Open Journal of Soil Science, 2012, 2, 289-298
http://dx.doi.org/10.4236/ojss.2012.23035 Published Online September 2012 (http://www.SciRP.org/journal/ojss)
289
Nutrient Budget for Optimal Oil Palm (Elaeis guineensis
Jacq) Yield on Coastal Plai n Sands Soils of Akwa Ibom
State Nigeria
Jude Chukwuma Obi*, Bassey Thomas Udoh
Department of Soil Science, University of Uyo, Uyo, Nigeria.
Received May 11th, 2012; revised June 15th, 2012; accepted June 30th, 2012
ABSTRACT
The objective of the study was to establish approximate relationships between yield and soil nutrients in oil palm pro-
duction. The study was conducted in Nigerian Institute for Oil Palm Research (NIFOR) substation Ibesit ekoi in Oruk
Anam Local Government Area of Akwa Ibom State Nigeria. Soil, rainfall and yield data were collected from oil palm
plantation established 49, 29, 9 and 0 (control) years ago in an area underlain by coastal plain sands. Descriptive statis-
tics, analysis of variance and multiple stepwise regression analysis were used to study variations, effect of land use on
soil properties at different depths and contributions of various soil nutrients at different depths to the yield (fresh fruit
bunch ‘FFB’ and palm oil) of oil palm. Results of coefficient of variability revealed that approx. 45.5% of the variables
were highly variable including available phosphorus, extractable zinc, FFB and palm oil, while others were either least
or moderately variable. Oil palm trees influenced soil development with its effect on silt content at 30 - 60 cm depth.
Uptake of phosphorus in oil palm land use system decreases with depth. This was further confirmed by the relative con-
tribution of available phosphorus to FFB yield that decreased from the surface of the soil downwards. Extractable zinc
contents of oil palm land use were not significantly different from each other (ranging between 9.65 mg·kg–1and 7.84
mg·kg–1) but significantly different from the control (23.99 mg·kg–1). In the modeling process, it was observed that the
absolute contribution of texture was minimal while exchangeable sodium was highest (i.e. 66.5%) in the quantity of oil
palm production. Also extractable copper and zinc were found to have made large contributions to FFB and oil palm.
Oil palm (Elaeis guineensis) is a high-yielding source of edible and technical oils but requires proper knowledge and
precise administration of nutrient demands for management of a major production constraint which is soil fertility.
Keywords: Soil Nutrient Budget; Oil Palm; Micronutrients; Modeling; Soil Development
1. Introduction
It is generally agreed that the Oil palm (Elaeis guineensis)
originated in the tropical rain forest region of West Af-
rica. It posses high economic importance because it is a
high-yielding source of edible and technical oils. Oil
palm is now grown as a plantation crop in most countries
with high rainfall (minimum 1600 mm/yr) in tropical
climates within 10° of the equator. Hence, the oil palm
(Elaeis guineensis) is an important economic tree crops
in the tropics. The African oil palm, Elaeis guineensis
Jacq, is a member of the Arecaceae family along with
coconut and date palms. Oil palm is the world’s number
one fruit crop, according to Rieger [1], world production
is approximately 153,578,600 million ton, which is ap-
proximately twice any other fruit crop production. Oil
palm is produced in 42 countries worldwide on about 27
million acres. Average yields are 10,000 lbs/acre (i.e.
1126.761 kg·ha–1), and per acre yield of oil from African
oil palm is more than 4-folds that of any other oil crop.
The most important constraint to oil palm production
is soil fertility. It was estimated that more than 95 percent
of oil palms grown in Southeast Asia are on acid, low
fertility and highly weathered soils [2]. This was cor-
roborated in the study conducted by Imogie et al. [3] in
Bayelsa State of Nigeria. Soyebo, et al. [4] further ob-
served that approximately 93 percent of oil palms found
in Osun State (Nigeria) are in wild comprising mostly
degraded lands. Soil physical properties such as depth,
texture and structure are important factors in determining
suitability for large scale oil palm production [2]. These
are based on the required clay loam texture that imposes
friable consistency, capacity to permits extensive root
development, firm anchorage, and capacity to stores suf-
ficient water and plant nutrients. Majority of oil palm
*Corresponding author.

