A GEDSTATISTICAL APPROACH TO THE MAPPING
OF ACID SULFATE SOILS
A THESIS SUBMITTED TO THE GRADUATE DIVISIC»J OF THE UNIYERSITY OF HAWAII IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN AGROMOMY AND SOIL SCIENCE
MAY 1985
By
FaricSah Hj Ahmad
Thesis Canmittee:
Dr. Russell S. Yost/ Chairman Dr. Goro Uehara Dr. Richard E. Green 11
We certify that we have read this thesis and that/ in our opinian/ it is satisfactory in scope and quality as a thesis for the degree of
Master of Science in Agroncxny and Soil Science.
THESIS COMMITTEE Ill
ACKNOWLEDGEMENT
I would like to thank the Government of Malaysia for the financial support and the Director General of the Department of Agriculture, West
Malaysia for extending my study leave.
I wish to express my sincere gratitude to Dr. Russell Yost, my major advisor for his dedication and encouragement, members of the comittee. Dr. Richard E. Green and Dr. Goro Uehara for their valuable suggestions in the revision of the thesis, the staff of the Department of Agriculture, Northwest Selangor Integrated Project area and Mr.
Shahruddin of the Drainage and Irrigation Scheme, West Malaysia for their assistance in soil and foliar sampling and analyses.
Appreciation is also extended to the soil survey staff for providing additional data and managers of Northwest Selangor Integrated Project and oil palm estates in the area for their cooperation.
I would also like to thank Dr. Bruce Trangmar and Mr. Keith Hayashi for their help in computer progremining and lastly but not least to my family and friends for their moral and emotional support. 'Terima
Kasih'. iv
I would like to dedicate this study to all farmers of the Northwest Selangor Integrated Project Area# West Malaysia. ABSTRACT
Acid sulfate soils are cotraion along the west coast of West
Malaysia. These soils occur in small isolated areas and are difficult
to locate. Cne approach is by analyses of spatial dependence of some
soil properties typical of acid sulfate soils determined on samples
in those areas. Geostatistics permits analysis of spatial dependence
and interpolation of soil properties at unsampled locations. These
methods were used to analyze soil properties in the Kuala Selangor
area/ West Malaysia which are usiially characteristic of acid sulfate
soils. Spatial dependence was observed in soil pH/ extractable Al/
soluble SO^ cind electrical conductivity.
The surface 0-15 cm of oxidized soil/ had approximately equal
ranges of spatial dependence of soil pH/ extractable aluminum/ soluble
SO^ and electrical conductivity. At the 15-30 cm depth/ extractable
Al had a greater range of spatial dependence than did soil pH.
Extractable Al is an important characteristic of acid sulfate soils
especially when considering soil acidity and liming. Range of spatial
dependence of extractable Al is useful in determining the sampling
distance. These data suggest that there should be no more than 4 km
between sanples and preferably much less. The map of individual
soil properties was useful in indicating location and extent of acid
sulfate areas. The coincidence of isarithms of soil pH/ extractable
aluminum/ soluble SO^ cind electrical conductivity at 0-15 cm and VI
15-30 cm depths indicates acid sulfate areas.
Sanpling frequency was probably inadequate to reveal spatial dependence in nutrient concentrations of oil palm in the Kuala
Selangor area. Detrimental effects of acid sulfate soils were evident in lower P concentrations in oil palm fronds.
In the wetland rice area/ pH of dry soil was used as an indication of possible acid sulfate soils. Using this criterion/ areas of potential acid sulfate soils were greater than those indicated by the soil survey map although there were large estimation variances. Low density sampling in some areas may account for some of the reasons.
Thus spatial dependence analyses by the geostatistical ajproach is useful in providing base information for the soil survey and also in mapping specific soil constraints. Locations and extent of soil constraints were napped with a known precision. Vll
TABLE OF CCOTENTS
page
ACKNOWLBGMENTS...... iii
ABSTRACT...... v
LIST OF TABLES...... vii
LIST OF FIGURES...... xii
LIST OF APPENDICES...... xviii
I. INTRODUCTION...... 1
II. LITERATURE REVIEW...... 4
III. MATERIAL AND METHODS...... 26
3.1 Area under study...... 26
3.2 The soils...... 27
3.3 Soil Sanpling...... 27
3.4 Soil Analysis...... 29
3.4 Foliar Sanpling and Analysis of Oil palm...... 35
3.5 Statistical and Geostatistical Analysis...... 35
IV. RESULTS AND DISCUSSION...... 41
4.1 SITE 1...... 41 viii
4.1.1 Soil Analyses...... 41
Soil Description...... 41
Statistical Analysis...... 43
Spatial Analysis...... 49
Soil pH...... 49
Ex tractable Aluminum...... 53
Water Soluble Sulfate...... 53
Electrical Ccxiductivity...... 56
Kriging...... 60
Summary of Soil Properties of Site 1.... 70
4.1.2 Oil Palm Analysis...... 72
Nutrient Levels...... 72
Nitrogen...... 72
Phosphorus...... 77
Potassium...... 80
Calcium and Magnesium...... 80
Manganese...... 81
Iron...... 83
Copper and Zinc...... 83
Boron...... 84
Spatial Analysis and kriging...... 84
Sumnary of the Nutrient Analysis...... 88
4.2 SITE II...... 89
4.2.1 Soil Analyses...... 89 ix
Correlation Matrix...... 89
Spatial Analysis...... 95
pH...... 95
Extractable Aluminum...... 98
Percentage Soluble Sulfate...... 98
Electrical Ccaiductivity...... 98
Kriging...... 106
Sumnary of Soil Properties of Site II.... 126
V. GENERAL SUMMARY...... 129
LITERATURE CITED...... 133
APPENDICES...... 141 LIST OF TABLES
Table page
4.1 Means and ranges of some soil chanical properties related to acidity/ site 1, Kuala Selangor...... 42
4.2 Correlation coefficients of some soil chemical properties related to acidity/ site 1/ Kuala Selangor...... 44
4.3 Regression coefficients of sulfate ions and hydrogen ions (itinolkg” )/ site 1/ Kuala Selangor...... 45
4.4 Regression cinalyses of soil pH with some variables related to acidity/ site 1/ Kuala Selangor...... 46
4.5 Regression coeficients of some variables related to soil pH/ site 1/ Kuala Selangor...... 48
4.6 Parameter estimates of isotropic semi-variograms of seme soil chemical properties/ site 1/ Kuala Selangor...... 50
4.7 Means and ranges of nutrient concentrations in oil palm frond/ site 1/ Kuala Selangor...... 73
4.8 Nutrient concentrations of oil palm frond in Malaysia - dry matter basis...... 74
4.9 Correlation coefficients of nutrient concentrations of oil palm frond with soil nutrients/ site 1/ Kuala Selangor 75
4.10 Regression analysis of P concentration in frond and soil factors related that are to acidity/ site 1/ Kuala Selangor...... 78 XI
LIST OF TABLES (continued)
Table page
4.11 Correlation coefficients between nutrient concentrations of oil palm frond/ site 1/ Kuala Selangor...... 82
4.12 Isotropic semi-variogram parameter estimates of nutrient concentrations of oil palm frond/ site 1/ Kuala Selangor.. 85
4.13 Means and ranges of some soil properties of site II/ Kuala Selangor...... 90
4.14 Correlation matrix of some variables related to soil acidity/ site II/ Kuala Selangor...... 92
4.15 Probability values in regression analyses of electrical conductivity (dependent variables) against some soluble salts/ site II/ Kuala Selangor...... 94
4.16 Parameter estimates isotropic semi-variogram of some chemical properties/ site II/ Kuala Selangor...... 96 Xll
LIST OF FIGURES
Figure Page
2.1 An ideal semi-varicjgram model where the curve passes through the origin (Adapted from Wilding and Drees/ 1982)...... 10
2.2 Linear/ spherical and mitscherlich semi-variogram models (Adapted and modified from Wilding and Drees/ 1982)...... 10
3.1 Area under study in Kuala Selangor/ Northwest Selangor Integrated Project area/ West Malaysia...... 28
3.2 Soil sampling sites (75), site 1/ (Block 1/ II/ III)/ Kuala Selangor...... 30
3.3 Soil sampling sites (58)/ site II/ wetland rice area/ Kuala Selangor...... 36
3.4 Foliar sanpling sites of oil palm (44), site I (Block 1/ II/ III)/ Kuala Selangor...... 37
3.5 Locations of 177 kriged points/ site I, Kuala Selangor. 39
3.6 Location of 50 kriged points/ site II/ wetland rice area/ Kuala Selangor...... 40
4.1 Isotropic semi-variogram for pH/ 0-15 cm depth/ site 1/ Kuala Selangor...... 51
4.2 Isotropic semi-variogram for pH/ 15-30 cm depth/ site 1/ Kuala Selangor...... 52 Xlll
LIST OF FIGURES (continued)
Figure F>age
4.3 Isotropic setni-variogram for extractable Al/ (anolkg~^) 0-15 on depth/ site 1/ Kuala Selangor...... 54
4.4 Isotropic semi-variogram for extractable Al/ (anolkg”^) 15-30 an depth/ site 1/ Kuala Selangor...... 55
4.5 Isotropic semi-variogram for soluble sulfate/ (%) 0-15 cm depth/ site 1/ Kuala Selangor...... 57
4.6 Isotropic semi-variogram for log transformed values of electrical conductivity (dSm” )/ 0-15 cm depth/ site 1, Kuala Selangor...... 58
4.7 Isotropic semi-variogram for_log transformed values of electrical conductivity (dSrn” )/ 15-30 cm depth/ site 1/ Kuala Selangor...... 59
4.8 Isarithm map of pH of 0-15 cm depth by punctual kriging fron 75 observed values/ site 1/ Kuala Selangor...... 61
4.9 Isarithm map of pH of 15-30 cm depth by punctual kriging fron 74 observed values/ site 1/ Kuala Selangor...... 62
4.10 Isarithm map of extractable Al (onolkg”^) of 0-15 cm depth by punctual kriging fron 75 observed values/ site 1 / Kuala Selangor...... 63
4.11 Isarithm map of extractable Al (cmolkg”^) of 15-30 cm depth by punctual kriging from 74 observed values/ site I Kuala Selangor...... 64 XIV
LIST OF FIGURES (continued)
Figure page
4.12 Isarithm map of soli:ible sulfate (%) of 0-15 cm depth by punctual kriging frcm 75 observed values/ site 1 / Kuala Selangor...... 65
4.13 Isarithm map of electrical conductivity (dsm~^) of 0-15 cm depth by punctual kriging from 75 observed values/ site 1/ Kuala Selangor...... 66
4.14 Isarithm map of electrical conductivity (dSm~^) of 15-30 cm depth by punctual kriging fron 74 (^Dserved values/ site 1/ Kuala Selangor...... 67
4.15 Three dimensional diagreun of extractable Al (cmolkg”^) by punctual Jcriged/ 15-30 an site I, Kuala Selangor. The low values of Al in the lower left comer coincided with the Selangor River terraces...... 68
4.16 Isotropic semi-variogram for log transformed values of frond Mn (rngkg” ) of oil palm/ site 1/ Kuala Selangor.. 86
4.17 Isotropic semi-yariogram for log treinsformed values of frond Fe (rngkg” ) of oil palm/ site 1/ Kuala Selangor.. 87
4.18 Isotropic semi-variogram for pH of fresh soil/ 0-15 cm depth/ site II/ Kuala Selangor. The values were log transformed...... 99
4.19 Isotropic semi-variogram for pH of dry soil/ 0-15 an depth/ site II/ Kuala Selangor. The values were log transformed...... 100
4.20 Isotropic semi-variogram for pH of dry soil/ 15-30 on depth/ site II/ Kuala Selangor. The values were log transformed...... 101 XV
LIST OF FIGURES (continued)
Figure page
4.21 Isotropic semi-variogram for fdl of fresh soil, 60-90 an depth, site II, Kuala Selangor...... 102
4.22 Isotropic semi-variogram for of dry soil, 60-90 cm depth, site II, Kuala Selangor...... 103
4.23 Isotropic semi-variogrcun for extractable Al, (cmolkg”^) 0-15 cm depth, site II, Kuala Selangor...... 104
4.24 Isotropic semi-variogram for soluble SO^ (%), 15-30 cm depth, site II, Kuala Selangor. The values were log transformed...... 105
4.25 Isotropic semi-variogram for electrical conductivity (dSm“^) of fresh soil, 0-15 on depth, site II, Kuala Selangor. The values were log transformed...... 107
4.26 Isotropic semi-variogram for electrical conductivity (dSm“^) of dry soil, 0-15 cm depth, site II, Kuala Selangor. The values were log transformed...... 108
4.27 Isotropic semi-variogram for electrical conductivity (dSm~^) of fresh soil, 15-30 cm depth, site II, Kuala Selangor. The values were log transformed...... 109
4.28 Isotropic semi-variogram for electrical conductivity (dSm“^) of fresh soil, 60-90 cm depth, site II, Kuala Selangor. The values were log transformed...... 110
4.29 Isarithm map of pH of fresh soil, 0-15 cm depth by punctual kriging from 56 observed values, site II, Kuala Selangor...... Ill xvi
LIST OF FIGURES (continued)
Figure page
4.30 Isarithm map of pH of dry soil/ 0-15 an depth by punctual kriging from 55 observed values/ site II Kuala Selangor...... 112
4.31 Isaritham map of pH of fresh soil/ 15-30 cm depth by punctual kriging from 56 observed values/ site II/ Kuala Selangor...... 113
4.32 Isarithm map of pH of fresh soil/ 60-90 cm depth by punctual kriging from 56 observed values/ site II/ Kuala Selangor...... 114
4.33 Isarithm map of pH of dry soil/ 60-90 an depth by punctual kriging from 55 observed values/ site II/ Kuala Selangor...... 115
4.34 Isaritham map of extractable Al (cmol(+)kg“^/ 0-15 cm depth by punctual kriging frcm 46 observed values/ site II/ Kuala Selangor...... 116
4.35 Isaritham map of original values of soluble SO. (%)/ 15-30 on depth from 57 observed values/ site II/ Kuala Selangor...... 117
4.36 Isarithm map of electrical conductivity of fresh soil (dSm~ )/ 0-15 on depth by punctual kriging frcm 52 observed values/ site II/ Kuala Selangor...... 118
4.37 Isarithm map of electrical conductivity of dry soil (dan / 0-15 on depth by punctual kriging from 55 observed values/ site II// Kuala Selangor...... 119
4.38 Isarithm map of electrical conductivity of fresh soil (dSm )/ 15-30 cm depth by punctual kriging from 51 observed values/ site II/ Kuala Selangor...... 120 xvii
LIST OF FIGURES (continued)
Figure page
4.39 Isarithm map of electrical conductivity of fresh soil (dSn” )/ 60-90 cm depth by punctual kriging from 51 observed values/ site II/ Kuala Selangor...... 121
4.40 Delineated areas of acid sulfate soil (Adapted from revised soil survey map/ Department of Agriculture/ West Malaysia) superimposed on isarithm map of pH of dry soil/ 15-30 cm depth/ site II/ Kuala Selangor 124
4.41 Delineated areas of potential acid sulfate soil (Adapted from revised soil survey map/ Department of Agriculture/ West Malaysia) superimposed on isarithm map of pH of dry soil/ 60-90 cm depth/ site II/ Kuala Selangor 125 xviii
LIST OF APPENDICES
Appendix page
A.l Estination variances of pH for isotropic punctual kriging/ 0-15 an depth/ site I,of Kuala Selangor. Isarithms in pH units ...... 142
A.2 Estimation variances of pH for isotropic punctual kriging/ 15-30 an depth/ site I of Kuala Selangor. Isarithms in pH units...... 143
A.3 Estimation variances of extractable Al for isotropic punctual kriging/ 0-15 cm depth/ site I of Kuala Selangor. Isarithms in (cmol(+)kg ) ...... 144
A.4 Estimation variances of extractable Al for isotropic punctual kriging/ 15-30 an depth/ site I of Kuala Selangor. Isarithms in (onol(+)kg ) ...... 145
A.5 Estimation variances of soluble SO^ for isotropic punctual krigingx 0-15 on depth/ site I of Kuala Selangor. Isarithms in (%) ...... 146
A . 6 Estimation variances of electrical conductivity for isotropic punctual kriging/ 0-15 on depth/ site I of Kuala Selangor. Isarithms in (dSm ) ...... 147
A.7 Estimation variances of electrical conductivity for isotropic punctual kriging/ 15-30 on depth/ site I of Kuala Selangor. Isarithms in (dShi ) ...... 148
A . 8 Estimation variances of pH of fresh soil for isotropic punctual kriging/ 0-15 on depth of site II/ Kuala Selangor. Isarithms in pH units...... 149 xix
LIST OF APPEM)ICES (continued)
Appendix page
A.9 Estimation variances of pH of dry soil for isotropic punctual kriging/ 0-15 cm depth of site II; Kuala Selangor. Isarithms in pH unit ...... 150
A.10 Estimation variances of pH of dry soil for isotropic punctual kriging/ 15^30 cm depth of site II/ Kuala Selangor. Isarithms in pH un i t ...... 151
A.11 Estimation variances of pH of fresh soil for isotropic punctual kriging/ 60-90 cm depth of site II/ Kuala Selangor. Isarithms in pH units...... 152
A.12 Estimation variances of pH of dry soil for isotropic punctual kriging/ 60-^ cm depth/ site II/ Kuala Selangor Isarithms in pH units...... 153
A.13 Estimation variances of extractable Al for isotropic punctual kriging/ 0-15 cm ^pth/ site II/ Kuala Selangor. Isarithms in (cmol(+)kg ) ...... 154
A.14 Estimation variances of electrical conductivity of fresh soil for isotropic p>unctxaal kriging^ 0-15 cm depth/ site II/ Kuala Selangor. Isarithms in (dSm ) ...... 155
A.15 Estimation variances of electrical conductivity of dry soil for isotropic punctual kriging/ 0-15 cm,depth/ site II/ Kuala Selangor. Isarithms in (dSm~ ...... 156
A.16 Estimation variances of electrical conductivity of fresh soil for isotropic punctual kriging/ 15-30 cm ^pth/ site II/ Kuala Selangor. Isarithms in (dSm ) ...... 157 XX
LIST OF APPENDICES (continued)
Appendix page
A.17 Isarithm map of exchangeable Ca (cmol(+)kg”^)/ 0-15 cm depth by punctual kriging fron 76 observed values/ site 1/ Kuala Selangor...... 158
A.18 Isarithm map of original values of exchangeable Mg (cmol(+)kg“ )/ 0-15 cm depth from 76 observed values/ site 1/ Kuala Selangor...... 159
A.19 Isarithm map of original values of percent clay/ 0-15 cm depth from 76 observed values/ site 1/ Kuala Selangor.... 160
A.20 Isarithm map of original values of percent clay/ 15-30 cm depth fron 75 observed values/ site 1/ Kuala Selangor.... 161
A.21 Isarithm map of original values of exchangeable K (cmol(+)kg“ )/ 0-15 cm depth fron 76 observed values/ site 1/ Kuala Selangor...... 162 INTRODUCTIC»I
Assessment of soil fertility status is important in planning agricultural land use. This is one reason why soils are sanpled and analyzed before land use interpretation. Because there is spatial variation in soil properties/ a precise statanent about a specific location cannot be made or if made/ its reliability is uncertain
(Beckett/ 1967; Webster et al./ 1968).
