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
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.
iii
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.
iv
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.
v
TABLE OF CONTENTS
Chapter 1
Chapter 2
Introduction
1
1.1 Research problem
3
1.2 Research objectives
4
1.3 Research questions
4
1.4 Research approach
5
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
vi
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
17
change
Chapter 3
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
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
vii
3.4.4 Crop management data
Chapter 4
29
3.5 Statistical Analysis
29
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
41
Taro export rejects
4.4 Correlation analysis between the selected meteorological variables,
42
taro yields and soil chemical indices
4.4.1 Dry production strata
42
4.4.2 Intermediate production strata
43
4.4.3 Wet production strata
44
viii
4.5 Relationship of selected chemical indices to taro corm yield
46
4.6
48
Comparison of soil chemical properties between pre and post 22
year cultivation period
4.7
Changes in selected soil management practices over 22 year
54
cultivation period
4.8
Chapter 5
Production constraints as identified by taro growers
57
59
Summary and Conclusions
References
61
Appendices
74
ix
LIST OF TABLES
Table 3.1
Research sites under each strata
Table 4.1 (a) Correlation matrix of selected meteorological and soil chemical
26
43
indices of taro soils from the dry production strata of Taveuni
(b) Correlation matrix of selected meteorological and soil chemical
44
indices of taro soils from the intermediate production strata of Taveuni
(c) Correlation matrix of selected meteorological and soil chemical
45
indices of taro soils from the wet production strata of Taveuni
Table 4.2
Estimates of parameters for the multiple linear regression analysis of
47
yield on soil pH, Olsen P, exchangeable Ca and exchangeable Mg
Table 4.3 (a) Paired sample t-test for the chemical indicators between pre and post
50
period of intensive cultivation
(b)
Soil chemical fertility decline resulting from 22 year intensive
51
cultivation
Table 4.4
Comparisons of end of research period levels against critical levels
52
and suggested ameliorative measures
Table 4.5 (a) Distribution of land tenure systems for the surveyed farms
(b)
Distribution of farm size under taro cultivation
x
55
56
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 33
for the island of Taveuni
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)
Figure 4.4 (a)
(b)
Figure 4.5 (a)
(b)
Figure 4.6 (a)
(b)
Figure 4.7 (a)
(b)
Figure 4.8 (a)
(b)
Figure 4.9 (a)
22 year mean pH trend for Taveuni
Total N trends for the three taro production strata
22 year mean Total N trend for Taveuni
Olsen available P trends for the three taro production strata
38
22 year mean Olsen available P trend for Taveuni
Exchangeable K trends for the three taro production strata
39
22 year mean Exchangeable K trend for Taveuni
Exchangeable Ca trends for the three taro production strata
37
22 year mean Exchangeable Ca trend for Taveuni
Exchangeable Mg trends for the three taro production strata
38
22 year mean Exchangeable Mg trend for Taveuni
Ca:Mg Ratio trends for the three taro production strata
(b)
22 year mean Ca:Mg ratio trend for Taveuni
(c)
Relative removal of Ca and Mg
Figure 4.10 (a)
36
Taro yield trends for the three taro production strata
xi
39
40
(b)
Figure 4.11 (a)
(b)
22 year mean yield trend for Taveuni
Taro export reject trends for the three taro production strata
41
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 54
intensive cultivation
Figure 4.14
Identification of production constraints by farmers
xii
57
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
76
and D) Taro production (1994 – 2013)
Appendix 4
Analysis of variance for between rainfall-zones (strata) comparison
Appendix 5
Paired sample t-test for comparisons of soil chemical indices pre and
82
90
post 22-year cultivation period
Appendix 6
Correlation analyses for association between indices for the dry zone
105
(strata) of Taveuni
Appendix 7
Correlation analyses for association between indices for the
105
intermediate zone (strata) of Taveuni
Appendix 8
Correlation analyses for association between indices for the wet zone
117
(strata) of Taveuni
Appendix 9
22- year trend regression analyses of variance
Appendix 10
Linear regression analyses of variance of taro yield on individual
129
134
chemical indices
Appendix 11
Multiple linear regression analyses of variance of taro yield on
137
significant individual chemical indices
Appendix 12
Farmer survey questionnaire
xiii
138
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
1
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
2
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.
3
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
1.
Research questions
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
4
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.
5
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
6
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. Twentythree 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.
7
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
8
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 (FAOUNESCO, 1974).
9
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 (FAOUNESCO, 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.
10
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
11
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.
12
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).
13
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 largescale 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
14
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
15
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.
16
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
17
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 onsite 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;
18
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.