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
290
roots are found within the first 60 cm of the soil [2].
Therefore, these requisite soil conditions and nutrient
status that favour growth and development is indispensi-
ble within the 0 - 60 cm depth, yet the importance of firm
anchorage creates the need for deeper soils (greater than
90 cm).
Large quantity of dry matter is generated in oil palm
production. These comprised those in the fresh fruit
bunch and large quantities sequestered in the standing
biomass. Therefore, oil palm has an established high de-
mand for nutrients. These nutrients must be supplied as
amendments and in a suitable balance [5] for yields to be
maximized and environment sustained as they may not
be released on sustained basis in the soil. Ng [6] suggested
that nutrient budget of oil palm must be compared with
the soils capacity while designing fertilizer or nutrient
management scheme for economic production. For in-
stance, increased supply of nitrogen and potassium with-
out an adequate supply of magnesium on soils with a low
magnesium status can lead to the development of Orange
Frond symptoms in younger palms (a nutritional disorder
which later depresses growth and eventually yields). Ap-
propriate micronutrients in the nutrient budget will
equally enhance the efficiency of use of nitrogen (N),
phosphorus (P) and potassium (K) and also meet the
crop’s needs. The deficiency of micronutrients is the nu-
tritional disorders that manifest with common incidence
of chlorotic and desiccated leaves due copper (Cu) and
zinc (Zn) deficiency [7,8].
The objective of this study was to establish approxi-
mate relationships between yield and soil nutrients in oil
palm production.
2. Materials and Methods
2.1. Site Description
The study was conducted in Nigerian Institute for Oil
Palm Research (NIFOR) substation Ibesit ekoi in Oruk
Anam Local Government Area of Akwa Ibom State Ni-
geria. Oruk Anam Local Government Area is bounded
within latitudes 4˚45′ and 5˚00′N and longitudes 7˚30′
and 7˚45′E. The climate is humid tropical characterised
by distinct rainy (February/March-November) and dry
(November-February/March) season. Rainfall ranges
from 3000 mm along the Atlantic coast to 2000 mm in
the hinterland [9]. The mean daily temperature is about
29˚C, relative humidity approximately 80% and sunshine
approximately 5 hours per day but changes during the
year in response to changes in climate and possesses. The
overall topography is typical of unconsolidated marine
and fluvial deposit formation. The State falls within the
sedimentary areas of Nigeria with up to 80% of the soil
formed on coastal plain sands (>70%) and alluvium [9-
11]. Oruk Anam Local Government Area falls within the
area covered by coastal plain sands. Soils on coastal
plain sands are normally deep, dominantly sandy with
low clay, organic matter content and pH [12]. The soils
are well drained, deeply weathered and formed on sandy,
coarse-loamy materials, have udic moisture regime, iso-
hypertermic temperature regime and broadly classified as
ultisol.
2.2. Field Work and Laboratory Analysis
The study was carried out on oil palm plantation in NI-
FOR substation in 2010. The sampling scheme was de-
signed based on various land uses characterized by ages
of oil palm trees. These include oil palm trees established
between 1960 and 2000 and divided into three groups
separated at twenty years intervals. Namely 49 years, 29
years, 9 years and 0 year (control) which resided at the
accompanying fallow land. Mean yield data (Fresh Fruit
Bunch weight) and quantity of oil (palm oil) produced
therein (in kg·ha–1·yr–1 and litres·ha–1·yr–1 respectively)
for each of the three groups was collected. In each land
use, four replicate sample plots were demarcated (corre-
sponding to the upper, middle, lower and valley bottom
slope positions) and 10 composite soil samples were
randomly collected from 0 - 60 cm depth comprising 0 -
15 cm, 15 - 30 cm and 30 - 60 cm depths. The available
annual (1977 to 2010) rainfall data was collected from
the Uyo (the nearest) weather station which is approxi-
mately 25 km (as the crow flies) away from NIFOR sub-
station. Annual yield data (both for weight of fresh fruit
bunch (FFB) and the corresponding quantity of oil pro-
duced were collected for each of the oil palm trees age
groups (land uses).
