement of intensive cultivation of the newly cleared forest sites while the latter

increase tends to reflect the use of chemical fertilisers for the taro crop, and application

of agricultural lime during the alternating fallow periods resulting in more plant biomass

that gets returned as organic matter to the soil. Significant differences (P=0.013) in total

soil N were found to exist between the three production strata with the wet and the

intermediate zone having higher total N than the dry zone. This can be attributed to the

differences in the native and fallow biomass production and subsequent biomass

additions to the soil ecosystems and is a strict function of rainfall. In intensive cropping

systems, where a non-tillage system is adopted, depletion or loss of organic matter has

been reported (Johnson et al., 2006), which may result in N deficiency.

4.2.3 Olsen available phosphorus

The mean Olsen available phosphorus (mg/kg) trends for the three taro production strata

and the general trend for the entire island of Taveuni for the 22 year review period are

given in Figure 4.5 (a) and (b) below.

(a) (b)

Figure 4.5 (a) Olsen P trends for the three taro production strata; (b) 22 year mean trend

for Taveuni

36

0

0.2

0.4

0.6

0.8

1989 1994 1999 2004 2009 2014

Exch

ange

able

K

(cm

ol(+

)/kg)

Year Dry Wet Intermediate

Y= 0.0038x - 7.1914 R² = 0.0578

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1989 1994 1999 2004 2009 2014

Exch

ange

able

K (c

mol

(+)/k

g)

Year

Trend analysis revealed a significant decline (R2=0.81; P<0.01) in the mean levels of

Olsen available P for all the three production strata over the 22 year review period. The

sharp initial decline can be attributed to the effect of continuous cultivation that

aggravates organic matter oxidation. In addition, it may have resulted from the decline in

soil pH leading to accelerated fixation of soil P. Taveuni soils, being of volcanic origin,

have a high tendency to fix soil P and this could be the reason for poor response of the

soils towards P fertilisation and liming during latter years of cultivation. Fageria et al.

(2004) reported that most of the acidic soils have very low levels of native fertility,

especially in terms of phosphorus. Holfords (1977) reported that when P fertilisers are

applied to replenish soil fertility, about 70-90% of the P fertiliser is adsorbed and

becomes locked in various soil P compounds of low solubility. There were significant

differences in Olsen P (P<0.001) between the three production strata with the drier and

the intermediate zone having lower P levels than the wet zone. This can be linked to

differences in the quantity of biomass production between the three zone as well the

heavy use of blended fertiliser in the wet zone.

4.2.4 Exchangeable K

The mean exchangeable K (cmol(+)/kg) trends for the three taro production strata and

the general trend for the entire island of Taveuni for the 22 year review period are given

in Figure 4.6 (a) and (b) below.

(a) (b)

Figure 4.6 (a) Exchangeable K trends for the three strata; (b) 22 year mean trend for

Taveuni

37

4

6

8

10

12

14

16

1989 1994 1999 2004 2009 2014Exch

ange

able

Ca

(cm

ol (+

)/kg)

Year Dry Wet Intermediate

Y = -0.223x + 456.16 R² = 0.4071

02468

10121416

1989 1994 1999 2004 2009 2014Exch

ange

able

Ca

(cm

ol(+

)/kg)

Year

There were no significant differences (P=0.255) in the exchangeable K levels between

the three production zone. In China, the increase in total K and available K could be

explained by unbalanced fertilisation strategy in the area, which was commonly

practiced by the farmers (Niu et al.,2011). Niu et al. (2011) further stated that according

to farmers’ opinion, the more K fertilisers were used, the higher yields could be

achieved, but this could also result in strong K accumulation in the soils.

4.2.5 Exchangeable Ca

The mean exchangeable Ca (cmol(+)/kg) trends for the three taro production zone and

the general trend for Taveuni for the 22 year review period are given in Figure 4.7 (a)

and (b) below.

(a) (b)

Figure 4.7 (a) Exchangeable Ca trends for the three zone; (b) 22 year mean trend for

Taveuni

Trend analysis revealed a strong significant decline (R2=0.41; P<0.01) in the mean

levels of exchangeable Ca for the entire three production zone over the 22 year review

period. The initial decline can be attributed to the decline in the native reserves of

organic matter and accelerated leaching of the Ca following continuous cultivation of

the newly cleared forest sites with no external inputs, while the latter fluctuations tend to

reflect the application of agricultural lime by some farmers during the alternating fallow

periods (See Section 4.7). There were no significant differences (P=0.915) in the

exchangeable Ca levels among the three production zone. Horsley et al. (2000) reported

38

0123456789

1989 1994 1999 2004 2009 2014Exch

ange

able

Mg

(cm

ol(+

)/kg)

Year

Dry Wet Intermediate

Y = -0.1592x + 323.74 R² = 0.5149

0123456789

1989 1994 1999 2004 2009 2014

Exch

ange

able

Mg

(cm

ol(+

)/kg)

Year

similar trends about the depletion of available soil calcium (Ca) due to nutrient removals

by forest harvesting and leaching induced by acid deposition and aluminium (Al)

mobilisation in acidified soils. This has led to a heightened interest in the role of base

cations, such as Ca, in forest health and productivity.

4.2.6 Exchangeable Mg

The mean exchangeable Mg (cmol(+)/kg) trends for the three taro production zone and

the general trend for the entire island of Taveuni for the 22 year review period are given

in Figure 4.8 (a) and (b) below.

(a) (b)

Figure 4.8 (a) Exchangeable Mg trends for the three taro production strata; (b) 22 year

mean trend for Taveuni

Trend analysis revealed a strong significant decline (R2=0.51; P<0.001) in the mean

levels of exchangeable Mg for the entire three production zone over the 22 year review

period. The declining trend can be attributed to crop uptake and accelerated leaching of

Mg following continuous cultivation of the newly cleared forest sites with no external

inputs, as well as leaching loss, before any lime application occurred. The agricultural

lime applied was mainly in the form of calcium carbonate, not Mg-containing dolomitic

limestone, so is not expected to raise Mg levels. Also, displacement of exchangeable Mg

by Ca in lime may lead to higher Mg leaching. There were no significant differences

39

0

1

2

3

4

5

1990 1995 2000 2005 2010 2015Dry Wet Intermediate

Y = 0.0195x - 37.056 R² = 0.0593

0

1

2

3

4

5

1990 1995 2000 2005 2010 2015

Y = 0.3913x + 1.2325 R² = 0.3797

0123456789

0 2 4 6 8 10 12 14 16

Exch

ange

able

Mg

Exchangeable Ca

(P=0.626) in the exchangeable Mg levels among the three production zone. Similar

results were reported by Adejuwon and Ekanade (1975) who reported that decline in

exchangeable Mg levels could be attributed to organic matter diminution and some may

be washed off by surface erosion following the exposure of forest cover.

4.2.7 Ca: Mg Ratio

The mean Ca: Mg trends for the three taro production zone and the general trend for the

entire island of Taveuni for the 22 year review period are given in Figure 4.9 (a), (b) and

(c) below. Analysis of Ca: Mg ratio revealed that exchangeable Mg is approximately

equal to 40% of corresponding exchangeable Ca (Fig. 4.9c).

(a) (b)

(c)

Figure 4.9(a) Ca: Mg trends for the three taro production zone; (b) 22 year mean trend

for Taveuni (c) Removal of Mg relative to Ca

40

0

6

12

18

24

30

36

1993 1998 2003 2008 2013

Cor

m y

ield

(t/h

a)

Year Dry Wet Intermediate

Y = 0.1121x2 - 450.54x + 452848 R² = 0.9292

05

10152025303540

1993 1998 2003 2008 2013C

orm

yie

ld (t

/ha)

Year

4.3 Taro production and export rejects

4.3.1 Taro yields

The mean taro yield (t/ha) trends for the three production zone and the general trend for

the entire island of Taveuni for the 20 year review period are given in Figure 4.10 (a)

and (b) below.

(a) (b)

Figure 4.10 (a) Taro yield (t/ha) trends for the three taro production strata; (b) 20 year

mean trend for Taveuni

Trend analysis revealed a strong significant decline (R2=0.93; P<0.01) in the mean yield

of taro for the entire three production zone over the 20 year review period. This decline

in yields can be attributed to the interactive response of the deterioration of soil

chemical, biological and physical properties, resulting from continuous monocropping,

coupled with shorter fallow durations, inadequate to rejuvenate the soils to native levels

of fertility. This has been evident from the trend analysis of soil pH as well as all the

macro nutrients, which all significantly declined over the 20 year review period with the

exception of K. In addition, rapid depletion of soil organic matter can also be regarded

as a major contributing factor for the sharp decline in taro yields. The three production

zone did not significantly differ (P=0.823) with regards to the decline in yields.

41

05

1015202530354045

1993 1998 2003 2008 2013

% T

aro

reje

cts

Year Dry Wet Intermediate

Y = 0.241x2 - 964.9x + 965788 R² = 0.6783

05

1015202530354045

1993 1998 2003 2008 2013

% R

ejec

ts

Year

4.3.2 Taro export rejects

The mean taro export rejects (%) trends for the three rainfall zones and the general trend

for the entire island of Taveuni for the 20 year review period are given in Figure 4.11 (a)

and (b) below.

(a) (b)

Figure 4.11 (a) Taro export rejects (%) trends for the three taro production strata; (b) 20

year mean trend for Taveuni

The general 20 year trend for the export rejects of taro from the island of Taveuni

followed a highly significant quadratic relationship (R2=0.68; P<0.001) with higher

percentages of rejects towards the start and the end of the research period. The higher

proportion of rejects towards the beginning of commercial production was largely due to

over-sized and overweight corms which did not meet the export weight requirements of

between 1 to 3 kg per corm (Appendix 2). This was indicative of a very fertile soil.

However, upon continuous cultivation and subsequent fertility depletion, the corm size

and weight gradually decreased and most of the corms produced satisfied the export

guidelines, thus rejects were low. As time progressed and soil fertility further depleted,

the mean corm size produced significantly became smaller and underweight to an extent

whereby they did not meet the export standards. This was coupled with the significant

infestation of taro by two pests’ namely mealy bugs and plant parasitic nematodes,

which were earlier kept under control by relatively higher levels of organic matter. Pest

42

infestation and reduced soil fertility also lead to corm deformities. All these factors

resulted in comparatively higher levels of rejects after 20 years of continuous cropping.

There were significant differences in percentage export rejects (P=0.04) between the

three production zone with the drier zone having higher rejects than the wet and the

intermediate zone. This can be partially explained by a weak but seasonally pronounced

dry period which causes physical corm deformities.