19
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
20
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)
21
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
22
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:
x
Have continuous roles as living demonstrations for farmers and academics of the
effects of organic and inorganic manures;
x
Enable the monitoring of trends in slow changing factors such as soil pH and other
soil fertility indices;
x
Provide data for long-term studies of the relationship between crop yield and
meteorological variables;
x
Provide data on the effects of atmospheric pollution; and,
x
Can be used to validate computer simulation of field processes over time.
23
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).
24
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
3.2
Location of the study area. (Source: Wikipedia, 2007)
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).
25
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
Rainfall zone
Research sites under each zone stratum
Location on the island
Site location
Mean
(village)
(mm)
annual
rainfall
Vunivasa
Dry
Northern end
Qeleni
1500 – 2500
Matei
Lamini
Intermediate
Central
Welagi
2000 - 3500
Qila
Waimaqere
Wet
Southern end
Delaivuna
Vuna
26
2500 - 4000
Figure 3.2
Soil sampling sites
27
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”
28
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
29
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).
30
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.
Mean annual rainfall (mm)
Mean monthly rainfall (mm)
4000
800
700
600
500
400
300
200
100
0
3500
3000
2500
2000
1500
1000
500
0
1989
1994
1999
2004
Month
Year
(a)
(b)
2009
2014
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.
31
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.
28
Mean annual temperature (oC)
26.6
Mean monthly temp. (oC)
26.8 Y = 0.0023x2 - 9.1131x + 9116.1
R² = 0.4149
26.4
26.2
26
25.8
27
26
25
24
25.6
23
1994
1999
2004
2009
2014
Month
Year
(a)
Figure 4.2
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
25.4
1989
(b)
(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.
32
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.
6.5
6.5
Y = 0.0011x2 - 4.4439x + 4464.8
6
Soil pH
Soil pH
R² = 0.2514
5.5
5
1989
1994
Dry
6
5.5
5
1989
2014
1999
2004
2009
Year
Wet
Intermediate
(a)
1994
1999 2004
Year
2009
2014
(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
33
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.
0.9
0.7
% Total N
Total N (%)
0.8
0.6
0.5
0.4
0.3
1989
1994
Dry
1999
2004
Year
Wet
2009
2014
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1989
Y = 0.002x2 - 7.8318x + 7841.5
R² = 0.4711
1994
1999
2004
2009
2014
Year
Intermediate
(a)
(b)
Figure 4.4 (a) Total N trends for the three taro production strata; (b) 22 year mean trend
for Taveuni
34
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
70
70
60
60
Olsen Available P (mg/kg)
Olsen available P (mg/kg)
given in Figure 4.5 (a) and (b) below.
50
40
30
20
10
0
1989
1994
Dry
1999
2004
2009
Year
Wet
Intermediate
2014
(a)
50
Y = 0.1512x2 - 606.63x + 608539
R² = 0.8104
40
30
20
10
0
1989
1994
1999
2004
Year
2009
(b)
Figure 4.5 (a) Olsen P trends for the three taro production strata; (b) 22 year mean trend
for Taveuni
35
2014
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
Exchangeable K
(cmol(+)/kg)
0.8
0.6
0.4
0.2
0
1989
1994
Dry
1999
2004
2009
2014
Year
Wet
Intermediate
(a)
Exchangeable K (cmol(+)/kg)
in Figure 4.6 (a) and (b) below.
0.7
0.6
0.5
0.4
0.3
0.2
Y= 0.0038x - 7.1914
R² = 0.0578
0.1
0
1989
1994
1999
2004
Year
2009
(b)
Figure 4.6 (a) Exchangeable K trends for the three strata; (b) 22 year mean trend for
Taveuni
36
2014
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)
16
14
12
10
8
6
4
1989
1994
Dry
1999 2004 2009 2014
Year
Wet
Intermediate
Exchangeable Ca (cmol(+)/kg)
Exchangeable Ca (cmol (+)/kg)
and (b) below.
(a)
16
14
12
10
8
6
4
2
0
1989
Y = -0.223x + 456.16
R² = 0.4071
1994
1999
2004
Year
2009
2014
(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
37
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
9
8
7
6
5
4
3
2
1
0
1989
Exchangeable Mg (cmol(+)/kg)
Exchangeable Mg (cmol(+)/kg)
in Figure 4.8 (a) and (b) below.
1994
1999
2004
2009
2014
Year
Dry
Wet
9
8
7
6
5
4
3
2
1
0
1989
Y = -0.1592x + 323.74
R² = 0.5149
1994
1999
2004
Year
2009
Intermediate
(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
38
2014
(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).
5
5
4
4
3
3
2
2
1
1
0
1990
1995
Dry
0
1990
2000
2005
2010
2015
Wet
Intermediate
Y = 0.0195x - 37.056
R² = 0.0593
1995
2000
Exchangeable Mg
(a)
2005
2010
(b)
9
8
7
6
5
4
3
2
1
0
Y = 0.3913x + 1.2325
R² = 0.3797
0
2
4
6
8
10
Exchangeable Ca
12
14
16
(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
39
2015
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.