The soil samples were air dried, pulverized and made
to pass through 2-mm mesh sieve. Particle size distribu-
tion was carried out through hydrometer method [13].
Organic carbon was determined by dichromate oxidation
[14] method. Soil pH was determined in a 1:2.5 (soil:
water) solution using pH meter [15]. Exchangeable bases,
available phosphorus (avail P) and micronutrients were
extracted with Mehlick No. 3 extraction [16]. Potassium
(K) and sodium (Na) content were read with the aid of
flame emission spectroscopy. Calcium (Ca2+) and mag-
nesium (Mg2+) and micronutrients (iron, zinc, copper and
manganese) were read with the aid of atomic absorption
spectroscopy (AAS), while total phosphorus was deter-
mined colorimetrically. Exchangeable acidity was ex-
tracted with un-buffered potassium chloride solution and
titration with 0.01 M-solution of sodium hydroxide to the
first permanent pink endpoint as described by Anderson
and Ingram [17]. Effective cation exchange capacity
(ECEC) was determined through summation [18].

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
291
2.3. Statistical Analysis
Data was collected in Randomized Complete Block De-
sign, therefore analysis of variance (ANOVA) was used
to study the effect of oil palm (i.e. land uses) on the soil
properties, while significantly different means were sepa-
rated using least significant difference (LSD) at 5%
probability level. Statistics of dispersion, normality of dis-
tribution [19] and measure of central tendencies were
carried out. Multiple stepwise regression analyses were
carried out with either fresh fruit bunch (FFB) or quantity
of palm oil as the dependent variable to model their rela-
tionship with soil nutrients as independent variables (ei-
ther micronutrient with particle size fraction (PSF) or
others with PSF in turns. All statistical analyses were
carried out using SAS [20].
3. Results and Discussion
The descriptive statistics of the data generated from the
study were shown in Table 1. This comprise mean, me-
dian and mode that measures location of central tendency,
skewness and kurtosis was used to estimate normality of
distribution while standard error and coefficient of varia-
tion were used as estimates of variability. The mean me-
dian and mode of the variable measured were similar.
Considering the levels of significances observed in uni-
variate normal distribution shown in Table 1, some vari-
ables were either skewed or kurtous (Pr < W) which was
based on Shipiro Wilk [19]. Even the non-normally dis-
tributed variables were not dominated by outliers and
therefore could be assumed to have originated from same
population. According to the Wilding [21] classification,
variables that had coefficient of variation (CV) greater
than 35% (i.e. neither least nor moderately, but highly
variable) included silt, (44.9%), organic carbon (57.6%),
available phosphorus (48.7%), electrical conductivity
(56%), exchangeable calcium (41.3%), exchangeable aci-
dity (39.0%), manganese (54.2%) and zinc (65.6%). In
addition are the Fresh fruit bunch and oil palm that had
coefficient of variation of 49.6% and 55.6% respectively.
Other variables including rainfall were either least vari-
able (<15%) or moderately variable (>15% < 35%). High
variability of available phosphorus and other properties
had been reported as common occurrence in soils espe-
cially due to intrinsic variation, land use and manage-
ment [22,23]. But the high CV of extractable zinc could
be of concern due to its important role in growth and
development of oil palm.
The properties of soil at various locations as influ-
enced by age of oil palm were compared. The particle
size fractions were dominated with sand fractions (coarse
and fine sand) with overall mean of approximately >836
g·kg–1 (Table 1 ). Coastal plain sands soils are character-
ised by dominance of sand fraction with coarse sand
contributing more than fine sand [12]. There were gener-
ally similarities in the distribution of the particle size
fractions (Table 2) and ratios of exchangeable bases
(Table 4) in the various land uses (i.e. ages of oil palm
including the control) at all depths with the exception of
silt content at the 60 cm depth. These were confirmations
that the soils of the study area are typically coastal plain
sands soils. The significant differences of silt content at
the 60 cm depth could be attributed to the effect of land
use on soil development [24] as this is the effective depth
for oil palm nutrition beyond which anchorage becomes
the important factor. Additionally it has been reported
that trees deepen soils, increases weathering and soil de-
velopment through their penetration and focusing mois-
ture flux, producing organic acids, facilitating microbial
activity and displacing loosened clasts [25].