This problen is particularly inportant in land use planning of
Malaysian soils especially where there are acid sulfate soils. In
Malaysia/ the acid sulfate soils and the potentially acid sulfate soils do not occur in large contiguous areas but in isolated areas within otherwise good soils (Zahari et al./ 1982; Foon/ 1977) . There are about 200/000 hectares of acid sulfate soils in Malaysia and of these
there are 20,000 hectares under oil palm cultivaticxi (Foon/ 1977). In
the district of Kuala Selangor/ the soils are among the best in the
country but there are some localized occurrences of potential acid
sulfate soils. Where excessive drainage occurs/ acid sulfate soils/
have developed/ especially during dry periods. As a result/ yields of
oil palm and coconut have deteriorated in recent years in these areas.
At present/ identification of acid sulfate soil areas is usually
made through observaticxi of crop yield and certain nutrient
deficiencies. So far this has been done through spot checking after
complaints from farmers. After the soil has become "acid sulfate" it is too late for rapid recuperation. Crops normlly require about 2-3 years to recuperate after ameliorative measures have begun. Thus/ the need is great to identify potentially acid sulfate soils as early as possible.
Although a semi-detailed soil survey is being conducted in the area
under study/ problem areas particularly in small farmer's fields cannot
be identified within a map unit. One possible approach to delineate
these problem areas is to quantify the spatial dependence of physical and chanical properties of soils of the area by methods of
geostatistics. Geostatistical methods can be used to improve land
management units (Burgess and Webster/ 1980a/ b; Hajrasuliha et
al./ 1980; Vieira et al./ 1981)/ mapping variability of specific
properties within plots (Trangmar/ 1982) and over large land areas
(Yost et al./ 1982a) and interpretation of soil genesis (Campbell/
1978; Yost et al./ 1982a).
The objectives of this study are:
1) To determine the spatial dependence of some physical and
chemical properties of soils in Kuala Selangor as they relate
to potentially acid sulfate soils.
2) To produce a general soil fertility map of the area, to
determine the most critical soil measuronents to be considered in predicting the potentially acid sulfate areas, and to produce a soil fertility map of these properties. II. LITERATURE REVIEW
Soil Spatial Variability
Previously/ much of the statistical analysis of crop yield has been based on treatment effects and plant responses. Little enphasis has been given to the variability of soil other than siiiply allocating this variability to blocking or replication error. This inplies that
statistical theory did not lead to direct correlation of soil properties with plant response or with land form parameters (Wilding and Drees/ 1983). In a study/ spatial structure of peanut yield
components and soil water content were analysed with methods of auto-correlation and cross-correlation (Bresler et al./ 1981). It was concluded that spatial variability in yield of a given crcp grown
under the same cultivation and management conditions is determined
mainly by soil variability.
Variability of soil properties would not be revealed in coimon bulk
soil sanpling. In short range distances/ a coefficient of variation of
33% was determined for exchangeable cations (except Na) in North Wales
within 1 meter radius of 22 sampling points (Ball and William/
1968). A study done on Richfield series/ Western Kansas/ variation
among fields at distances varying from 8 to 145 km contributed the most
to the total variance simong sanples (Cipra et al. / 1972). The results show more than 57% of the variance of organic matter/ phosphorus and pH of the A horizon and of potassium in B and C horizons occured among fields. Over a wide range of distances/ soils under
"Cerrado" vegetation in Brazil had ranges in extractable phosphorus of
0.1-16.5 ugkg”^/ extractable potassium of 7.8-237.9 ugkg”^/ exchangeable calcium of 8-1362 ugkg”^/ aluminum saturation of
1.1-89.4%/ and particle size distribution of sand fraction 4.3-93.9%/ silt fraction 1.6-57.4% and clay fraction 4.5-72.4% (Lopes and Cox,
1977). Several other studies have reported similarly wide ranges of variability over distances (Campbell/ 1977; Cameron et al./ 1971; and Lee et ^ . / 1974). This also indicates that a comnon method of analysis of variance also does not permit concise and complete description of changes over distance (Campbell/ 1978). Use of these results for fertilizer recommendations in the area would be quite misleading. For long-term observations/ analysis of individual saitpling is therefore recommended in order to test the variability at any site involving soil chemical properties (Ball and William/ 1968).
In conditions of high variation/ the result of a fertilizer test conducted on a soil of a given type is not necessarily more accurately applicable to the same soil type than to different soil types (Davis/
1936).
The same problem is faced in using soil maps because the boundaries of soil survey maps are drawn by soil surveyors according to their
inspection and observation (DaviS/ 1936; Cruikshank/ 1972). Often accurate predictions are difficult using soil maps due to the variation of the soil properties that have been grouped together into a presumably homogeneous class (Beckett, 1967; Webster and Beckett, 1968;
Miller et , 1979). In a study done in Western Kansas on two selected soil series, pH and silt exhibited very gradual change across
the boundary, but the sand content differed by 8% within a distance of
20 meters at some places along the boundary (Campbell, 1977).
Moreover, the reliability or purity of a mapping linit decreases as the
definition becomes more specific (Cruikshank, 1972). With increasing
need and capability to quantitatively determine soil areas, the
question of purity of mapping units is recurrent.
In mining too, proper prediction of ore quantity and quality in an
area has long been a problem. Putting grades of samples on a histogram
will not portray their location nor their continuity (Matheron, 1963).
Matheron further stressed that comnon statistics are not able to
quantify the spatial aspect which is precisely its most important
feature.
Thus, a key factor in soil management is soil variability. Whether
quantifying pedogenesis, designing map units, determining crop response
to a given set of management system, or identifying soil property-plant
growth response, a knowledge of soil variability is fundamental
(Wilding and Drees, 1983). The soil is not static and seldom, if ever,
exhibits clear-cut boundries (Davis, 1936).
Lee et al., 1974, broadly attributed sources of soil
variation to the field and laboratory. In the field, time and space
are important factors, while in the laboratory, subsampling and the analytical method used are important. Field error was the greatest of the two errors. Spatial and teitporal variation are again classified into systematic and random (Vander Zaag et al., 1980). Natural systonatic spatial variability is listed as a function of landforms/ geomorphic elements/ soil forming factors (chronosequences/ lithosequences/ toposequences/ biosequences and climatosequences) and the interactions of the above factors (Wilding and Drees/ 1971). This systematic spatial variability varies again with cultivated landscape.
Intensive farming/ intense grazing/ fertilizer afplication/ plowing and subsoiling/ drainage and irrigation tend to superimpose additional
heterogenity of soil chemical and physical properties (Beckett and
Webster/ 1971).
Random spatial variation was considered non-visual and unmappable.
Seasonal changes in soil temperature and soil moisture are examples of
systematic temporal variability and unpredictable weather exemplifies
randan temporal variability. In contrast to the latter/ the
systematic temporal variability is mappable (Vander Zaag et al./
1981). Of all these variations/ systematic spatial variability is
the most significant (Cameron et al./ 1971; Canpbell/ 1978) and
this is the main factor that has led mining engineers and geologists to
formulate methods of statistics called geostatistics (Clark/ 1982). Theory of Regionalised Variables
When a variable is distributed in space/ it is said to be regionalized (Journel and HuijbregtS/ 1978). A regionalized variable is an actual function/ taking a definite value in each point of space (Matheron/ 1963). This theory of regionalized variables was developed for estimating ores in deposits (Clark/ 1982). The
theory is based on the relationship between the variance of variables
of each pair of sample points and distance. Isotropically/ the graphs
obtained will normally reflect the zone of influence of each variable within a distance (David/ 1977). This will permit estimating the
minimum distance for independent samplings (Cambell/ 1978). They are
anisotropic when every direction shows dissimilarity in variability.
The theory also provides the theoretical background for the "kriging"
procedure which permits extrapolaticxi of sample values to unsampled
areas (Burgess and Webster/ 1980a/ 1980b). This theory can also be
used in other sciences including population density in demography/
rainfall measurement in pluviometry and harvest yield in agronony
(Joumel and HuijbregtS/ 1978).
The Theory of Regionalized Variables was broadly outlined by
Matheron/ 1963; Journel and HuijbregtS/ 1978; Clark/ 1982; Campbell/
1978 and Davis/ 1973. The main idea of this theory is to consider
distance from each sample point. It is assummed that the properties
found at one sample point are almost similar to those of its closest
neighboring point. As the sample points are placed further frcm the original point/ the less related they beccane. Distant points normally becane independent of each other (Clark/ 1982). A brief summary of the theory is given below.
A variance of the difference in value with distance could be formulated as:
2 Y(h) = 1 Z[z(x.) - z(x.+h.)]^ (1 )
^i where 2 Y(h) = variance of z at distance h^, z(x^)/ z(x^+h^) = values of z at location x^ and (xj^+h^)
= number of sample pairs at each distance of interval h^.
Then/ y (h) = 1 Z [z(x^)-z(xj^+h^)(2)
(2N.) and Y(h) is termed as semi-variance at distance h^.
Fran formula (2) above/ two variables of variance of properties against distance interval could be represented on a graph (figure 2 .1 ) known as a semi-variogram. Thus/ semi-variances are smaller at smaller distances implying more similarity than those further away. The range
is the distance over which pairs of observations are related. Beyond
this range/ the values becomes independent of each other. This means
that the semi-variance is constant at or greater than the range. The ufper horizontal line in which the graph levels off is the sill (Fig.
2.1). The semi-variograms of soil properties seldom follow the
idealized model (Fig. 2.1) where the curve passes through the origin
(Burgess and Webster/ 1980a/ 1980b; Campbell/ 1978). This is due to 10
Fig. 2.1 An ideal semi-variogram model where the ciirve passes through the origin (Adapted from Wilding and Drees, 1982).