40
30
35
Corm yield (t/ha)
Corm yield (t/ha)
36
24
18
12
6
0
1993
Y = 0.1121x2 - 450.54x + 452848
R² = 0.9292
30
25
20
15
10
5
1998
Dry
2003
Year
Wet
2008
0
1993
2013
Intermediate
(a)
1998
2003
Year
2008
2013
(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.
40
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)
45
40
35
30
25
20
15
10
5
0
1993
% Rejects
% Taro rejects
and (b) below.
1998
Dry
2003
Year
Wet
2008
2013
45
40
35
30
25
20
15
10
5
0
1993
Y = 0.241x2 - 964.9x + 965788
R² = 0.6783
1998
2003
Year
2008
Intermediate
(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
41
2013
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).
42
Table 4.1(a)
Correlation matrix of selected meteorological and soil chemical indices of
taro soils from the dry zone of Taveuni
Yield
Rainfall
Yield Rainfall
Temp.
pH
N
P
K
Ca
Mg
1.0
0.13
-0.56*
0.43
0.40
0.76**
-0.14
0.51*
0.69**
1.0
-0.09
0.02
0.35
0.18
-0.03
-0.18
0.25
1.0
-0.28
-0.24
-0.33
0.25
-0.54*
-0.56*
1.0
0.34
0.23
0.01
0.47*
0.70**
1.0
0.54*
0.49*
0.49*
0.61**
1.0
0.01
0.38
0.52*
1.0
0.37
0.07
1.0
0.69**
Temp.
pH
N
P
K
Ca
1.0
Mg
*
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).
43
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
Yield Rainfall
Temp.
pH
N
P
K
Ca
Mg
1.0
0.05
-0.54*
0.41
-0.03
0.71**
-0.50*
0.46*
0.59**
1.0
-0.03
-0.46*
0.46*
-0.20
-0.32
0.09
0.10
1.0
-0.48*
0.33
-0.54*
0.27
-0.35
-0.72**
1.0
-0.28
0.49*
-0.22
0.38
0.49*
1.0
0.03
-0.39
-0.10
-0.09
1.0
-0.23
0.44*
0.59**
1.0
-0.51*
-0.27
1.0
0.26
Rainfall
Temp.
pH
N
P
K
Ca
1.0
Mg
*
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).
44
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
Yield Rainfall
Temp. pH
N
P
K
Ca
Mg
1.0
0.09
-0.53*
0.33
0.27
0.88***
0.24
0.63**
0.66**
1.0
-0.09
0.12
0.37
-0.03
0.36
-0.12
0.00
1.0
-0.14
0.05
-0.52*
-0.47*
-0.22
-0.27
1.0
0.44
0.53*
0.09
0.59**
0.64**
1.0
0.39
0.15
-0.04
0.15
1.0
0.31
0.65**
0.64**
1.0
0.08
0.17
1.0
0.65**
N
P
K
Ca
1.0
Mg
*
Significant at the <0.05, **<0.01, and ***<0.001 levels.
45
Relationship of selected chemical indices to taro corm yield
5.8
6.0
(d)
40
35
30
25
20
15
10
5
0
40
35
30
25
20
15
10
5
0
4
Y = 2.3135x - 4.1593
R² = 0.2734
9
14
Exchangeable Ca (cmol (+)/kg)
(e)
Total N (%)
1
Y = 16.039x + 9.1655
R² = 0.0388
0.3 0.4 0.5 0.6 0.7 0.8 0.9
(b)
40
35
30
25
20
15
10
5
0
40
35
30
25
20
15
10
5
0
2
0
Y = 1.1229x + 5.32
R² = 0.5814
(f)
(d) Exchangeable K (cmol(+)/kg); (e) Exchangeable Ca (cmol(+)/kg); and, (f) Exchangeable Mg (cmol(+)/kg).
46
40
8
Y = 4.8925x - 5.5472
R² = 0.4057
10
20
30
Olsen available P (mg/kg)
(c)
4
6
Exchangeable Mg (cmol (+)/Kg)
Regression of taro yield on (a) soil pH; (b) Total N (%); (c) Olsen available P (mg/kg);
0.7
Y= -14.605x + 23.453
R² = 0.0259
6.2
Y = 18.718x - 88.731
R² = 0.1111
Soil pH (H2O)
5.6
(a)
0.3
0.5
Exchangeable K (cmol (+)/kg)
5.4
Figure 4.12
0.1
5.2
40
35
30
25
20
15
10
5
0
0
5
10
15
20
25
30
35
40
The relationship between individual chemical indices and the yield if taros are given in Figure 4.12 (a-f) below.