4.4 Correlation analyses between the selected meteorological, taro yields and

soil chemical indices

The correlation matrices for the three individual production strata are presented in Table

4.1 (a), (b) and (c) below. Associations between variables differed between the three

rainfall zones.

4.4.1 Dry zone

The mean yield of taro showed significant positive associations with mean levels of

Olsen available P (P<0.01), exchangeable Ca (P<0.05) and exchangeable Mg (P<0.05)

(Table 4.1a). Mean daily temperature showed significant negative associations with

mean levels of exchangeable Ca and Mg (P<0.05). Soil pH was positively correlated

with mean levels of exchangeable Ca (P<0.05) and Mg (P<0.01). Total soil N showed

significant association with all the other macronutrients in the dry zone: Olsen available

P (P<0.05); exchangeable K (P<0.05); exchangeable Ca (P<0.05) and exchangeable Mg

(P<0.01). Exchangeable Mg showed significant association with Olsen available P

(P<0.05) and a highly significant association with exchangeable Ca (P<0.01).

43

Table 4.1(a) Correlation matrix of selected meteorological and soil chemical indices of

taro soils from the dry zone of Taveuni

Yield Rainfall Temp. pH N P K Ca Mg

Yield 1.0 0.13 -0.56* 0.43 0.40 0.76** -0.14 0.51* 0.69**

Rainfall 1.0 -0.09 0.02 0.35 0.18 -0.03 -0.18 0.25

Temp. 1.0 -0.28 -0.24 -0.33 0.25 -0.54* -0.56*

pH 1.0 0.34 0.23 0.01 0.47* 0.70**

N 1.0 0.54* 0.49* 0.49* 0.61**

P 1.0 0.01 0.38 0.52*

K 1.0 0.37 0.07

Ca 1.0 0.69**

Mg 1.0

*Significant at the <0.05, **<0.01, and ***<0.001 levels.

4.4.2 Intermediate Zone

The yield of taro positively correlated with exchangeable Ca (P<0.05) and highly

correlated with Olsen available P and exchangeable Mg (P<0.01). However, mean daily

temperature and exchangeable K correlated negatively with taro yield (P<0.05) (Table

4.1b). Annual rainfall correlated negatively with soil pH (P<0.05), while positively with

total soil N (P<0.05). Mean daily temperature significantly correlated negatively with

soil pH (P<0.05), Olsen available P (P<0.05) and exchangeable Mg (P<0.01). Soil pH

showed significant positive associations with Olsen available P and exchangeable Mg

(P<0.05). Olsen available P correlated positively with exchangeable Ca (P<0.05) and

exchangeable Mg (P<0.01). Exchangeable K showed a significant negative association

with exchangeable Ca (P<0.05).

44

Table 4.1(b) Correlation matrix of selected meteorological, taro yields and soil

chemical indices of taro soils from the intermediate zone of Taveuni

Yield Rainfall Temp. pH N P K Ca Mg

Yield 1.0 0.05 -0.54* 0.41 -0.03 0.71** -0.50* 0.46* 0.59**

Rainfall 1.0 -0.03 -0.46* 0.46* -0.20 -0.32 0.09 0.10

Temp. 1.0 -0.48* 0.33 -0.54* 0.27 -0.35 -0.72**

pH 1.0 -0.28 0.49* -0.22 0.38 0.49*

N 1.0 0.03 -0.39 -0.10 -0.09

P 1.0 -0.23 0.44* 0.59**

K 1.0 -0.51* -0.27

Ca 1.0 0.26

Mg 1.0

*Significant at the <0.05, **<0.01, and ***<0.001 levels.

4.4.3 Wet zone

The yield of taro highly positively correlated with Olsen available P (P<0.001),

exchangeable Ca (P<0.01) and exchangeable Mg (P<0.01). However, mean daily

temperature correlated negatively with taro yield (P<0.05) (Table 4.1c). Mean daily

temperature correlated negatively with Olsen available P and exchangeable K (P<0.05).

Soil pH showed significant associations with available P (P<0.05) and exchangeable Ca

and Mg (P<0.01). Olsen available P highly correlated with exchangeable Ca and Mg

(P<0.01). Exchangeable Ca and Mg showed strong association (P<0.01).

45

Table 4.1(c) Correlation matrix of selected meteorological, taro yields and soil

chemical indices of taro soils from the wet zone of Taveuni

Yield Rainfall Temp. pH N P K Ca Mg

Yield 1.0 0.09 -0.53* 0.33 0.27 0.88*** 0.24 0.63** 0.66**

Rainfall 1.0 -0.09 0.12 0.37 -0.03 0.36 -0.12 0.00

Temp. 1.0 -0.14 0.05 -0.52* -0.47* -0.22 -0.27

pH 1.0 0.44 0.53* 0.09 0.59** 0.64**

N 1.0 0.39 0.15 -0.04 0.15

P 1.0 0.31 0.65** 0.64**

K 1.0 0.08 0.17

Ca 1.0 0.65**

Mg 1.0

*Significant at the <0.05, **<0.01, and ***<0.001 levels.

46

Y =

18.

718x

- 88

.731

R

² = 0

.111

1

0510152025303540

5.2

5.4

5.6

5.8

6.0

6.2

Taro corm yield (t/ha)

Soil

pH (H

2O)

(a)

Y =

16.

039x

+ 9

.165

5 R

² = 0

.038

8

0510152025303540

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Tota

l N

(%)

(b)

Y =

1.1

229x

+ 5

.32

R² =

0.5

814

0510152025303540

010

2030

40O

lsen

ava

ilabl

e P

(mg/

kg)

(c)

Y=

-14.

605x

+ 2

3.45

3 R

² = 0

.025

9

0510152025303540

0.1

0.3

0.5

0.7

Taro corm yield (t/ha)

Exch

ange

able

K (c

mol

(+)/k

g)

(d)

Y =

2.3

135x

- 4.

1593

R

² = 0

.273

4

0510152025303540

49

14Ex

chan

geab

le C

a (c

mol

(+)/k

g)

(e)

Y =

4.8

925x

- 5.

5472

R

² = 0

.405

7

0510152025303540

24

68

Exch

ange

able

Mg

(cm

ol (+

)/Kg)

(f)

4.5

Rel

atio

nshi

p of

sele

cted

che

mic

al in

dice

s to

taro

cor

m y

ield

The

rela

tions

hip

betw

een

indi

vidu

al c

hem

ical

indi

ces a

nd th

e yi

eld

if ta

ros a

re g

iven

in F

igur

e 4.

12 (a

-f) b

elow

.

Figu

re 4

.12

Reg

ress

ion

of ta

ro y

ield

on

(a) s

oil p

H; (

b) T

otal

N (%

); (c

) Ols

en a

vaila

ble

P (m

g/kg

);

(d) E

xcha

ngea

ble

K (c

mol

(+)/k

g); (

e) E

xcha

ngea

ble

Ca

(cm

ol(+

)/kg)

; and

, (f)

Exc

hang

eabl

e M

g (c

mol

(+)/k

g).

47

The linear regression analysis of yield data of taro against the individual soil chemical

indices for the 20 year review period (Figure 4.12 a-f) revealed significant dependence

of yield on soil pH (R2=0.11; P<0.011); Olsen available P (R2=0.58; P<0.001);

exchangeable Ca (R2=0.27; P<0.001); and, exchangeable Mg (R2=0.41; P<0.001). On

the contrary, Total N (R2=0.04; P<0.14) and exchangeable K (R2=0.03; P<0.23), did not

significantly influence the yield of taro. In general, soil total N is not a good predictor of

crop yield as this variable does not reflect N availability to plants.

Multiple linear regression analysis was then carried out using only the chemical indices

which significantly influenced taro yields, that is, soil pH, Olsen available P,

exchangeable Ca and Mg. This showed a highly significant overall relationship

(R2=0.65, P<0.001) between the yield and the interactive response of the four

parameters. However, the estimation of parameters revealed that only Olsen available P

and exchangeable Mg had a significant effect in predicting the yield of taro as outlined

in Table 2 below:

Table 4.2 Estimates of parameters for multiple linear regression analysis

Parameter Estimate S.E. t-value (df=52) P – value

Constant 36.9 28.7 1.29 0.204

Ca 0.639 0.424 1.51 0.138

Mg 2.395 0.814 2.94 0.005

P 0.868 0.147 5.91 <0.001

pH -8.13 5.33 -1.52 0.133

48

It can therefore, be said that the yield of taro can reasonably be estimated using the

following predictor equation based on soil chemical indices:

Y= 36.9 + 0.868 (Olsen P) + 2.395 (Exchangeable Mg)

N=20; R2=0.65; P< 0.001

4.6 Comparison of soil chemical properties between pre and post 22 year cultivation period

Comparison of soil chemical indices between the pre and post 22 year intensive

cultivation period using paired sample t-test revealed highly significant reduction in

levels of soil organic carbon (P<0.001), Olsen P (P<0.001), exchangeable Ca (P=0.005)

and Mg (P=0.003) (Table 4.3a). This can be attributed to depletion of the natural levels

of these nutrient elements following forest clearing and subsequent cropping. Although

P supplementation were made through use of complete chemical fertilisers, most of

these inorganic P has been rendered unavailable for plants largely due to fixation, a most

common limiting characteristic of many soils. Ca and Mg supplementation through

liming were also made over the review period, particularly during the latter stages, but

was most likely counter balanced by leaching and crop removal losses.

There were no significant declines in the soil pH (P=0.370), total N (P=0.241) and

exchangeable K (P=0.242) over the research period (Table 4.3a). This can be partially

explained by organic matter additions during the periodic fallow phases as well as

inorganic inputs of N and K. Liming towards the latter stages of the research period

partially compensated for the earlier decline in soil pH. The survey data reveals that

prior to year 2000; none of the farmers applied any form of fertiliser or liming material

and depended entirely on the natural levels of soil fertility. However, nearly 90% of the

total farmers surveyed depended on fertiliser and lime applications to sustain yields

thereafter.

49

Yield comparison between the pre and post 22 year intensive cultivation period using

paired sample t-test revealed a highly significant reduction (P<0.001). This can be

attributed to the corresponding decline in soil organic carbon, Olsen P, exchangeable Ca

and Mg which all significantly correlated with taro yield. From the multiple regression

analysis, it can be deduced that Olsen P and exchangeable Mg may be two of the most

limiting nutrient elements for taro soils of Taveuni.