The soil properties that vary significantly (p < 0.05)
among those indicated in Table 3 (i.e. excluding particle
size fraction (Table 2), ratios of exchangeable bases
(Table 4) and micronutrients (Table 5)) include avail-
able phosphorus at 15 cm and 30 cm depths, base satura-
tion at 30 cm and 60 cm depths, exchangeable sodium at
15 cm, 30 cm and 60 cm depths and exchangeable acidity
at 15 cm and 60 cm depths. The result in Table 3 indi-
cated that oil palm significantly influenced the available
phosphorus in the soil especially at 0 - 15 cm depth
which had the control containing higher amount of
available phosphorus. This effect repeated at 15 - 30 cm
depth, but with location (i.e. land use) that experience
longer period of oil palm production (49 years) possess-
ing significantly lower amount of available phosphorus
than the control. This effect decreases with depth as sig-
nificant difference was not observed at 30 - 60 cm depth.
Hence it could be inferred that utilization of phosphorus
in oil palm land use system decreases with depth. This
was further confirmed by the relative contribution of
available phosphorus to FFB that decreased from the
surface of the soil downwards and inverted in the corre-
sponding quantity of palm oil which decreased down-
wards (Table 7). Suggesting that the amount of available
phosphorus may need to be critically adjusted in the nu-
trient management scheme for acceptable balance be-
tween yield in FFB and corresponding palm oil produced
therein. In contrast to the available phosphorus, base
saturation at the control (79.1%) was significantly lower
than that at the oldest plantation (86.6%) at the 15 - 30
cm depth. But more typical is the scenario at the 30 - 60
cm depth that base saturation decreased from 49 years to
0 years (control). The ratios of the exchangeable bases
were not significantly different which still confirmed that
the soils were actually from similar parent material
[26-28] and that oil palm production or uptake has not

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
292
Table 1. Descriptive statistics of the soil properties in the palm plantation.
Mean Median Mode SE CV Skew Kurtosis Pr < W
Coarse Sand 454.9 444.0 444.0 1.31 20.01 −0.09 −0.71 0.578*
Fine Sand 381.7 380.0 320.0 1.21 22.02 0.19 −0.79 0.24*
Silt 47.6 41.0 41.0 0.31 44.9 0.80 2.17 0.00
Clay 115.4 115.0 155.0 0.56 33.74 0.31 −0.63 0.01*
pH (H2O) 5.77 5.76 5.76 0.02 2.46 1.02 1.84 0.00
Organic Carbon 1.05 0.95 1.40 0.89 57.6 0.18 −0.83 0.97*
Available Phosphorus 7.33 6.60 6.60 0.51 48.46 0.66 −0.28 0.03*
Base Saturation 81.34 81.50 81.10 0.97 8.23 −0.31 1.16 0.33*
Electrical Conductivity 0.01 0.01 0.01 0.001 55.95 2.97 10.99 0.00
Calcium 3.49 3.20 2.67 0.21 41.34 2.06 5.41 0.00
Magnesium 5.59 5.33 5.86 0.22 27.57 1.81 6.28 0.00
Potassium 0.06 0.06 0.05 0.00 27.24 −0.84 2.23 0.00
Sodium 0.05 0.05 0.04 0.00 14.11 0.66 −0.51 0.00
Exchangeable
Acidity 2.05 1.76 1.60 0.12 39.04 1.37 2.85 0.00
ECEC 11.22 10.75 - 0.45 27.64 1.55 3.39 0.00
Manganese 9.88 9.21 6.01 0.77 54.2 0.71 0.12 0.08*
Zinc 11.51 8.64 - 1.09 65.6 1.983 4.01 0.00
Copper 11.69 10.72 - 0.55 32.57 0.75 0.35 0.04*
Extractable
Iron 100.96 100.51 78.78 2.06 14.13 0.27 −0.31 0.28*
Fresh Fruit Bunch 333.67 296.00 261.00 47.83 49.63 0.39 −1.65 0.00
Palm oil (litres) 2207.01 1827.34 1995.70 354.31 55.61 0.52 −1.65 0.00
Rainfall 7861.13 7828.34 7997.73 186.57 9.49 0.18 −0.17 0.92*
*Neither significant at 1% nor 5%; SE: standard error; CV: coefficient of variation; Skew: skewness.