4.5
Taro corm yield (t/ha)
Taro corm yield (t/ha)
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
47
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.
48
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.
49
0.66
-7.13
-4.10
< 0.001 ***
0.16
-0.53
0.22
0.370 ns
Standard error of mean
difference (Sd)
95% Confidence Interval
p-value
0.58
0.44
0.65
0.65
0.80
0.10
0.32
0.44
2012
0.23
0.241 ns
-0.49
0.14
0.14
-26.6
0.3-0.6
-0.17
0.28
0.70
0.80
0.56
0.41
1.00
0.67
1.00
1990
0.34
N (%)
50
8.23
5.47
8.71
5.75
6.30
5.80
6.25
6.57
2012
8.77
< 0.001 ***
-51.15
-21.75
6.37
-84.1
20-30
-36.45
19.8
22.0
53.0
68.0
64.0
56.3
38.0
46.0
1990
22.8
P (mg/kg)
ns – not significant; ** - significant at P < 0.01; *** - significant at P < 0.001
-56.4
4-10
-2.7
-5.6
4.1
Relative %
decline/increase
9.2
4.2
3.8
5.1
5.9
5.4
3.5
3.7
2012
3.4
5.3-6.5
5.70
9.8
8.2
9.8
15.4
11.5
10.6
9.4
1990
5.7
Critical range
6.32
Qila
5.30
5.30
6.45
5.90
6.20
5.90
5.50
2012
4.80
OC (%)
-0.16
5.70
5.80
Lamini
Welagi
5.70
Vuna
6.00
Waimaqere
6.20
5.52
Matei
Delaivuna
5.73
1990
5.48
pH (H2O)
Qeleni
Vunivasa
Village
0.09
0.66
0.52
0.26
0.48
0.30
0.27
0.66
0.37
0.242 ns
-0.07
0.25
0.07
+22.5
0.5-0.8
0.44
0.48
0.40
0.30
0.16
0.40
0.12
0.33
K
(cmol(+)/kg)
1990 2012
0.19 0.11
Soil chemical indicators
6.30
9.90
8.37
3.70
7.89
2.40
5.11
4.60
0.005 **
-8.31
-2.11
1.35
-43.7
5-10
-0.52
6.70
9.80
13.80 2.71
1.94
2.37
2.73
0.003 **
-4.09
-1.15
0.64
-47.0
1-3
-2.62
2.60
6.80
7.40
5.88
2.70
2.59
5.96
2.17
Mg
(cmol(+)/kg)
1990 2012
2.60 0.27
18.80 14.62 8.40
17.36 7.90
9.60
12.42 8.28
8.10
Ca
(cmol(+)/kg)
1990 2012
10.70 0.98
Paired sample t-test for the chemical indicators between pre and post period of intensive cultivation
Mean Difference (d)
Intermediate
Wet
Dry
Climatic
Zone
Table 4.3a
9.1
9.4
9.6
9.1
8.9
10.4
8.4
9.8
< 0.001 ***
-25.51
-22.78
0.59
-73.0
12-15
-24.14
33.1
36
36
33.6
31.4
31.5
33.6
33
Taro corm
yield
(t/ha)
1993 2012
32.9 9.1
Table 4.3b Soil chemical fertility decline resulting from 22 year intensive cultivation
Soil Chemical
Property
pH
Total N (%)
Olsen P (mg/kg)
Exchangeable K
(cmol(+)/kg)
Organic carbon
(%)
Exchangeable
Ca (cmol(+)/kg)
Farms below critical range
Critical Range
5.3 – 6.5
0.3 – 0.6
20 - 30
0.5 – 0.8
4 - 10
5 - 10
Exchangeable
Mg
(cmol(+)/kg)
1-3
Rainfall Zone
No. of
Percentage of
farms
farms
Dry
8
27
Wet
8
27
Intermediate
14
47
Dry
19
63
Wet
11
37
Intermediate
13
43
Dry
30
100
Wet
30
100
Intermediate
30
100
Dry
20
67
Wet
30
100
Intermediate
20
67
Dry
30
100
Wet
3
10
Intermediate
15
50
Dry
10
33
Wet
4
13
Intermediate
20
67
Dry
10
33
Wet
4
13
Intermediate
7
23
51
5.67
4.34
0.46
Total OC
Total N (%)
(2012)
values
Mean
Soil pH
Parameters
Soil
0.3 – 0.6
4-10
5.3 – 6.5
Rangea
Critical
Table 4.4
52
synthetic N fertilisers and organic matter accumulation through improved leguminous fallow is paramount.