Data from the farmer survey conducted in all the zones revealed Olsen P to be the most

limiting nutrient across all the zone with 100% of the farms surveyed being below the

critical levels for the element (Table 4.3b). Largest proportion of farms having organic C

(100%) and total N (63%) below the critical levels were in the dry strata. This can be

ascribed to the least biomass production and organic matter addition in the dry strata

comparatively. The proportion of farms having exchangeable K levels below the critical

range was highest in the wet zone (100%) (Table 4.3b).This can be partially explained

by relatively higher leaching losses as well as higher uptake of the nutrient as yield

levels were higher for the zone.

50

Tabl

e 4.

3a

Paire

d sa

mpl

e t-t

est f

or th

e ch

emic

al in

dica

tors

bet

wee

n pr

e an

d po

st p

erio

d of

inte

nsiv

e cu

ltiva

tion

Clim

atic

Zo

ne

Vill

age

Soil

chem

ical

indi

cato

rs

Tar

o co

rm

yiel

d (t

/ha)

pH

(H2O

) O

C (%

) N

(%)

P (m

g/kg

) K

(c

mol

(+)/k

g)

Ca

(cm

ol(+

)/kg)

M

g (c

mol

(+)/k

g)

1990

20

12

1990

20

12

1990

20

12

1990

20

12

1990

20

12

1990

20

12

1990

20

12

1993

20

12

Dry

Vun

ivas

a 5.

48

4.80

5.

7 3.

4 0.

34

0.23

22

.8

8.77

0.

19

0.11

10

.70

0.98

2.

60

0.27

32

.9

9.1

Qel

eni

5.73

5.

50

9.4

3.7

1.00

0.

44

46.0

6.

57

0.33

0.

37

8.10

6.

30

2.40

2.

17

33

9.8

Mat

ei

5.52

5.

90

10.6

3.

5 0.

67

0.32

38

.0

6.25

0.

12

0.66

12

.42

8.28

7.

89

5.96

33

.6

8.4

Wet

Wai

maq

ere

6.00

6.

20

11.5

5.

4 1.

00

0.10

56

.3

5.80

0.

40

0.27

9.

60

9.90

3.

70

2.59

31

.5

10.4

Del

aivu

na

6.20

5.

90

15.4

5.

9 0.

41

0.80

64

.0

6.30

0.

16

0.30

17

.36

7.90

8.

37

2.70

31

.4

8.9

Vun

a 5.

70

6.45

9.

8 5.

1 0.

56

0.65

68

.0

5.75

0.

30

0.48

18

.80

14.6

2 8.

40

5.88

33

.6

9.1

Inte

rmed

iate

Lam

ini

5.80

5.

30

8.2

3.8

0.80

0.

65

53.0

8.

71

0.40

0.

26

13.8

0 2.

71

7.40

2.

73

36

9.6

Wel

agi

5.70

5.

30

9.8

4.2

0.70

0.

44

22.0

5.

47

0.48

0.

52

9.80

4.

60

6.80

2.

37

36

9.4

Qila

6.

32

5.70

9.

2 4.

1 0.

28

0.58

19

.8

8.23

0.

44

0.66

6.

70

5.11

2.

60

1.94

33

.1

9.1

Mea

n D

iffer

ence

(d)

-0.1

6 -5

.6

-0.1

7 -3

6.45

0.

09

-0.5

2 -2

.62

-24.

14

Crit

ical

rang

e 5.

3-6.

5 4-

10

0.3-

0.6

20-3

0 0.

5-0.

8 5-

10

1-3

12-1

5

Rel

ativ

e %

de

clin

e/in

crea

se

-2.7

-5

6.4

-26.

6 -8

4.1

+22.

5 -4

3.7

-47.

0 -7

3.0

Stan

dard

err

or o

f mea

n di

ffer

ence

(Sd)

0.16

0.

66

0.14

6.

37

0.07

1.

35

0.64

0.

59

95%

Con

fiden

ce In

terv

al

-0.5

3 0.

22

-7.1

3 -4

.10

-0.4

9 0.

14

-51.

15

-21.

75

-0.0

7 0.

25

-8.3

1 -2

.11

-4.0

9 -1

.15

-25.

51

-22.

78

p-va

lue

0.37

0 ns

<

0.00

1 **

* 0.

241

ns

< 0.

001

***

0.24

2 ns

0.

005

**

0.00

3 **

<

0.00

1 **

*

ns –

not

sign

ifica

nt; *

* - s

igni

fican

t at P

< 0

.01;

***

- si

gnifi

cant

at P

< 0

.001

51

Table 4.3b Soil chemical fertility decline resulting from 22 year intensive cultivation

Soil Chemical

Property Critical Range Rainfall Zone

Farms below critical range

No. of

farms

Percentage of

farms

pH 5.3 – 6.5

Dry 8 27

Wet 8 27

Intermediate 14 47

Total N (%) 0.3 – 0.6

Dry 19 63

Wet 11 37

Intermediate 13 43

Olsen P (mg/kg) 20 - 30

Dry 30 100

Wet 30 100

Intermediate 30 100

Exchangeable K

(cmol(+)/kg) 0.5 – 0.8

Dry 20 67

Wet 30 100

Intermediate 20 67

Organic carbon

(%) 4 - 10

Dry 30 100

Wet 3 10

Intermediate 15 50

Exchangeable

Ca (cmol(+)/kg) 5 - 10

Dry 10 33

Wet 4 13

Intermediate 20 67

Exchangeable

Mg

(cmol(+)/kg)

1 - 3

Dry 10 33

Wet 4 13

Intermediate 7 23

52

Tabl

e 4.

4 C

ompa

rison

of e

nd o

f res

earc

h pe

riod

leve

ls a

gain

st c

ritic

al le

vels

and

sugg

este

d am

elio

rativ

e m

easu

res

Soil

Para

met

ers

Mea

n

valu

es

(201

2)

Crit

ical

Ran

gea

Sugg

este

d A

mel

iora

tive

Tech

niqu

es

Soil

pH

5.67

5.

3 –

6.5

Soil

pH le

vel o

f ta

ro s

oils

in T

aveu

ni is

with

in th

e cr

itica

l ran

ge. T

his

coul

d be

attr

ibut

ed to

app

licat

ion

of li

me

and

othe

r org

anic

mat

eria

ls. T

hus,

to m

aint

ain

soil

pH le

vel w

ithin

the

criti

cal r

ange

agr

icul

tura

l lim

e (p

refe

rabl

y do

lom

itic)

shou

ld b

e ap

plie

d be

fore

eve

ry p

lant

ing

cycl

e. A

pplic

atio

n ra

te, t

ype

of li

min

g m

ater

ials

and

tim

e of

app

licat

ion

shou

ld

be d

one

in c

onsu

ltatio

n w

ith a

gric

ultu

re d

epar

tmen

t.

Tota

l OC

4.

34

4-10

Mea

n va

lues

for

tot

al O

C fo

r Ta

veun

i so

ils a

re w

ithin

the

crit

ical

ran

ge.

This

cou

ld b

e du

e to

per

iodi

c fa

llow

ing

prac

tice

with

hig

h bi

omas

s inp

uts,

as a

dopt

ed b

y th

e fa

rmer

s. To

enh

ance

car

bon

build

up,

num

ber o

f org

anic

mat

eria

ls

coul

d be

exp

lore

d su

ch a

s us

e of

by-

prod

ucts

of

fish

cann

ing

proc

essi

ng p

lant

, sea

wee

d, a

nd s

hred

ded

coco

nut h

usk,

whi

ch a

re r

eadi

ly a

vaila

ble

to t

he f

arm

ers

on t

he i

slan

d. A

void

bur

ning

of

plan

t lit

ter

afte

r fo

rest

cle

arin

g an

d us

e

legu

min

ous f

allo

w to

impr

ove

the

leve

l of c

arbo

n. P

erha

ps p

rolo

ng n

atur

al fa

llow

may

ass

ist a

llevi

atin

g ca

rbon

leve

l as

wel

l as c

arbo

n se

ques

tratio

n th

roug

h us

e of

bio

char

.

Tota

l N (%

) 0.

46

0.3

– 0.

6

Mea

n va

lues

for t

otal

N fo

r Tav

euni

soi

ls a

re w

ithin

the

criti

cal r

ange

. Thi

s co

uld

be d

ue to

app

licat

ion

of m

iner

al N

ferti

liser

s. So

me

farm

ers i

n Ta

veun

i als

o pr

actic

es o

ther

mea

ns o

f en

hanc

ing

orga

nic

mat

ter s

uch

as th

e us

e of

legu

mes

(muc

una

bean

s),

agro

for

estry

pra

ctic

es a

nd f

allo

win

g pr

actic

es.

To m

aint

ain

tota

l N

lev

els

with

in a

n id

eal

rang

e,

synt

hetic

N fe

rtilis

ers a

nd o

rgan

ic m

atte

r acc

umul

atio

n th

roug

h im

prov

ed le

gum

inou

s fal

low

is p

aram

ount

.

53

Ols

en

avai

labl

e P

(mg/

kg)

6.87

20

- 30

Soil

avai

labl

e P

is c

ritic

ally

low

for

all

taro

gro

win

g si

tes

in T

aveu

ni. I

t is

cruc

ial t

o co

rrec

t the

soi

l pH

leve

ls w

ith

dolo

mtic

lim

ing

mat

eria

ls b

efor

e ap

plyi

ng m

iner

al o

r or

gani

c P

ferti

liser

s. Fe

rtilis

ers

avai

labl

e in

Fiji

are

: R

ock

phos

phat

e, D

i- am

mon

ium

pho

spha

te, m

ono-

amm

oniu

m p

hosp

hate

, NPK

13:

13:2

1, s

ingl

e su

perp

hosp

hate

and

trip

le

supe

rpho

spha

te. S

ole

relia

nce

on c

hem

ical

ferti

liser

s sh

ould

be

redu

ced

as th

ey te

nd to

aci

dify

the

soil.

The

sol

ubili

ty

of o

rgan

ic P

mat

eria

ls is

ver

y lo

w th

eref

ore,

par

ticle

s sho

uld

be fi

nely

gro

und

and

inco

rpor

ated

in th

e so

il.

Exch

ange

able

K

(cm

ol(+

)/kg)

0.40

0.

5 –

0.8

Exch

ange

able

K le

vels

wer

e fo

und

to b

e cr

itica

lly lo

w in

all

taro

gro

win

g si

tes,

desp

ite m

iner

al K

sup

plem

enta

tion.

