CHAPTER FOUR - University Of Nigeria Nsukka

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CHAPTER FOUR - University Of Nigeria Nsukka
1
NWITE JAMES NTE
PG/Ph.D./2004/39086
EVALUATION OF THE PRODUCTIVITY OF A SPENT AUTOMOBILE OILCONTAMINATED SOIL AMENDED WITH ORGANIC WASTES IN ABAKALIKI,
SOUTH EASTERN NIGERIA
DEPARTMENT OF SOIL SCIENCE
FACULTY OF AGRICULTURE
Chukwuma Ugwuoke
Digitally Signed by: Content manager‟s Name
DN : CN = Webmaster‟s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
2i
EVALUATION OF THE PRODUCTIVITY OF A SPENT AUTOMOBILE OILCONTAMINATED SOIL AMENDED WITH ORGANIC WASTES IN ABAKALIKI,
SOUTHEASTERN NIGERIA
BY
NWITE JAMES NTE
B. AGRIC (ESUT), M.Sc (Nig)
PG/Ph.D/2004/39086
DEPARTMENT OF SOIL SCIENCE
UNIVERSITY OF NIGERIA, NSUKKA
APRIL, 2013
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CHAPTER O NE
1.0
INTRODUCTION
Soil is a primary recipient of a myriad of waste products and chemicals used in modern
industrial society (Brady and Weil, 2002). Modern industrialized societies have developed
plastics and plasticizers, automobiles and refrigerants, fuels and solvents, pesticides and
preservatives. Organic chemicals may enter the soil as contaminants in wastes applied on soils
or as fertilizer (Lauhanen et al., 2004), in large or small automobile oil and fuel leaks and as
sprays applied to control pests (Adesodun, 2004). Some of these wastes; fertilizers, automobile
oil, fuel leaks, pesticides, preservatives etc are toxic even in very small concentrations. Once
waste materials enter the soil, they become part of a biological cycle that affects all forms of life.
Contamination of a soil with toxic substances can degrade its capacity to provide habitat for
crops (Brady and Weil, 2002).
In Nigeria, the common sources of soil contamination are household wastes, agricultural
wastes, gas flaring and spent automobile oil. Soil and water contamination by crude oil is a
sensitive issue, particularly in the Niger-Delta areas (Anon, 1985). The impact of contamination
by spent automobile oil in the environment has been shown to be more widespread than
contamination by crude oil (Atuanya, 1987). For instance, Nigeria was reported to account for
more than 87 million litres of spent oil waste annually (Anon, 1985) and adequate attention has
not been given to its disposal (Anoliefo and Vwioko, 1994). Contamination of soil and
groundwater with spent automobile oil otherwise called “condemned” engine oil obtained after
servicing of automobiles, is a common phenomenon in the mechanic village, popularly known
as “site” in Abakaliki. The spent automobile oil is disposed off indiscriminately into the
surrounding environment by “motor mechanics”.
Reclamation of lands contaminated with waste organic materials coupled with enhanced
awareness of their potential adverse effects on the human and environment, has received
increasing international attention in recent years (Susan and Kelvin, 1993; NRC, 2002). Physical
and chemical methods most widely used for land treatment of oil-based waste have been
criticized as grossly inadequate and in-effective (Abu and Ogiji, 1996). Besides, these methods
could result in further contamination of the environment (Steven, 1991). Since oil degradation is
limited by temperature, pH, oxygen and scarcity of nutrients such as nitrogen and phosphorus
(Leahy and Colwell, 1990; Ladousse and Tramier, 1991), bioremediation of organic wastes has
been recommended (Atlas et al., 1991), as it is commonly accepted as the most efficient,
environmentally safe and cost-effective method of treatment of hydrocarbon contaminated soils
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with simultaneous introduction of nutrients in the form of organic matter to the contaminated
soil (Odookuma and Dickson, 2003).
There is the need to reclaim such soils using cheap amendments such as burnt rice husk
dust, fresh or unburnt rice husk dust and sawdust which, in the study area, are available to local
farmers. Even though, reclamation of crude-oil contaminated soils has increased, little or no
research has been carried out to reclaim the spent automobile oil-contaminated soils, which are
abundant in many cities (Odokuma and Dickson, 2003). The main objective of this study was to
evaluate the productivity of spent automobile oil - contaminated soil amended with organic
wastes.
The specific objectives of the work were to:
i.
assess the physicochemical properties of spent automobile oil-contaminated and
organic wastes amended soil;
ii.
quantify the productivity of spent automobile oil- contaminated and organic wastes
amended soil ;
iii.
evaluate maize grain yields of a spent automobile oil-contaminated and organic
wastes amended soil as well as;
iv.
make recommendation based on findings for improvement of a spent automobile oilcontaminated and organic wastes amended soil.
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CHAPTER TWO
2.0
2.1
LITERATURE REVIEW
CONTAMINANTS
The term contaminant could be defined as any physical, chemical, biological or
radiological substance or matter in an environment (air, water and land or soil) (Adesodun,
2004). Essentially, contaminants are substances introduced into the environments as a result of
natural or human induced activities regardless of whether or not the concentrations reached
levels that cause significant degradation or any harm (Adesodun, 2004). Contaminants include
pesticides, fertilizers, plastics, preservatives, automobile oil, solvents, emissions or discharges
from industries or agriculture etc. Pollution, on the other hand, results when contaminant
concentration reaches levels that are considered to be harmful (Freeze and Cherry, 1997).
Furthermore, a situation in which the concentration of a substance is higher than would naturally
occur also indicates the existence of contamination. Pollution occurs when soil contamination
continues to fix chemical elements by, say, the well-known phenomena of adsorption and
complexing (Lacatusu, 1998). Some examples of the common chemical contaminants are
organic substances (pesticides, solvents and petroleum hydrocarbons) and inorganic materials
(heavy metals and other trace elements). According to Orange County Health Care Agency
(OCHCA. 2002), used oil, spent solvents, cleaning compounds, discarded paints, by-products of
chemical processes and chemical formulations are regarded as chemical contaminants.
Furthermore, wastes that contain toxic substances in excess of specified concentration are
considered hazardous wastes. The terms toxic wastes, hazardous wastes and hazardous
substances refer to pollutants. The added distinction of „toxic‟ or „hazardous‟ is merely used for
substances that can be acutely or chronically toxic to humans (OCHCA, 2002).
2.2
Sources of contaminants
Contaminant sources vary widely but can be broadly classified as those originating from
anthropogenic sources or natural sources. For example, a volcano may place a greater quantity
of noxious substances and particulate matter into the atmosphere than the combined output
from a large number of electric power plants, although, some would not consider the output
from the volcano as a pollutant because of its natural origin (Pierzyneski et al., 2000). In
Abakaliki areas there are stone mining and quarry activities which release substantial quantities
of noxious gases and particulate matter into the atmosphere. Similarly, metal mining may
contaminate soils with heavy metals, yet soils with high concentration of heavy metals can
occur naturally because of their proximity to metal ore deposits (Adesodun, 2004).
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Potential sources of contaminants include both point and non-point sources. Point
sources are direct discharges or emissions from discrete sources, or identifiable point of
discharges such as outfall pipe, landfill, or spill point sources of pollution within urbanized
areas and include industries and municipalities which discharge directly into streams and rivers
as well as releases from chemical spills, and leaking underground storage tanks (Adesodun,
2004).
The chemical sources that are not specifically characterized which may encompass
numerous individual discharges or emissions, or an obvious single point of discharge are nonpoint sources which, according to Adesodun (2004), include agricultural, urban storm run-off
and construction sites as well as automobile emission (discharges). Non-point sources have the
potential to contribute significant pollutant load from run-off and the atmospheric deposition
into the river. It was estimated that over 99% of the polychlorinated biphenyls (PCBs) in the soil
and river were due to sediment re-suspension, volatilization, and/or dissolution of PCBs from
the sediments (Pierzynski et al., 2000). These processes also control the occurrence of other
organic and inorganic compounds within sediments and water.
Two important characteristics of pollutants in the environment are persistence and
residence time (Pierzynski et al., 2000). Persistence refers to the length of time a given pollutant
remains unmodified while present in the soil, water or air. It also means resistance of a
substance to break down into less complex substances by abiotic or biotic processes. This,
according to Pierzynski et al. (2000), sometimes quantifies a half-life term. Residence time, on
the other hand, is the length of time it takes a pollutant to move from one compartment to
another in the environment. Example of compartment would be the atmosphere, soil,
groundwater, surface waters, or any other location.
Persistence and residence time play major roles in pollution management and control,
since soils have varying capacities for adsorption of both inorganic and organic substances,
those substances that are not readily adsorbed by the soil will have a short residence time in the
soil environment. These substances will likely become groundwater contaminants, while
substances that cannot be easily broken down into simpler entities will be persistent regardless
of time. Residence is not an issue for substances that are not persistent (Pierzynski et al.,2000).