Table 2. Particle size fractions (g·kg–1) of soil of the palm plantation.
Age of Oil Palm Plantation (years)
0 9 29 49 LSD0.05
0 - 15 cm Depth
Coarse Sand 467.5 432.5 467.5 412.5 142.8
Fine Sand 335.0 430.0 395.0 395.0 116.0
Silt 66.8 46.0 39.5 56.8 50.8
Clay 100.8 91.0 98.0 135.8 62.5
15 - 30 cm Depth
Coarse Sand 504.0 389.0 402.5 299.3 326.8
Fine Sand 325.0 350.0 425.0 430.0 130.7
Silt 59.5 35.0 51.8 40.3 28.1
Clay 111.5 105.0 120.8 90.8 58.4
30 - 60 cm Depth
Coarse Sand 472.5 365.3 391.0 311.0 300.2
Fine Sand 360.0 486.0 395.0 541.0 347.0
Silt 44.5 60.3 37.5 76.0 38.3
Clay 123.0 183.5 176.5 147.0 127.7

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
293
Table 3. Some chemical properties of soil of the palm plantation.
Age of Oil Palm Plantation (Years)
0 9 29 49 LSD0.05
0 - 15 cm Depth
pH (H2O) 5.89 5.74 5.89 5.80 0.29
Organic Carbon 1.43 1.33 0.85 1.43 0.70
Available Phosphorus 7.35 3.87 3.85 3.20 2.73
Base Saturation 81.78 75.75 83.65 82.95 7.75
Electrical Conductivity 0.010 0.015 0.020 0.0125 0.02
Calcium 5.60 5.19 5.20 5.46 2.38
Magnesium 3.58 3.34 3.07 3.87 2.93
Potassium 0.075 0.055 0.070 0.057 0.03
Sodium 0.05 0.04 0.04 0.05 0.006
Exchangeable
Acidity 2.00 2.77 1.60 1.72 1.13
Effective Cation Exchange Capacity 11.19 11.38 9.97 11.16 5.82
15 - 30 cm Depth
pH (H2O) 5.78 5.70 5.88 5.72 0.23
Organic Carbon 1.10 1.10 0.60 1.13 0.87
Available Phosphorus 10.51 7.56 7.16 4.34 3.90
Base Saturation 79.08 74.05 82.98 86.55 7.26
Electrical Conductivity 0.010 0.010 0.0125 0.010 0.004
Calcium 5.60 4.93 5.20 7.86 3.35
Magnesium 3.60 2.94 3.20 5.34 2.86
Potassium 0.083 0.053 0.063 0.065 0.018
Sodium 0.05 0.04 0.04 0.05 0.004
Exchangeable
Acidity 2.48 2.76 1.72 1.92 1.10
Effective Cation Exchange Capacity 11.81 10.71 10.22 15.24 6.70
30 - 60 cm Depth
pH (H2O) 5.77 5.72 5.73 5.70 0.16
Organic Carbon 1.05 0.80 0.85 0.95 1.19
Available Phosphorus 11.00 10.69 9.11 9.34 4.92
Base Saturation 74.18 80.95 86.30 87.70 9.07
Electrical Conductivity 0.010 0.0175 0.020 0.0150 0.01
Calcium 4.93 5.28 5.06 6.80 1.96
Magnesium 2.80 3.07 2.94 4.14 1.70
Potassium 0.065 0.048 0.057 0.050 0.030
Sodium 0.05 0.04 0.04 0.05 0.004
Exchangeable
Acidity 2.69 1.92 1.34 1.72 0.93
Effective Cation Exchange Capacity 10.54 10.27 9.43 12.76 3.88

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
294
Table 4. Ratios of some exchangeable bases.