This

can

be

due

to g

reat

er r

emov

al o

f K

rel

ativ

e to

N, b

y ro

ot c

rops

. Man

agem

ent p

ract

ices

incl

ude

the

follo

win

g:

Lim

ing

acid

soi

ls w

ith a

ppro

pria

te li

min

g m

ater

ial,

appl

ying

ade

quat

e ra

te o

f K fe

rtilis

er, a

ppro

pria

te ti

me

and

met

hod

of a

pplic

atio

n, in

corp

orat

ing

crop

resi

dues

, sup

plyi

ng a

dequ

ate

moi

stur

e an

d us

e of

farm

yard

man

ures

. In

addi

tion

to

thes

e, o

ther

pra

ctic

es s

uch

as c

rop

rota

tion,

ado

ptin

g co

nser

vatio

n til

lage

, im

prov

ing

orga

nic

mat

ter c

onte

nt in

the

soil

and

husb

andr

y pr

actic

es is

impo

rtant

(Fag

eria

and

Bal

igar

, 200

3b).

Exch

ange

able

Ca

(cm

ol(+

)/kg)

6.14

5

- 10

Thou

gh th

e C

a an

d M

g ar

e w

ithin

the

criti

cal r

ange

, the

pra

ctic

e of

lim

ing

usin

g do

lom

itic

limin

g m

ater

ials

sho

uld

be

cont

inue

d to

mai

ntai

n th

e le

vels

. Ex

chan

geab

le

Mg(

cmol

(+)/

kg)

2.95

1

- 3

a Bla

kem

ore

et a

l. ra

tings

(198

7)

54

0

20

40

60

80

100

1989 1994 1999 2004 2009 2014

% fa

rmer

s

Year continuous cultivation shifting cultivationfertiliser application lime application

4.7 Changes in selected soil management practices over 22 year cultivation

period

The changes in the adoption of selected management practices, namely continuous

cultivation, shifting cultivation and application of lime and mineral fertilisers, over time

were recorded through surveys, to assess how attempts have been made to maintain soil

fertility (Figure 4.13).

Figure 4.13 Farmer adoption of various management practices to support intensive

taro cultivation

Prior to the commencement of commercial taro production in Taveuni (before 1993),

almost all the farmers practiced shifting cultivation and continuous cultivation was not

necessary as most of the farmers grew taro on a smallholder scale (Fig. 4.13). However,

with the introduction of the lucrative taro export markets, shifting cultivation was soon

phased out and continuous cultivation practices were adopted to maintain market

consistency and take advantage of the rewarding prices that the export markets had to

offer, as majority of the farmers were constrained by farm size. As time progressed,

55

significant yield declines were experienced and this system was no longer considered to

be sustainable. Farmers at first turned towards the use of chemical fertilisers for a quick

fix solution. However, later researches revealed that the soil pH and available P were the

most limiting factors for optimum taro productivity. As such, liming was duly

recommended and remedial actions were taken by farmers, exploiting various sources of

liming materials such as calcitic and dolomitic lime as well as ground coral. However,

adoption of the practice of liming never exceeded 32% of the total farmers due to the

high cost factor involved and so did not result in significantly raising the pH and

exchangeable Ca levels of the soils overall. Therefore, farmers continue to rely heavily

on chemical fertilisers alone to date. The application rate of lime is highly variable

between and within zone ranging from 300 to 800kg per hectare. The lime application

rate varies due to factors like soil pH, rainfall and the soil type in each stratum. Since

taro is spot planted in Taveuni with minimum tillage practices, lime is placed in the

planting holes during planting. The application rates currently used by farmers are far

below the Ministry of Agriculture recommendation of 3- 4 t/ha.

Table 4.5a Distribution of land tenure systems for the surveyed farms

Land tenure No. farms

Freehold 43 (48)

Freehold lease 19 (21)

Native lease 20 (22)

Communal lease 8 (8)

*The figures in parenthesis denote percentage of farms surveyed.

The farmer survey data revealed that 48% of the surveyed farms in Taveuni - fall under

freehold form of land ownership. Furthermore, large freehold estates subdivided as

smaller leased out fragments constituted of 21%, while the native lease and communal

tenure systems accounted for the remainder 22% and 8%, respectively (Table 4.5a). The

freehold leases were only for a short term duration (3-4 years) imposing severe

56

restrictions on adoption of conservation practices. Under this arrangement of taro

cultivation, continuous cropping targeting maximum output per unit area of land is the

paramount interest of the farmers.

The native land and communal leases under taro production comprise of larger units

with longer terms of lease. Under these forms of ownerships, some conservation

practices, such as crop rotation and seasonal fallowing are adopted.

Table 4.5b Distribution of farm size under taro cultivation

Farm size (acres) No. of Farms

1 - 5 32 (36)

6 - 10 41 (45)

11 - 15 10 (12)

16 - 20 3 (3)

21 - 25 2 (2)

26 - 30 2 (2)

*The figures in parenthesis denote percentage of farms surveyed.

The farmer survey data revealed that 81% of the farm holdings in Taveuni were 10 acres

(0.4ha) or less. These small fragmented holdings contribute to approximately 80% of the

total taro grown for the export market. This small scale of operations coupled with the

constrained economic climate of these holdings limit the adoption of most the

recommended husbandry practices which advocate sustainable production. As such,

yield decline under these holdings turn out to be inevitable.

On the hand, the remaining 19% of the relatively larger production units were in a better

position to adopt alternative sustainable package of taro cultivation, such as crop

rotation, shifting cultivation and fallowing. In addition, these are the units that are

comparatively more financially capable with regards to the usage of agro inputs.

57

0102030405060708090

100

Production cost Inconsistency ofsupply of agro

inputs

Roading/Access Instability ofmarket prices

Lack of technicalknow how

% o

f far

mer

s

Constraint

From the survey, it was evident that the average farm size and the management decisions

that they dictate, significantly contribute to the overall fertility levels and declines

experienced by the taro farmers.

4.8 Production constraints as identified by taro growers

The production constraints were systematically categorised fewer than five predominant

classes. Some growers identified multiple constraints to be the limiting factors and were

recorded as such (Fig. 4.14).

Figure 4.14 Identification of production constraints by farmers

Instability of market prices resulting from inconsistencies in production was revealed to

be the most severe constraint facing taro growers of Taveuni with all the surveyed

farmers (100%) identifying it as a significant determinant of their net farm income. Road

access, lack of technical knowledge and high variable production costs constitute the

other constraints limiting the full realisation of taro farming output. As far as lack of

technical knowledge is concerned, Nisha et al. (2014) evaluated the soil nutrient

management practices of taro farmers in Taveuni and highlighted that the main cause of

low use of fertilisers was that the farmers do not know the fertility status of their farms

and majority of them are also not fully aware of various low-cost organic methods of

maintaining the soil fertility of their farms.

58

These constraints largely determine the economic position of the farmers and dictate the

underlying factors affecting the degree of adoption of sustainable crop and soil

conservation management practices needed to maintain soil chemical fertility.

59

CHAPTER 5

CONCLUSIONS

5.1 Summary Sustainability, although a dynamic concept, implies some sort of equilibrium or steady

state. The analyses presented in this research work has shown that many soil chemical

properties significantly change with time, and it can be argued that land-use systems in

which significant soil fertility decline takes place are not sustainable in the long term.

This research has used a set of basic soil chemical properties (pH, total N, Olsen P and

exchangeable cations) to investigate changes under taro cropping systems in Taveuni,

Fiji, over a 22 year period of intensive cultivation with little to no fallow. Each of the

property shows a degree of natural variation that is affected by soil management and the

cropping system. Since taro is an annual crop, decline in soil fertility is comparatively

larger than other land-use systems, which thus have a significant effect on crop

productivity. The high native fertility levels and production potential of Taveuni soils

declined rapidly when the forest cover was replaced by the annual crop of taro.

5.2 Conclusions

This was particularly evident from the trend analyses of the nutrient elements which,

altogether with soil pH and taro yields, revealed significant declines over the 22 year

cropping period, with the exception of exchangeable K. Significant associations between

and dependence of taro yields on soil pH, Olsen P, exchangeable Ca and exchangeable

Mg were also observed. In addition, significant changes in these four chemical

parameters were observed when the pre and the post cultivation levels were compared.

Olsen P and exchangeable Mg were identified to be the most limiting nutrients for the

taro soils of Taveuni.

The increased use of inorganic fertilisers and lime was deemed necessary towards the

latter years of the research period in an attempt to sustain yields and continuing research

60

needs to be undertaken to ascertain any resultant significant changes. Obviously, soil

fertility is a complex issue consisting of several attributes that interact over time.

Measurements require long-term research commitments as well as detailed knowledge

about spatial and temporal variability. Systematic, consistent measurements of soil

properties should be undertaken, since soil attributes are an important component of land

cover change.

5.3 Recommendations

1. The balanced and efficient use of plant nutrients from both organic and

inorganic sources, at the farm and community levels, should be emphasized; the

use of local sources of organic matter and other soil amendments should be

promoted; and successful cases of integrated plant nutrient management should

be analyzed, documented, and disseminated.

2. More closer cooperation and coordination between farmers and researchers to

exchange information and disseminate developed technologies that take into

account immediate farmer immediate needs along with longer-term soil fertility

and agricultural sustainability requirements

3. Participatory forms of design, testing, and extension of improved plant nutrient

management strategies that build upon local institutions and social organisations,

including trained farmer groups should be promoted.

4. Improvement of security of access to land leases on long terms is critical for the

intensification of fertiliser use and the successful promotion of integrated plant

nutrient management approaches.

61

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(ed) Soil Acidity and Liming. ASA, Madison, Wisconsin, pp. 211 - 265.

Adejuwon, J.O. and O. Ekanade, 1975. Soil changes associated with forest/savanna

boundary. Nigerian Geogr. J., 27(1-2)

Alemayehu, T. 1990. Soil and irrigation management in the state farms. pp. 47-52. In:

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All Fiji, 2011. Taveuni weather, [Online], Available at:

http://allfiji.com.au/taveuni/weather/ Accessed June 12, 2011.

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APPENDICES

APPENDIX 1

SOIL AND LAND USE CAPABILITY MAP OF TAVEUNI

75

APPENDIX 2

EXPORT SPECIFICATIONS FOR TARO IN FIJI

Variety: Tausala ni Samoa

Cleanliness: Washed clean

Appearance: Conical in shape

Corm flesh: Light yellow

Maturity: Corm should be seven months old

Maximum corm weight: 1 - 3 kg

Size of corm: 15 - 20 cm in length and 10 - 12 cm in

maximum diameter, free from buds/shoots

and shaggy hair

Decay: No surface mould or corm softening.