2.3
Fate of contaminant in the soil and its transport
Brady and Weil (2002) pointed out that the environment played a key role in the ultimate
fate and transport of contaminants. According to them, the specific fate of contaminants,
following their release into the environment, depends on their chemical structure, which is
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highly variable, abiotic factors within the receiving environment (e.g. organic carbon, pH,
surface water), and interaction with the biotic environment. The influence of pH is due, in part,
to increased competition between protons (H+) and metal ions for the same binding sites, and
through the formation of hydroxide complexes (Boulding, 1995). The redox potential
determines the oxidation state of the metals. Interaction with biotic environment to a great
extent determines fate of contaminants in soil.
Biotransformation refers to the alteration of compound due to the influence of living
organisms. It is the most prevalent process causing the break down of organic compounds in the
subsurface (Boulding,1995). Boulding (1995) pointed out that biodegradation was a more
specific term used to describe the biologically mediated change of a chemical into simpler
products, although, the simpler “daughter” products may be as toxic as or more toxic than the
original compound. For example, anaerobic biotransformation of tetra-and trichloroethylene
yields equally toxic or more persistent dichloroethylene and vinyl-chloride (Wood et al., 1995).
2.4
Anthropogenic sources of soil contamination
The contamination of agricultural land by toxic elements is usually caused by long-term
use of pesticides, and by the use of contaminated sewage sludge as a soil conditioner (Freedman,
1989). Freedman (1989) observed that the use of inorganic pesticides had been particularly
important in fruit orchards, where chemicals such as lead arsenate, calcium arsenate and copper
sulphate were used to control fungal pathogens and arthropods for more than a century.
Concentrations as high as 890 ppm Pb and 126 ppm As in the surface of soil grown with apple
(Malus pumila) orchards compared with background levels of these elements of <25 ppm and
<10 ppm, respectively had been reported by Frank et al. (1976).
Sewage sludge is a by-product of the secondary treatment of municipal sewage (Adesodun,
2004). Adesodun (2004) stated that sewage sludge had a favourable soil conditioning property
due to its high concentration of humified organic matter, and large concentration of nitrogen and
phosphorus. As a result, it is frequently disposed off by application to agricultural lands.
Unfortunately, most of this sewage sludge, especially those having a significant industrial input
contain considerable quantities of toxic elements (Freedman, 1989). Page (1974) reported that of
various elements, Cadium, Nickel, Copper and Zinc, were the most likely to cause phytotoxicity
when sludge was applied to agricultural lands.
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2.5
Oil pollution
Oils in general are relatively insoluble in water and are , therefore, associated primarily
with the particulate phase, especially, the organic matter (OM) (Means et al.,1980). The
contamination of soil and ground water with mineral oils, hydrocarbon or mineral oil-based
products, is among the most common negative effects of the industrial society (Adesodun,
2004). Adesodun (2004) noted that there were multiple cases ranging from production and
transportation of mineral oil in the upstream area to refining, transportation and trading of oilbased products in the downstream area. Petroleum hydrocarbons are common and widespread in
the environment, even to the extent of being carried on particles of dust (Gelpi et al.,1970). In
Nigeria, the phrase “oil pollution” has become synonymous with pollution of the environment
with crude oil, its refined oil and its refined products (Odu, 1996).
Umoh (2004) pointed out that crude oil exploration and exploitation activities in the
Niger Delta had resulted in pollution hazards ranging from accidental oil spillage to oil pipeline
vandalization. He emphasized that oil spillage often led to fire disaster and large-scale
enviromental destruction. According to the official estimates of the Nigerian National Petroleum
Corporation (NNPC, 2000), approximately 2,300 cubic metres of oil are spilled in 3000 separate
incidents annually. Major spill of more than 40,000 barrels of crude oil leaked from the pipeline
on shore terminal in Akwa Ibom State (NDDC, 2003).
World Bank (1996) reported about 75% of total gas production in Nigeria as flared,
while 95% of the “associated gas” was produced from a by-product of crude oil extraction from
reservoirs in which oil and gas are mixed. Flaring, particularly in Nigeria, contributes a
measureable percentage of the world‟s total emission of green house gases which, due to low
efficiency of the flares, result in the release of methane which has a high warming potential
(World Bank, 1996).
Akpe (2003) reported pollution of water-ways and farmlands through oil spillage and gas
flaring. He also emphasized that food security problem and low agricultural productivity were
major problems caused by spillage and gas flaring. Akpe (2003), observed that air, leaf and soil
temperatures were increased up to 80 0C or 100 0C, and there were changes in species
composition of vegetation (biodiversity).
2.5.1 Effect of Automobile oil on physical and chemical properties of soil
Amadi et al. (1996) reported the effect of heavy and moderate oil spill on soil properties
of rainforest ecosystem in Nigeria after 17 years of oil spillage. They found that the pH status of
the soils in contaminated zones varied from acidic (4.0) to near neutral (6.0) levels. Furthermore,
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the organic carbon content of the soils decreased from 3.6% at the heavy impact (HI) zones to
2.8% at the moderate impact (MI) zones. Total nitrogen (N) in the HI and MI zones differed by a
fraction of 0.10%. Available phosphorus (P) was higher at the MI than HI zones, while cation
exchange capacity (CEC) decreased from a mean of 6.48 at the HI zones to 4.46 at the MI zones.
The residual oil content at the HI zones showed persistence of high level of oil, about 788 times
higher than the biogenic threshold level for most tropical soils, inspite of the time lapse between
spill and the time of investigation. Their findings indicate that although organic C, total N, C/N
ratio, available P, exchangeable K and CEC were higher at the MI than HI zones, the treatment
effects were, nevertheless, not significant.
Amadi et al. (1996), concluded that indiscriminate disposal of oily wastes or spill might
lead to formation of oily scum. As a result, oily scum on soil surface would impede O2 and
water availability to biota, and anaerobic condition created in the subsoil would aid the
persistence of the oil.
Similarly, Udo (1984) discovered that soil properties usually underwent considerable
changes following pollution by oil. These changes, amongst others, included increase in water
holding capacity, loss of soil structure, exclusion of air which introduced reducing conditions
and the production of hydrogen sulphide.
Diana et al. (2004) noted that hydrocarbon oil contaminated soils had significantly
higher pH values and C: N ratios, and lower total nitrogen and available phosphorus than
uncontaminated soils. They attributed the higher C: N ratio in contaminated soil to lower
nitrogen rather than higher carbon. Similar findings were reported by Diana et al., (2004) who
noted that electrical conductivity (EC), total carbon, available potassium were significantly
lower in contaminated soils relative to uncontaminated soils. They further reported that total
petroleum hydrocarbons were low (49 to 64μg/g) and pointed out that the plots with the highest
concentration of hydrocarbons also had the highest C: N ratios.
Ogboghodo et al. (2001) noted that land degradation due to oil pollution could lead to
alteration of the physico-chemical properties of the soil such as soil structure, reduction in soil
permeability, surface sealing, compaction and decrease in macro-pores. They further pointed out
that infiltration rate, and hydraulic conductivity were affected by oil pollution. Odu (1981)
observed that oil pollution had less effect on soil physical properties such as texture than on the
chemical properties. Total nitrogen, organic carbon, exchangeable acidity, phosphorus,
exchangeable cations, carbon-nitrogen ratio and percentage base saturation of soil were reduced
by oil pollution (Ogboghodo et al., 2001).
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However, Asuquo et al. (2001) observed that organic carbon, total nitrogen, soil pH,
redox potential and hydrocarbon contents of soil increased with crude oil contamination. They
also noted that hydraulic conductivity increased with oil contamination of the soil.
2.5.2 Effect of Automobile oil on crop development
Nwankwo (1989) reported that the effect of oil on seed germination had been shown to
be inhibitry. Inhibitry is variously associated with tar-mat induced loss of vaibility (Rowell,
1977) or unfavourable soil conditions (Anoliefo and Vwioko, 1995). De Jong (1980) reported
that oils in soil created unsatisfactory condition, probably due to insufficient aeration of the soil
caused by displacement of air or the demand for oxygen by activities of micro-organisms (Gudin
and Syratt, 1975). Naegele (1974) observed that plants responded differently to pollution effect
due to an innate genetic response of the plant system as modified by environmental influences.
Oil penetrates and accumulates in plant thereby causing damage to all membranes and leakage
of cell contents (Baker, 1970). The growth of cereals is adversely affected in oil polluted soil
causing chlorosis of leaves and plant dehydration (Udo and Fayemi, 1975).
Amadi et al. (1993) in a greenhouse study on maize germination in oil-polluted soils
corroborated the findings of Schwindinger (1968), Udo and Fayemi (1975), and Mc Gill and
Nybory (1975) that increasing the concentration of oil beyond 3% in soil reduced
the
percentage germination, by oil coating on seed surfaces thereby affecting physiological
functions within the seed. However, by decreasing the soil bulk density with sawdust, in this
experiment, the soil volume available for contact with oil was reduced. Consequently, the degree
of inhibition of the physiological functions was reduced.
Rowell (1977) reported that oil exerts adverse effect on soil condition and this was
supported by Baker (1970) who noted adverse effect of oil on microorganisms and plants.