(mucuna beans), agro forestry practices and fallowing practices. To maintain total N levels within an ideal range,
fertilisers. Some farmers in Taveuni also practices other means of enhancing organic matter such as the use of legumes
Mean values for total N for Taveuni soils are within the critical range. This could be due to application of mineral N
well as carbon sequestration through use of biochar.
leguminous fallow to improve the level of carbon. Perhaps prolong natural fallow may assist alleviating carbon level as
which are readily available to the farmers on the island. Avoid burning of plant litter after forest clearing and use
could be explored such as use of by-products of fish canning processing plant, seaweed, and shredded coconut husk,
practice with high biomass inputs, as adopted by the farmers. To enhance carbon build up, number of organic materials
Mean values for total OC for Taveuni soils are within the critical range. This could be due to periodic fallowing
be done in consultation with agriculture department.
should be applied before every planting cycle. Application rate, type of liming materials and time of application should
other organic materials. Thus, to maintain soil pH level within the critical range agricultural lime (preferably dolomitic)
Soil pH level of taro soils in Taveuni is within the critical range. This could be attributed to application of lime and
Suggested Ameliorative Techniques
Comparison of end of research period levels against critical levels and suggested ameliorative measures
kg)
Mg(cmol(+)/
a
1-3
Blakemore et al. ratings (1987)
2.95
53
continued to maintain the levels.
and husbandry practices is important (Fageria and Baligar, 2003b).
these, other practices such as crop rotation, adopting conservation tillage, improving organic matter content in the soil
of application, incorporating crop residues, supplying adequate moisture and use of farmyard manures. In addition to
Liming acid soils with appropriate liming material, applying adequate rate of K fertiliser, appropriate time and method
This can be due to greater removal of K relative to N, by root crops. Management practices include the following:
Exchangeable K levels were found to be critically low in all taro growing sites, despite mineral K supplementation.
of organic P materials is very low therefore, particles should be finely ground and incorporated in the soil.
superphosphate. Sole reliance on chemical fertilisers should be reduced as they tend to acidify the soil. The solubility
phosphate, Di- ammonium phosphate, mono-ammonium phosphate, NPK 13:13:21, single superphosphate and triple
Exchangeable
5 - 10
0.5 – 0.8
20 - 30
Though the Ca and Mg are within the critical range, the practice of liming using dolomitic liming materials should be
6.14
0.40
6.87
dolomtic liming materials before applying mineral or organic P fertilisers. Fertilisers available in Fiji are: Rock
(cmol(+)/kg)
Ca
Exchangeable
(cmol(+)/kg)
K
Exchangeable
(mg/kg)
available P
Olsen
Soil available P is critically low for all taro growing sites in Taveuni. It is crucial to correct the soil pH levels with
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).
100
% farmers
80
60
40
20
0
1989
1994
1999
2004
2009
2014
Year
continuous cultivation
fertiliser application
Figure 4.13
shifting cultivation
lime application
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,
54
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
55
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.
56
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).
100
90
80
% of farmers
70
60
50
40
30
20
10
0
Production cost
Figure 4.14
Inconsistency of
supply of agro
inputs
Roading/Access
Constraint
Instability of
market prices
Lack of technical
know how
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.
57
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.
58
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
59
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
1.
Recommendations
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.
60
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APPENDICES
APPENDIX 1
SOIL AND LAND USE CAPABILITY MAP OF TAVEUNI
74
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
deformations
(Source: Robin, 2000)
75
bruises,
injuries
and
APPENDIX 3
1990 – 2012 Data on: A) Soil Fertility, B) Temperature, C)
Dry Zone
Rainfall and D) Taro production (1994 – 2013)
pH
Nitrogen
Olsen P
H2O
(%)
(mg/kg)
Ca me%
Mg me%
K me%
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
76
Exchangeable Bases
Wet zone
pH
Nitrogen
Olsen P
H2O
(%)
(mg/kg)
Ca me%
Mg me%
K me%
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
77
Exchangable Bases
Intermediate Zone
pH
Nitrogen
Olsen P
H2O
(%)
(mg/kg)
Ca me%
Mg me%
K me%
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
78
Exchangable Bases
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
79
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
80
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
81
APPENDIX 4
ANALYSIS OF VARIANCE FOR BETWEEN RAINFALL-ZONES (STRATA)
COMPARISON
Variate: Ca
Source of variation
d.f.
s.s.
m.s.
v.r.
22
207.563
9.435
2.55
2
0.656
0.328
0.09
Residual
44
162.537
3.694
Total
68
370.756
Year stratum
F pr.
Year.*Units* stratum
Strata
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
82
0.915
Variate: K
Source of variation
d.f.
s.s.
m.s.
v.r.