Postharvest: No physical bruises, injuries and

deformations

(Source: Robin, 2000)

76

APPENDIX 3

1990 – 2012 Data on: A) Soil Fertility, B) Temperature, C)

Rainfall and D) Taro production (1994 – 2013)

pH

H2O

Nitrogen

(%)

Olsen P

(mg/kg)

Exchangeable Bases

Ca me% Mg me% K me%

Dry

Zon

e

1990 6.0 0.67 31.8 10.4 7.57 0.21

1991 5.8 0.71 38.1 12.88 6.36 0.24

1992 5.7 1.0 29.7 9.8 5.35 0.33

1993 5.7 0.68 22.8 11.2 6.2 0.31

1994 5.7 0.64 23.4 11.7 6.1 0.32

1995 5.7 0.63 23.6 11.8 6.2 0.33

1996 5.7 0.55 14.8 11.22 6.6 0.31

1997 5.6 0.52 13.7 11.51 5.8 0.32

1998 5.8 0.54 9.3 12.3 5.3 0.30

1999 5.6 0.61 7.8 9.56 6.1 0.29

2000 5.7 0.41 6.4 10.55 5.2 0.23

2001 5.8 0.44 6.5 11.26 4.2 0.27

2002 5.9 0.35 7.2 8.23 4.9 0.26

2003 5.5 0.34 6.8 8.88 3.2 0.23

2004 5.4 0.34 6.4 8.93 3.2 0.22

2005 5.3 0.51 6.3 9.01 3.4 0.26

2006 5.28 0.46 7.74 8.84 3.56 0.40

2007 5.47 1.15 10.35 6.63 4.09 0.17

2008 5.00 0.45 5.48 5.16 2.86 0.37

2009 5.65 0.63 7.13 13.95 5.51 1.03

2010 5.70 0.57 6.75 11.83 5.13 0.55

2011 5.49 0.48 8.91 8.89 4.51 0.41

2012 5.80 0.61 6.91 8.05 6.02 2.34

77

pH

H2O

Nitrogen

(%)

Olsen P

(mg/kg)

Exchangable Bases

Ca me% Mg me% K me%

Wet

zon

e

1990 5.7 0.81 54 13.64 8.57 0.2

1991 5.7 0.61 59 14.95 7.41 0.5

1992 5.9 0.76 59.3 10.95 6.45 0.35

1993 5.9 0.70 30 10.4 7.52 0.40

1994 6.1 0.77 32.5 11.52 6.52 0.42

1995 6.2 0.83 35.5 13.43 6.32 0.48

1996 5.9 0.69 21.3 10.42 6.95 0.53

1997 5.8 0.67 15.6 9.24 5.64 0.44

1998 5.6 0.42 15.1 11.2 5.63 0.33

1999 5.8 0.42 15.2 11.1 4.23 0.42

2000 5.6 0.56 13.4 9.31 2.36 0.57

2001 5.8 0.48 9.4 8.25 4.89 0.52

2002 5.7 0.58 12.3 8.56 4.56 0.28

2003 5.8 0.51 12.8 9.26 5.63 0.41

2004 6.0 0.52 7.8 10.71 5.89 0.36

2005 5.9 0.44 6.1 9.93 4.23 0.42

2006 5.28 0.46 7.74 8.84 3.56 0.40

2007 5.47 1.15 10.35 6.63 4.09 0.17

2008 5.00 0.45 5.48 5.16 2.86 0.37

2009 5.65 0.63 7.13 13.95 5.51 1.03

2010 5.70 0.57 6.75 11.83 5.13 0.55

2011 5.49 0.48 8.91 8.89 4.51 0.41

2012 5.80 0.61 6.91 8.05 6.02 2.34

78

pH

H2O

Nitrogen

(%)

Olsen P

(mg/kg)

Exchangable Bases

Ca me% Mg me% K me%

In

term

edia

te Z

one

1990 6.4 0.39 45 13.64 8.57 0.4

1991 6.2 0.71 40 14.68 7.95 0.33

1992 5.9 0.62 48.4 11.9 4.4 0.33

1993 5.73 0.63 22.05 11.06 5.01 0.3

1994 5.7 0.62 21.4 11.8 5.53 0.31

1995 5.8 0.62 21.5 12.06 6.48 0.28

1996 5.7 0.59 13.2 9.06 6.69 0.33

1997 5.8 0.47 12.5 9.65 6.45 0.41

1998 5.8 0.58 15.2 10.25 4.52 0.28

1999 5.7 0.55 13.5 12.56 4.02 0.23

2000 5.7 0.50 9.2 11.25 5.21 0.36

2001 5.7 0.42 10.2 9.25 6.72 0.31

2002 5.6 0.42 9.8 10.64 3.22 0.44

2003 5.8 0.53 13.1 6.45 4.52 0.56

2004 5.7 0.48 7.8 7.28 5.21 0.66

2005 5.8 0.51 5.8 8.43 4.87 0.43

2006 5.76 1.13 4.46 11.38 4.23 0.26

2007 5.47 0.72 4.30 7.09 2.92 0.43

2008 5.72 0.59 12.16 13.70 3.54 0.45

2009 5.78 0.38 9.00 8.49 4.55 0.48

2010 5.73 0.55 3.30 5.90 2.42 0.36

2011 5.53 0.53 7.81 5.08 2.78 0.57

2012 5.61 0.89 10.58 6.31 4.99 2.33

Mean Annual Max and Min Temperature

79

Year Mean Annual Max Temp Mean Annual Min Temp

1990 28.7 23.5

1991 28.8 23.3

1992 28.5 23.3

1993 28.5 23.1

1994 28.5 23.1

1995 28.8 23.3

1996 29.1 23.1

1997 28.3 23

1998 29.3 23.1

1999 29.0 25.7

2000 28 25.8

2001 28.8 22.8

2002 29.3 23.8

2003 28.9 23.2

2004 29.2 23.0

2005 29.2 23.4

2006 29.1 26.6

2007 29.4 23.3

2008 29.3 23.5

2009 28.9 22.9

2010 29.2 23.7

2011 29.4 24.0

2012 29.0 23.8

80

Annual Rainfall (mm)

Year Annual Rainfall (mm)

Dry Intermediate Wet

1990 3425.7 3494.2 3596.9

1991 2864.8 2922.0 3008.0

1992 2116.6 2158.9 2222.4

1993 2025.6 2066.1 2126.8

1994 2177.9 2221.4 2286.7

1995 3030.2 3090.0 3181.7

1996 2724.2 2778.6 2860.4

1997 3489.9 3558.9 3663.6

1998 1901 1939.0 1996.5

1999 2786 2925.3 2869.5

2000 2854 2996.7 2911.5

2001 1158.4 1181.5 1216.3

2002 2116.3 2158.6 2222.1

2003 1443.3 1472.1 1515.4

2004 2081.1 2122.7 2185.1

2005 2338.4 2385.1 2455.3

2006 2765.4 2903.6 2848.1

2007 3192.3 3256.1 3351.9

2008 2791.6 2847.4 2931.1

2009 2228 2272.5 2339.4

2010 2157.4 2200.5 2265.2

2011 2682.6 2736.2 2816.7

2012 3308.3 3374.4 3473.7

81

Taro Production – 1994 - 2013

Year Dry zone Wet zone Intermediate zone

t/ha

1994 32.9 36.0 31.5

1995 33.0 36.0 31.4

1996 33.6 30.8 33.6

1997 33.6 29.7 32.3

1998 30.8 33.1 34.1

1999 23.1 22.3 25.2

2000 18.9 18.4 19.8

2001 15.8 15.2 15.8

2002 16.5 15.0 15.8

2003 13.2 12.0 12.6

2004 11.6 11.0 11.0

2005 10.5 10.0 10.0

2006 10.5 10.0 11.4

2007 9.9 10.0 10.0

2008 9.9 9.9 10.0

2009 9.6 9.6 9.6

2010 9.1 10.4 9.6

2011 9.1 9.6 10.4

2012 9.8 9.4 8.9

2013 8.4 9.1 9.1

82

APPENDIX 4

ANALYSIS OF VARIANCE FOR BETWEEN RAINFALL-ZONES (STRATA) COMPARISON

Variate: Ca Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 22 207.563 9.435 2.55

Year.*Units* stratum

Strata 2 0.656 0.328 0.09 0.915

Residual 44 162.537 3.694

Total 68 370.756

Tables of means

Variate: Ca

Grand mean 10.00

Strata 1 2 3

10.11 10.01 9.87

Standard errors of means Table Strata rep. 23 d.f. 44 e.s.e. 0.401 Standard errors of differences of means Table Strata rep. 23 d.f. 44 s.e.d. 0.567 Least significant differences of means (5% level) Table Strata rep. 23 d.f. 44 l.s.d. 1.142

83

Variate: K Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 22 3.5646 0.1620 0.74

Year.*Units* stratum

Strata 2 0.6134 0.3067 1.41 0.255

Residual 44 9.5707 0.2175

Total 68 13.7488

Tables of means

Variate: K

Grand mean 0.421

Strata 1 2 3

0.307 0.417 0.538

Standard errors of means Table Strata rep. 23 d.f. 44 e.s.e. 0.0972 Standard errors of differences of means Table Strata rep. 23 d.f. 44 s.e.d. 0.1375 Least significant differences of means (5% level) Table Strata rep. 23 d.f. 44 l.s.d. 0.2772

84

Variate: Mg Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 22 110.2061 5.0094 5.73

Year.*Units* stratum

Strata 2 0.8268 0.4134 0.47 0.626

Residual 44 38.4353 0.8735

Total 68 149.4681

Tables of means

Variate: Mg

Grand mean 5.14

Strata 1 2 3

5.22 5.22 4.99

Standard errors of means Table Strata rep. 23 d.f. 44 e.s.e. 0.195 Standard errors of differences of means Table Strata rep. 23 d.f. 44 s.e.d. 0.276 Least significant differences of means (5% level) Table Strata rep. 23 d.f. 44 l.s.d. 0.555

85

Variate: N Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 22 0.639855 0.029084 3.92

Year.*Units* stratum

Strata 2 0.071829 0.035914 4.84 0.013

Residual 44 0.326571 0.007422

Total 68 1.038255

Tables of means

Variate: N

Grand mean 0.572

Strata 1 2 3

0.530 0.609 0.576

Standard errors of means Table Strata rep. 23 d.f. 44 e.s.e. 0.0180 Standard errors of differences of means Table Strata rep. 23 d.f. 44 s.e.d. 0.0254 Least significant differences of means (5% level) Table Strata rep. 23 d.f. 44 l.s.d. 0.0512

86

Variate: P Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 22 11107.93 504.91 21.04