Conversely, only a few beneficial effects of well-degraded oil on soil biota have been reported
(Mc Gill, 1980). A few investigators have examined the effect of post-oil spill rehabilitation
measures on rate of soil recovery and crop improvement (Amadi et al., 1993; Toogood et al.,
1977). In all cases of oil pollution of soil ecosystem, N and P were observed to be limiting to
both biodegradation of oil and crop development (Amadi et al., 1993; Bossert and Bartha,
1984).
Germination of seeds is grossly affected by oil pollution (Udo and Fayemi, 1975). Udo
and Fayemi (1975) maintained that seeds planted in oil-polluted soils generally absorbed the oil
and got destroyed. Fekumo (2001) reported that oil contamination reduced crop yield and plant
growth.
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Contaminated plots had significantly lower total vegetation cover and litter than
uncontaminated plots (Diana et al., 2004). Diana et al. (2004) reported that at seven sites, total
vegetation cover on contaminated plots was significantly lower than on uncontaminated ones,
the average being about 43 % on contaminated and 58 % on uncontaminated plots. All
contaminated plots had significantly more bare ground than uncontaminated ones, about 44 %
compared with 6 % respectively (Diana et al., 2004).
2.6 Soil remediation
The remediation of lands contaminated with hazardous materials has received increasing
international attention with enhanced awareness of the potential adverse effects on the
environment and human health. Since the mid-1980s, contamination of soils with petroleum
hydrocarbons has become a critical issue in the world (Yeung et al., 1997). Currently, physical
and chemical methods are the most widely used procedures employed in minimizing the effects
of oil spills. Nonetheless, these methods are grossly inadequate and ineffective, and may even
result in further contamination of the environment (Steven, 1991).
Abu and Ogiji (1996) stated that bioremediation was a process whereby the natural
biodegradative capabilities were enhanced by nutrient addition and innoculation with
microorganisms, which was among the most important techniques currently in use. Most
bioremediation methods use natural soil microorganisms. This technology according to Rose et
al. (1988) has such advantages as cost effectiveness and the potential to remediate an
environment without causing much damage. Furthermore, the use of bioremediation techniques
in conjunction with chemical and physical treatment procedures, i.e., the use of a “treatment
trains”, is also an effective means for comprehensive site-specific remediation (Rose et al.,
1988).
Bioremediation is usually accomplished “in-situ”, but polluted soil may also be
excavated and hauled to a treatment site where such techniques as high temperature compositing
may be used to destroy the organic contaminations in the soil. The use of bioremediation
technology that assists the naturally occurring microbial populations in breaking down
chemicals is called biostimulation. Usually, the soil naturally contains some bacteria or other
microorganism that can degrade specific contaminants. However, the rate of natural degradation
may be far too slow to be very effective (Brady and Weil, 2002). Brady and Weil (2002)
reported further that both growth rate and metabolic rate of organisms capable of using
contaminants as sources of carbon are often limited by insufficient mineral nutrients, especially
nitrogen and phosphorus. Therefore, nutrients and oxygen are often added to speed up the
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process. Bioremediation is used extensively to break down petroleum constituents, including the
more resistant polynuclear aromatic hydrocarbons (PAHs) as well as several synthetic
compounds such as polychlorinated biphenyls (PCBs) and trichloroethylene (TCE).
Generally, Nyer and Skladany (1993) have reported four major ways to remediate soils
contaminated with hydrocarbons as follows: excavation and off-site disposal land fill, in-situ soil
venting, in-situ biodegradation and above ground or in-situ chemical oxidation.
Excavation of site soils may result in the loss of the volatile compounds present. As these
soils are exposed to the atmosphere, petroleum products with high vapour pressures and low
boiling points tend to volatilize. According to Nadim et al. (2000) in-situ soil venting is to move
air past the contaminated soils and transfer the organic from the liquid phase to vapour phase.
This technique, Nadim et al. (2000), described as soil vapour extraction (SVE) which could also
be referred to as in-situ clean-up process to remove volatile and some semi-volatile organic
compounds (VOCs). This mass transfer process would effectively remove the hydrocarbons
from the soil (Nyer and Skladany, 1993).
According to Nyer and Skladany (1993), chemical oxidation relied on the use of
hydrogen peroxide. On the other hand, when dealing with petroleum-contaminated soils, one can
measure the concentration of individual petroleum constituents or the total petroleum
hydrocarbon (TPH) concentration. Furthermore, analytic test procedures normally used to assess
soil contaminated by petroleum products are petroleum hydrocarbons and heavy metals
determination (Kelly and Tata, 1998; Massoud et al., 1996; Onianwa, 1995; Hewari et al., 1995;
Hasty and Revesz, 1994; Hayes et al., 1985). However, Coyne (1999), Kelly and Tata (1998)
and Amadi et al. (1996) pointed out that the degree of soil contamination by petroleum
hydrocarbons and heavy metal could impact soil ecosystems sufficiently to result in significant
losses in soil quality.
2.7
Soil quality assessment
Sims et al. (1997) stated that soil quality had emerged as an issue of vital importance to
the use and mangement of land, water and air. According to Arshad and Martin (2002), a
significant decline in soil quality had occurred world wide through adverse changes in its
physical, chemical and biological properties and contamination by in-organic and organic
chemicals. Consequently, Sims et al. (1997) pointed out that soils must be maintained in a
“clean” state that is suitable for agriculture, that minimizes the pollution of water and air, and
that allows for the safe and productive use of wastes and by- products as amendments.
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The concept of soil quality is deceptively simple and attractive (Davidson, 2000), new,
highly controversial and poorly defined from a scientific perspective. Arshad and Martin (2002)
pointed out that many definitions of soil quality had been proposed and this was supported by
Karlen et al. (1997); Doran and Parkin (1996) and Arshad and Colen (1992). Karlen et al.
(1997) gave an expanded definition of soil quality as the capacity of a specific kind of soil to
function, within natural or managed ecosystem boundaries,to sustain plant and animal
productivity,
maintatin or enhance water and air quality, and support human health and
habitation. Soil quality could be explained as the capacity of the soil to promote the growth of
plants, protect watershed by regulating the infiltration and partitioning of precipitation, and
prevent water and air pollution by buffering potential pollutants such as agricultural chemicals,
organic wastes, and industrial chemicals. Therefore, quantitative definition of soil quality,
analogous to air and water quality, is with reference to a specific objective. For example Sims et
al. (1997), pointed out that measures of soil quality
important for enviromentally sound
agronomic crop production would not always be the most appropriate for contaminated soils
where cost effective remediation was often the primary objective. Hence, Doran and Parkin
(1996) proposed a minimum data set (MDS) of physical, chemical and biological indicators of
soil quality (Table 1) which refers to measurable soil attributes that influence the capacity of soil
to perform its basic functions. The selection of MDS parameter (Wander and Drinkwater, 2000)
has been based upon a wealth of soil management research that relates soil attributes to soil
function, and ideally relates management practices to soil attributes. Davidson (2000) noted that
the properties listed in Table 1 were all relevant to the ability of soils to perform the range of
functions, and thus such MDS could be used to evaluate soil quality. For instance, Reeves
(1997) described soil organic carbon (SOC) as a cornerstone soil quality indicator, and in a
review of long-term studies, concluded that continuous cropping resulted in decline of SOC
athough amelioration was possible by careful choice of management system. Bolinder et al.
(1999) compared the responses and consistency of different soil organic matter (SOM) fractions.
Bolinder et al. (1999) found that macro-organic carbon and nitrogen, microbial biomass carbon
and total soil carbon provided the most responsive soil quality indicators for different land
managements systems. Since soil biota plays an important role in the development (Dijkstra,
1998) of humus profiles because they are responsible for the decomposition of SOM, microbial
activity is a good indicator for soil quality.
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Table 1. Soil Quality; Minimum Data Set of Soil Physical, Chemical and Biological Indicators
Physical properties
Chemical properties
Biological properties
1. Texture
1. Soil organic carbon
1. Microbial biomass Carbon
2. Depth of soil, top soil
2. pH
2. Potentially mineralizable N
3. Electrical conductivity
3. Soil respiration density
and rooting depth
3. Infitration and bulk
Density
4. Soil drainage and water
4. Extractable N, P and K
capacity holding
Source: Davidson (2000); Doran and Parkin (1996)
15
2.8
Evaluation of soil productivity
According to Brady and Weil (2002), Soil Science Society of America (1975), soil
productivity is the capacity of a soil for producing a particular plant or sequence of plants under
a specified set of management practices. Soil Science Society of America (1975) noted that the
soil must be in its normal environment and stated further that because of the emphasis on a
soil‟s capacity to produce crops, productivity should be expressed in terms of crop yields.
Maintaining soil productivity today is probably the most difficult on fragile land and in arid
regions because soil tilth and organic matter are difficult to restore without adequate water
(McCinty et al., 1979).
Soil physical and chemical properties such as bulk density, available water capacity;
total porosity, texture, pH and nutrient storage are important indicators of soil productivity.