Fig. 2.2 Linear, spherical and mitscherlich semi- variogram models (Adapted and modified from Wilding and Drees, 1982). 11
the nugget variance which arises because of variation at distances much shorter than the sampling interval. In this case/ the curve will not pass through the origin but intercept the Y axis between 0 and the sill
(Canpbell/ 1978)/ Fig. 2.2. Fig. 2.2 also shows that the semi-variance is the difference between the variance and the covariance (Hajrasuliha et al./ 1980). The most ccximon models often used in the curve fitting are the linear/ spherical and mitscherlich (Fig. 2.2). From
Vieira et al./ 1983/ the models can be expressed as:
a) linear y (h) = Co + Bh 0 < h < a
(h) = Co + Cl h > a
Co is the nugget effect/ Co + Cl is the sill and B is the slope, h
is the distance between each sample pair.
b) Spherical Y (h) = Co + Cl[(3(h)) - l(h^)]
2 a 2 a^
when 0 < h < a
Y (h) = Co + Cl when h > a
The spherical model is obtained by first selecting the nugget effect/
C^, and the sill value/ Co + Cl. a is the range of the
semi-variogram. The range is normally 3/2a' in which a' is a distance
where a line intercepting the y-axis at Co and tangent to the point
near the origin will reach the sill, a' = 2/3 a. The spherical model
behaves linearly up to approximately l/3a. 12
c) Mitscherlich:
Y (h) = Co + Cl[1-exp(-h/a^)] 0 Kriging is a term used in estimating or interpolating values for unsampled areas. In the procedure/ estimation of a particular point is based on other points surroimding it within the "neighborhood". A certain weight is given for each neighboring point/ with the nearest point receiving the heaviest weight. In general/ nearer points carry more weight than distant points/ points that occur in clusters carry less weight than lone points/ and points lying between the point to be interpolated and more distant points screen the distant points so that the latter have less weight than they would otherwise (Burgess and Webster/ 1981). As given by Clark/ 1982/ the linear estimation is as follows: T' = Wlgl + W2g2 + W3g3 +...... Wngn (3) where T' = estimated point value Wl/ W2/...Wn = weights assigned to each sample and gl/ g2 /...gn = values for n samples. 13 Equation (3) can also be written as: Z(Xo) = Z®AZ(Xi) (4) i=l "■ where n = number of sanple values/ Z(Xi) = estimates of sample values Z(Xo) = estimates of unsample points and X are the weights (Burgess and Webster/ 1980a). In estimating the point/ an error is created and is given as: E = T - T' (5) where T is the true value of the estimated point and T' is the kriged values. In order that kriging be the best linear estimator/ two constraints are set (Burgess and Webster/ 1981; Clark/ 1982). Firstly/ the estimation must be unbiased. This is expressed by the constraints: E[Y'o - Yo] = 0 (6 ) where Y'o is the estimated value at point 0/ and E[ ] indicates expected values. Secondly/ estimation variance should be a minimum. Estination variance [ (Y'o - Yo)] = minimum (7) From equation (5)/ E = t - T' Variance of the errors = a ^ E = average squared deviation from the mean error . 2 = average of (E - E) = average of E^ since E = 0 2 = average of (T - T') By simply considering two points/ for instance/ an unknown A and 14 point 1 / the estimation variance could be obtained by taking the value at point A/ subtracting the value at point 1 / squaring the result/ repeating the process over all possible pairs of such points and then averaging the values. E = 2ZX,Y(Xi/Xo) - z" Z^X X Y(Xi/Xj) i=li i=l j=i i j where (Xi/Xo) are semi-variances between observed locations Xi and kriged location Xo. (Xi/Xj) are semivariances between observed locations Xi and other sampling points Xj. X /X are the weights i j respectively (Burgess and Webster/1980a). Standard deviation could then be calculated from the variance obtained and the reliability of the sanpling could then be known. To determine the weight for each point/ the sum of the weights must be made equal to 1 for an unbiased estimator. Therefore/ an additional equation Z X = 1 has to be added. A Lagrangian multiplier/ U/ is added to ensure unbiasedness and hence optimal estimates. The weighted values could be obtained from matrix [A] and vector [B] comprising of (n+1 ) by (n+1 ) equations where n is the number of samples. 15 In matrix notation, (from Burgess and Webster, 1980), [A][X] = [B] [X] = [B][A]-1 where, [A] = (XI,XI) (X2,X1)..... (Xn,Xl) 1 (X1,X2) (X2,X2)..... (Xn,Xl) 1 (XI,Xn) (X2,Xn), (Xn,Xn) 1 1 1 1 0 (9) [B] = (XI,Xo) (X2,Xo) (Xn,Xo) 1 (10) X and [X] = (11) y 16 The matrix A is the semi-variances between the c3ata points XI to Xn and vector B is the semi-variances between the data points and the points to be estimated. All these conputations will be done using a cOTiputer. The weights obtained can then be substituted in equation (3) or (4) for the kriged values. The previous kriging procedure described is known as punctual kriging. These kriged data points can be presented on maps as isarittms/ by drawing line contours of equal property value. Sometimes/ the resulting contour maps tend to show discontinuities in the surface as a result of large nugget variance. These can be smoothened by block kriging. In block kriging/ interpolaticxi can be made for an unsampled area or region by determining its center Xo. The semi-variances between the data points and the interpolated point are replaced by the average semi-variances between the data points and all points in the region. From equation (10)/ each (Xi/Xo) in matrix B is replaced by the integral/ / (Xi/X)p(x)dx where i = 1 / 2 / /n p(x)= 1/Hv if X belongs to v Hv is a region V of area H or otherwise/ p(x) = 0 and p(x)dx =1 The coefficients for block kriging are given as: 17 = A~^ S, where S = / Y (Xl,X)p(x)dx / Y (X2,X)p(x)dx / Y (Xn,X)p(x)dx 1 Estimation variance of the area Hv is given as: (x,y)p(x)p(y)dxdy This method of analysis allows estimating the results of a sampling scheme without actually taking a map (Clark, 1982). Semi-variances may also be calculated as a function of time instead of distance which may identify how frequently a field should be sampled (Hajrasuliha et al., 1980). For kriging to be accurate, data should be collected at intervals which exploit the spatial dependence. If the nugget variance is large, punctual kriging may produce undesirable effects (Burgess and Webster, 1980). 18 Applications Using the geostatistical approach, a study of 3 sites in a seini-arid region at an 80 meter grid sampling was presented in Hajrasuliha et , (1980). At site 1, the semi-variogram revealed no variance structure suggesting that variation in soil salinity was randomly distributed and not spatially dependent. This might be due to small physical size of each sample and a large spacing between samples. At site 2, electrical conductivity measurements were spatially related at less than 800 meters. At another site, the semi-variogram did not show a rauige which revealed the discontinuity between adjacent seimples taken at distances less than 80 meters. This was assumed to be due to the spacing of the sampling points and errors in measurements. Using kriging values, several high salinity areas were located in the maps. This information was useful in optimizing a soil scimpling scheme. In the Island of Hawaii, the maximum variation was found to occur at large distances (Yost et al., 1982a). The zcxie of influence for exchangeable Ca, Mg, K, extractable Si, and P sorption were approximately 23-45 km at 0-15 cm depth. Parent material and rainfall were thought to have influenced these soil properties. The zone of influence for these variables was smaller for the subsoil, probably indicating less influence of rainfall, weathering reaction and organic material accumulation. The kriging estimates were discussed in their second paper (Yost et al., 1982b). Another study in Rwanda showed a range of more than 50 km for pH, Ca, effective CEC (ECEC), log Si and log NH4. The estimation variance was less when the neighbors were 19 uniformly distributed around the krige point suggesting a better collection of sanples on a uniform grid than in clusters or transects. These points hcwever, must fall within the zone spatial dependence (Van der Zaag ^ / 1981). Spatial variation of sand and measurements were studied on Ladysmith and Pawnee series in Eastern Kansas (Campbell/ 1978). The pH has a raindom variation in both areas. Maximum variation of sand content was observed within 30 m in Ladysmith series/ and 40 m in Pawnee series. In Zhang Wu County of North East China/ the soil pH showed the strongest spatial dependence with an exponential semi-variogram. Soil pH was a good indication of an alkaline zcxie in the valley of Liu River (Xu Jiyan and Webster/ 1984). Another study in Scotland indicated areas copper-deficient soil (containing less than 1 mg of soluble cofper/kg) (Mcbratney et al./ 1982). The copper deficient areas were associated with the Old Red Sandstone sediments and Fluvio-Glacial sands. A similar approach in evaluating soil spatial variability was used by Burgess and Webster/ 1980a; 1980b; Huddleston and Riecken/ 1973; Yost and Fox, 1983). In relation to plcuntS/ a study at Pinal Count/ Arizona/ showed petiole nitrates of cotton plants were spatially dependent (Tabor et al./ 1984). The semi-variograms and kriged maps of petiole nitrates suggested a strong influence of direction of rows of irrigation. A study on corn on a Tropeptic Eutrustox in Molokai/ Hawaii/ shews a significant correlation between leaf P and soil P (Trangmar/ 1982). The range of spatial dependence of soil P and leaf P is less than 15 m 20 X 8 m plots indicating marked heterogenity of nutrient concentration within each plot. Acid Sulfate Soils Acid sulfate soils are characterized by extremely low pH/ far lower than the normal highly leached soils of the tropics. The pH of some acid sulfate soils in Malaysia can be as low as 2.7 (Zahari et al./ 1982). Another prominent feature of these soils is the formation of yellow jarosite mottles on dry exposed soil surfaces and along root channels. Acid sulfate soils frequently occur in soils originating from marine clay deposits having highly sulfidic materials and low in bases (Ponnamperuma/ 1984). The soils occur in spotted areas/ often found in anaerobic coastal or estuarine areas and are normally influenced by tidal swamps (Bloomfield and Coulter/ 1973; Ponnamperuma/ 1984). In Malaysia/ most of acid sulfate soils have formed under tidal brackish water and waterlogged conditions together with high spring tides (Kanapathy/ 1976). Under these conditions/ the reduction process is enhanced resulting in pyrite formation. Ponammperuma/ 1984/ outlined the chanical process as follows: S0^^“ + IOh'*’ + 8e" = H2S + 4H2O H2S = h"^ + HS“ HS“ = H"^ + 2+ 2— Fe"^ + = FeS H2S + 1/202 = S + H2O 21 FeS + S = FeS2 (pyrite) The process involves the reduction of sulfate to hydrogen sulfide by Desulfovibrio and Desulfotomoculum bacteria/ precipitation of iron tnonosulfide/ oxidation of hydrogen sulfide to sulfur chemically and biochemically and the formation of pyrite. Mangrove swaitp vegetation is normally prohibited on this submerged soil. Bloonfield and Zahari/ 1982/ represented the sequence of the reduction as: Anaerobic Desulfovibrio desulfuricans 2- 2+ S + Fe'^ --- > FeS S^“ + Fe^"^ or O2 --- > S FeS + S --- > FeS^ On draining/ the processes are reversed. The pyrite (FeS2 ) will undergo oxidation producing sulfuric acid/ which causes the soil to become very acid. The end product normally formed is jarosite. The oxidation processes as described by Van Breemen/ 1982; Bloonfield and Zahari/ 1982 are as follows: FeS2 + 7/2 O 2 + H 2 O -- > Fe^"^ + 2S0^^“ + 2H"^ Upon corplete reduction/ the hydrolysis of iron to Fe (III) oxides yields 2 moles of sulfuric acid per mole of pyrite. FeS2 + I 5/4 O 2 + 7/2 H2O > Fe(0H)3 + 2S0^^“ + 4H"^ Chemoautotropic bacteria/ Bacillus ferroxidans enhance the oxidation process of pyrite. Thus/ 22 FeS 2 + 14Fe^'^ + 8 H2 O------> 15 Fe^"*" + 168"^ + 2S0^^ Otherwise/ Blaanfield and Coulter (1973)/ listed the oxidation process as follows; Initial chemical reaction in moist condition/ FeS2 + H2O + 70 = FeSO^ + H2SO4 Action of Thiobacillus ferroxidans: 2FeS0^ + O + H2S0^ = Fe2(804)3 ^2° On subsequent chemical reaction: Fe2(S04)3 + FeS2 =3FeS0^ + 28 28 + 6Fe2 (80^)3 + 8H2O = 12Fe80^ + 8H28O4 The pyrite is also oxidized by Fe^'*’ yielding 8 Fe82 + 2Fe^'*’ = 3Fe^"^ + 28° The 82 generated prcxnote the oxidation process by another bacteria species/ the Thiobaccillus thiooxidans. 8 + 30 + H2O = 2H''' + 80^^“ The oxidation of sulfur would decreases soil pH and bring more Fe into solution. Oxidation Products Most of the iron (Fell) is further oxidized to ferric oxides (Van Breemen/ 1982). However/ the reaction does not normally go to completion if there is sufficient calcium carbonate to neutralize the acid (Bloomfield and Coulter/ 1973). Most of the sulfate remains in solution or is ranoved by leaching. The remainder of the sulfate is 23 precipitated as jarosite and as basic aluminum sulfate and partly adsorbed, especially at low pH (Van Breemen, 1982). Jarosite is a pale yellow (2.5 - 5 Y 8/3 - 8/6 ) of [KFe2 (SO^)2 (OH)g] (Van Breemen, 1982) and is the most caimonly formed jarosite although Na and Hydronium can substitute for K and Al can substitute for Fe. This material can be seen on soil surfaces, along drainage canals during dry seasons and persists for many years (Breemen, 1982). pH The final pH of the soil depends on the amount of pyrite and the buffering capacity of the soil (Bloomfield and Coulter, 1973). The two sources of bases for neutralizing the acid are the minerals, mainly CaCO^ or seashells and the metal cations on the exchange complex (Bloomfield and Coulter, 1978; Foon, 1977). Normally an acid sulfate soil has a high pyrite content and is low in bases (Kanapathy, 1976). Exchangeable bases are renoved by acids and replaced by H, and spontaneously forming clays saturated with Al^^ ions (Coleman and Craig, 1961). Hydrogen ions are very weakly adsorbed relative to Al (Coulter, 1969). The source of exchangeable Al is mainly from the layer silicates (Bloomfield and Coulter, 1973). Froduction of sulfuric acid during the oxidation process may rapidly weather aluminum silicates such as feldspar, mica and smectites to alunite, jarosite, halloysite and amorphous silica and allc^hane-like phases (Keller et 24 al./ 1967). Some of the unneutralized acids will attack the clay minerals and release aluminum in great amounts which is toxic to plant roots and microorganism (Pons/ 1973). Usually/ however/ the pH of acid sulfate soils in coastal plains is too low to cause extensive silicate weathering (Van Breemen/ 1982). Effects on Plants Crops grown poorly on these soils because of high acidity and toxicities of Al/ Fe and semetimes Mn (Zahari et ^ . / 1982). The extractable Al can range frcm 5 to 24 cmol(Al^'*’)kg“^ of soil (Ting and Zahari/ 1976). Oil palm will tolerate acidity within the range of pH 4-5 (Turner/ 1981). At lower soil prfl the plants will frequently show multiple nutrient deficiencies/ frequently severe Mg and K deficiencies (Turner/ 1981)/ severe dessication/ and premature necrosis and stunted growth (Zahari et , 1982). An increased number of unopen oilpalm spears/ indicating moisture stress/ was also noted (Poon/ 1983; Turner/ 1981). Without proper ameliorative measures/ yields can be greatly affected and may not exceed 5 tons fresh fruit bunch/hectare per annum (Yeow et al./ 1977). After raising the water table/ the yield of oil palm has increased to 11.44 tons fresh fruit bunch/hectare (an increase of 36.3%) in a 4 year period (Zahari et al./ 1982). In the next two consecutive 4 year periods the yield improved further to an average of 17.79 tons fresh fruit bunch/ha/year. 25 Bloomfield and Zahari, 1982 stated that paddy rice plants were stunted in growth in areas of single cropping. The plants displayed Fe and Al toxicity symptoms and the yield ranged fran 0 to 1,680 kg/ha. However, under double cropping, the yield was improved probably due to the field being flooded most of the time and thus hindering oxidation. Yields of 7 to 8 ton/ha have been obtained frcm acid sulfate areas in Sekinchan area in Selangor (Paramananthan, 1978). 