22
3.5646
0.1620
0.74
2
0.6134
0.3067
1.41
Residual
44
9.5707
0.2175
Total
68
13.7488
Year stratum
F pr.
Year.*Units* stratum
Strata
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
83
0.255
Variate: Mg
Source of variation
d.f.
s.s.
m.s.
v.r.
22
110.2061
5.0094
5.73
2
0.8268
0.4134
0.47
Residual
44
38.4353
0.8735
Total
68
149.4681
Year stratum
F pr.
Year.*Units* stratum
Strata
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
84
0.626
Variate: N
Source of variation
d.f.
s.s.
m.s.
v.r.
22
0.639855
0.029084
3.92
2
0.071829
0.035914
4.84
Residual
44
0.326571
0.007422
Total
68
1.038255
Year stratum
F pr.
Year.*Units* stratum
Strata
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
85
0.013
Variate: P
Source of variation
d.f.
s.s.
m.s.
v.r.
22
11107.93
504.91
21.04
2
406.87
203.44
8.48
Residual
44
1056.10
24.00
Total
68
12570.90
Year stratum
F pr.
Year.*Units* stratum
Strata
Tables of means
Variate: P
Grand mean 16.2
Strata
1
13.4
Standard errors of means
TableStrata
rep.
d.f.
e.s.e.
2
19.3
3
16.0
23
44
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
86
<.001
Variate: pH
Source of variation
d.f.
s.s.
m.s.
v.r.
22
0.87292
0.03968
1.24
2
0.29797
0.14899
4.67
Residual
44
1.40349
0.03190
Total
68
2.57438
Year stratum
F pr.
Year.*Units* stratum
Strata
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
87
0.014
Variate: %_Rejects
Source of variation
d.f.
s.s.
m.s.
v.r.
19
5672.11
298.53
14.00
2
143.61
71.80
3.37
Residual
38
810.01
21.32
Total
59
6625.73
Year stratum
F pr.
Year.*Units* stratum
Strata
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
88
0.045
Variate: Yield
Source of variation
d.f.
s.s.
m.s.
v.r.
19
5475.301
288.174
213.53
2
0.529
0.264
0.20
Residual
38
51.284
1.350
Total
59
5527.114
Year stratum
F pr.
Year.*Units* stratum
Strata
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
89
0.823
APPENDIX 5
PAIRED SAMPLE T-TEST FOR COMPARISON OF SOIL CHEMICAL
INDICES AND YIELDS PRE AND POST- 22- YEAR CULTIVATION PERIOD
Variate: Ca.
Summary
Sample
Present-Previous
Size
9
Mean
-5.209
Variance
16.28
Standard
deviation
4.035
Standard error
of mean
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
Sample
Present-Previous
Size
9
Mean
0.09000
Variance
0.04565
Standard
deviation
0.2137
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
90
Standard error
of mean
0.07122
Variate: Mg
Summary
Sample
Present-Previous
Size
9
Mean
-2.617
Variance
3.648
Standard
deviation
1.910
Standard error
of mean
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
Sample
Present-Previous
Size
9
Mean
-0.1722
Variance
0.1664
Standard
deviation
0.4080
Standard error
of mean
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
Sample
Present-Previous
Size
9
Mean
-36.45
Variance
365.7
Standard
deviation
19.12
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
91
Standard error
of mean
6.374
Variate: pH
Summary
Sample
Present-Previous
Size
9
Mean
-0.1556
Variance
0.2411
Standard
deviation
0.4910
Standard error
of mean
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
Sample
Present-Previous
Size
9
Mean
-24.14
Variance
3.170
Standard
deviation
1.781
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
92
Standard error
of mean
0.5935
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
93
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
94
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
95
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
96
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
97
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
98
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
99
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
100
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
101
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
102
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
103
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
104
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
105
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
106
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
107
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
108
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
109
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
110
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
111
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
112
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
113
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
114
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
115
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
116
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
117
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
118
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
119
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
120
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
121
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
122
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
123
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
124
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
125
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
126
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
127
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
128
APPENDIX 9
22 - YEAR TREND REGRESSION ANALYSIS OF VARIANCE
Response variate:Exchangeable Ca
Fitted terms: Constant, Year
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
67
68
s.s.
150.9
219.8
370.8
m.s.
150.938
3.281
5.452
v.r.
46.01
F pr.
<.001
Percentage variance accounted for 39.8
Standard error of observations is estimated to be 1.81.
Estimates of parameters
Parameterestimate
s.e.
Constant
456.2
Year
-0.2230
t(67)
t pr.
65.8
0.0329
6.93
-6.78
<.001
<.001
Response variate: Exchangeable K
Fitted terms: Constant, Year
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
67
68
s.s.
0.0433
0.7058
0.7491
m.s.