Hence, National Soil Erosion-Soil Productivity Research Planning Committee (1981), Follet
and Stewart (1985) and American Society of Agricultural Engineers (ASAE, 1985) noted that
relationships between soil properties and a soil‟s capacity for producing plants or soil
productivity were the focus of a number of research projects. These research projects, according
to Gantzer and McCarty (1987), had grown out of a need to increase the knoweldge of
quantitative relationships between plant growth and soil properties. The productive capacity
and/or expectable yields are useful in determining the suitability of any soil for agriculural use
(De La Rosa et al., 1981). Consequently, estimates have been made of the productivity of
individual kinds of soil such as ultisol and inceptisol (Anikwe, 2000). Attempts have beens
made to key the yields of crops or pastures to limited number of soil properties (Stone, 1950;
Carstea, 1964; System, 1964; De La Rose et al., 1976).
The productivity of a soil is reduced through such soil degradation processes as erosion
and desertificaiton. The reduction may manifest as soil constraints such as loss of plant
nutrients, loss of storage capacity for plant available water, degradation of soil structure and
decreased uniformity of soil conditions within a field (Williams et al., 1990). According to
Woomer and Muchena (1993), soil productivity constraints in tropical Africa have been grouped
into four broad categories namely: nutreint availability and retention, nutrient toxicities, water
availability and physical degradation. Obi and Ebo (1995) noted that soil physical constraints in
West Africa are high crusting propensity, easy compatibility of the topsoils and droughtiness.
According to El-Fring (1983) erosion was not the only process that could damage the
productivity of a nation‟s cropland and rangelands, but it was the most pervasive. Mbagwu
(1985) explained that the serious problems of exposed subsoil infertility and means of
reclaiming areas degraded by erosion in the tropics had not received adequate attention. The
16
erosion hazard in tropical agro-environment is so high that it detrimentally affects the
productivity of the soil. The causes of this high erosion hazard have been discussed by some
researchers (El-Swaify; 1989; Pierce et al., 1983; Dumenski et al., 1991). Consequently, there is
a steady decline in food production in sub-sahara Africa which leads to direct human suffering
(Tarawali and Ogundibe, 1995). International Soil Conservation Organization (ISCO, 1996)
indicated that rapid depletion of soil fertility and non-sustainable land use particularly in
developing countries, for example Nigeria, were both the cause of the widespread poverty.
Although limited research has been devoted to the soil productivity problem,
considerable effort had gone into most of the important processes involved. These include
hydrology, nutrient recycling, crop growth and tillage (National Soil Erosion-Soil Productivity
Research Planning Committee, 1981). The problem , according to National Soil Erosion-Soil
Productivity Research Planning Committee (1981) was that these necessary components had
not been linked in a model appropriate for studying the productivity problem.
2.8.1 Productivity index (PI)
According to Lindstrom et al. (1992), productivity index (PI) is an algorithm based on
the assumption that crop yield is a function of root-growth, including rooting depth, which in
turn, is controlled by the soil environment. The model was first developed by Neill (1979) and
was later modified by Kiniry et al. (1983). Pierce et al. (1983), further modified the productivity
index model of Kiniry et al. (1983) in order to accommodate more sufficiency parameters such
as nutrient storage.
Simulation models enable quantitative estimates of the growth and production of the
main agrcultural crops under a wide range of soils. For instance, Williams et al. (1983) and
Pierce et al. (1983) stated that many models had been used to assess the productivity of
agricultural lands. These include the Universal Soil Loss Equation (USLE) and Erosion
Productivity Impact Calculator (EPIC). Neill (1979) and Kiniry et al. (1983) corroborated that
the productivity index (PI) was a model which related root growth to soil properties within a
profile. The approach assumes that properties of soil layers within the rooting zone are major
factors constraining crop growth and yield (Gantzer and McCarty, 1987). According to Gantzer
and McCarty (1987) soil suitability for plant growth was assumed to be the sum of the
suitability of each layer of soil. The soil layers would vary in their importance to plant growth.
In the extreme case, layers 1m deep have essentially no influence on crop growth. However, soil
layers up to 60 cm deep have small influence on plant growth because crops growing in them
will have little restriction in root penetration. Essentially, layers less then 30 cm usually exert
17
major influence on plant growth and development. Previously, only the soil environment,
characterized by the bulk density, available water capacity and pH was used as input for the
model of Gantzer and McCarty (1987) and other variables such as climate, management and
genetic plant potential are known to affect plant growth, but are not currently included. Each
model property was related to suitability or sufficiency for growth, ranging from 0.0
representing conditions which would have no growth, to 1.0, representing growth in an ideal
medium (Gantzer and MaCarty, 1987).
Neill (1987) considered five soil parameters, namely available water capacity, bulk
density, aeration, pH and electrical conductivity as the parameters most influential to root
growth. According to Gantzer and McCarty (1987), productivity index (PI) model requires
fewer inputs of soil property values than Erosion Productivity Impact Calculator (EPIC). The
model‟s simplicity might involve some loss in description of soil productivity relationships and
subsequently might reduce the model‟s accuracy. However, by requiring fewer inputs, this
model should provide a less costly tool for analysis.
Rijsberman and Wolman (1985) concluded, that the model would have promise on a
world scale because of its simplicity and input data. Simple method of FAO (1978, 1979) was
difficult to be linked in a model and the assumption used by Pierce et al. (1983; 1984) might
best fit where crops rely heavily on stored soil water. Rijsberman and Wolman (1985) observed
that although, a variety of soil productivity indices existed, the International Federal Institutes of
Advanced Study (IFIAS) used the productivity index model because it dealt explicitly with
changes in soil profile characteristics with depth. They further stressed that the productivity
index approach was based on the assumption that soil was a major determinant of crop yield
because of the environment it provided for root growth.
The productivity index model was tested in the United States. Furthermore, comparative
data were obtained from other regions, such as North Central United States (Rijsberman and
Wolman, 1985). According to Rijsberman and Wolman (1985), practice and management
potential were place and time dependent. Daniels et al. (1987) noted that to quantify
productivity, all factors except those soil properties affected by erosion such as soil moisture
must be held constant. Extension of these uses might be possible at a global level where
estimates of the potential impact of erosion on productivity, are desired or where productivity
index may serve as one measure of the relative suitability of different lands for agricultural
development (Rijsberman and Wolman, 1985). Kiniry et al. (1983) corroborated by Pierce et al.
(1983; 1985), however indicated that there had been little quantification of the impact of
progressive soil losses on future productivity.
18
2.9
Role of organic wastes in the soil
In the Abakaliki agro-ecological zone of southeastern Nigeria, large quantities of fresh
and partially burnt rice-mill wastes accumulate from the numerous rice-mills located in the area.
Despite, the magnitude of these wastes generated daily and the possible adverse effects on the
environment no serious attempts have been made regarding either their effective utilization or
safe disposal (Nnabude and Mbagwu, 1999). Rice husks are the largest milling by-product of
rice, constituting one-fifth by weight (Beagle, 1978) and a potential pollutant of the
environment. The typical practice in Nigeria is to burn the wastes, resulting in both air pollution
and fire hazards (Omaliko and Agbim, 1983). At Abakaliki, large quantities of agro-rice mill
wastes (fresh and partially burnt) have formed artificial “mountains” occupying a considerable
portion of arable land (Nnabude, 1995).
It has been suggested that rice husk dust with high specific surface area and organic
carbon content can be used as a soil amendment to improve the productivity of the soil (Anikwe,
2000). Similarly, rice husk dust contains valuable inorganic material that can improve soil
productive capacity (Karikari and Yayock, 1987). It can, however, be used to maintain longterm productivity. According to Hornick and Parr (1987) cereal straws and wood bark (saw
dust) just like rice husk dust have high organic stability and could be used to reclaim marginal
soils.
Apart from variations arising from the nature of the wastes, it has been recognized
(Mbagwu, 1991; 1992, b) that the ability of these wastes to restore productivity in soils was
climate and crop-specific.
According to Okonkwo and Ogu (2002), agricultural production in low-input systems in
the tropics relies partly on nutrient cycling and the maintenance of soil fertility through
biological processes. Consequently, alternative food production systems that are productive and
sustainable are being developed (Okonkwo and Ogu, 2002). One way was the application of
organic materials. Organic materials used as soil amendment included residue mulch materials
such as farm wastes, compost manure, green manures and fresh grasses (Okonkwo and Ogu,
2002). Though, there is abundant variety of organic wastes for soil amendment, the potentials of
agricultural wastes such as agro-rice mill wastes for soil improvement and their effects on
tropical soil productivity have not been studied extensively.