26 III. MATERIAL AND METHODS 3.1 Area Under Study The area to be studied is Kuala Selangor district in the North West Selangor Integrated Agricultural Development Project Area, Malaysia. The area extends from 3° 20' to 3° 50' north latitude and from 100° 50' to 101° 30' east longitude (Acton, 1966). The district covers an area of 52400 hectares (Ministry of Agriculture, Malaysia, 1982). The area is low-lying, below 15.25 m contour with few undulations in the southern part (Acton, 1966). The main river, the Sungai Selangor and smaller rivers, the Sungai Buloh and Sungai Tengi drain the area. The major crops are wetland rice in the lowlying areas, coconut near the coast and oil palm, rubber, cocoa and coffee on the higher land. Typical of the humid tropic climate, the area has a relatively high average rainfall of 1800 irm, about 83% relative humidity, mean annual temperature of 26°C and only small variation in the day length (World Bank Report, 1978). The distribution of annual rainfal is largely determined by the Northeast Monsoon (October-March) and some convectional rain during the March-April and Septent»er-November periods. The influence of the Southwest Monsoon (May-september) is weakened by the sheltering effect of North Sumatra. The driest months 27 are February-March and June-July and the wettest months are October-November and April-May although at times the dry and rainy periods are unreliable. Occasional extended flooding, due to heavy localised rainfall and overflowing of rivers has caused some dcimage to tree crops (Wbrld Bank Report, 1978). 3.2 The Soils The soils of the region are mainly Entisols and Inceptisols which have developed under marine or brackish water conditions (Paramananthan, pers. comm.). There are also organic muck and peat soils developed under inpeded drainge. The coastal plain is fringed by the Straits of Malacca, and the subsoil underlying the peat has the influence of marine depositions and high amount of sulfudic materials. Upon drainage, the soils can undergo oxidation and acid sulfate soils result depending on the amount of sulfidic material present and the degree of oxidation. 3.3 Soil Sampling A total area of 5100 hectares was selected in this study (Fig. 3.1). The area consists of oxidized (site I) and reduced (site II) potential acid sulfate soils. Site I of 3850 hectares comprised of Block I, II, and III. The soils in site I were sampled at about 2 km intervals along irregular transects and at 2 levels, 0-15 cm and 15-30 Fig. 3.1 Area under study in Kuala Selangor, Northwest Integrated Project area, West Malaysia. to 00 29 cm. There were 75 sampling sites (Fig. 3.2). Site II (1250 hectares) is a wetland rice area of Sawah Senpadan and Sungai Burong/ (Fig. 3.1). There were 58 sampling sites (Fig. 3.4) and sampling was carried out at three depths (0-15 cm, 15-30 cm, 60-90 cm). These data were obtained from the soil surveyors. Department of Agriculture, Malaysia. The same data were used to delineate the potential acid sulfate and the acid sulfate areas in the semi-detailed soil map of the area. The sanpling was done along irregular transects of 1.75 km at intervals of 0.75 km apart. The soil analyses done were the same as in the former area with additional analyses of fresh soil electrical conductivity and exchangeable Na. 3.4 Soil Analysis The study is based cxi the standard soil analysis of the Department of Agriculture, Malaysia. The following soil analyses were performed according to the procedure of Lim (1975): Soil pH (1:2.5 H2O) Twenty-five ml of distilled water were added to 10 g of air-dry soil and allowed to stand overnight. The samples were then shaken for 1 hour. The pH was measured by inserting the electrode of a pH meter and agitating the beaker until a steady reading was obtained. Fig. 3.2 Soil sampling sites (75), site I, (Block I, II, III), Kuala Selangor. 31 Electrical Conductivity One hundred and fifty ml of distilled water were added to 30 g of 2 nri soils and shaken for 1 hour in an end-over-end shaker. The samples were allowed to stand overnight and the electrical conductivity was determined using a conductivity bridge. Percentage Soluble Sulfate Sulfate cind chloride were analysed if the electrical conductivity exceeded 400 dShi”^. The filtrate used in determining the electrical conductivity was also used for sulfate and chloride determination. An aliquot (5 ml if E.C. > 2500 dSirn"^/ 10 ml if E.C. is 1000-2,500 dSm~^ and 25 ml if E.C. < 1000 dSm”^) was heated to boiling with 4 ml of 50% HCl solution. Ten ml of 10% Barium chloride solution was added to precipitate the sulfate. The solution was boiled for 5 minutes and allowed to cool overnight. The filtered solution was then tested with barium chloride crystal for precipitate. If there was still precipitate the barium chloride precipitation procedure was repeated. Otherwise, the precipitate was washed with distilled water and placed in muffle furnace at 750°C for half an hour. After cooling overnight, the amount of barium sulfate precipitated out was calculated by weight difference. 32 Percentage Soluble Chloride An aliquot of the filtrate obtained from the soil suspension was used for the conductivity measurement. The suspension was filtered through a Whatman No. 1 paper. A few drops of 0.1 N nitric acid was added to flocculate the suspended particles if the filtrate was turbid and the solution was refiltered. An aliquot (the amount was also based on the conductivity measurement as in sulfate determination) was transfered to a 100 ml beaker. A drop of phenolphthalein was added and the pH was adjusted to 8.2 using sodium carbonate until pink color appeared. The color was removed by adding a drop of sulfuric acid. Any iron precipitate formed was filtered off. Eight drops of chromate indicator was then added and the solution was titrated with the standard silver nitrate solution until the first permanent red color appeared. A blank titration was prepared using the same volume of distilled water and subtracted frcm the reading. Percentage Organic Carbon by Walkley Black Method Walkley and Black's Rapid Titration method (Piper, 1950) was employed using 1 g of ground soil samples. The carbon present as organic matter in the soil was estimated by oxidizing it with a known excess of potassium dichromate in concentrated sulfuric acid. The utilized dichromate was back-titrated with a standard solution of ferrous ammonium sulfate using diphenylamine as an internal indicator. 33 An efficiency factor of 1.3 was considered in the calculation of organic carbon. The organic matter was obtained by multiplying the amount of carbon by 1.724. Total Nitrogen The soil was digested by the classical Kjeldahl method. The digested solution in which nearly all nitrogen was converted into ammoniacal cotpounds was determined colorimetrically or using an auto-nanalyzer. Acid Fluoride Soluble P by Bray and Kurtz No.2 Twenty ml of extracting solution (0.1 N HCl and 0.03 N NH^F with the pH adjusted to 1.8) was added to 2 g of air-dry 60-mesh sieved soil. The solution was shaken by wrist inversion for 1 minute and the suspension filtered through no. 2 Whatnan paper. Ten ml aliquot of the filtrate was used for color development by adding 7.5 ml of boric acid, 2 ml of molybdate solution and 0.4 ml of stannous chloride. The solution was shaked and diluted to 50 ml. The P sorption curve was as outlined by Lim (1975). 34 Exchangeable Cations of Ca, Mg and K Ten g air-dry soil samples were mixed with acid washed sand and were continously leached for 5 - 6 hours with 100 ml IN ammonium acetate (pH 7) in a batch system. Potassium was determined using flame photometer while Ca and Mg were determined using atomic absorption. Extractable Aluminum Ten g of air-dry 2 mm soil were mixed with acid washed sand were leached with 100 ml IN KCl solution for 3 hours. The aliquot was pipetted into a test tube and diluted to 25 ml with distilled water. Two ml of thioglycollic acid solution was added and mixed. Ten ml of aluminon reagent was added to the aliquot and mixed again. The test tube was heated in boiling water bath for exactly 16 minutes. After cooling for 1 1/2 to 2 hours, % of light tramsmittance was measured using a spectrophotometer. The extractable aluminum was determined by using aluminum standard solution standard curve as outlined by Lim (1975). Free Iron Oxide Free iron oxide was determined based on the reduction of ferric ions into ferrous ions by sodium dithionite and chelating of the soluble ferrous ions by sodium citrate. 35 Mechanical Analysis Mechanical analysis of of percentage clay, silt, fine sand and course sand was determined by the pipette method. 3.5 Foliar Sampling and Analysis of Oil Palm Oil palm leaves were also sampled in site I, at 44 sites where the soil samples were taken (Fig. 3.3). The 17th frond was selected from palms of more than 4 years old (Yeoh, 1975). Foliar analyses included percentage N, P, K, Mg, Mn (^iti), Cu (jpm), Zn (jpm) and B (ppm). N was extracted by standard micro-kjeldahl method and determined by Technicon auto-analyzer. Percentages of P, K, Ca, Mg and Fe were determined in 1 : 1 nitric acid-water extract of a dry-ashed sampled . Phosphorus and Fe was determined colorimetrically using auto-analyzer, K using a flame photometer while Ca and Mg were determined with an atomic absorption spectrophotometer. Trace elements of Mn, Cu, Zn and B were also determined in the wet-ashed samples. Manganese, Cu and Zn were determined using atomic absorption while B was determined colorimetrically using a spectrophotcxneter. 3.6 Statistical and Geostatistical Analysis Percentage sulfate, pH, electrical conductivity, exchangeable Aluminum (Van Breeman, 1976) and organic matter (Van de Kevie, 1972) 36 Fig. 3.3 Soil sampling sites (58), site II, wetland rice area, Kuala Selangor. Fig, 3.4 Foliar sampling sites of oil palm (44), site I, (Block I, II, III), Kuala Selangor. OJ 38 were expected to delineate the acid sulfate soils. Sane of the data were lognormally distributed and were log transformed prior to analysis. Correlation and regressiai analyses were used to examine variables related to soil acidity. Semi-variograms of pH, % soluble sulfate, electrical conductivity and extractable aluminum were constructed and the range of spatial dependence and sill determined. Appropriate semi-variogram models were fitted to the semi-variances for the kriging program in order to estimate the properties at unsampled locations. For isarithmic mapping, a punctual kriging procedure using the Fortran program was used to compute 177 kriged points (Fig. 3.5). All maps of punctual and estimation variance were computed using the Splot algorithm (Bridges and Becker, 1976). Semi-variograms of foliar analysis were also determined and isarithmic maps were plotted based on 177 kriged points for variables that showed some structure. Superimposing these maps on the isarithmic maps of soils in the area, the relationships between nutrient uptake and soil properties in the prcfclem areas were observed. Semi-variogram and isarithmic maps of soil variables from the flooded, reduced condition area (site II) were determined and the potential acid sulfate areas delineated. The isarithmic maps of this area were based on 50 kriged points (Fig. 3.6). Isarithms of fresh and dry soil pH might be useful in determining the potential acid sulfate soil and the acid sulfate soil. Isarithms of dry soil pH at different depths were compared to the areas of potential acid sulfate and acid sulfate soils delineated in the soil survey map. Fig. 3.5 Locations of 177 kriged points, site I, Kuala Selangor. OJ vo 40 Fig. 3.6 Location of 50 kriged points, site II, wetland rice area, Kuala Selangor. 41 IV. RESULTS AND DISCUSSION 4.1 SITE I 4.1.1 Soil Analyses Soil Description Analyses of soils of the area showed wide ranges of pH values, extractable aluminum, percentage soluble sulfate, electrical conductivity and clay in both the top and subsoil (Table 4.1). Several samples indicate of less than 4.0, soli±>le sulfate of more than 0.1 % and electrical conductivity values exceeding 400 dSm~^ indicating problem areas of acid sulfate soils. These factors are the main criteria used by the Department of Agriculture, Malaysia to identify acid sulfate areas besides the presence of jarosite. High levels of extractable aluminum in some of the areas are also frequently observed in acid sulfate soils. This is so because most soils developed from marine sediment especially along the west coast of West Malaysia which have more than 50 % 2:1 clay minerals such as smectites, vermiculites as well as mixed layer minerals (Paramananthan, 1978). 42 Table 4.1 Means and ranges of some soil chemical properties related to acidity, site 1, Kuala Selangor. Variables means^ minimum maximum standard distribution median deviation (0-15 an) pH 5.045 3.6 7.0 0.69 normal SO4 (%) 0.0094 trace 0.14 0.03 normal Extr. Al 7.899 trace 17.00 4.59 normal (onolkg” ) E.C. 112.00 16.00 1120.00 218.76 logarithnic (dSm”-^) clay (%) 52.36 12.10 79.40 12.64 normal (15-30 on) pH 4.85 3.3 6.50 0.70 normal SO4 (%) 0.0145 trace 0.239 0.042 normal Extr. Al^ 9.37 0.10 19.30 5.38 normal (onolkg" ) E.C. , 152.00 24.00 3040.00 394.37 logarithmic (dSm”-^) clay (%) 55.83 11.60 74.50 12.65 normal Median values are given for lognormally distributed data. E.C. - Electrical conductivity. 