0.04333
0.01053
0.01102
v.r.
4.11
Percentage variance accounted for 4.4
Standard error of observations is estimated to be 0.103.
Estimates of parameters
Parameter
estimate
Constant
-7.19
Year
0.00378
s.e.
3.73
0.00186
129
t(67)
-1.93
2.03
t pr.
0.058
0.047
F pr.
0.047
Response variate:Exchangeable Mg
Fitted terms: Constant, Year
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
67
68
s.s.
76.96
72.51
149.47
m.s.
76.962
1.082
2.198
v.r.
71.12
F pr.
<.001
Percentage variance accounted for 50.8
Standard error of observations is estimated to be 1.04.
Estimates of parameters
Parameter
estimate
Constant
323.7
Year
-0.1592
s.e.
37.8
0.0189
t(67)t pr.
8.57 <.001
-8.43 <.001
Response variate: Total N
Fitted terms: Constant + Year
Submodels: POL(Year; 2)
Summary of analysis
Source
Regression
Residual
Total
d.f.
2
66
68
s.s.
0.4892
0.5491
1.0383
m.s.
0.244578
0.008320
0.015268
v.r.
29.40
F pr.
<.001
Percentage variance accounted for 45.5
Standard error of observations is estimated to be 0.0912.
Estimates of parameters
Parameter
Constant
Year Lin
Year Quad
estimate
7841.
-7.83
0.001956
130
s.e.
1120.
1.12
0.000280
t(66)
7.00
-6.99
6.99
t pr.
<.001
<.001
<.001
Response variate: Olsen P
Fitted terms: Constant + Year
Submodels: POL(Year; 2)
Summary of analysis
Source
Regression
Residual
Total
d.f.
2
66
68
s.s.
10188.
2383.
12571.
m.s.
5093.84
36.11
184.87
v.r.
141.07
F pr.
<.001
Percentage variance accounted for 80.5
Standard error of observations is estimated to be 6.01.
Estimates of parameters
Parameter
Constant
Year Lin
Year Quad
estimate
608539.
-606.6
0.1512
s.e.
t(66)
73810.
73.8
0.0184
t pr.
8.24
-8.22
8.20
<.001
<.001
<.001
Response variate:Soil pH
Fitted terms: Constant + Year
Sub models: POL(Year; 2)
Summary of analysis
Source
Regression
Residual
Total
d.f.
2
66
68
s.s.
0.639
1.936
2.574
m.s.
0.31936
0.02933
0.03786
v.r.
10.89
F pr.
<.001
Percentage variance accounted for 22.5
Standard error of observations is estimated to be 0.171.
Estimates of parameters
Parameter
Constant
Year Lin
Year Quad
estimate
4465.
-4.44
0.001107
131
s.e.
t(66)t pr.
2104.
2.12
2.10
-2.11
0.000525
2.11
0.038
0.038
0.039
Response variate:% Taro Rejects
Fitted terms: Constant + Year
Sub models: POL(Year; 2)
Summary of analysis
Source
Regression
Residual
Total
d.f.
2
57
59
s.s.
4336.
2057.
6393.
m.s.
2168.21
36.08
108.36
v.r.
60.09
F pr.
<.001
Percentage variance accounted for 66.7
Standard error of observations is estimated to be 6.01.
Estimates of parameters
Parameter
Constant
Year Lin
Year Quad
estimate
965788.
-965.
0.2410
s.e.
105066.
105.
0.0262
t(57)
9.19
-9.20
9.21
Response variate: Av_Temp
Fitted terms: Constant, Year
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
21
22
s.s.
0.769
1.530
2.299
m.s.
0.76918
0.07285
0.10451
v.r.
10.56
Percentage variance accounted for 30.3
Standard error of observations is estimated to be 0.270.
Estimates of parameters
Parameter
estimate
Constant
-29.0
Year
0.02757
s.e.
17.0
0.00848
132
t(21)
-1.71
3.25
t pr.
0.102
0.004
F pr.
0.004
t pr.
<.001
<.001
<.001
Response variate:
Yield
Fitted terms: Constant + Year
Sub models: POL(Year; 2)
Summary of analysis
Source
Regression
Residual
Total
d.f.
2
57
59
s.s.
5142.2
391.7
5533.9
m.s.
2571.119
6.872
93.796
v.r.
374.14
F pr.
<.001
Percentage variance accounted for 92.7
Standard error of observations is estimated to be 2.62.
Estimates of parameters
Parameter
Constant
Year Lin
Year Quad
estimate
452848.
-450.5
0.1121
s.e.
45851.
45.8
0.0114
t(57)
9.88
-9.84
9.81
t pr.