Age of Oil Palm Plantation (Years)
0 9 29 49 LSD0.05
0 - 15 cm Depth
Calcium/Magnesium 1.59 1.62 1.69 1.73 0.59
Calcium/Potassium 75.49 96.75 77.56 177.56 169.45
(Calcium + Magnesium) / Potassium 125.50 152.4 126.2 341.3 281.3
Magnesium + Potassium 48.14 62.69 46.13 108.93 97.63
15 - 30 cm Depth
Calcium/Magnesium 1.64 1.70 1.64 1.51 0.30
Calcium/Potassium 67.35 94.60 86.41 129.80 77.57
(Calcium + Magnesium) / Potassium 101.48 151.08 140.01 219.0 139.86
Magnesium + Potassium 43.12 56.48 53.60 89.22 62.51
30 - 60 cm Depth
Calcium/Magnesium 1.77 1.74 1.74 1.74 0.33
Calcium/Potassium 75.82 111.35 88.00 231.85 192.24
(Calcium + Magnesium) / Potassium 118.70 176.10 139.00 365.90 296.99
Magnesium + Potassium 42.90 64.74 51.02 134.04 105.33
Table 5. Extractable micronutrient contents (mg·kg–1) of soils of the palm plantation.
Age of Oil Palm Plantation (Years)
0 9 29 49 LSD0.05
0 - 15 cm Depth
Manganese 14.97 13.10 14.35 9.21 9.57
Zinc 12.42 8.49 14.52 7.26 8.44
Copper 9.29 9.54 13.07 15.17 6.71
Iron 94.34 99.40 111.32 107.16 24.40
15 - 30 cm Depth
Manganese 13.25 11.41 12.00 4.60 5.27
Zinc 23.99 9.65 8.30 7.84 10.08
Copper 8.76 10.29 12.32 15.84 3.48
Iron 96.89 96.31 107.67 104.75 24.41
30 - 60 cm Depth
Manganese 8.65 7.51 7.54 3.47 5.03
Zinc 21.27 9.43 6.46 8.48 11.36
Copper 9.35 9.75 11.49 15.39 4.73
Iron 92.77 86.65 104.09 110.20 22.43

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
295
Table 6. Regression equations for total weight (kg) of harvested oil palm bunches and corresponding quantity of oil (litres)
produced versus environmental characteristics
Dependent Independent
Micronutrients, Rainfall and Particle Size Fractions (PSF)
Total Weight of Bunches = 1308.163 + 0.075Mn(1) – 5.796Zn(2) + 48.751Cu(2) – 19.826Mn(3) – 9.408Fe(3) – 0.590Coarse sand(1)
(R2 = 0.98, p < 0.05).
Quantity of Palm Oil
= 3230.275 – 0.033Rainfall – 125.551Mn(1) – 34.031Fe(1) + 0.512Mn(2) + 380.597Cu(2) – 54.397Mn(3) –
43.801Fe(3) + 0.371Fine sand(1) – 2.736Coarse sand(1) + 5.180Clay(2) + 0.162Coarse sand(2) + 4.257Fine
sand(3) + 1.142Coarse sand(3)
(R2 = 0.99, p < 0.05)
Rainfall and Other Soil Properties
Total Weight of Bunches
= −1067.015 – 0.053Rainfall – 5.520Available phosphorus(1) + 10.913Available phosphorus(2) +
4.805Available phosphorus(3) – 4.730Base saturation(1) – 1.562Base saturation(2) – 2.345Base saturation(3)
+ 14.584Exchangeable acidity(1) – 779.269Electrial conductivity(1) + 13.597Effective cation exchange
capacity(3) + 25732Sodium(2) + 40.976Organic carbon(1) + 84.599 Organic carbon(2) + 0.601pH(3)
(R2 = 0.99, p < 0.05)
Quantity of Palm Oil
= 14865 + 0.280Rainfall – 249.682Available phosphorus(1) − 44.194Available phosphorus(2) + 4.221Base
saturation(2) + 42.750Exchangeable acidity(2) – 53923Electrical conductivity(1) – 21758Electrical
conductivity(3) – 41.701 Effective cation exchange capacity(1) + 9.792 Effective cation exchange
capacity(2) + 49.374 Effective cation exchange capacity(3) + 107905Sodium(1) – 287.107Organic carbon(2)
– 2999.668pH(1) – 15.773pH(3)
(R2 = 0.99, p < 0.05)
1, 2, 3 in parenthesis corresponds to 0 - 15 cm, 15 - 30 cm and 30 - 60 cm depths respectively.