43 Statistical Analysis A correlation analyses showed significant correlation between soil pH, extractable aluminum, % soluble sulfate, electrical conductivity as well as % clay (Table 4.2). In many acid tropical soils, soil pH is dependent on the amount of unbuffered KCl extractable aluminum present which is indirectly a measure of the aluminum activity. Thus a significant negative correlation is evident between pH and exchangeable aluminum on both the topsoil and subsoil. Positive correlation between extractable aluminun and clay indicates that extractable aluminum present is also dependent on the amount of clay present which in turn affects the acidity of the soil. The significant negative correlation of % soluble sulfate with pH suggests the importance of ions in soil acidity. This was indicated by the regression of concentration (itmolkg”^) of H'*' ions on SO^^” ions (Table 4.3). The regression of both topsoil and subsoil pH on the individual variables of % sulfate, extractable aluminum and aluminum saturation was significant (Tables 4.4, 4.5). The regression coefficient relating pH to extractable aluminum was highly significant, indicating the role of exchangeable aluminum on soil acidity. Unlike many acid tropical soils, additional acidity of acid sulfate soils is due to the presence of sulfuric acid. The extremely acid condition as a result of oxidation of sulfides fiarther increases the extractable Al^'*’ resulting in marked decrease in pH. The degree of acidity also depends 44 Table 4.2 Correlation coefficients of some soil properties related to acidity, site 1, Kuala Selangor. 0 -1 5 an pH E.C. Extr. Al (dSm -‘■) (onolkg ) pH 1 -0 .4 5 -0 .2 9 -0 .5 0 6 (0.00 0 1) (0 .0 0 1 ) (0.00 0 1) E.C. -0 .4 5 1 0.80 - (dSm"^) (0.00 0 1) (0.00 0 1) clay -0 .3 3 0.31 - 0 .27 (%) (0 .0 0 4 ) (0 .0 0 7 ) (0.01 9 6) Exch. Ca 0.31 0.28 — -0 .5 7 ( onolkg” )(0 .0 0 7 ) (0 .0 1 5 ) (0.00 0 1) 15-30 on pH E.C. SO. Extr. Al (dSm"-*-) • (% f (onolkg” ) pH 1 -0 .3 0 -0 .3 5 -0 .5 5 (0 .0 0 9 ) (0 .0 0 2 ) (0.00 0 1) E.C. -0 .3 0 1 0.77 -0 .1 9 (dShi“ ) (0 .0 0 9 ) (0.00 0 1) (0.10 9 7) clay -0 .2 7 0.26 - 0.29 (%) (0.00 0 2) (0.00 1 1) (0.00 0 1) Exch. Ca 0.33 0.38 — -0 .4 8 8 (onolkg" )(0 .0 0 0 4 ) (0.00 1 0) (0.00 0 1) Values in parentheses indicate the probability of a chance occurrence of the statistics. Exch. Ca = Exchangeable Ca 45 Tcible 4.3 Regressicxi coefficients of sulfate ions and Hydrogen ions (ninolkg ), site 1, Kuala Selangor. 0-15 cm 2 Independent Dependent Intercepts Coefficient R Probability Variables Variables SO4 H-^ 0.01977 0.002759 0.0528 0.0488 ' 15-30 an SO4 H-" 0.03256 0.00691 0.1517 0.0006 46 Table 4.4 Regression analyses of soil pH with some variables related to acidity, site 1 , Kuala Selangor. 0-15 cm Equation Independent Intercepts Regression R^ Probability no. Variables Coefficient 1 . Extr. Al 5.68 0.075 0.25 0.0001 2 . % SO., Extr? Al % SO^ 5.83 -8.90 0.39 0.0002 Extr. Al 5.83 -0.084 0.39 0.0001 3. Aluminum 5.51 -0.007 0.08 0.012 Saturation (%) 4. % SO., Al Saturation (%) % SO^ 5.75 -9.54 0.23 0.0004 Al 5.75 -0.009 0.23 0.0004 Saturation (%) 47 Contd. from Table 4.4 15-30 cm Equation Independent Intercept Regression R^ Probability No. Variables Coefficient 1 . Extr. Al 5.54 -0.072 0.30 0.0001 2 . % SO^/ Extr. Al % SO^ 5.67 -6.695 0.46 0.0001 Extr. Al 5.67 -0.076 0.46 0.0001 3. Aluminum 5.36 -0.007 0.098 0.0006 saturation (%) 4. % SO^, Al saturation (%) % SO^ 5.66 -7.78 0.29 0.0001 Al 5.66 -0.01 0.29 0.0001 Saturation (%) 48 Table 4.5 Regression coefficients of scxne variables related to soil pH, site 1, Kuala Selangor. 0 - 15 cm Independent Regression Standard Probability Variables Coefficients Error Intercept 6.1467 0.2635 0.0000 Extr. Al -0.0836 0.01357 0.0000 Clay 0.0005 0.00527 0.9297 OM -0.0053 0.01344 0.692 SO4 2.6861 3.4727 0.4419 E.C. -0.0019 0.00047 0.0002 15 - 30 cm Independent Regression Standard Probability Variable Coefficient Error Intercept 5.9050 0.2836 0.0000 Extr. Al -0.0777 0.0126 0.0000 Clay -0.00084 0.0055 0.8795 OM -0.02029 0.01565 0.1992 SO4 -3.2572 2.3530 0.1708 E.C. -0.0005 0.00027 0.077 OM = organic matter E.C. = Electrical Conductivity 49 on the amount of bases in the soil. In the analyses, exchcingeable Ca was significantly negatively correlated with extractable aluminum and directly correlated with pH (Table 4.2). This probably is the result of displacement of exchangeable aluminum by exchangable calcium. As a result of the correlation analysis, soil frfl, % soluble sulfate, electrical conductivity and extractable aluminum were selected for structural analyses and kriging with the aim of delineating the problem areas. Spatial Analysis Using the theory of regionalized variables, each of the above properties was analyzed for spatial dependence. The semi-variances, Y (h) = 1/(2N) Z [z(x)-z(x+h)]^ were computed. The parameters of each of the variables are shown in Table 4.6. All the variables analyzed showed a small amount of anisotrc^y. Soil pH At 0-15 cm, the semi-variogram of pH tend to show a linear structure Y(h) = 0.1764 + 0.0815h although a few points were scattered (Fig. 4.1). At 15-30 cm, a linear model, Y(h) = 0.2555 + 0.07233h (Fig. 4.2) fit the data better than a spherical or mitscherlich model. The of each equation was significant at 0.01%. Large intercepts at both depths indicated large nugget variances implying that there 50 Table 4.6 Parameter estimates of isotropic semi-variograms of some chemical properties, site 1, Kuala Selangor. 1/ ^ variables depths range nugget sill % of sill— general model (cm) (km) variance variance ** pH 0 — 15 3.73 0.176 0.477 37.05 0.477 L2/ 0.37 ** 15 - 30 2.55 0.256 0.490 51.00 0.490 L 0.42 ** Extr. 0 15 3.37 6.118 21.71 48.00 21.08 S 0.48 Al ** 15 - 30 4.60 12.325 27.12 45.00 28.93 S 0.42 ** %S04 0 - 15 3.75 0.0003 - 17.64 0.00083 L 0.43 15 - 30 - - No structure - --- ** E.C. 0 - 15 3.36 0.570 0.799 377.45 0.78 S 0.35 15 - 30 6.92 0.299 0.991 57.54 0.94 s 0.46** V L - Linear S - Spherical nugget variance 2/ % sill X 100 sill ** Significant at <0.01 probability. ** 0. 5-1 r2=0.42 0. 4- S E N I 0. 3- V fl R 0.2- I R N C 0. 1- E 0.0- T— — I— — I— — I— — I— 0.0 0.5 1. 0 1.5 2.0 2. 5 OISTflNCE (KM) Fig. 4.1 Isotropic semi-variogram for pH, 0-15 cm depth, site I, Kuala Selangor. U1 0. 45- E 0. 35- M I V R R I fl 0. 25- N C E 0. 15- 5 " I------’------1------’------r ------^------1------*------1------■------T 0.0 0.5 1.0 1.5 2.0 2.5 3.0 DISTANCE (KM) Fig. 4.2 Isotropic semi-variogram for pH, 15-30 cm depth, site I, Kuala SeleUigor. U1 NJ 53 still is consic3erable variation at distances smaller than the sampling distances. Heavy liming which is normally done by broadcasting probably affects the topsoil more than the subsoil. Extractable Aluminum Semi-variograms of extractable aluminum for both topsoil and subsoil had less scatter than semi-variograms for soil pH. Spherical models were fitted to both the semi-variograms (Fig. 4.3, 4.4). The semi-variances for both the topsoil and the sx±isoil Al were Y(h) = 6.117 + 6.297h + 0.1523h^ and Y(h) = 12.325 + 4.8165h - 0.0757h^ respectively. At 0-15 cm, the fit near the ordinate was poor, nevertheless, the value was 0.48 and significant at 0.01% (75 samples). Extractable Al had greater range (4.60 km) in subsoil than in the ufper horizcxi (3.37 km). The range of spatial dependence of extractable Al was also greater than soil pH in the si±»soils. This result may indicate consideration of the use of the range of spatial dependence of soil extractable Al as a sampling interval in the area. Besides, lime requiranent of crops is determined by the levels of extractable Al. Soluble Sulfate The isotropic linear model gave the best fit for soli±>le sulfate 2 with R values of 0.427 (significant at 0.01%) for topsoil. A large 25.0- 22. 5- s 20.0- E M 17. 5- I - 15.0- V ft 12.5- R I 10. 0- fl N 7. 5- C E 5.0- 2.5- T---- •---- 1— —I------1--•— — ^------, ------p 0.0 0.8 1.6 2.4 3. 2 4.0 4. 8 DISTANCE (KM) Fig. 4.3 Isotropic semi-variogreun for extractable Al (cmolkg“l), 0-15 cm depth, site I, Kuala Selangor. IS DISTANCE (KM) Fig. 4.4 Isotropic semi-variogram for extractable Al, (cmolkg ), 15-30 cm depth, site I, Kuala Selangor. U1 VI 56 intercept and a moderate slope, Y (h) = 0.0003 + O.OOOllh, was obtained (Fig. 4.5). The range was similar to the range value of pH of the topsoil. At lower depths, the semi-variogram provides no structure or indication of spatial dependence. Sulfur accumulations in marine sediments in typical coastal plain soils of Malaysia apparently were randomly distributed. Electrical Conductivity For both the topsoil and the subsoil, the spherical model gave the best fit compared with the linear and mitscherlich models. The semi-variances are Y (h) = 0.299 + 0.223h - 0.00659h^ and Y(h) = 0.5709 + 0.09117h - 0.000633h^ for topsoil and subsoil respectively (Fig. 4.6, 4.7). The range was greater in subsoil (6.92 km) than the topsoil (3.36 km). Electrical conductivity is a measure of the dissolved salts especially of sulfates and chlorides. Sulfate and chloride contents were higher in the subsoil than in the topsoil in these areas . The sulfates and chlorides probably leached through the surface soil and accumulated at various depths in the lower soil layer. This may cause the range to be greater in the subsoil. The above soil chemical properties, soil pH, extractable Al, percent sulfate and electrical conductivity had nearly similar ranges in the topsoil suggesting the possibility they resulted frcm a similar process or set of reactions. At lower depths, extractable aluminum and electrical conductivity had the greatest ranges of spatial dependence. 0.0008- 0.0006- S E H I V 0.0004- ft R I R N C 0.0002- E 0.0000- -1 1------' T" T 1 2 3 4 DISTRNCE (KH) Fig. 4.5 Isotropic semi-variogram for soluble sulfate (%), 0-15 cm depth, site I, Kuala Selangor. (ji -J s E H I V R R I R N C E 10 DISTRNCE (KM) Fig. 4.6 Isotropic semi-variogram for log transformed values of electrical conductivity (dSm-1), 0-15 cm depth, site I, Kuala Selangor. cn 00 1. 05- ** r 2=0.A6 1. 00- 0. 95- s 0. 90- E M 0. 85- I - 0. 80- V R 0. 75- R I 0. 70- R N 0. 65- C E 0. 60- 0. 55- 0. 50- -r ~r -r- ~r T- -r — r 2 3 5 6 7 8 10 11 DISTHNCE (KM) Fig. 4.7 Isotropic semi-variogram for log transformed values of electrical conductivity (dSm“l), 15-30 cm depth, site I, Kuala Selangor. U1 VO 60 Electrical conductivity had the greatest range indicating much more spatial dependence in the property. However, the range soil was smallest. There was no spatial structure in percentage soluble sulfate. Perhap, the lack of structure in subsoil percent soluble sulfate may relate to the shorter range of spatial dependence of soil pH. Other factors such as temporal variability, vegetation, climate, geomorphic and human activity may have influenced the spatial variation of these properties. Kriging Although selected variables have nugget effects, most show important spatial dependence. Using a punctual kriging and computer graphics procedure, isarithm maps of both the topsoil and subsoil were prepared. These maps point out locations where these variables coincide with each other. Isarithms in these specific locations indicate coincident low and high extractable aluminum with high sulfate content and high electrical conductivity (Fig. 4.8, 4.9, 4.10, 4.11, 4.12, 4.13, 4.14). These are possible acid sulfate areas. Some of the pH values in these problem areas were greater than 4.0. Liming may have caused the pH to decrease. Extractable aluminum (15-30 cm) was plotted 3-dimensionally (Fig. 4.15). High values of extractable aluminum were observed in those problem areas. A marked decreased in extractable Al corresponded to the the largest river in the area, Sungai Selangor. The maps of estimation variance showed Fig. 4.8 Isarithm map of pH of 0-15 cm depth by punctual kriging from 75 observed values, site I, Kuala Selangor. to Fig. 4.10 Isarithm map of extractcible Al (cinolkg"^) of 0-15 can depth by punctual kriging from 75 observed values, site I, Kuala Selcuigor. cn LJ Fig. 4.11 Isarithm map of extractable Al (cmolkg”^) of 15-30 cm depth by punctual kriging from 74 observed values, site I, Kuala Selangor. Fig. 4.12 Isarithm map of soluble sulfate (%) of 0-15 cm depth by punctual kriging from 75 observed values, site I, Kuala Selangor. as U1 Fig. 4.13 Isarithm map of electrical conductivity (dSm~^) of 0-15 cm depth by punctual kriging from 75 observed values, site I, Kuala Selangor. Fig. 4.14 Isarithm map of electrical conductivity (dSm of 15-30 cm depth by punctual kriging from 74 observed values, site I, Kuala Selangor. o> -J 68 Fig. 4.15 Three dimentional diagram of extractable Al (cmol(+)kg“ ) by punctual kriged, 15-30 cm depth, site I, Kuala Selangor. The low values of Al^'*’ in the lower left coincided with the Selangor River terraces. 69 a decreasing reliability towards the coast (southwest) and northeast as expected because fewer samples were taken from those areas (Appendix A.l, A.2, A.3, A.4, A.5, A.6 , A.7). Kriging can be performed even if there is lack of structure in the semi-variograms. Higher nugget variances which are shown by higher 2 estimation variances are not translated by low R or lack of fit. Kriging provides a valid method of determining the degree of variation of the variable kriged (Yost, pers. ccxim.). A cross validation was computed by omitting a sample value, estimating it from the surrounding samples and then cortparing the estimated and the measured values. The kriged values displayed on the map will approximate the original values depending on the inherent variability and suitability of the semi-variogram model selected. A cross-validation was computed for soil pH (0-15 cm). The cross-validation mean square was 0.49 which was higher than the estimation variances suggesting that the estimation variances were under-estimated (Appendix A.l). This appears to be due to the rather poor fit of the semi-variogram equation to the semi-variances (Fig. 4.1). In particular, the linear model appears to have under-estimated the nugget variance in this case. Because the nugget variance forms a major component of the estimation variance it also appears to have been under-estimated. 70 Summary of soil properties of site I 1) Weak structure was apparent in the semi-variograms of sane variables especially pH and sulfate. The considerable variability in these properties may result from either less sampling points or the properties themselves do not exhibit much spatial structure in the variance. 2) Soil pH, extractable aluminum, percentage soluble sulfate and electrical conductivity together delineate the high acidity areas. Precision necessary for adequate interpretation will be given by kriging map. The confidence limits of the border line constraints can be measured by the estimation variance. Maps of these kriged variables can then be helpful in selecting representative soil sainpling sites to further specify acid sulfate areas. Use of such maps would be useful in exposing relationships between soils, topography and types of vegetation in the problem area. 3) In this specific area, extractable aluminum is an important variable to be considered in mapping soil constraints. This is because of its detrimental influence on plant growth and nutrition. Levels of extractable Al can also serve as an estimate of lime requirement. 