<.001
<.001
<.001
Response variate: 20 year mean monthly rainfall
Fitted terms: Constant + Month
Sub models: POL(Month; 2)
Summary of analysis
Source
Regression
Residual
Total
d.f.
2
9
11
s.s.
264847.
29549.
294396.
m.s.
132423.
3283.
26763.
v.r.
40.33
F pr.
<.001
Percentage variance accounted for 87.7
Standard error of observations is estimated to be 57.3.
Estimates of parameters
Parameter
Constant
Month Lin
Month Quad
estimate
908.4
-183.7
12.69
133
s.e.
59.2
20.9
1.57
t(9)
15.34
-8.77
8.09
t pr.
<.001
<.001
<.001
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
Regression
Residual
Total
d.f.
1
55
56
s.s.
1449.
3850.
5299.
m.s.
1448.78
70.00
94.63
v.r.
20.70
F pr.
<.001
Percentage variance accounted for 26.0
Standard error of observations is estimated to be 8.37.
Estimates of parameters
Parameter
estimate
Constant
-4.16
Ca
2.314
Response variate:Taro Yield;
s.e.
4.98
0.509
t(55)
-0.83
4.55
t pr.
0.407
<.001
Fitted terms: Constant, Exchangeable K
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
55
56
s.s.
137.
5162.
5299.
m.s.
136.99
93.86
94.63
v.r.
1.46
Percentage variance accounted for 0.8
Standard error of observations is estimated to be 9.69.
Estimates of parameters
Parameter
estimate
Constant
23.45
K
-14.6
s.e.
4.74
12.1
134
t(55)
4.95
-1.21
t pr.
<.001
0.232
F pr.
0.232
Response variate:Taro Yield;
Fitted terms: Constant, Exchangeable Mg
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
55
56
s.s.
2150.
3149.
5299.
m.s.
2149.90
57.26
94.63
v.r.
37.55
F pr.
<.001
Percentage variance accounted for 39.5
Standard error of observations is estimated to be 7.57.
Estimates of parameters
Parameter
estimate
Constant
-5.55
Mg
4.893
Response variate:Taro Yield;
s.e.
3.96
0.798
t(55)
-1.40
6.13
t pr.
0.167
<.001
Fitted terms: Constant, % Total N
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
55
56
s.s.
206.
5093.
5299.
m.s.
205.81
92.60
94.63
v.r.
2.22
Percentage variance accounted for 2.1
Standard error of observations is estimated to be 9.62.
Estimates of parameters
Parameter
estimate
Constant
9.17
N
16.0
s.e.
6.02
10.8
135
t(55)
1.52
1.49
t pr.
0.134
0.142
F pr.
0.142
Response variate:Taro Yield;
Fitted terms: Constant, Olsen P
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
55
56
s.s.
3081.
2218.
5299.
m.s.
3080.74
40.33
94.63
v.r.
76.38
F pr.
<.001
Percentage variance accounted for 57.4
Standard error of observations is estimated to be 6.35.
Estimates of parameters
Parameter
estimate
Constant
5.32
P
1.123
s.e.
1.67
0.128
Response variate:Taro Yield;
t(55)
3.18
8.74
t pr.
0.002
<.001
Fitted terms: Constant, Soil pH
Summary of analysis
Source
Regression
Residual
Total
d.f.
1
55
56
s.s.
589.
4710.
5299.
m.s.
588.98
85.64
94.63
v.r.
6.88
Percentage variance accounted for 9.5
Standard error of observations is estimated to be 9.25.
Estimates of parameters
Parameter
estimate
Constant
-88.7
pH
18.72
s.e.
40.7
7.14
136
t(55)
-2.18
2.62
t pr.
0.034
0.011
F pr.
0.011
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
Regression
Residual
Total
d.f.
4
52
56
s.s.
3579.
1720.
5299.
m.s.
894.67
33.08
94.63
v.r.
27.04
Percentage variance accounted for 65.0
Standard error of observations is estimated to be 5.75.
Estimates of parameters
Parameter
Constant
Ca
Mg
P
pH
estimate
36.9
0.639
2.395
0.868
-8.13
s.e.
28.7
0.424
0.814
0.147
5.33
137
t(52)
1.29
1.51
2.94
5.91
-1.52
t pr.
0.204
0.138
0.005
<.001
0.133
F pr.
<.001
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:
(b)
(a)
____________________
_____________________
Smallholder/semi-commercial
Commercial
138
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?
_________________________________________________________________
__________
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12.
13.
a.
Do you use fertiliser?________________________________________
b.
Which fertiliser?
c.
How much? ______________________________________________
d.
Since when? ______________________________________________
e.
Do you receive government assistance on fertilisers?_______________
________________________________________
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?
_________________________________________________________________
_________________________________________________________________
_________________________________________________________________
______________________________
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