2. 9.1 Impact of organic wastes on soil physical properties
The role of organic wastes in reducing bulk density and increasing total porosity has
been well reported in literature (Mbagwu 1992b; Mbagwu and Piccolo, 1990; Hulugalle and
19
Lal, 1984). Dutart et al. (1993) observed from soils they studied that the amount and
composition of organic materials, particularly the humic substances, influenced the structure of
sandy soils. Cheshire et al. (1990, 1983) noted that carbohydrates were positively involved in
soil aggregate stability. Several workers (Mbagwu and Piccolo, 1990; Hu et al. 1995) obtained
high correlation between aggregate stability and polysaccharide contents of the soil. Similarly,
the ability of organic wastes to improve bulk density and extreme consistency conditions had
been studied by Nnabude and Mbagwu (1999). Furthermore, Allison (1973) reported the ability
of organic wastes to improve soil structural impediments and thereby increase water infiltration,
water holding capacity and aeration status. Anjaiah et al. (1987) reported an increase in the final
infiltration rate of the soil. All the infiltration parameters were reduced significantly 3 years
after continuous cropping (Wilkinson and Aina, 1976) and this was corroborated by Nnabude
and Mbagwu (1999). In a similar work using different rates of fertilizer, rice shavings and
poultry manure as soil amendments, Mbagwu (1992) observed that at 3 months and 3 years after
cropping, the differences in cumulative infiltration, infiltration rate and time to reach final
infiltration rate between the control and inorganic fertilizer treatments were not significant.
Variations in water retention and available water capacity for different soils amended
with organic wastes have been reported by Parr and Papendick (1978); and Mbagwu and
Ekwealor (1990). According to Mbagwu and Ekwealor (1990), soil water content in an Ultisol
generally increased with increasing rate of brewer‟s spent grain but did not change the total and
readily available water. On the other hand, these parameters tended to change progressively with
higher amounts of brewer‟s spent grain in an Entisol (Nnabude and Mbagwu, 1999). Although
most organic wastes exerted influence on soil water conditions, the magnitude of such influence
was soil and waste specific (Nnabude and Mbagwu, 1999).
2.9.2 The impact of organic wastes on soil chemical properties
Okonkwo and Ogu (2002) reported that application of elephant grass Panicum maximum
and Gliricidia sepium biomass to the soil increased the percentage organic carbon,
exchangeable calcium, total nitrogen, available phophorus and and exchangeable magnesium
and potassium compared to other treaments and control.
Nnabude and Mbagwu (2001) pointed out that fresh and burnt rice wastes amendment on
soil recorded positve and significant influence on N and OC status of the soil, and the influence
increased with increase in the rate of application. Mbagwu (1992a, b) and Hoffman (1982)
observed that increasing the levels of fresh and burnt rice wastes could have a long-term effect
on soil OC and N. However, application of the wastes failed to improve the soil pH, which
20
remained extremely acidic (Nnabude and Mbagwu, 2001). Opara-Nadi et al. (1987) and Arya et
al. (1991) reported that pH increases following application of wastes as well as evolution of
CO2 during the same process could acount for low pH in soil (Nnabude and Mbagwu, 2001).
They also reported increasing levels of cation exchange capacity (CEC) and base saturation
(BS) in soil amended with organic wastes relative to the unamended soil, although the observed
increase was not corresponding to increase in rate of application. They particularly noted that
burnt rice waste made generally marginal increases relative to the control. Residual base
saturation declined substantially compared with the initial values and this situation was
attributed to accelerated leaching of the basic cations as a result of the high intensity of rainfall
(Nnabude and Mbagwu, 2001).
Most organic materials contain significant quantities of plant nutrients, especially
phosphorus (P), nitrogen (N) and potassium (K) (Anon, 2005). High levels of organic material
applied to soil may increase salts in soil. In addition, methods of waste application to the soil
can affect the nutrient availability in the soil especially nitrogen. Organic wastes should be
incorporated into the soil as soon as possible since most Nitrogen losses occur within 24hours of
application. Phosphorus and potassium contained in organic waste are
generally readily
available in the soil unless removed by surface runoff or soil erosion. Organic material should
be tested prior to application since nutrient composition of organic waste is affected by
collection, storage and the waste handling system. Soils receiving the organic materials should
also be tested for available nutrients before application (Anon, 2005).
Organic wastes serve as a nutrient store from which basic cations are slowly released
and this accounts for over 80% cation exchange capacity (CEC) of the humid tropical soil
(Agboola and Fagbenro, 1985). The quantity of nutrients in the soil at any given time indicated
its productivity. This also demonstrates the capacity of the soil to renew the supply when the
nutrients in solution had been utilized.
2.10 Conclusion
The soil is a sink for many contaminants and pollutants. These organic and inorganic
waste materials could occur naturally or anthropogenically. The common sources of soil
contamination are household wastes, agricultural wastes, industrial wastes, gas flaring, crude oil
and spent automobile oil (Anon, 1985). The quality and productivity of contaminated soil are
often affected. In order to ensure a good environment conducive for increased food production
and human health, there is need to reclaim such soils. Physical and chemical methods, although
most widely used for land treatment of oil-based wastes (Abu and Ogiji, 1996), have been
criticized to be grossly inadequate and ineffective and according to Steven (1991) may result in
21
further contamination of soil. Since oil degradation is limited by temperature, pH, oxygen and
scarcity of nutrients such as N and P (Leahy and Colwell, 1990 and Ladousse and Tramier,
1991), bioremediation of organic wastes is recommended (Atlas et al., 1991). Bioremediation is
commonly accepted as the most efficient, environmentally safe and cost-effective method of
treatment of hydrocarbon contaminated soils (Atlas et al., 1991). Odokuma and Dickson (2003)
noted that bioremedation was the introduction of nutrients in form of orgnanic matter to the
contaminated soil.
Soil productivity is believed to be influenced by physical and chemical parameters.
These soil physical and chemical parameters can be linked up in a model (Pierce et al., 1983) to
predict the productivity of the soil. The positive effects of organic wastes on improving soil
productivity has been reported by many reserachers (Tisdall, 1996 and Okonkwo and Ogu,
2002) among others. Generally, improved physical and chemical parameters predict high
productivity of soil relative to the marginal one.
22
CHAPTER THREE
3.0
3.1
MATERIALS AND METHODS
Experimental Site
The study was carried out at the Teaching and Research Farm of Faculty of Agriculture
and Natural Resources Management, Ebonyi State University, Abakaliki. The Teaching and
Research Farm of Faculty of Agriculture and Natural Resources Management, Ebonyi State
University is located by latitude 0604/N and longitude 08065/E in the derived savannah zone of
the southeast agro-ecological area of Nigeria. The rainfall pattern is bimodal (April-July and
September-November), with a short dry spell in August normally referred to as “August break”.
The total annual rainfall in the area ranges from 1500 to 2000 mm, with a mean of 1,800 mm. At
the onset of rainfall, it is torrential and violent, sometimes lasting for one to two hours
(Okonkwo and Ogu, 2002). The area is characterized by high temperatures with minimum mean
daily temperature of 27 0C and maximum mean daily temperature of 31 0C throughout the year.
Humidity is high (80%) with lowest (60%) levels occurring during the dry season between
December to April, before the rainy season begins (ODNRI, 1989). The underlying geological
material in the area is the sedimentary rocks derived from successive marine deposits of the
cretaceous and tertiary periods. According to the Federal Department of Agricultural Land
Resources (FDALR, 1985), Abakaliki agricultural zone lies within „Asu River group‟ and
consists of olive brown sandy shales, fine-grained sandstones and mudstones. The soils are
shallow with unconsolidated parent materials (shale residuum) within 1 m of the soil surface.
The soils belong to the order, ultisols and are classified as Typic Haplustult (FDALR, 1985).
The vegetation of the area is primarily derived savannah, with bush regrowth, and scanty
economic trees. The site had history of previous cultivation of yam (Dioscorea spp) and cassava
(Manihot spp). There is growth of native vegetation such as Tridax spp, Odoratum spp, Aspilla
africana, Imperata cylindrica, Panicum maximum, Pennisetum purperum, Sporobulus
pyramidalis and other herbs and shrubs.
3.2
Field Methods
3.2.1 Field design/layout and treatment application
The vegetation was cleared manually using matchet and hoe. The debris left after
clearing was removed before seedbed preparation. An area of land that measured 28 m x15 m
(0.042ha) was used for the study. The land was demarcated into plots and replicates. The plots
were laid out as split plot in Randomized Complete Block Design (RCBD). The main plots
measured 10 m x 2 m and were separated by 1m spaces. Each subplot measured 2 m x 2 m with
23
a plot alley of 0.5 m spacing. The four replicates were separated by 2 m spaces. The main plot
treatments were as follows: 0% spent automobile oil contamination
5% spent automobile oil equivalent to 50,000 mg kg-1
The subplot treatments consisted of:
control (C) i.e. no application of oil or organic wastes
burnt rice husk dust (B) 20 t ha-1 equivalent to 8 kg/plot
unburnt rice husk dust (U) 20 t ha-1 equivalent to 8 kg/ plot
sawdust (S) 20 t ha-1 equivalent to 8 kg/plot and
5% spent automobile oil (O) equivalent to 50,000 mg kg-1.
oil+20 t ha-1 of burnt rice husk dust (OB) equivalent to 8 kg/ plot
oil+20 t ha-1 of unburnt rice husk dust (OU) equivalent to 8 kg/ plot
oil+ 20 t ha-1 of sawdust (OS) equivalent to 8 kg/ plot
The treatments, spent automobile oil (O), burnt rice husk dust(B), fresh or unburnt rice
husk dust(U) and sawdust(S) were sourced from the “mechanic village”, agro-rice mill industry
and timber shade market, Abakaliki, respectively. Spent automobile oil was sprayed uniformly
on each main plot that received it, with a spraying machine to serve as a source of soil
contamination. The organic wastes, namely, burnt rice husk dust, unburnt rice husk dust and
sawdust were spread on the subplots one week after application of spent automobile oil on the
soil. They were incorporated into the soil during seedbed preparation using traditional hoe. The
beds were allowed to age for two weeks after incorporation of treatments before planting the test
crop. The main plot and subplot treatments were replicated four times to give a total of thirtytwo (32) subplots in the study.