Table 7. Absolute contribution of soil properties and rainfall to the modeling of palm oil production in the study area +.
Dependent (%) Dependent (%)
Independent FFB* Palm Oil
Independent Palm Oil FFB*
Micronutrients Other Soil Properties
Intercept 39.88 18.15 Intercept 14.61 38.16
Rainfall - 1.46 Rainfall 5.71 5.65
0 - 15 cm Depth 0 - 15 cm Depth
Manganese 0.03 9.10 pH(H2O) - 44.90
Iron - 19.70 Organic Carbon 0.71 0.00
15 - 30 cm Depth Available Phosphorus 0.35 2.93
Manganese - 0.03 Base Saturation 5.25 0.00
Zinc 2.20 - Electrical Conductivity 0.15 1.99
Copper 17.54 25.23 Exchangeable Sodium 66.50 0.00
30 - 60 cm Depth Effective Cation Exchange Capacity - 1.17
Manganese 4.11 2.08 15 - 30 cm Depth
Iron 28.23 - Organic Carbon 1.14 0.72
0 - 15 cm Depth Available Phosphorus 0.49 0.84
Coarse Sand 8.00 6.84 Base Saturation 1.73 0.87
Fine Sand - 0.81 Exchangeable Acidity 0.03 0.00
15 - 30 cm Depth Effective Cation Exchange Capacity - 0.30
Coarse Sand - 0.36 30 - 60 cm Depth
Clay - 3.11 pH (H2O) 0.05 0.23
30 – 60 cm Depth Available Phosphorus 0.66 0.00
Coarse Sand 2.47 Base Saturation 2.64 -
Fine Sand 10.65 Electrical Conductivity - 0.87
100 Effective Cation Exchange Capacity - 1.36
+variable that occurred in the table are those that influence the modelling process; *Fresh Fruit Bunch.

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
296
affected their distribution.
The contribution of micronutrients in the growth and
development of oil palm may be responsible for the sig-
nificant (p < 0.05) differences in manganese (Mn), zinc
(Zn) and copper (Cu) at 15 - 60 cm depth, and Fe at 30 -
60 cm (Table 5). The micronutrients at the surface (0 -
15 cm depth) soil were not significantly different at the
various age groups of the oil palm plantation. Whereas
soil on the 15 - 30 cm depth indicated that manganese
content at the control, 9 years and 29 years old oil palm
plantations were not significantly different from each
other. A more impressive result was found in zinc where
the effect of land use resulted in locations with oil palm
plantation not significantly different from each other
(ranging between 9.65 mg·kg–1 and 7.84 mg·kg–1) but
significantly different from the control (23.99 mg·kg–1).
This was a confirmation that zinc is required for proper
growth and development of oil palm. Additionally, the
effect of large biomass of oil palm that tend to sequester
large amount of soil nutrients and additional removals in
FFB contribute to locations with oil palm manifesting
lower nutrient status [5]. The reverse of the Zn trend was
observed in Cu content at 15 - 30 cm depth with the con-
trol and 9 years old oil palm plantation not significantly
different from each other. This may be an indication that
as oil palm gets older, it tends to release some of ex-
tractable copper previously sequestered in the dry matter
back to the soil. The trend of distribution of Mn, Zn and
Cu at the 15 - 30 cm depth was replicated at the 30 - 60
cm depth. The only exception was in the distribution of
extractable iron which had not been found to have sig-
nificantly changed at other depths but 30 - 60 cm depth.
These typically indicated that different nutrients are ei-
ther required or taken up at different depths within the 0 -
60 cm depths reported as effective feeding depth for oil
palm roots [2].