4) Seme estimates of nai-oxidized sulfur conpounds (e.g. pyrite) or 71 total sulfur may help point out potential acid sulfate soils. 72 4.1.2 Oil Palm Analysis The nutrient status of oil palm (frond 17) of the area (Table 4.7) was compared with the proposed optimum levels of nutrient concentrations listed (Table 4.8). The mean nutrient concentrations of N, P, K, Ca, Mg, Fe, Cu, Zn and B were not greatly different from the reconrnended concentration for most nutrients when considering the standard deviation. However, seme of the nutrient contents were lower than the optimum level in areas with high soil acidity. Deterioration in yields of oil palm from such areas are particularly frequent during the dry season. Nutrient levels Nitrogen Nitrogen deficiency of oil palm is found in many soil types (Turner and Gillbanks, 1974). Nitrogen availability is largely influenced by the activities of soil orgcinisms that fix atmospheric nitrogen and those that break down organic matter such as dead fronds. Percent N in the frond was correlated positively with subsoil organic matter but was not correlated with topsoil organic matter (Table 4.9). Nitrogen probably would be supplied by the legxanes in most estates in the area. Cover crops such as Centrosema and Pueraria planted on acid sulfate soils with pH of 4.0 grow well if given adequate phosphate (Blocxnfield 73 Table 4.7 Means and ranges of nutrient concentrations in oil palm frond, Site 1, Kuala Selangor. No. variables mecin/ minimun maximum standard distribution median deviation 1 N (%) 2.527 2.04 3.01 0.242 normal 2 P (%) 0.148 0.114 0.173 0.010 logarithmic 3 K (%) 0.901 0.651 1.730 0.168 logarithmic 4 Ca (%) 0.485 0.254 0.900 0.159 logarithmic 5 Mg (%) 0.283 0.153 0.624 0.097 logarithmic 6 Mn T 384 78 826 192 logarithmic (mgkg“ ) 7 Fe_i 121 76 203 34.80 logarithmic (mgkg ■*■) 8 Cu_i 5.84 3.2 7.40 1.0026 normal (mgkg ) 9 14.6 10.7 19.70 2.327 logarithmic (mgkg^ - 1 ) 10 14.78 10.0 23.70 3.145 logarithmic (mgkg ) 74 Table 4.8 Nutrient concentrations of oil palm frond in Malaysia - dry matter basis. Nutrients Tentative optimum References levels N (%) 2.70-2.80 Rosenquist, (1966) 2.60-2.70 Coulter, (1958) P (%) 0.18-0.19 Rosenquist, (1966) 0.17 Coulter, (1958) 0.17-0.19 Kanapathy, (1§76)- (frond 9) data from DOA , Malaysia. K (%) 1.30 Rosenquist, (1966) 1 . 00- 1.20 Coulter, (1958) 1.20-1.40 Kanapathy, (1976)- (frond 9) data from DOA, Malaysia Ca (%) 0.30-0.70 Coulter, (1958) 0.30-0.50 Kanapathy, (1976) (frond 9) data from DOA, Malaysia Mg (%) 0.25-0.29 Coulter, (1958) 0.30-0.35 Rosenquist, (1966) 0.30-0.40 Kanapathy, (1976)- (frond 9) data from DOA, Malaysia. Fe (mgkg“^) 50-120 Coulter, (1958) Mn (mgkg”^) 200-350 Coulter, (1958) 150-200 Rosenquist, (1966) Cu (mgkg“^) 5-8 Rosenquist, (1966) Zn (mgkg ) 15-20 Rosenquist, (1966) B (mgkg-^) 10-20 Rosenquist, (1966) DOA - Department of Agriculture. 75 Table 4.9 Correlation coefficients of nutrient ccxicentrations of oil palm frond with soil nutrients, site 1, Kuala Selangor. 0 - 15 an pH SO4 Organic Clay Extr. Al Ca Mg K Fe natter frond P 0.26 -0.44 - - 0.27 (0.098) (0.003)1 (0.079) K -0.25 - - - 0.35 -0.27 - - - (0.099) (0.02) (0.08) Ca 0.44 - - -0.28 -0.44 ' - - - - (0.003) (0.069) (0.003) Mg - - - 0.26 -0.26 0.42 0.27 0.25 - (0.09) (0.09) (0.006) (0.08) (0.09) Mn - - -0.283 0.45 - 0.43 0.29 0.29 - (0.066) (0.004) (0.004)(0.064)(0.061) Fe -- -- -0.368 - - - (0.015) Cu - -- 0.31 - - - - 0.36 (0.042) (0.05) Zn - - - 0.31 - - - - 0.31 (0.048) (0.047) B — — — 0.42 — - - - 0.30 (0.0046) (0.05) Values in parentheses indicate the probability of chance occurrence of the statistics. 76 contd. (Table 4.9) 15 - 30 cm pH Organic P Clay Extr. Al Ca Mg Fe matter __ , , - - 1 mgkg"^ Firond N - 0.38 - - -0.25 -- (0.01) (0.1 1 ) P 0.32 -- - -0.26 0.31 - - (0.04) (0.09) (0.04) K -0.31 - -- 0.26 - -0.28 - (0.04) (0.09) (0.07) Ca 0.31 -0.30 - -0.30 -0.35 -- (0.04) (0.05) (0.05) (0.02) Mg - - - - -0.26 0.59 - (0.09) (0.0001) Mn -- -0.28 0.404 - 0.46 - (0.08) (0.008) (0.002) Cu - - -0.36 0.28 - - 0.37 (0.02) (0.07) (0.016) Zn - -- 0.33 - - - (0.034) B ——— 0.36 — - - - (0.02) Values in parentheses indicate the probability of chance occurrence of the statistics. 77 et al., 1968). Nevertheless, the lowest nutrient concentration of N in oil palm analyses was found in the high acidity areas; with subsoil pH of 3.9. The inhibition of microbial activity is probable in strcxigly acid soil ( restrict nitrification at low pH (Jackson, 1967). Phosphorus Low phosphorus availability is often associated with excess aluminum (Tisdale and Nelson, 1975) probably due to its tightly sorbed, especially in heavy clay soils of high aluminum or iron content (Turner and GillbanJcs, 1974). Adding fertilizer P to an acid soil can result in precipitation reactions between Al^'*’ and added P and the formation of insoluble Al-P compound (Coleman et al., 1960). Aluminum also inhibit the absorption of P euid seriously limits the growth of susceptible species (Rorison, 1973) because of precipitation of aluminum phosphate outside the roots and in intercellular spaces of the cortex. There was a significant negative correlation coefficient of P concentration in frond and soil extractable aluminum in the subsoil but there was no correlation with extractable alumintm in the topsoil (Table 4.9). A highly significant negative correlaticxi was also observed between topsoil soluble sulfate and P concentration in the frond. This perhaps was due to an indirect effect of acidity. No significant negative correlation was observed between subsoil soliable 78 Table 4.10 Regression Analysis of P cc»icentration in frond and soil factors that are related to soil acidity, site I, Kuala Selangor. Independent Regression Standard Probability Variables Coefficient Error Constant 0.1095 0.0286453 0.0006 pH (0-15 cm) 0.0023 0.0034991 0.5153 pH (15-30 cm) 0.0050 0.0039142 0.2084 Extr. Al (0-15 cm) 0.00033 0.0006486 0.6155 Extr. Al (15-30 cm) -0.00043 0.0005082 0.4055 SO^ (0-15 cm) -0.3152 0.1070 0.0059 SO^ (15-30 cm) 0.0866 0.1021 0.4026 E.C. (0-15 cm) 0.00003 0.0000236 0.2347 E.C. (15-30 cm) 0.0000003 0.0000181 0.9852 79 sulfate and frond P cx>ncentration. Only soluble sulfate in the topsoil was significantly negatively correlated with P concentration in the frond in regression analysis (Table 4.10). In the problem areas, it seemed that the frond concentration of phosphorus was not as low as might be expected (Table 4.7) although the lowest value was found in one of the areas of high acidity. One reason is that oil palm is an acid tolerant plant. Soils with pH between 4.0-5.0 can still support excellent palm growth (Turner, 1981). Differential Al tolerance of plant species was clodtely associated with the ability of plants to absorb and utilize P in the presence of excess aluminum (Foy and Brown, 1964). In their stixSy, Al-tolerant buckwheat was much more effective than Al-sensitive barley in absorbing P in acid Bladen soils or in nutrient solutions containing excess aluminum. Deep rooting species tolerant to high Al levels are also able to recycle leached Ca frcm the subsoil (Friesen et al., 1982). A positive correlation between concentration of frond phosphorus and calcium (Table 4.10) in the analysis may indicate another reason for the moderate levels of frond P. Application of lime which in turn increase soil Ca and Mg in the area might have neutralized the exchangeable aluminum thereby increasing the pH of the soil (Kamprath, 1970). Liming also increased organic P mineralization (Lathwell et al., 1979). 80 Potassium The mean K concentration in the frond was lower than the mean optimal level listed (Table 4.8). Acid sulfate soils are likely to be deficient in K (Rorison, 1973) because most bases are ranoved as sulfates and replaced cai the exchangeable coctplex by aluminum. However, differing results were observed (Table 4.9). There were negative correlations between K concentration in frond and pii of both the topsoil and subsoil (Table 4.9). Positive correlations were observed between K concentration in the frond and extractable Al of both the topsoil and si±>soil. Availability of K also depends on the amount and types of clay present. Potassium can be bound tightly to clay minerals of 2 :1 types which is the dominant clay in the area. Liming, too, may result in an important shift of solution K to exchangeable phase as pH increases (Adams, 1984). In Malaysia, potassium deficiency in oil palm can occur in almost all soil types because of heavy continued removal of fruit bunches from the field (Turner and Gillbanks, 1974). Calcium and Magnesium The mean concentration of frond Ca and Mg did not differ much from suggested optimal values in Table 4.8. However, Ca and Mg levels were lower in the problem areas. These cations were probably leached as CaSO^ and MgSO^ and were being replaced by aluminum ions on the 81 exchange complex (Bloomfield and Coulter 1973., Rorison, 1973), even though a considerable amount of ground magnesium limestone was applied in those areas to reduce acidity. Manganese Frond Mn levels in oil palm vary considerably, ranging from belcw 10 mgkg”^ in some seimples of frond 17 from the New Britain area to figures approaching 1000 rngkg”^ with an average figure of 200-400 mgkg”^ of dry matter (Turner and Gillbanks, 1974). Manganese is known to be soluble at pH values less than 5.5 (Black, 1967). This could relate to the manganese concentration observed (Table 4.7) which was higher in the extremely acid areas. High manganese can be neutralized by supplying lime (Kamprath, 1984., Suttai and Hallsworth, 1958). Positive correlations were observed between manganese concentration in the frond and soil calcium, and between potassium and magnesium (Table 4.9). Mn toxicity can also be ameliorated by the presence of high amount of silica (Peaslee and Frink, 1969), Al, Fe and NH^'*' (Jackson, 1967). A positive correlation was observed between frond Mn concentration and percent of clay which is predcminantly of the 2:1 type (Table 4.9). Manganese was negatively correlated with K and positively correlated with frond Mg, Cu, Zn and B (Table 4.11). 82 Table 4.11 Correlation coefficients between nutrient concentrations of oil palm frond, site I, Kuala Selangor. N P K Ca Mg Mn Fe Cu Zn B N 1 0.47 - - - - -0.29 (0.0009) (0.05) P - 1 - 0.35 - - -0.29 - (0.017) (0.053) K -- 1 -0.37 -0.58 - -0.34 0.38 (0 .012) (0 .0001) (0.021) (0.0102) Ca - 0.35 -0.37 1 - - - - - (0.02) (0.0 1) Mg -- - 1 0.57 - 0.36 0.38 (0 .0001) (0.0136) (0.0094) Mn -- -0.53 - - 1 - - - (0.0001) Cu ------1 0.47 - (0.001) B —— « • — — 0.37 - 0.34 0.39 (0.0 1) (0.02) (0.007)' Values in parentheses indicate the prdaability of chance occurence of the statistics. 83 Iron Iron toxicity is normally associated with flooded soil condition where the soil has high iron content. Iron can be the main cause of poor growth of wetland rice on acid sulfate soils (Tanaka and Navasero, 1966b). The toxic effects of iron appear to be due to the oxidised layer of Fe surrounding the roots which impedes nutrient absorption and thus would not likely be reflected in higher foliar iron levels. Ferric ircxi is not soluble unless the pH falls below 3.5 (Bloomfield and Coulter, 1973). This might account for the negative correlation between Fe concentration in plants and soil calcium (Table 4.9). The concentration of Fe (Table 4.7) was probably adequate (Table 4.8) although there were higher Fe concentrations in frcxids from sane areas of low pH. There were no significant correlations with other foliar nutrient concentration (Table 4.11). Copper and Zinc Ccpper and Zinc deficiencies are often associated with peat soils. Heavy applications of phosphate fertilizer or increasing soil organic matter both lead to induced zinc deficiency (Turner and Gillbanks, 1974). No correlation was observed between frond Cu and Zn concentrations with soil phosphate and organic matter. Positive correlations between frond Cu and Zn with percent of clay and iron in the topsoil were observed (Table 4.9). Copper and zinc concentrations 84 in the frond were probably adequate. Boron Boron availability largely depends on parent rocks and organic matter (Turner and Gillbanks, 1974). It is readily available fran clays of marine origin and shale but is tightly held by organic natter. There were positive correlation coefficients between concentration of boron in the frond and percent of clay from both the topsoil and subsoil (Table 4.9). Significant correlations were also observed with magnesium, potassium and manganese concentrations in the frond (Table 4.11). Spatial Analysis and Kriging The nutrients N, P, K, Ca, Cu, Zn and B did not show any structure. The lack of structure and high nugget effect in ttese nutrients was probably due to the small number of samples (44 sanpling points). Table 4.12 shows their semi-variogram parameter estimates. Only manganese and iron shewed strong spatial dependence. The spherical models of manganese and iron are given in Fig. 4.16 and 4.17. The equations are y h = 0.05804 + 0.1368h - 0.00725(h)^ and Y (h) = 0.01288 + 0.0546(h) - 0.006977(h)^ respectively. Range of spatial dependence of Mn (2.524 km) was almost similar to the range value of soil pH (2.55 km) at 15-30 cm. The range for Fe was much smaller 85 Table 4.12 Isotropic semi-variogram parameter estimates of nutrient concentrations of oil palm frond, site I, Kuala Selangor. Variables Range Nugget % of sill sample model R^ (km) variance sill variance of model N - No Structure - --- P - No Structure -- -- K - No Structure - - - - Ca - No Structure --- - Mg - No Structure - - - - *★ Fe 1.615 0.01288 17.97 0.0717 0.0718 S 0.64 Mn 2.524 0.05804 19.92 0.2193 0.3433 s 0.39* Cu - No Structure - - - - Zn - No Structure -- -- B- No Structure ———— Significant at <0.1 % probability. % Significant at <0.01 % probability. VO 00 0. 35- 0. 30- s E 0. 25- M I 0. 20- V R R 0. 15- I R N 0. 10- C E 0.05- 0. 00- -T------,------.------r- - r - —r 0 2 3 4 5 6 DISTANCE (KM) Fig. 4.16 Isotropic semi-variograun for log transformed values of frond Mn (mgkg“^) of oil palm, site I, Kuala Selangor. r~ CO S E M I H R I fl N DISTANCE (KM) Fig. 4.17 Isotropic semi-variogram for log transformed values of frond Fe (mg)cg~^) of oil palm, site I, Kuala Selangor. 88 (1.615 km). Sairple density was inadequate to develop useful isarithm of Fe and Mn. Suninary of the Nutrient Analysis A high negative correlation was observed between phosphorus concentration in the frond and percent sulfate in the soil. No correlation was observed between manganese concentration in frond and soil acidity. More samples are needed to determine whether other oil palm nutrient ccxicentrations shew spatial variability. 