Maize seed (suwan-1-SR-hybrid variety) sourced from Ebonyi State Agricultural
Development Programme (EBADEP) was planted (2 seeds per hole) at 5 cm depth and spacing
distance of 25 x 75 cm. Two weeks after emergence (WAE), the plants were thinned down to
one plant per hole while lost stands were replaced. Weak plants were rogued out and replaced
leaving a plant population of approximately 53, 000 stands per hectare. There was application of
NPK (20:10:10) fertilizer at 400 kg ha-1 to all the subplots two weeks after plant emergence
(WAPE). The fertilizer was banded and placed 5 cm away from the maize plants. Weeds were
removed at three-weekly intervals up till harvest. In the second year, the procedure was repeated
while residual effect was tested in the third year of study without fresh application of treatments.
24
3.2.2 Agronomic Data
The cobs were harvested at plant maturity. This was when the
husks were dried. The
cobs were dehusked and further dried before shelling and grain yield determined at 14%
moisture content. Percentage relative yields treatment factor was calculated from amended and
control plots by using the formula
TF
=
Ya – 1
Yb
x 100 -------------------------------------------------1
1
where
TF = is the treatment factor
Ya = maize grain yield in amended plots
Yb = maize grain yield in control plots
Agronomic yield data were taken on twelve tagged plants representing 25% of plant
population per plot.
3.2.3 Soil Sampling
Initial soil samples were collected from the 0-20 cm depth, using auger at different
points in the study site before application of spent automobile oil, organic wastes and
cultivation. The auger samples were composited and used for routine laboratory analysis. Core
and auger samples were collected at 0-20 cm from each plot, at three points i.e. 3 cores and 3
augers in each subplot after the planting for post harvest soil analysis. Soil samples were further
collected (both core and auger) at 0-15, 15-30, 30-45 and 45-60 cm depths in each subplot and
used for soil productivity evaluation. Core samples were used to determine some soil physical
properties while auger samples were air-dried at room temperature (about 26 0C) and passed
through a 2 mm sieve. These were used for chemical analysis.
25
3.3
Laboratory Determination
The laboratory determination was categorized as follows:
3.3.1 Soil physical properties
3.3.1.1 Particle size distribution
Particle size distribution was determined by the hydrometer method as described by Gee
and Bauder (1986). The result from particle size distribution was reported as percentage sand,
silt and clay respectively.
3.3.1.2 Bulk density
Dry bulk density was determined as described by Blake and Hartge (1986).
3.3.1.3 Soil porosity
Total porosity was derived from measurement of dry bulk density (Pb) and soil particle
density (Ps) assumed to be 2.65 Mgm-3.
Thus, St =
1- Pb
Ps
x 100 --------------------------------------------------------------------- 2
1
Macro- porosity was determined using cores on a tension table subjected to a potential of – 6
kPa according to Obi (2000). Micro- porosity was calculated as the difference between total
porosity and macro-porosity.
3.3.1.4 Aggregate stability and Mean weight diameter
The distribution of aggregates was estimated by the wet sieving technique described by
Kemper and Rosenau (1986). In this procedure, 50 g of the < 4.76 mm aggregates were placed
on the topmost of a nest of sieves of diameters 2, 1, 0.5 and 0.25 mm. The samples were presoaked in distilled water for 10 minutes before oscillating vertically in water 20 times (along a
4 cm amplitude). The resistant aggregates on each sieve were dried at 105
0
C for 24 hours,
and weighed. The mass of < 0.25 mm fraction was obtained by difference between the initial
sample weight and the sum of sample weights collected on the 2, 1, 0.5 and 0.25 mm sieve
nests. The percent water-stable aggregates (WSA) on each sieve was determined, thus:
WSA = Ma + S - Ms
Mt -Ms
x 100 --------------------------------------------------- 3
1
where;
Ma + S = Mass of the resistant aggregates plus sand (g)
26
Ms = The Mass of the sand fraction alone (g)
Mt =The total mass of the sieved soil (g)
All soil samples that fell within 4. 76 and 0.25 mm were used to express WSA > 0.25
mm as the index of stability.
The method of Van Bavel (1950) as modified by Kemper and Rosenau (1986) was used
to evaluate the mean weight diameter of aggregates. This is expressed as:
n
MWD = Σ Xi Wi ----------------------------------------------------------------- 4
i=1
Where;
MWD = is the mean weight diameter of water-stable aggregates (mm)
Xi = The mean diameter of each size fraction and,
Wi = The proportion of the total sample weight (AS) in the
corresponding size fraction, after deducting the weight of stone (upon dispersion
and passing through the same sieve).
Higher values of MWD indicate the dominance of the less erodible, large
aggregates of the soil (Piccolo et al., 1997).
3.3.1.5 Soil moisture retention
Water retention was determined by the hanging water column technique as described by
Obi (2000). Hanging water column procedure involved collection of undisturbed core soil
samples. The metal cores used had dimensions of 4.8 cm (internal diameter, ID) and 5.6 cm
height. After saturation for 24 hours, the cores were used to measure soil water retention at
matric potential of – 6 kPa and then oven-dried at 105oC for 24 hours. Soil water content was
measured gravimetrically.
3.3.1.6 Available water capacity
Moisture retained at – 10 and – 1500 kPa matric potentials were estimated based on the
saturation water percentage (Sp) models of Mbagwu and Mbah (1998). The models are:
Ө.01 (FC) = - 6. 22 + 0.79 (Sp). -------------------------------------------- 5
Ө. 15 (PWP) = - 8.65 + 0.51 (Sp). ------------------------------------------- 6
Available water capacity (AWC) was computed as the difference between moisture
retained at 10 kPa and 1500 kPa matric potentials, where;
FC – field capacity
PWP – permanent wilting point
27
3.3.1.7 Saturated hydraulic conductivity
Saturated hydraulic conductivity (Ks) determination was by the constant- head soil core
method of Reynolds (1993) as adapted from Elrick et al. (1981). This is transposed as:
where,
Ks = Q x
At
∆H --------------------------------------------------------7
L
Ks = Mean volume of water conducted x Hydraulic head change
Cross sectional area of core x Time Soil sample Length
3.3.2 Soil chemical properties
The soil chemical properties were determined as follows:
3.3.2.1 Soil pH
The pH determination of the soil was in duplicates both in distilled water and in 0.1N
KCl solution, using a soil / water ratio of 1:2.5. After stirring for 30 minutes, the pH values
were read off using a Beckman zeromatic pH meter (Peech, 1965).
3.3.2.2 Total nitrogen
The total nitrogen was determined using the micro-Kjeldhal distillation method of
Bremner (1996). The ammonia from the digestion was distilled with 45% Na0H into 2.5% boric
acid and determined by titrating with 0.05 N KCl.
3.3.2.3 Available phosphorus
Available phosphorus was by the Bray-2 method. This method involved weighing 2 g of
soil sample into a test tube. Then, 20 ml of 0.03 NH4F in 0.1NHCl was added to the sample of
soil in the test tube. The test tube was closed and shaken for a minute. It was allowed to settle
and filtered with 608 filter paper. 1ml of the filterate was pipetted into a 50 ml of volumetric
flask. Then, 7ml of distilled water, 1ml of NH4 molybdate and 1ml of ascorbic acid were
added to the sample. The flask was made up to the mark with distilled water and allowed to
stand for 15 minutes before taking the reading. The available phosphorus was read off from
the standard curve obtained from optical density using a colorimeter.
3.3.2.4 Organic carbon
Organic carbon determination was by using the method described by Nelson and
Sommers (1982). The percentage organic matter was calculated by multiplying the value for
organic carbon by the “Van Bemmeler factor” of 1.724, which is based on the assumption that
soil organic matter (SOM) contains 58% C (Allison, 1982).
28
3.3.2.5 Exchangeable bases
Calcium (Ca) and magnesium (Mg) were determined by titration method (Mba,2004).
Sodium (Na) and potassium (K) were extracted with 1N ammonium acetate solution
(NH4OAC), and determined using flame photometer.
3.3.2.6 Total exchangeable acidity
The titrimetric method using 1 NKCl extract of McLean (1982) was used in the
determination of total exchangeable acidity (Al3+ and H+).
3.3.2.7 Effective cation exchange capacity (ECEC)
This was evaluated by the summation method as follows:
ECEC = TEB + TEA ----------------------------------------------------------------
8
where;
ECEC = Effective cation exchange capacity (cmolkg-1 soil)
TEB = Total exchangeable bases (cmolkg-1 soil)
TEA = Total exchangeable acidity (cmolkg-1 soil)
3.3.2.8 Cation exchange capacity
This was determined by ammonium acetate (NH4OAC) displacement (Jackson, 1958).