Multiple stepwise regression analysis (Table 6) was
used to separately model the relationship between the
soil properties measured at various depths and either
fresh fruit bunch (FFB) yield (kg·ha–1·yr–1) or the quan-
tity of palm oil generated therein (litres ha–1·yr–1). The
analyses were carried out in two stages for each yield
parameter (i.e. FFB and palm oil) and the results were as
shown in Table 6 . The first stage modeled micronutrients,
rainfall and particle size fractions (PSF) against either
FFB or palm oil, while the second stage modeled other
soil properties and rainfall. The discrimination was made
between micronutrients and other soil properties as a
result of the perceived importance of micronutrients in
oil palm production and to reduce the quantity of data
that will be used in modeling to a manageable size. This
is not without mindfulness of the fact that their availabi-
lity may influence each other. Therefore the soil proper-
ties that significantly contribute to the models were
brought together as the dimensionality had been drastic-
cally reduced. But it was impossible to establish in the
models that the variable could act together. Yet the inte-
gration of rainfall and PSF in the various stages of the
modeling process was in consideration of the importance
of PSF in solute transport [29] and that of water from the
rainfall especially in coastal plain sands soils [30-32].
Generally, rainfall was identified as an integral as it
manifested its importance in almost the entire models.
The contribution of rainfall manifested more in the other
soil properties and to a less extent in micronutrients. It
was observed that the relationship between micronutri-
ents and FFB largely depended on the manganese content
at the 0 - 15 cm and 30 - 60 cm depth, zinc and copper at
the 15 - 30 cm depth and coarse sand at the 0 - 15 cm
depth. The micronutrients present in the modeling of
quantity of palm oil included Mn (0 - 60 cm depth), Fe (0
- 15 cm and 30 - 60 cm depths), Cu (15 - 30 cm depth)
and particle size fractions. In consideration of other soil
properties, the importance of available phosphorus and
base saturation especially in the FFB and ECEC in the
quantity of palm oil was manifested in the modeling
process. In as much as the exchangeable bases were not
found to have significantly influenced production, the
participation of ECEC indicated that compound fertility
indicators are more important than their individual con-
tribution. The presence of negative signs in the models
indicated that in-as-much-as those nutrients were re-
quired, increases beyond a particular threshold that was
not determined in this study may be detrimental to the
performance of the crop.
The relative contribution of the different variables to
the model equation was computed as the mean values of
the variables (Table 1) multiplied by their coefficients in
the regression equation or model and expressed as the
percentage of the absolute total (i.e. irrespective of ac-
companying signs), their values will determine corre-
sponding contribution they made in the overall value of
the dependent variables. This means that the variable
which posses highest value will exert highest influence
either in increasing or decreasing yield. Table 7 indi-
cated that effects of PSF are minimal while exchangeable
sodium was highest in overall contribution (i.e. 66.5 per-
cent) in the quantity of oil palm production. Also ex-
tractable copper and zinc were found to have made large
contributions to FFB and oil palm. The unexplained (in-
tercept) was very similar and high in the FFB for the soil
properties indicative that the outcome of the modeling
activity may not be sporadic as the overall level of sig-
nificance was <5% and the model R2 for the processes
was >0.98.

Nutrient Budget for Optimal Oil Palm (Elaeis guineensis Jacq) Yield on Coastal Plain
Sands Soils of Akwa Ibom State Nigeria
297
4. Conclusion
The oil palm may have influenced soil development with
its effect on the silt content at the 30 - 60 cm depth. The
soil properties apart from micronutrients and texture that
vary significantly among the land uses are equally those
that significantly influence the modeling process. Land
use was found to significantly influence the micronutri-
ents in the soils. The importance of zinc and its removal
resulted in significantly higher zinc contents in control
compared to oil palm bearing soils. Sequestration of nu-
trients in large biomass and removals in FFB of oil palm
grossly diminishes soil nutrients and creates the need for
proper nutrient management in oil palm enterprises.
Notwithstanding pragmatic complexities that may be
involved, if locations (i.e. depth) and yield components
were adequately considered in the planning and man-
agement of nutrient supplementation, there may be in-
creases and minimal variability in the yield of fresh fruit
bunch and palm oil especially on coastal plain sands
soils.
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