89 4.2 SITE II 4.2.1 Soil Analyses The reduced soil exhibited a large diversity in characteristics compared to oxidised soils (Site 1). Drainage system and types of crop grown in the area probably influence the development of chemical properties of the soil. The soil also had a wide range in pH values (fresh and dry), electrical conductivity, sulfate content and extractable aluminum (Table 4.13). Electrical conductivity and percentage soluble sulfate increased with increasing depth. The highest values were at 60-90 cm. Lower pH values and higher electrical conductivity were observed as the soil was dried. The heterogenity of the lowest horizon were more than the upper horizons as indicated by larger standard deviations. Correlation Matrix Electrical conductivity, both fresh and dry, extractable aluminum and clay were highly correlated with pH (fresh and dry) (Table 4.14). In contrast to soils of the oxidized area, percentage soluble sulfate was not highly correlated with pH. Normally, under reduced condition the sulfate is reduced to hydrogen sulfide. Electrical conductivity of both fresh and dry samples was significantly correlated with 90 Table 4.13 Means and ranges of some soil properties of site II, Kuala Selangor. 0-15 cm Variables No of Mean/ Standard Minimum1 Maximum Distribution Sairples Median Deviation F«(f)* 56 4.60 1.18 3.20 8.17 logarithmic pH(d)* 55 4.50 1.10 3.20 7.30 logarithmic E.C.(f)* 52 163.40 270.00 25.00 681.50 logarithmic E.C.(d)* 55 326.00 378.28 65.18 2023.00 logarithmic SO4 (%) 58 trace 0.0357 0.01 0.16 logarithmic Extr. Al 46 2.84 2.38 0.01 10.40 normal (cmolkg” ) Clay (%) 47 53.92 14.42 17.68 78.60 normal 15-30 cm Variables No of Mean/ Standard Minimum Maxiiraan Distribution Sanples Median Deviation pH(f) 57 4.6 1.10 2.90 8.00 logarithmic pH(d) 56 4.3 1.12 2.90 7.10 logarithmic E.C.(f) 51 246.0 383.16 21.00 2000.00 logarithmic E.C.(f) 55 445.0 790.96 39.00 4050.00 logarithmic SO4 (%) 57 trace 0.09 0.02 0.55 logarithmic Extr. Al 47 3.5 3.38 0.01 12.00 logarithmic (anolJcg“ ) clay (%) 48 59.65 11.44 22.87 75.70 normal Median values were used for logarithmic transformed data. *(f) for fresh soils, *(d) for dry soils and E.C. for electrical conductivity (dsm ). 91 contd. (Table 4.13). 60-90 cm Variables No of Mean/ Standard Minimum1 Maximum Distribution Sanples Median Deviaticxi pH(f)* 56 6.08 1.56 3.07 8.44 normal pH(d)* 55 4.71 1.52 2.20 7.63 normal E.C.(f) 52 609.00 1245.23 34.75 6040.00 logarithmic E.C.(d) 52 1880.00 2606.51 62.20 10500.00 logarithmic SO4 (%) 55 0.26 0.66 0.02 3.59 logarithmic Extr. Al 2.31 4.65 0.01 20.34 logarithmic (anolkg” Clay (%) 46 57.92 11.82 22.20 74.50 normal Median values were used for logarithmic transformed data. (f) for fresh soils, (d) for dry soils and E.C. for electrical conductivity (dSm” ). 92 Table 4.14 Correlation matrix of some variables related to acidity, site II, Kuala Selangor. 0-15 cm pH(f) pH(d) E.C.(f)_E.C.(d) Extr. Al Clay Fine Sand [anol)tg ) pH(f)* 1 0.94201 0.3416 0.3623 -0.7188 0.5596 -0.3659 (0.001) (0.0132)(0.0066) (0.0001) (0.0001) (0.0115) pH(d)* - 1 0.3759 0.3072 -0.7579 0.5733 -0.3740 (0.0066)(0.0225) (0.0001) (0.0022) (0.0105) E.C.(f) - - 1 0.6882 0.3796 0.4545 -0.4859 (0.0001) (0.0132) (0.0020) (0.0010) E.C.(f) - - 1 - 0.3249 -0.3836 (0.0279) (0.0085) SO4 - — 0.4362 0.8483 — -- (0.0006)(0.0001) 15-30 cm pH(f) pH(d) E.C.(f) E-C.(d) Extr. Al Clay Fine Sand Q. pH(f) 1 0.9330 0.5037 0.3534 -0.5349 0.4736 -0.3177 (0.0001) (0.0002)(0.0081) (0.0001) (0.0007) (0.0278) pH(d) - 1 0.5468 0.4115 -0.6160 0.5039 -0.3392 (0.0001)(0.0018) (0.0001) (0.0003) (0.0197) E.C.(f) - - • 1 0.9682 -0.5457 0.4373 -0.4233 (0.001) (0.0002) (0.0038) (0.0052) E.C.(d) - - 1 -0.3983 0.3272 -0.3191 (0.0067) (0.0265) (0.0306) SO4 (%) - — 0.8351 0.9273 - -- (0.0001)(0.0001) fresh soils, d = dry soils. 93 contd. (Table 4.14). 60-90 cm pH(f) pH(d) E.C.(f) E.C.(d) Extr. Al Clay Fine Sand (anolkg”^ pH(f)* 1 0.7251 0.6990 0.5964 0.6320 - -0.3680 (0.0001)(0.0001)(0.0001) (0.0001) (0.0119) pH(d)* - 1 0.5295 0.3826 -0.6589 0.2596 -0.2771 (0.0001)(0.0051) (0.0001) (0.0850) (0.0653) E.C.(f) - - 1 0.6740 -0.3102 - - (0.0001) (0.0514) E.C.(d) - - 1 - -0.2604 - (0.0917) SO4 (%) - — 0.3318 0.6658 -0.3151 — — (0.0318)(0.0001) (0.0842) ★ ★ f = fresh soils, d = dry soils 94 Table 4.15 Probability values in regression analyses of electrical conductivity (dependent variables) against sane soluble salts, site II, Kuala Selangor. Independent E.C.(f)* E.C.(d)* E.C.(f) E.C.(d) E.C.(f) EC(d) Variables Depth (on) 0-15 0-15 15-30 15-30 60-90 60-90 Intercept 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 SO4 0.0067 0.0001 0.0671 0.0001 0.0011 0.0001 Cl 0.6123 0.5247 0.0001 0.0001 0.0049 0.0001 Ca 0.0325 0.8246 0.5349 0.7424 0.4187 0.4073 Mg 0.0227 0.5237 0.5576 0.6531 0.4059 0.8749 Na 0.0244 0.2891 0.4504 0.5470 0.0660 0.5144 K 0.0114 0.6710 0.6299 0.7274 0.2217 0.5164 Extr. Al 0.0825 0.0833 0.1097 0.0949 0.1091 0.6190 Sum 0.0266 0.5714 0.5395 0.6550 0.6567 0.7379 CEC 0.1047 0.3807 0.8613 0.3643 0.3317 0.6389 0.1056 0.8348 0.9794 ^2°5 0.9220 0.2324 0.9284 K2O 0.7210 0.3422 0.3892 0.6572 0.4036 0.8953 Sum = Sum of cations (Ca, Mg, K, Na) *f = dry soils, *d => fresh soils SO^ and Cl were expressed^as %, Ca, Mg, Na, K, Extr. Al, Sum, CEC were expressed as cmolkg , and and K2O as mgkg"-*-. 95 percentage soluble SO^, Cl, Ca, Mg, Na, K and sum of cations for the three depths. However, sulfate and chloride provide the highest significant correlation coefficient on regression analysis (Table 4.15). Spatial Analysis Only isotropic semi-variograms display structure in the variance. There was very little anisotropy which means that the spatial dependence of pH, extractable Al, electrical conductivity and percentage soluble sulfate was not directional. Semi-variogram parameter estimates for these variables are given in Teible 4.16. pH The ranges of the semi-variograms did not change much with depth (Table 4.16). The pH of dry soil (15-30 cm) had a smaller range value than the range of dry soil pH of the surface soil (2.99 km): and its value (0.39) is significant at 0.05 probability. At lower depths, 60-90 cm, the range of pH (fresh) increases to 3.27 km. This perhaps relates to the amount of clay present at each depth and their nature of deposition. The ranges of pH of dry soils increase with depths indicating more spatial dependence in lower horizons. The semi-variogram of fresh soil pH (0-15 cm) increased without a sill, suggesting nonstationarity. A linear equation of (h) = 0.00084 96 Table 4.16 Parameter estimates of isotropic semi-variograms of some chemical properties, site II, Kuala Selangor. Variables depths ranges sill nugget % of sill— general model (cm) (km) variance variance pH(f)* 0-15 2.60 0.0143 0.00084 5.89 0.046 l2/ 0.79** ** pH(d)* 3.45 0.0302 0.0047 15.56 0.0484 S 0.71 pH(f) 15-30 - No structure - 0.047 - - ** pH(d) 2.99 0.0418 0.0177 42.31 0.052 s 0.39 ** pH(f). 60-90 3.27 5.1529 0.3075 5.96 2.4513 s 0.83 pH(d) 4.86 2.1513 1.1279 52.43 2.317 s 0.83** Extr. Al 5.55 5.55 0.6211 11.19 5.55 L 0.94** (cmolkg“1 15-30 - No Structure - 11.42 - - 60-90 - No structure - 7.033 - - SO^ (%) 0-15 - No structure - 15.455 - - 15-30 3.06 17.360 8.6275 49.69 34.967 s 0.59** 60-90 — No stioicture — 39.96 -- = fresh soils d = dry soils V % of sill = nugget variance X 100 sill ^ L = Linear S = Spherical Significant at <0.01 probability 97 Contd. (Table 4.16} Variables depths ranges sill nugget % of silli^general model (cm) (km) variance variance Hit B C ( f ) * 0-15 2.6 0.9530 0.2009 21.08 0.953 Li/ 0.56 E C ( d ) * 5.70 0.8015 0.2796 34.88 0.8015 L 0.74** E x : ( f ) 15-30 4.80 1.4990 0.1547 10.32 1.4990 L 0.82** E C ( d ) - No Structure - 1.4119 - - B C ( f ) 60-90 4.80 2.0911 0.4952 23.68 2.0911 L 0.72** Ex : ( d ) - No structure - 1.868 - - f^ = fresh soils d = dry soils Significant at <0.01 probability. V % of sill = nugget variance x 100 sill 2/ L = Linear ~ S = Spherical 98 + 0.01425h was fitted (Fig. 4.18). Spherical mcxtels of pH (dry) 0-15 cm and 15-30 cm, pH at 60-90 cm both fresh and dry were Y(h) = 0.0047 + O.OllOh - 0.0003096 h^ (Fig. 4.19), Y (h) = 0.0177 + 0.01208h - 0.00045h^ (Fig. 4.20), Y(h) = 0.3075 + 0.8626h - 0.0269h^ (4.21), Y (h) = 1.1279 + 0.3157h -0.000445h^ (Fig. 4.22) respectively. Extractable Aluminum At the 0-15 cm depth, semi-variograms of extractable aluminum showed a linear trend of y (h) = 0.6211 + 1.2554h with a range of 5.5 km (Fig. 4.23). Unlike the oxidized area, extractable Al was spatially dependent over a large distances than was soil as indicated by its larger range. At greater depths, no structure was apparent in the semi-variograms of extractable Al. Percentage Soluble Sulfate Spatial dependence was observed only at the 15-30 cm depth. Other depths show random variation indicating no spatial structure. A spherical model of Y (h) = 8.6275 + 4.2799h - 0.1522h^ was fitted (Fig. 4.24). Electrical Ccxiductivity Linear equations were used to model the sani-variances of 0.040- 0. 035- 0.030- s E M 0.025- I V 0.020- A R I 0.015- A N C 0.010- E 0.005- 0.000- 1 1 1 1— — I— r 0.2 0.6 1.0 1.4 1.8 2. 2 2.6 DISTANCE (KM) Fig. 4.18 Isotropic semi-variogram for pH of fresh soil, 0-15 cm depth, site II, Kuala Selangor. vn The values were log transformed. 0. 04- 0.03- S E H I V 0. 02- ft R I R N C 0.01- E 0. 00- r- -T" -r 0 2 3 DISTANCE (KM) Fig. 4.19 Isotropic semi-variogram for pH of dry soil, 0-15 cm depth, site II, Kuala Selangor. The values were log transformed. o o 0. D6D- D. D55- D. D50- 2 ** s D.D45- R =0.39 E M D.D4D- I - D. D35- V A D. D3D- R I D. D25- A N D.D2D- C E D.D15- D.DID- D.DD5- -r- -r- -r —r 2 3 4 5 DISTANCE (KM) Fig. 4.20 Isotropic semi-variogram for pH of dry soil, 15-30 cm depth, site II, Kuala Selangor. The values were log transformed. 2. 4-1 ♦ ♦ 2. 0- S E 1.6- M I V 1.2- fl R I R 0.8- N C E 0. 4- 0. 0- T- -T- -r -r 0 2 3 4 5 OISTRNCE (KM) Fig. 4.21 Isotropic semi-variogram for pH of fresh soil, 60-90 cm depth, site II, Kuala Selangor. o to 2. 4H 2. 0- S E 1. 6- M I V 1. 2- n R I fl 0. 6- N C E 0. 4- 0. 0- T------,------1------,------p - r - -r 0 2 3 4 5 6 OISTflNCE (KM) Fig. 4.22 Isotropic semi-variogram for pH of dry soil. 60-90 cm depth, site II, Kuala Selangor. OJo 5. 5-J 4.5- r2=0.94** 3 E H I 3.5- V R R 2. 5- I R N C 1.5 E 0.5- T- -r- r -T 2 3 4 5 OISTRNCE (KH) Fig. 4.23 Isotropic semi-variogram for extractable Al, (cmolkg”^), 0-15 cm depth, site II, Kuala Selangor. o 20-1 15-1 S E M I V 10- A R I A N C 5- E 0- T- 'T- -r- -r 0 2 3 4 DISTANCE (KH) Fig. 4.24 Isotropic semi-variogram for soluble SO (%), 15-30 cm depth, site II, Kuala SelangorT The values were log transformed. 106 electrical conductivity of fresh and dry soil. At the 0-15 an depth, the range of spatial dependence was smaller for electrical conductivity (fresh samples). At lower depths, (15-30, 60-90 on), there was no structure in the variance of electrical conductivity of dry soil. The fitted equations of semi-variograms of electrical conductivity for both fresh and dry soil (0-15 cm), electrical conductivity (fresh) at 15-30 cm and 60-90 cm depths were Y(h) = 0.2009 + 0.235h, Y (h) = 0.2796 + 0.1207h, Y(h) = 0.1548 + 0.2614h, Y (h) = 0.495 + 0.2744h respectively (Fig. 4.25, 4.26, 4.27, 4.28). Kriging Each property mentioned above was interpolated at 50 kriged points. Isarithmic maps were drawn for each property that showed spatial dependence. To the northeast of the region (Svingai Burong area), isarithms of high pH, both fresh and dry soil pH seemed to coincide well at the three depths (Fig. 4.29, 4.30, 4.31, 4.32, 4.33). At 0-15 cm, extractable aluminum coincides with pH. Low values of electrical conductivity and percent sulfate were, however, observed in this particular area at upper layers although at 60-90 cm, there was a 600 dSm”^ isarithm of electrical conductivity of fresh soil (Fig. 4.35, 4.36, 4.37, 4.38, 4.39). Sulfate salt can decrease under reduced conditions with the formation of hydrogen and iron sulfide. Most of the rice land in the area developed frcm shallow peat overlying the D. 9- 0.8- r2=0.56** s 0.7- E H D. 6- I 0. 5- V R 0. 4- R I 0. 3- R N 0. 2- C E 0. 1- 0. 0- 1------1------1------1------.------1------.------,------,------r — 0.2 0. 6 1. 0 1.4 1. 8 2.2 2.6 DISTRNCE (KN) Fig. 4.25 Isotropic semi-variogram for electrical conductivity (dSm ) of fresh soil, 0-15 cm depth, site II, Kuala Selangor. The values were log transformed. o 1. 2-1 0. 9- S E H I V 0. 6- H R I n N c 0. 3- E 0. 0- *r - i •------1------■------r -T" -r 2 3 4 5 6 DISTANCE (KM) Fig. 4.26 Isotropic semi-variogram for electrical conductivity (dSm~^) of dry soil, 0-15 cm depth, site II, Kuala Selangor. The values were log transformed. o 00 DISTANCE (KM) Fig. 4.27 Isotropic semi-variogram for electrical conductivity (dSm"^) of fresh soil, 15-30 cm depth, site II, Kuala Selangor. The values were log transformed. o 2.25- 2.00- s 1.75- E M 1.50- I 1.25- V A 1. 00- R I 0.75- A N 0. 50- C E 0. 25- 0. 00- -t- -r -r 2 4 5 DISTANCE (KM) Fig. 4.28 Isotropic semi-variogram for electrical conductivity (dSm“i) of fresh soil, 60-90 cm depth, site II, Kuala Selangor. The values were log transformed. Ill Fig. 4.29 Isarithm map of pH of fresh soil, 0-15 cm depth by punctual kriging from 56 observed values, site II, Kuala Selangor. 112 Fig. 4.30 Iseurithm map of pH of dry soil, 0-15 cm depth by punctual kriging from 55 observed values, site II. Kuala Selangor. 113 Fig. 4.31 Isarithm map of pH of fresh soil, 15-30 an depth by punctual kriging from 56 observed values, site II, Kuala Selcuigor. 114 Fig. 4.32 Isarithm map of of fresh soil, 60-90 cm depth by punctual kriging from 56 observed values, site II, Kuala Selangor. 115 Fig. 4.33 Isarithm map of pH of dry soil, 60-90 an depth by punctual kriging from 55 observed Vedues, site II, Kuala Selangor. 116 Fig. 4.34 Isarithm map of extractable Al (cmol(+)kg~^), 0-15 cm depth by punctual kriging from 46 observed values, site II, Kuala Selcuigor. 117 Fig. 4.35 Isarithm map of original values of soluble SO4 (%), 15-30 cm depth from 57 observed values, site II, Kuala Selangor. 118 Fig. 4.36 Isarithm map of electrical conductivity of fresh soil (dSm“^), 0-15 cm depth by punctual kriging from 52 observed values, site II, Kuala Selauigor. 119 Fig. 4.37 Isarithm map of electricad conductivity of dry soil (dSm“^), 0-15 cm depth by punctual kriging from 55 observed values, site II, Kuala Selangor. 120 Fig. 4.38 Isarithm map of electrical conductivity of fresh soil (dSm"^), 15-30 cm depth by punctual kriging from 51 observed values, site II, Kuala Selsmgor. 121 Fig. 4.39 Isarithm map of electrical conductivity of fresh soil (dSm“^), 60-90 cm depth by punctual kriging from 51 observed vad.ues, site II, Kuala Selangor. 