3.3.2.9 Base saturation
Base saturation (BS) was calculated by dividing total exchangeable bases (TEB) with
cation exchange capacity value and multiplying by 100. The expression is thus:
%BS = TEB x 100 ------------------------------------------------------------------CEC 1
9
3.3.2.10 Aluminium saturation
Percentage aluminium saturation was obtained by calculation. This is expressed as
follows:
Al3+ saturation% = (Exchangeable Al3+)
TEB
where;
x 100 ------------------1
10
29
Al3+ saturation% = percentage aluminium saturation
TEB = Total exchangeable bases
3.3.2.11
Determination of heavy metals in soil
The heavy metals copper (Cu), Zinc (Zn), lead (Pb) and cadmium (Cd) were determined
as follows: 1g of the soil sample was digested and made up to 50 ml in a 50 ml volumetric flask.
The sample was further analyzed using the Atomic Absorption Spectrophotometer (A.A.S).
Each element had a cathode lamp. Heavy metal concentration was calculated as follows:
Let heavy metal conc. Mgl-1 be .
i.e. 1000 ml digest contains
mg element
:. 50 ml digest would contain 50 mg element
1000
Since this is from 1g soil
:. 1000g will contain 50
x 1000mg
1000
1
:. Heavy metal mgkg-1 = 50
where;
= concentration (mgkg-1)
3.3.3 Study on automobile oil, burnt and unburnt rice husk dust and saw dust
The amendments were evaluated using the following procedures
3.3.3.1 Organic wastes
The burnt rice husk dust(B), unburnt or fresh rice husk dust(U) and sawdust(S) organic
wastes were analyzed for sodium (Na), Potassium (K), Calcium (Ca), Magnesium (Mg),
Nitrogen (N), Phosphorus (P), organic carbon (OC) and C:N ratio using the method of Juo
(1983).
30
3.3.3.2 Mineralization rate
At the end of the study, the residual organic matter content was determined and used to
calculate the mineralization rate constant using the equation proposed by Gilmour et al. (1977)
as follows:
K = (2.303/(t2 - t1) log (C1/C2) ------------------------------------------------- 11
where;
K = organic carbon mineralizaiton rate constant/day
C1 = amount of organic carbon in the soil (g) at the beginning of
the experiment (t1)
C2 = the residual amount of organic carbon (g) at the end of study
(t2) (with t1 – t2 expressed in days).
3.3.3.3 Hydrocarbon
Total hydrocarbon content was determined gravimetrically by toluene extraction (cold
extraction) method of Odu et al. (1989) to provide an estimate of the organic and available
forms of total hydrocarbon content (THC) at the end of harvest. In this procedure, 10 g of soil
sample was weighed into 50 ml flask and 20 ml toluene (Analar grade) added. It was shaken for
30 minutes after which the liquid phase of the extract was measured spectrophotometrically at
420 nm using Jenway 6100 spectrophotometer. The total hydrocarbon content (THC) in soil was
estimated using standard curve derived from fresh spent oil diluted with toluene.
3.3.3.4 Half-life
The half-life (T50) of the organic wastes was determined using the equation:
T50 = ln (0.50) = 0.693 ---------------------------------------------------K
K
12
where;
T50 = is the half-life i.e. the time it took to mineralize 50% of the wastes (day)
K = organic carbon mineralizaiton rate (day) (Mbah, 2004).
3.3.3.4.1
Spent Automobile oil
This was analyzed by the total extraction method of (Benka - Coker and Ekundayo,
1995). In this method, 1g of the sample was extracted with 10 ml of toluene. Then, 5 ml extract
31
was pipetted into a 50 ml volumetric flask and colour developed. This was read with the
spectrophotometer.
The calculation was as follows:
Let the ppm of curve be x
5 ml extract contain 50x µgTHC
10 ml will contain 10 x 50µg
5
This was from 1g sample
:. THC (ppm) = 10 x 50x
5
= 100x
:. THC (ppm) was converted to % THC
3.4
Evaluation of soil productivity
The second aspect of the study involved evaluation of soil productivity.
3.4.1 Pierce et al. (1983) productivity index
The Pierce et al. (1983) productivity index is expressed thus:
PI =
r
Σ(Ai x Bi X Ci x Di x Ei x Wfi) ---------------------------------------i =1
13
where;
PI = productivity index
Ai = sufficiency for available water capacity for the ith soil layer
Bi = sufficiency for aeration for the ith soil layer
Ci = sufficiency for pH for the ith soil layer
Di = sufficiency for bulk density for the ith soil layer
Ei = sufficiency for electrical conductivity for the ith soil layer
Wfi = Root weighting factor
r = Number of horizons in the rooting zone
3.4.2 Modified Pierce et al. (1983) productivity index
Pierce et al. (1983) model of productivity index as used in this work was modified to
exclude sufficiency for aeration since it could be predicted from bulk density and sufficiency for
electrical conductivity. Hence the modified productivity index.
r
PIM = Σ(Ai x Ci x Di x Wfi) ---------------------------------------------i =1
14
32
where;
PIM = Modified productivity index
Ai = sufficiency for available water capacity for the ith soil layer
Ci = sufficiency for pH for the ith soil layer
Di = sufficiency for bulk density for the ith soil layer
Wfi = Root weighting factor.
3.5
Data analysis
The data collected from this experiment were subjected to Statistical Analysis System
(SAS, 1985) method. Significant treatment effect was reported at 5% probability level.
Correlation and regression analyses according to Steel and Torrie (1980) were used to determine
the relationship between soil productivity indictors and yield data.
33
CHAPTER FOUR
4.0
4.1
RESULTS AND DISCUSSION
Properties of the Soil at initiation of the study
Table 2 shows some properties of soil at the initiation of study. The particle size
distribution analysis indicates that the textural class was sandy loam. The pH in KCl was 5.1
indicating that the soil was strongly acidic according to the rating of USDA – SCS (1974). The
percentage organic matter was 3.17 and rated low (Enwezor et al., 1981). The percentage total N
(0.16) was low (Enwezor et al., 1981). The soil exchange complex was dominated by calcium
and magnesium (5.20 and 3.80 cmolkg-1, respectively).
Low values of 0.17 and 0.18 cmolkg-1 according to Asadu and Nweke (1999) were
recorded for sodium and potassium, respectively. The available phosphorus was low with value
of 4.70 mgkg–1 (Landon, 1991). The soil was very low (6.8%) in base saturation (Landon, 1991)
confirming its strongly acidic nature. Exchangeable acidity (EA) was 0.7cmolkg-1. The soil
cation exchange capacity (CEC) and effective cation exchange capacity (ECEC) were 10.3 and
7.97 cmolkg-1, respectively and rated low (Asadu and Nweke, 1999; Landon, 1991).
4.2
Nutrient Composition of Amendments
The nutrient compositions of organic wastes and spent automobile oil applied to the soil
are presented in Table 3. The nutrient contents of the organic wastes were generally low.
Exchangeable cations were low in burnt rice husk dust, saw dust and unburnt (fresh) rice husk
dust compared to the soil. The values of exchangeable cations were low in burnt rice husk dust,
sawdust and unburnt rice husk dust according to Howeler (1996) and Landon (1991),
respectively. The percentage organic carbon and total N ranged from 6.92 to 16.39 and 0.28 to
0.48 in the organic wastes and rated high (Landon, 1991). Available phosphorus ranged from
3.00 to 14.00 mgkg-1 in the organic wastes and rated low using critical values established for
soils by Enwezor et al. (1989) and Landon (1991). The C:N ratios were 23, 32 and 34 for B, S
and U, respectively.
The observed values of Cu, Zn and Pb in spent automobile oil were within the normal
levels in soil as recommended by Alloway (1990). However, Cd reached the critical value
(Alloway, 1990).
The percentage of organic carbon (0C) and total N were 17.3 and 6.8
respectively and rated high (Enwezor et al., 1989). Available phosphorus was very low with
value of 0.02 mgkg-1 (Landon, 1991). The C:N ratio and total hydrocarbon values were 11.38
and 33.4% (Table 3).
34
Table 2. Some properties of the Soil at the initiation of the study
Soil Properties
Unit
Values
Sand
gkg-1
660
Silt
gkg-1
210
Clay
gkg-1
130
Textural class
Sandy loam
pH kcl
5.1
OC
%
1.84
OM
%
3.17
N
Cmolkg
0.16
Na
Cmolkg
0.17
K
Cmolkg
0.18
Ca
Comolkg
5.20
Mg
Comolkg
3.80
Available P
Mgkg-1
4.70
Base saturation
%
68.0
CEC
comolkg-1
10.3
EA
comolkg-1
0.7
ECEC
comolkg-1
7.97
OC – organic carbon, OM – organic matter, CEC – cation exchange capacity, N – nitrogen, EA
– exchangeable acidity, ECEC – effective cation exchange capacity.