Year.*Units* stratum

Strata 2 406.87 203.44 8.48 <.001

Residual 44 1056.10 24.00

Total 68 12570.90

Tables of means

Variate: P

Grand mean 16.2

Strata 1 2 3 13.4 19.3 16.0 Standard errors of means TableStrata rep. 23 d.f. 44 e.s.e. 1.02 Standard errors of differences of means Table Strata rep. 23 d.f. 44 s.e.d. 1.44 Least significant differences of means (5% level) Table Strata rep. 23 d.f. 44 l.s.d. 2.91

87

Variate: pH

Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 22 0.87292 0.03968 1.24

Year.*Units* stratum

Strata 2 0.29797 0.14899 4.67 0.014

Residual 44 1.40349 0.03190

Total 68 2.57438

Tables of means

Variate: pH

Grand mean 5.7

Strata 1 2 3

5.6 5.8 5.8

Standard errors of means Table Strata rep. 23 d.f. 44 e.s.e. 0.04 Standard errors of differences of means Table Strata rep. 23 d.f. 44 s.e.d. 0.05 Least significant differences of means (5% level) Table Strata rep. 23 d.f. 44 l.s.d. 0.11

88

Variate: %_Rejects

Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 19 5672.11 298.53 14.00

Year.*Units* stratum

Strata 2 143.61 71.80 3.37 0.045

Residual 38 810.01 21.32

Total 59 6625.73

Tables of means

Variate: %_Rejects

Grand mean 13.46

Strata 1 2 3

15.25 11.48 13.65

Standard errors of means TableStrata rep. 20 d.f. 38 e.s.e. 1.032 Standard errors of differences of means Table Strata rep. 20 d.f. 38 s.e.d. 1.460 Least significant differences of means (5% level) Table Strata rep. 20 d.f. 38 l.s.d. 2.956

89

Variate: Yield Source of variation d.f. s.s. m.s. v.r. F pr.

Year stratum 19 5475.301 288.174 213.53

Year.*Units* stratum

Strata 2 0.529 0.264 0.20 0.823

Residual 38 51.284 1.350

Total 59 5527.114

Tables of means

Variate: Yield

Grand mean 17.49

Strata 1 2 3

17.49 17.60 17.38

Standard errors of means TableStrata rep. 20 d.f. 38 e.s.e. 0.260 Standard errors of differences of means Table Strata rep. 20 d.f. 38 s.e.d. 0.367 Least significant differences of means (5% level) Table Strata rep. 20 d.f. 38 l.s.d. 0.744

90

APPENDIX 5

PAIRED SAMPLE T-TEST FOR COMPARISON OF SOIL CHEMICAL

INDICES AND YIELDS PRE AND POST- 22- YEAR CULTIVATION PERIOD

Variate: Ca. Summary Standard Standard error Sample Size Mean Variance deviation of mean Present-Previous 9 -5.209 16.28 4.035 1.345 95% confidence interval for mean: (-8.311, -2.107) Test of null hypothesis that mean of Present-Previous is equal to 0 Test statistic t = -3.87 on 8 d.f. Probability = 0.005 Variate: K Summary Standard Standard error Sample Size Mean Variance deviation of mean Present-Previous 9 0.09000 0.04565 0.2137 0.07122 95% confidence interval for mean: (-0.07423, 0.2542) Test of null hypothesis that mean of Present-Previous is equal to 0 Test statistic t = 1.26 on 8 d.f. Probability = 0.242

91

Variate: Mg Summary Standard Standard error Sample Size Mean Variance deviation of mean Present-Previous 9 -2.617 3.648 1.910 0.6367 95% confidence interval for mean: (-4.085, -1.149) Test of null hypothesis that mean of Present-Previous is equal to 0 Test statistic t = -4.11 on 8 d.f. Probability = 0.003 Variate: N Summary Standard Standard error Sample Size Mean Variance deviation of mean Present-Previous 9 -0.1722 0.1664 0.4080 0.1360 95% confidence interval for mean: (-0.4858, 0.1414) Test of null hypothesis that mean of Present-Previous is equal to 0 Test statistic t = -1.27 on 8 d.f. Probability = 0.241 Variate: P Summary Standard Standard error Sample Size Mean Variance deviation of mean Present-Previous 9 -36.45 365.7 19.12 6.374 95% confidence interval for mean: (-51.15, -21.75) Test of null hypothesis that mean of Present-Previous is equal to 0 Test statistic t = -5.72 on 8 d.f. Probability < 0.001

92

Variate: pH Summary Standard Standard error Sample Size Mean Variance deviation of mean Present-Previous 9 -0.1556 0.2411 0.4910 0.1637 95% confidence interval for mean: (-0.5330, 0.2219) Test of null hypothesis that mean of Present-Previous is equal to 0 Test statistic t = -0.95 on 8 d.f. Probability = 0.370 Variate: Yield Summary Standard Standard error Sample Size Mean Variance deviation of mean Present-Previous 9 -24.14 3.170 1.781 0.5935 95% confidence interval for mean: (-25.51, -22.78) Test of null hypothesis that mean of Present-Previous is equal to 0 Test statistic t = -40.68 on 8 d.f. Probability < 0.001

93

APPENDIX 6

CORRELATION ANALYSES FOR ASSOCIATION BETWEEN INDICES FOR DRY ZONE (STRATA) OF TAVEUNI

Correlation: Yield vs Rainfall Yield Rainfall 0.1323 Yield Rainfall Number of observations: 19 Two-sided test of correlations different from zero probabilities Yield Rainfall 0.5893 Yield Rainfall Correlation: Yield vs Temperature Temperature Yield -0.5615 Temperature Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Yield 0.0124 Temperature Yield Correlation: Soil pH vs Yield pH Yield 0.4254 pH Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Yield 0.0694 pH Yield

94

Correlation: Total N vs Yield N Yield 0.4018 N Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities N Yield 0.0881 N Yield Correlation: Olsen P vs Yield P Yield 0.7600 P Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities P Yield 0.0002 P Yield Correlation: Exchangeable K vs Yield K Yield -0.1357 K Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities K Yield 0.5798 K Yield

95

Correlation: Exchangeable Ca vs Yield Ca Yield 0.5080 Ca Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Ca Yield 0.0264 Ca Yield Correlation: Exchangeable Mg vs Yield Mg Yield 0.6863 Mg Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Mg Yield 0.0012 Mg Yield Correlation: Rainfall vs Temperature Rainfall Temperature -0.0911 Rainfall Temperature Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Temperature 0.7106 Rainfall Temperature

96

Correlation: Rainfall vs Soil pH Rainfall pH 0.0223 Rainfall pH Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall pH 0.9279 Rainfall pH Correlation: Total N vs Rainfall Rainfall N 0.3536 Rainfall N Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall N 0.1376 Rainfall N Correlation: Rainfall vs Olsen P Rainfall P 0.1783 Rainfall P Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall P 0.4653 Rainfall P

97

Correlation: Rainfall vs Exchangeable K Rainfall K -0.0328 Rainfall K Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall K 0.8940 Rainfall K Correlation: Rainfall vs Exchangeable Ca

Rainfall Ca -0.1786 Rainfall Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Ca 0.4645 Rainfall Ca Correlation: Rainfall vs Exchangeable Mg Rainfall Mg 0.2497 Rainfall Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Mg 0.3026 Rainfall Mg

98

Correlation: Temperature vs Soil pH Temperature pH -0.2800 Temperature pH Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature pH 0.2456 Temperature pH Correlation: Temperature vs Total N Temperature N -0.2382 Temperature N Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature N 0.3262 Temperature N Correlation: Temperature vs Olsen P Temperature P -0.3332 Temperature P Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature P 0.1634 Temperature P

99

Correlation: Temperature vs Exchangeable K Temperature K 0.2519 Temperature K Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature K 0.2981 Temperature K Correlation: Temperature vs Exchangeable Ca Temperature Ca -0.5407 Temperature Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Ca 0.0168 Temperature Ca Correlation: Temperature vs Exchangeable Mg Temperature Mg -0.5635 Temperature Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Mg 0.0120 Temperature Mg

100

Correlation: Soil pH vs Total N pH N 0.3392 pH N Number of observations: 19 Two-sided test of correlations different from zero probabilities pH N 0.1554 pH N Correlation: Soil pH vs Olsen P pH P 0.2251 pH P Number of observations: 19 Two-sided test of correlations different from zero probabilities pH P 0.3542 pH P Correlation: Soil pH vs Exchangeable K pH K 0.0069 pH K Number of observations: 19 Two-sided test of correlations different from zero probabilities pH K 0.9775 pH K

101

Correlation: Soil pH vs Exchangeable Ca pH Ca 0.4659 pH Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Ca 0.0444 pH Ca Correlation: Soil pH vs Exchangeable Mg pH Mg 0.6996 pH Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Mg 0.0009 pH Mg Correlation: Total N vs Olsen P N P 0.5413 N P Number of observations: 19 Two-sided test of correlations different from zero probabilities N P 0.0167 N P

102

Correlation: Total N vs Exchangeable K N K 0.4911 N K Number of observations: 19 Two-sided test of correlations different from zero probabilities N K 0.0327 N K Correlation: Total N vs Exchangeable Ca N Ca 0.4882 N Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities N Ca 0.0340 N Ca Correlation: Total N vs Exchangeable Mg N Mg 0.6093 N Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities N Mg 0.0056 N Mg

103

Correlation: Olsen P vs Exchangeable K K P 0.0141 K P Number of observations: 19 Two-sided test of correlations different from zero probabilities K P 0.9545 K P Correlation: Olsen P vs Exchangeable Ca P Ca 0.3823 P Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities P Ca 0.1062 P Ca Correlation: Olsen P vs Exchangeable Mg P Mg 0.5186 P Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities P Mg 0.0229 P Mg

104

Correlation: Exchangeable K vs Exchangeable Ca K Ca 0.3717 K Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities K Ca 0.1171 K Ca Correlation: Exchangeable K vs Exchangeable Mg K Mg 0.0699 K Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities K Mg 0.7761 K Mg Correlation: Exchangeable Ca vs Exchangeable Mg Ca Mg 0.6923 Ca Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Ca Mg 0.0010 Ca Mg

105

APPENDIX 7

CORRELATION ANALYSES FOR ASSOCIATION BETWEEN INDICES FOR

INTERMEDIATE ZONE (STRATA) OF TAVEUNI

Correlation: Yield vs Rainfall Yield Rainfall 0.0538 Yield Rainfall Number of observations: 19 Two-sided test of correlations different from zero probabilities Yield Rainfall 0.8268 Yield Rainfall Correlation: Yield vs Temperature Temperature Yield -0.5441 Temperature Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Yield 0.0160 Temperature Yield Correlation: Soil pH vs Yield pH Yield 0.4139 pH Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Yield 0.0782 pH Yield