122 sulfidic horizons. Peat, too, may cause the pH to decrease. The lack of structure and noncoincidence of electrical conductivity, percentage SO4 , extractable aluminum and pH may also be due to the extended sampling time. Samples were collected during a two year period. To the southeast (Sawah Senpadan area), the wetland rice area had relatively high pH of fresh soil with low electrical conductivity and low extractable aluminum at all depths. Nevertheless, isarithms of pH of dry soil were between 3.5 to 4.0 at 15-30 cm and 60-90 cm depths. Extractable aluminum (Fig. 4.34) and electrical conductivity of both fresh and dry soil and percentage sulfate were also low in this area. Maps of the estimation variance of electrical conductivity (fresh and dry soil) in these two areas were large suggesting great variation within short distances (Appendix A.14, A.15, A.16). The values of kriged pH of dry soil at 15-30 cm and 60-90 cm depths (Fig. 4.31, 4.33), suggest these two areas can be considered acid sulfate areas. Areas of acid sulfate soil and potential acid sulfate soil of semi-detailed soil survey map were superimposed an isarithm maps of pH at 15-30 cm and 60-90 cm depths (Fig. 4.40, 4.41). On the map of sanples from the 15-30 cm depth, (Fig. 4.40), most areas delineated as acid sulfate soils (acid sulfate layer between 0-50 cm depth) of the soil survey map did not coincide with isarithms of pH of 3.0-3.5. Even at 0-15 cm depth, kriged isarithms of pfl of fresh and dry soils with pH of less than 4 and high extractable Al were observed to the northwest of the area (Sungai Burong), indicating an extremely acid areas. The soil survey map, however, displays only anall isolated areas of acid 123 Fig. 4.40 Delineated areas of acid sulfate soil (Adapted from revised soil survey map, Department of Agriculture, West Malaysia) superimposed on isarithm map of pH of dry soil, 15-30 cm depth, site II, Kuala Selangor. 124 Fig. 4.41 Delineated areas of potential acid sulfate soil (Adapted from revised soil survey map. Department of Agriculture, West Malaysia) superimposed on isarithm map of pH of dry soil, 60-90 cm depth, site II, Kuala Selangor. 125 sulfate soil although these areas coincide approxinetely with the isarithms of pH 3.0-3.5. Lower sampling density in the Sungai Burong area could account for this omission which was reflected in the large estimation variances (Appendix A.8, A.9). This suggests that preliminary low density sampling followed by geostatistical analysis can indicate areas where additional sampling is likely to be beneficial. Another region to the southeast of the area (Sawah Sempadan) was also mapped as acid sulfate areas by the soil surveyors. However by kriging, only isarithms of pH of 4.0 were observed in the area. At 60-90 cm depth, (Fig. 4.41), isarithms of pH of less than 4.0 were observed at many areas towards the interior. Nevertheless, areas of potential acid sulfate soils of the soil survey map coincided with isarithms of pH of 3.5-4.0 in only some areas. Tcward the coast, percentage soluble sulfate and electrical conductivity of fresh and dry soils increased sharply with increasing pH. These areas were si±»jected to salinity with exchangeable Na reaching 40 cmol(+)kg“^ in some of the samples. Areas of acid sulfate soil and potential acid sulfate soils of the soil survey map were also observed in these areas of high pH and high electrical conductivity isarithms. It might be possible to have acid sulfate soil and potential acid sulfate soil because there was high sulfate content in these areas. Estimation variances of all variables were high (Appendix A.8, A.9, A.10, A.11, A.12, A.13, A.14, A.15, A.16) indicating high variability in pH of fresh and dry soils, extractable 126 aluminum and electrical conductivity. Unexpectedly, there was no marked decrease in pH of dry soils as the soil depth increased even though there was pyrite layer below. Apparently, the soil pH might not be effected in the presence of high salts. Thus, the delineated areas of both potential acid sulfate soil and acid sulfate soil by kriging and by soil survey did not coincide. One reason was the sampling schane. Sanpling points were not uniform which may cause large estimation variances in the kriging procedure at areas where there were fewer sanple points. A question may also arise in the efficiency of the criteria used to delineate the potential acid sulfate soil and the acid sulfate soil under reduced condition. Should potential acid sulfate areas be estimated by the the depth of pyrite layer or by soil constraints resulting fron acid sulfate soils or both ?. Another factor was that the area was under reduced condition. There were numerous factors especially in the chemical reactions which might not be reflected in the chemical analyses. Sunmary of soil properties of site II. 1) Areas of Sungai Burong and Sawah Sempadan appear to have potential acid sulfate soil. The areas indicated by the map of kriged estimates generally did not coincide with the areas indicated in the semi-detailed soil survey map, especially at 15-30 cm depth. Kriging maps also showed larger areas of 127 potential acid sulfate soils. High acidity areas were also indicated by the kriged map even in the topsoil. Low density of sampling points in the soil survey could be an important reason for this, as it might have resulted in a selection of nonrepresentative sample pedons used in delineating the soil series in the area. The geostatistical ajproach of interpolation can help uncover this type of error and indicate areas which require further sampling. Crop and soil contraints may also suggest an important criteria to be used in mapping the potential acid sulfate soil and the acid sulfate soil. 2) Soil pH of fresh cind dry sanples and extractable aluminum could be considered in indicating the probable acid sulfate areas on soils which have been partially oxidized. Extractable aluminum displayed little spatial structure in the lower layers which probably relates to localized reduced conditions and fil increases. At lower depths, low pH of dried soils suggested that the soil may become acid sulfate if drained. 3) Percentage soluble sulfate may not adequately indicate an acid sulfate soil if pyrite is present. Therefore, determination of total sulfur or pyrite may be more diagnostic. 4) The lack of spatial structure in seme semi-variograms and the noncoincidence of high extractable aluminum, electrical 128 conductivity and sulfate in certain areas may be <3ue to the extencted sampling pericxS of two years. 129 GENERAL SUMMARY Geostatistical theory can be used to analyze soil properties related to acid sulfate soils. Based on the geostatistical analyses carried out, the following conclusions were made. 1) Soil variables of pH, extractable Al, soluble SO^ and electrical conductivity displayed isotrcpic semi-variograms. There was no evidence of strong anisotropy, however the sanpling density was probably not adequate for a satisfactory test. 2) In areas which had been drained for several years (site I), the soil pH, extractable Al, electrical conductivity and percentage sulfate were used to map the acid sulfate areas. The isarithms of these properties generally coincided well with areas where evidence of acid sulfate soils had been observed. At 0-15 cm soil depth, extractable Al had almost the same range of spatial dependence as pH, electrical conductivity and soluble sulfate. At lower depth, the range of spatial dependence of extractable Al was greater than pH of the soil. Thus, fewer samples would be required to characterized extractable Al than pH. Extractable Al is likely to be an important constraint to crop growth on acid sulfate soils. Based on the range of spatial dependence of extractable Al at 15-30 cm depth, sampling should be no farther than 4.0 km apart. 130 3) Under wetland rice areas (site II), the time of sampling can be inportant. Chemical reactions differ depending on the degree of reduction or oxidation. Variables that relate to acid sulfate soils showed sane spatial structure but not at every depth. The variables of electrical conductivity and percentage sulfate did not coincide well with pH and extractable aluminum. Most isarithms of kriged pH of dry soil of less than 3.5 did not coincide well with the potential acid sulfate soil and acid sulfate soil delineated in the soil map. In Sungai Burong and Sawah Sempadan areas, the isarithms of kriged pH of 3.5 of dry soil at 0-15 cm, 15-30 cm and 60-90 cm depths, which are indications of potential acid sulfate soils were larger than the area delineated in the soil survey map. Only about 59% of the area kriged (pH of <4.0 of dry soil) coincided with the acid sulfate soil delineated in the soil survey map. Sanpling density may account for such a great contrast as indicated by the large estimation variances. Besides depth of pyrite layer, consideration of soil/crop constraint such as pH of fresh and dry soils and extractable Al may be helpful in delineating the acid sulfate and the potential acid sulfate soils. This also suggests that preliminary low density sanpling followed by the geostatistical analyses could indicate areas where high density samplings may be necessary to fully itep the acid sulfate areas. Coastal areas of site II displayed saline areas with high electrical conductivity and pH. Sulfate percent was also high but extractable Al was low. The soil survey map indicated sane areas of 131 potential acid sulfate soil and acid sulfate soil in the area where the kriged pH isarithms were high. The kriged isarithms of pH were high in both the topsoils and subsoils of the area. Afparently the pyrite/jarosite layer did not result in high acidity in areas with some influence of salinity. 4) Ability to predict whether reduced soil will become an acid sulfate soil would be a useful soil management tool. Determination of jarosite or reduced forms of sulfur rather than soluble sulfate might be more meaningful in determining the potential acid sulfate areas. This is because the percentage sulfate measures only S which is already in the oxidized form. Depth of pyrite layer and water table can be also useful. A measure of the difference between soil pH or electrical conductivity values in fresh soil and dried soil (after sulfur oxidation) might be a another approach. Soils that contain sulfidic materials will show a drop in pH of at least 0.5 unit to pH below 3.5 within 4 weeks if incubated as a 1 cm thick layers under moist aerobic conditions at room tonperature (Van Breeman, 1982). If isarithms of such measurements could be obtained, the potential of reduced soil areas to become an acid sulfate could be predicted. Then, the drainage program and crop suitability could be more appropriately designed. 5) Nutrient uptake and yield of crop could provide a useful spatial dependence that could relate to the acidity of the soils. Based on 132 the analyses done in the Kuala Selangor area, phosphorus concentration in the frond could be useful because it is significantly correlated with soluble soil sulfate. Closer spaced sanples would be necessary to further examine the spatial dependence of nutrient concentrations. Sampling time probably accounts for some of the lack of correlation because the concentrations of nutrient in plants vary with season. Sampling during the dry seascxi may indicate the extreme acidity of the soils. Thus, these data suggest that graphical display and geostatistical analysis of soil data Ccin be useful in at least two ways. In a new area, general trends in soil properties can be sampled and displayed as base information for the soil survey. In the second case, critical soil constraints can be sampled or calculated from soil survey data or specifically sampled to delineate location and extent of the soil constraint with a known precision. 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Abd. W^hab, C.C. Ting, and M. Abd. Rahim. 1982. Distribution, characterization and utilization of problem soil in Malaysia, p. 41-55. A country report in Tropical Agricultural Research Series No.15. 141 APPENDICES Appendix A.l Estimation variances of pH for isotropic punctual kriging, 0-15 cm depth, site I of Kuala Selangor. Isarithms in pH unit^. K) Appendix A.2 Estimation variances of pH for isotropic punctual kriging, 15-30 cm depth, site I of Kuala Selangor. Isarithms in pH units^. u> Appendix A.3 Estimation variances of extractable Al for isotropic punctual kriging, 0-15 cm depth, site I of Kuala Selangor. Isarithms in (cmol(+)kg“^)^. < < « Appendix A.4 Estimation variances of extractable Al for isotropic punctual kriging, 15-30 cm depth, site I of Kuala Selangor. Isarithms in (cmol(+)kg"^) , ii^ U1 Appendix A.5 Estimation variances of soluble SO4 for isotropic punctual kriging, 0-15 cm depth, site I of Kuala Selangor. Isarithms in (%)^. Appendix A . 6 Estimation variances of electrical conductivity for isotropic punctual kriging, 0-15 cm depth, site I of Kuala Selangor. 0^ Isarithms in (dSm“^)2, -J Appendix A.7 Estimation variances of electrical conductivity for isotropic punctual kriging, 15-30 cm depth, site I of Kuala Selangor. Isarithms in (dSm“l)2. 00 149 Appendix A.8 Estimation variances of pH of fresh soil for isotropic punctual kriging, 0-15 cm depth of site II, Kuala Selangor. Isarithms in pH units^. 150 Appendix A.9 Estimation variances of pH of dry soil for isotropic punctual kriging, 0-15 cm depth of site II, Kuala Selangor. Isarithms in pH units^. 151 Appendix A.10 Estimation variances of pH of dry soil for isotropic punctual kriging, 15-30 cm depth of site II, Kuala Selangor. Isarithms in pH unit^, 152 Appendix A.11 Estimation varieuices of pH of fresh soil for isotropic punctual kriging, 60-90 cm depth of site II, Kuala Selangor. Isarithms in pH units'” 153 Appendix A.12 Estimation varieuices of pH of dry soil for isotropic punctual kriging, 60-90 cm depth, site II, Kuala Selangor. Isarithms in pH units^. 154 Appendix A.13 Estimation variances of extractable Al for isotropic punctual kriging, 0-15 cm depth, site II, Kuala Selangor. Isarithms in (cmol(+)kg“^)^. 155 Appendix A.14 Estimation varieuices of electrical conductivity of fresh soil for isotropic punctual kriging, 0-15 cm depth, site II, Kuala Selangor. Isarithms in (dSm~^)2, 156 Appendix A.15 Estimation variances of electrical conductivity of dry soil for isotropic punctual kriging, 0-15 cm depth, site II, Kuala Selangor. Isarithms in (dSm“^)2. 157 Appendix A.16 Estimation variances of electrical conductivity of fresh soil for isotropic punctual kriging, 15-30 cm depth, site II, Kuala Selangor. Isarithms in (dSm-l)^. Appendix A.17 Isarithm map of exchangeable Ca (cmol(+)kg“^), 0-15 cm depth by punctual kriging from 76 observed values, site I, Kuala Selangor. tn 00 Appendix A.18 Isarithm map of original values of exchangeable Mg (cmol(+)kg"^), 0-15 cm depth from 76 observed values, site I, Kuala Selangor. VO Appendix A.19 Isarithm map of original values of percent clay, 0-15 cm depth from 76 observed values, site I, Kuala Selcingor. oO' Appendix A.20 Isarithm map of original values of percent clay, 15-30 cm depth from 75 observed values, site I, Kuala Selangor.