106

Correlation: Total N vs Yield N Yield -0.0298 N Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities N Yield 0.9038 N Yield Correlation: Olsen P vs Yield P Yield 0.7140 P Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities P Yield 0.0006 P Yield Correlation: Exchangeable K vs Yield K Yield -0.5043 K Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities K Yield 0.0277 K Yield

107

Correlation: Exchangeable Ca vs Yield Ca Yield 0.4632 Ca Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Ca Yield 0.0458 Ca Yield Correlation: Exchangeable Mg vs Yield Mg Yield 0.5868 Mg Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Mg Yield 0.0083 Mg Yield Correlation: Rainfall vs Temperature Rainfall Temperature -0.0361 Rainfall Temperature Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Temperature 0.8833 Rainfall Temperature

108

Correlation: Rainfall vs Soil pH Rainfall pH -0.4572 Rainfall pH Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall pH 0.0490 Rainfall pH Correlation: Total N vs Rainfall Rainfall N 0.4638 Rainfall N Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall N 0.0455 Rainfall N Correlation: Rainfall vs Olsen P Rainfall P -0.2000 Rainfall P Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall P 0.4117 Rainfall P

109

Correlation: Rainfall vs Exchangeable K Rainfall K -0.3158 Rainfall K Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall K 0.1880 Rainfall K Correlation: Rainfall vs Exchangeable Ca

Rainfall Ca 0.0930 Rainfall Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Ca 0.7048 Rainfall Ca Correlation: Rainfall vs Exchangeable Mg Rainfall Mg 0.1024 Rainfall Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Mg 0.6766 Rainfall Mg

110

Correlation: Temperature vs Soil pH Temperature pH -0.4760 Temperature pH Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature pH 0.0394 Temperature pH Correlation: Temperature vs Total N Temperature N 0.3274 Temperature N Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature N 0.1712 Temperature N Correlation: Temperature vs Olsen P Temperature P -0.5396 Temperature P Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature P 0.0171 Temperature P

111

Correlation: Temperature vs Exchangeable K Temperature K 0.2660 Temperature K Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature K 0.2710 Temperature K Correlation: Temperature vs Exchangeable Ca Temperature Ca -0.3534 Temperature Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Ca 0.1377 Temperature Ca Correlation: Temperature vs Exchangeable Mg Temperature Mg -0.7227 Temperature Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Mg 0.0005 Temperature Mg

112

Correlation: Soil pH vs Total N pH N -0.2876 pH N Number of observations: 19 Two-sided test of correlations different from zero probabilities pH N 0.2324 pH N Correlation: Soil pH vs Olsen P pH P 0.4862 pH P Number of observations: 19 Two-sided test of correlations different from zero probabilities pH P 0.0348 pH P Correlation: Soil pH vs Exchangeable K pH K -0.2231 pH K Number of observations: 19 Two-sided test of correlations different from zero probabilities pH K 0.3587 pH K

113

Correlation: Soil pH vs Exchangeable Ca pH Ca 0.3786 pH Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Ca 0.1100 pH Ca Correlation: Soil pH vs Exchangeable Mg pH Mg 0.4929 pH Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Mg 0.0320 pH Mg Correlation: Total N vs Olsen P N P 0.0313 N P Number of observations: 19 Two-sided test of correlations different from zero probabilities N P 0.8987 N P

114

Correlation: Total N vs Exchangeable K N K -0.3926 N K Number of observations: 19 Two-sided test of correlations different from zero probabilities N K 0.0963 N K Correlation: Total N vs Exchangeable Ca N Ca -0.0985 N Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities N Ca 0.6884 N Ca Correlation: Total N vs Exchangeable Mg N Mg -0.0891 N Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities N Mg 0.7168 N Mg

115

Correlation: Olsen P vs Exchangeable K K P -0.2264 K P Number of observations: 19 Two-sided test of correlations different from zero probabilities K P 0.3513 K P Correlation: Olsen P vs Exchangeable Ca P Ca 0.4431 P Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities P Ca 0.0574 P Ca Correlation: Olsen P vs Exchangeable Mg P Mg 0.5866 P Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities P Mg 0.0083 P Mg

116

Correlation: Exchangeable K vs Exchangeable Ca K Ca -0.5113 K Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities K Ca 0.0253 K Ca Correlation: Exchangeable K vs Exchangeable Mg K Mg -0.2734 K Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities K Mg 0.2574 K Mg Correlation: Exchangeable Ca vs Exchangeable Mg Ca Mg 0.2587 Ca Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Ca Mg 0.2848 Ca Mg

117

APPENDIX 8

CORRELATION ANALYSES FOR ASSOCIATION BETWEEN INDICES FOR

WET ZONE (STRATA) OF TAVEUNI Correlation: Yield vs Rainfall Yield Rainfall 0.0852 Yield Rainfall Number of observations: 19 Two-sided test of correlations different from zero probabilities Yield Rainfall 0.7287 Yield Rainfall Correlation: Yield vs Temperature Temperature Yield -0.5342 Temperature Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Yield 0.0185 Temperature Yield Correlation: Soil pH vs Yield pH Yield 0.3274 pH Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Yield 0.1713 pH Yield

118

Correlation: Total N vs Yield N Yield 0.2703 N Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities N Yield 0.2630 N Yield Correlation: Olsen P vs Yield P Yield 0.8773 P Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities P Yield 0.0000 P Yield Correlation: Exchangeable K vs Yield K Yield 0.2417 K Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities K Yield 0.3187 K Yield

119

Correlation: Exchangeable Ca vs Yield Ca Yield 0.6292 Ca Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Ca Yield 0.0039 Ca Yield Correlation: Exchangeable Mg vs Yield Mg Yield 0.6563 Mg Yield Number of observations: 19 Two-sided test of correlations different from zero probabilities Mg Yield 0.0023 Mg Yield Correlation: Rainfall vs Temperature Rainfall Temperature -0.0911 Rainfall Temperature Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Temperature 0.7106 Rainfall Temperature

120

Correlation: Rainfall vs Soil pH Rainfall pH 0.1244 Rainfall pH Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall pH 0.6119 Rainfall pH Correlation: Total N vs Rainfall Rainfall N 0.3708 Rainfall N Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall N 0.1180 Rainfall N Correlation: Rainfall vs Olsen P Rainfall P -0.0323 Rainfall P Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall P 0.8954 Rainfall P

121

Correlation: Rainfall vs Exchangeable K Rainfall K 0.3629 Rainfall K Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall K 0.1268 Rainfall K Correlation: Rainfall vs Exchangeable Ca

Rainfall Ca -0.1231 Rainfall Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Ca 0.6156 Rainfall Ca Correlation: Rainfall vs Exchangeable Mg Rainfall Mg -0.0009 Rainfall Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Rainfall Mg 0.9971 Rainfall Mg

122

Correlation: Temperature vs Soil pH Temperature pH -0.1372 Temperature pH Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature pH 0.5755 Temperature pH Correlation: Temperature vs Total N Temperature N 0.0517 Temperature N Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature N 0.8334 Temperature N Correlation: Temperature vs Olsen P Temperature P -0.5150 Temperature P Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature P 0.0241 Temperature P

123

Correlation: Temperature vs Exchangeable K Temperature K -0.4689 Temperature K Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature K 0.0428 Temperature K Correlation: Temperature vs Exchangeable Ca Temperature Ca -0.2287 Temperature Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Ca 0.3462 Temperature Ca Correlation: Temperature vs Exchangeable Mg Temperature Mg -0.2701 Temperature Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Temperature Mg 0.2630 Temperature Mg

124

Correlation: Soil pH vs Total N pH N 0.4435 pH N Number of observations: 19 Two-sided test of correlations different from zero probabilities pH N 0.0572 pH N Correlation: Soil pH vs Olsen P pH P 0.5261 pH P Number of observations: 19 Two-sided test of correlations different from zero probabilities pH P 0.0207 pH P Correlation: Soil pH vs Exchangeable K pH K 0.0858 pH K Number of observations: 19 Two-sided test of correlations different from zero probabilities pH K 0.7268 pH K

125

Correlation: Soil pH vs Exchangeable Ca pH Ca 0.5921 pH Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Ca 0.0076 pH Ca Correlation: Soil pH vs Exchangeable Mg pH Mg 0.6359 pH Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities pH Mg 0.0034 pH Mg Correlation: Total N vs Olsen P N P 0.3870 N P Number of observations: 19 Two-sided test of correlations different from zero probabilities N P 0.1017 N P

126

Correlation: Total N vs Exchangeable K N K 0.1545 N K Number of observations: 19 Two-sided test of correlations different from zero probabilities N K 0.5276 N K Correlation: Total N vs Exchangeable Ca N Ca -0.0442 N Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities N Ca 0.8573 N Ca Correlation: Total N vs Exchangeable Mg N Mg 0.1500 N Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities N Mg 0.5399 N Mg

127

Correlation: Olsen P vs Exchangeable K K P 0.3128 K P Number of observations: 19 Two-sided test of correlations different from zero probabilities K P 0.1923 K P Correlation: Olsen P vs Exchangeable Ca P Ca 0.6543 P Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities P Ca 0.0024 P Ca Correlation: Olsen P vs Exchangeable Mg P Mg 0.6385 P Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities P Mg 0.0033 P Mg

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Correlation: Exchangeable K vs Exchangeable Ca K Ca 0.0781 K Ca Number of observations: 19 Two-sided test of correlations different from zero probabilities K Ca 0.7507 K Ca Correlation: Exchangeable K vs Exchangeable Mg K Mg 0.1669 K Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities K Mg 0.4945 K Mg Correlation: Exchangeable Ca vs Exchangeable Mg Ca Mg 0.6531 Ca Mg Number of observations: 19 Two-sided test of correlations different from zero probabilities Ca Mg 0.0024 Ca Mg

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APPENDIX 9

22 - YEAR TREND REGRESSION ANALYSIS OF VARIANCE

Response variate:Exchangeable Ca

Fitted terms: Constant, Year Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 150.9 150.938 46.01 <.001 Residual 67 219.8 3.281 Total 68 370.8 5.452 Percentage variance accounted for 39.8 Standard error of observations is estimated to be 1.81. Estimates of parameters Parameterestimate s.e. t(67) t pr. Constant 456.2 65.8 6.93 <.001 Year -0.2230 0.0329 -6.78 <.001 Response variate: Exchangeable K Fitted terms: Constant, Year Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 0.0433 0.04333 4.11 0.047 Residual 67 0.7058 0.01053 Total 68 0.7491 0.01102 Percentage variance accounted for 4.4 Standard error of observations is estimated to be 0.103. Estimates of parameters Parameter estimate s.e. t(67) t pr. Constant -7.19 3.73 -1.93 0.058 Year 0.00378 0.00186 2.03 0.047

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Response variate:Exchangeable Mg Fitted terms: Constant, Year Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 76.96 76.962 71.12 <.001 Residual 67 72.51 1.082 Total 68 149.47 2.198 Percentage variance accounted for 50.8 Standard error of observations is estimated to be 1.04. Estimates of parameters Parameter estimate s.e. t(67)t pr. Constant 323.7 37.8 8.57 <.001 Year -0.1592 0.0189 -8.43 <.001 Response variate: Total N

Fitted terms: Constant + Year

Submodels: POL(Year; 2)

Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 2 0.4892 0.244578 29.40 <.001 Residual 66 0.5491 0.008320 Total 68 1.0383 0.015268 Percentage variance accounted for 45.5 Standard error of observations is estimated to be 0.0912. Estimates of parameters Parameter estimate s.e. t(66) t pr. Constant 7841. 1120. 7.00 <.001 Year Lin -7.83 1.12 -6.99 <.001 Year Quad 0.001956 0.000280 6.99 <.001

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Response variate: Olsen P

Fitted terms: Constant + Year

Submodels: POL(Year; 2)

Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 2 10188. 5093.84 141.07 <.001 Residual 66 2383. 36.11 Total 68 12571. 184.87 Percentage variance accounted for 80.5 Standard error of observations is estimated to be 6.01. Estimates of parameters Parameter estimate s.e. t(66) t pr. Constant 608539. 73810. 8.24 <.001 Year Lin -606.6 73.8 -8.22 <.001 Year Quad 0.1512 0.0184 8.20 <.001 Response variate:Soil pH

Fitted terms: Constant + Year

Sub models: POL(Year; 2)

Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 2 0.639 0.31936 10.89 <.001 Residual 66 1.936 0.02933 Total 68 2.574 0.03786 Percentage variance accounted for 22.5 Standard error of observations is estimated to be 0.171. Estimates of parameters Parameter estimate s.e. t(66)t pr. Constant 4465. 2104. 2.12 0.038 Year Lin -4.44 2.10 -2.11 0.038 Year Quad 0.001107 0.000525 2.11 0.039

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Response variate:% Taro Rejects

Fitted terms: Constant + Year

Sub models: POL(Year; 2)

Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 2 4336. 2168.21 60.09 <.001 Residual 57 2057. 36.08 Total 59 6393. 108.36 Percentage variance accounted for 66.7 Standard error of observations is estimated to be 6.01. Estimates of parameters Parameter estimate s.e. t(57) t pr. Constant 965788. 105066. 9.19 <.001 Year Lin -965. 105. -9.20 <.001 Year Quad 0.2410 0.0262 9.21 <.001 Response variate: Av_Temp Fitted terms: Constant, Year Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 0.769 0.76918 10.56 0.004 Residual 21 1.530 0.07285 Total 22 2.299 0.10451 Percentage variance accounted for 30.3 Standard error of observations is estimated to be 0.270. Estimates of parameters Parameter estimate s.e. t(21) t pr. Constant -29.0 17.0 -1.71 0.102 Year 0.02757 0.00848 3.25 0.004

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Response variate: Yield Fitted terms: Constant + Year

Sub models: POL(Year; 2)

Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 2 5142.2 2571.119 374.14 <.001 Residual 57 391.7 6.872 Total 59 5533.9 93.796 Percentage variance accounted for 92.7 Standard error of observations is estimated to be 2.62. Estimates of parameters Parameter estimate s.e. t(57) t pr. Constant 452848. 45851. 9.88 <.001 Year Lin -450.5 45.8 -9.84 <.001 Year Quad 0.1121 0.0114 9.81 <.001 Response variate: 20 year mean monthly rainfall

Fitted terms: Constant + Month

Sub models: POL(Month; 2)

Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 2 264847. 132423. 40.33 <.001 Residual 9 29549. 3283. Total 11 294396. 26763. Percentage variance accounted for 87.7 Standard error of observations is estimated to be 57.3. Estimates of parameters Parameter estimate s.e. t(9) t pr. Constant 908.4 59.2 15.34 <.001 Month Lin -183.7 20.9 -8.77 <.001 Month Quad 12.69 1.57 8.09 <.001

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APPENDIX 10

LINEAR REGRESSION ANALYSIS OF VARIANCE OF TARO YIELD ON

INDIVIDUAL CHEMICAL INDICES

Response variate:Taro Yield; Fitted terms: Constant, Exchangeable Ca Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 1449. 1448.78 20.70 <.001 Residual 55 3850. 70.00 Total 56 5299. 94.63 Percentage variance accounted for 26.0 Standard error of observations is estimated to be 8.37. Estimates of parameters Parameter estimate s.e. t(55) t pr. Constant -4.16 4.98 -0.83 0.407 Ca 2.314 0.509 4.55 <.001 Response variate:Taro Yield; Fitted terms: Constant, Exchangeable K Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 137. 136.99 1.46 0.232 Residual 55 5162. 93.86 Total 56 5299. 94.63 Percentage variance accounted for 0.8 Standard error of observations is estimated to be 9.69. Estimates of parameters Parameter estimate s.e. t(55) t pr. Constant 23.45 4.74 4.95 <.001 K -14.6 12.1 -1.21 0.232

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Response variate:Taro Yield; Fitted terms: Constant, Exchangeable Mg Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 2150. 2149.90 37.55 <.001 Residual 55 3149. 57.26 Total 56 5299. 94.63 Percentage variance accounted for 39.5 Standard error of observations is estimated to be 7.57. Estimates of parameters Parameter estimate s.e. t(55) t pr. Constant -5.55 3.96 -1.40 0.167 Mg 4.893 0.798 6.13 <.001 Response variate:Taro Yield; Fitted terms: Constant, % Total N Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 206. 205.81 2.22 0.142 Residual 55 5093. 92.60 Total 56 5299. 94.63 Percentage variance accounted for 2.1 Standard error of observations is estimated to be 9.62. Estimates of parameters Parameter estimate s.e. t(55) t pr. Constant 9.17 6.02 1.52 0.134 N 16.0 10.8 1.49 0.142

136

Response variate:Taro Yield; Fitted terms: Constant, Olsen P Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 3081. 3080.74 76.38 <.001 Residual 55 2218. 40.33 Total 56 5299. 94.63 Percentage variance accounted for 57.4 Standard error of observations is estimated to be 6.35. Estimates of parameters Parameter estimate s.e. t(55) t pr. Constant 5.32 1.67 3.18 0.002 P 1.123 0.128 8.74 <.001 Response variate:Taro Yield; Fitted terms: Constant, Soil pH Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 1 589. 588.98 6.88 0.011 Residual 55 4710. 85.64 Total 56 5299. 94.63 Percentage variance accounted for 9.5 Standard error of observations is estimated to be 9.25. Estimates of parameters Parameter estimate s.e. t(55) t pr. Constant -88.7 40.7 -2.18 0.034 pH 18.72 7.14 2.62 0.011

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APPENDIX 11

MULTIPLE LINEAR REGRESSION ANALYSIS OF VARIANCE OF TARO

YIELD ON SIGNIFICANT INDIVIDUAL CHEMICAL INDICES

Response variate:Taro Yield;Fitted terms: Constant, Exchangeable Ca, Mg, Olsen P, pH Summary of analysis Source d.f. s.s. m.s. v.r. F pr. Regression 4 3579. 894.67 27.04 <.001 Residual 52 1720. 33.08 Total 56 5299. 94.63 Percentage variance accounted for 65.0 Standard error of observations is estimated to be 5.75. Estimates of parameters Parameter estimate s.e. t(52) t pr. Constant 36.9 28.7 1.29 0.204 Ca 0.639 0.424 1.51 0.138 Mg 2.395 0.814 2.94 0.005 P 0.868 0.147 5.91 <.001 pH -8.13 5.33 -1.52 0.133

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APPENDIX 12

FARMER SURVEY QUESTIONNAIRE

SECTION A – FARMER DETAILS

1. Name of the farmer (optional) : ____________________________

2. Age (optional) : ____________________________

3. Gender (optional) : ____________________________

4. Educational level : ____________________________

5. Race : ____________________________

SECTION B – FARM DETAILS

1. Location of the farm:

(a) Stratum: ____________________ (Rainfall zone)

(b) Village : ____________________

(c) District: ____________________

2. Size of the farm: ____________________

3. (a) Land Tenure : ____________________

(b) Term of lease : ____________________

(c) Loan requirement/mortgage obligations: _____________________

Scale of operation: (a) Smallholder/semi-commercial

(b) Commercial

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SECTION C: FARMING DETAILS

1. How long you have been farming? ______________ years

2. Which crops you started off with?

____________________________________________

____________________________________________

3. When did you venture into large scale taro cultivation? And state the reasons:

_________________________________________________________________

________

_________________________________________________________________

________

_________________________________________________________________

________

4. Which taro varieties do you grow? __________________________

__________________________

5. What trends in taro yield have you noticed over the years?

_________________________________________________________________

_________________________________________________________________

_________________________________________________________________

_________________________________________________________________

________________________________________

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6. Do you grow taro on previously cropped land or do you open up new forested

area for growing?

_________________________________________________________________

_________________________________________________________________

____________________

7. Do you practice crop rotation?_____________________________________

8. Do you practice fallowing?________________________________________ 9. (a) How long after continuous cropping/cultivation do you practice fallowing?

_________________________________________________________

(b) What is your fallow period?____________________________________

(c) Have your fallow durations remained constant or changed over the years?

_______________________________________________________________________

_

10. What kind of fallow: (a) Natural

(b) Improved

11. Do you get your soil tested regularly? _________________________________________________________________

__________

141

12. a. Do you use fertiliser?________________________________________

b. Which fertiliser? ________________________________________

c. How much? ______________________________________________

d. Since when? ______________________________________________

e. Do you receive government assistance on fertilisers?_______________

13. Over the years, have you noticed any significant change (shift) in the weather

pattern within your area? _________________________________________________________________

_________________________________________________________________

_________________________________________________________________

______________________________

14. After how many cropping cycle, especially in the newly opened area, you add

fertiliser to get your desired yield? _________________________________________________________________

_________________________________________________________________

____________________

15. Without the use of fertiliser, are you able to meet the requirements of export specifications?

_________________________________________________________________

_________________________________________________________________

_________________________________________________________________

______________________________

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16. What are some of the major constraints you are currently facing?

_________________________________________________________________

_________________________________________________________________

____________________

17. How has the taro industry evolved (changed) over the years?

_________________________________________________________________

_________________________________________________________________

_________________________________________________________________

______________________________