35
Table 3. Some Properties of Organic Wastes and Spent Automobile Oil
Treatment
Parameter
Unit
B
Na
cmolkg-1
0.04
-1
0.06
Ca
-1
cmolkg
1.17
Mg
cmolkg-1
0.27
OC
%
6.92
N
%
K
P
Value
cmolkg
0.30
-1
cmolkg
C:N
S
Na
K
Ca
23
-1
0.07
-1
0.13
-1
0.30
-1
cmolkg
cmolkg
cmolkg
Mg
cmolkg
0.10
OC
%
8.99
N
%
P
0.28
-1
cmolkg
C:N
U
3.00
32
cmolkg-1
0.07
-1
0.24
Ca
-1
cmolkg
0.50
Mg
cmolkg-1
0.12
OC
%
16.39
N
%
Na
K
P
cmolkg
0.48
-1
cmolkg
C:N
Spent automobile oil
14.00
7.00
34
-1
Cd
cmolkg
15.6
Cu
cmolkg-1
9.1
Zn
cmolkg-1
31.2
-1
Pb
Mgkg
4.0
OC
%
17.3
N
%
P
Mgkg
C:N
THC
6.8
-1
0.02
11.38
%
33.4
B-burnt rice husk dust, S-sawdust, U-unburnt rice husk dust, OC-organic carbon, C:N-carbonnitrogen ratio, THC – total hydrocarbons
36
4.3
Effects of Automobile oil and organic wastes on soil Physical Properties
4.3.1 Particle Size Distribution
The results (Table 4) indicate that particle size distribution did not vary appreciably in
the soils. Sand was the dominant fraction. Sand fractions were 18 and 11 % higher under oil
contamination and oil contaminated amended with burnt rice husk dust in 2006 season relative
to same treatments in 2007 season. Conversely, silt and clay fractions were 32, 22, 17, 25% and
50,18, 9 % lower under oil contamination and oil contaminated plots amended with organic
wastes in 2006 cropping season compared to their counterparts in 2007 season except sawdust
amended plot. Clay fractions decreased by 25, 31,25 and 46 % under oil contamination and oil
contaminated and amended with organic wastes in 2008 season relative to same treatments in
2007 season. Silt fractions were 33,18, 21 and 27% higher under oil contamination and oil
contaminated and amended with organic wastes in 2007 season compared to their counterparts
in 2008 season. Furthermore, sand fractions decreased by 14 and 13 % in control and unburnt
rice husk dust amended plots in 2006 season compared to same treatments in 2007 season. Silt
and clay fractions increased by 20, 16, 18, 8% and 30, 8, 29 and 40% respectively under control,
burnt rice husk dust; unburnt rice husk dust and sawdust respectively in 2007 season relative to
same treatments in 2006 season. Clay fractions were 23, 28 and 14 % higher in respective
organic wastes amended plots in 2008 season compared to their counterparts in 2007 season.
Generally, clay fractions decreased by 25, 9, 27, 9%, and 8, 50, 27 % respectively for control,
burnt rice husk dust, unburnt rice husk dust and sawdust relative to oil contamination, oil
contaminated amended with burnt rice husk dust and unburnt rice husk dust except for sawdust
in 2007 season. However, clay fractions increased by 41 and 25 % in burnt rice husk dust and
sawdust amended plots in 2008 season compared to oil contamination amended with same
organic wastes in 2007 season.
The high content of sand in the soils seems to be related to the parent material and
climate of the region (FDALR, 1985). Sand content of the soils in southeastern region is a
characteristic of sand formed on unconsolidated coastal plain and sand-stones from „Asu River‟
(FDALR, 1987). Sandy soil is vulnerable to drought (Abii and Nwosu, 2009).
The textural class consistently remained sandy loam in contaminated and
uncontaminated soils for the three cropping seasons. Obi (2000) stated that texture was a
“permanent property” of soil. Texture has good relationship with nutrient storage, water
retention, porosity (Foth and Turk, 1972) and specific surface area, soil compactibility and
compressibility (Smith et al., 1998), which affect inherent productivity of the soil.
37
Table 4. Particle Size Distribution (%) following amendments
Contaminated Soil
2006
Uncomtaminated Soil
2007
2008
2006
2007
2008
Trt
Sand%
Silt%
Clay%
Texture
Sand%
Silt%
Clay%
Texture
Sand%
Silt%
Clay%
Texture
Trt
Sand%
Silt%
Clay%
Texture
Sand%
Silt%
Clay%
Texture
Sand%
Silt%
Clay%
Texture
O
71
22
8
SL
58
30
12
SL
65
20
15
SL
C
66
25
10
SL
57
30
13
SL
65
21
14
SL
OB 66
23
11
SL
59
28
13
SL
60
23
17
SL
B
63
25
12
SL
58
29
13
SL
62
28
10
SL
OU 65
24
11
SL
60
28
12
SL
63
22
15
SL
U
64
22
14
SL
56
26
18
SL
60
27
13
SL
OS 65
24
11
SL
59
30
11
SL
62
22
16
SL
S
65
25
10
SL
59
27
14
SL
63
25
12
SL
O – Oil contamination, OB – oil treated with burnt rice husk dust, OU – oil treated with unburnt rice husk dust, OS – oil treated with saw dust, C
– control not treated with oil or organic wastes, B – burnt rice husk dust treatment, U – unburt rice husk dust treatment, S – saw dust treatment,
SL – sandy loam, Trt – treatment.
38
4.3.2
Some Properties of Soil at selected depths
Some properties of soil at selected depths are presented in Table 5. The percent sand
fraction was generally high ranging from 520-650 gkg-1 at the soil surface (0-15 cm depth) in
uncontaminated and contaminated soils. There was a general decrease of silt and increase of clay
fractions with soil depth in both soils. Bulk density also increased with depth in two soils. Total
porosity had inverse relationship with bulk density. Available water capacity and pH were higher
in uncontaminated soil relative to contaminated one.
Nwite and Obi (2008) and Nwite et al. (2007) reported low sand content as depth of soil
increased. Bulk density values obtained fall within critical ratings of Arshad et al. (1996) from 1560 cm depth in contaminated soil. The pH values were very strongly acidic (Schoeneberger et al.,
2002) in the soils.
4.3.3 Bulk Density
Table 6 shows the effect of the amendments on bulk density for three cropping seasons.
Significantly (P < 0.05) higher bulk density was observed in spent automobile oil contaminated
soil relative to oil contaminated soil amended with organic wastes in 2006 and 2007 cropping
seasons. The observed bulk density values ranged from 1.57 - 1.70 and 1.60 – 1.73 Mgm-3 in
2006 and 2007 cropping seasons respectively in the automobile contaminated soil. The lowest
values of bulk density of 1.57 and 1.60 Mgm-3 were obtained in spent automobile oil
contaminated soil amended with burnt rice husk dust for the two cropping seasons. Amendment
of spent automobile oil contaminated soil using organic wastes lowered the bulk density of the
soil. The bulk densities of oil contaminated soil treated with unburnt rice husk dust and saw dust
were significantly higher compared to those of oil contaminated soil amended with burnt rice
husk dust for the two years.
Similarly, there was significantly higher bulk density in the control compared to organic
wastes amended plots for 2006 and 2007 study seasons. The bulk densities in uncontaminated
soil ranged from 1.54 – 1.61 and 1.56 – 1.69 Mgm-3 for the 2006 and 2007 study years
respectively. The bulk densities of the soils of the burnt rice husk dust treated plots recorded
lowest values of 1.54 and 1.56 Mgm-3 for the two seasons respectively. The bulk densities of
uncontaminated soil amended with unburnt rice husk dust and saw dust were significantly higher
relative to those of burnt rice husk dust treated plots for the two cropping seasons. Generally, the
bulk densities increased after first cropping season in the automobile oil contaminated and
organic wastes amended soils. Again, the bulk densities in oil contaminated plots were higher
compared to control and organic wastes amended plots in automobile oil contaminated and
organic wastes amended soils.
39
Table 5. Some properties of soil at selected depths (cm)
Contaminated Soil
Soil depth (cm)
Uncontaminated Soil
Soil property
gkg-1
-3
Soil depth (cm)
Sand
Silt
Clay
BD(mgm )
AWC(cm/cm)
TP (%)
PH (Kcl)
0-15
650
230
120
1.59
0.18
40
4.3
15 – 30
560
200
150
1.62
0.19
39
30 – 45
540
180
210
1.66
0.20
45 – 60
230
170
240
1.80
0.21
gkg-1
Soil property
-3
Sand
Silt
Clay
BD(mgm )
AWC(cm/cm) TP (%)
PH (Kcl)
0-15
600
240
160
1.48
0.20
44
4.8
4.2
15 – 30
580
220
200
1.50
0.21
44
4.8
37
4.1
30 – 45
540
190
270
1.51
0.22
43
4.7
32
3.8
45 – 60
520
180
300
1.66
0.23
37
4.3
BD – Bulk density, AWC – available water capacity, TP – total porosity
40
Table 6. Effect of amendments on bulk density (Mgm-3)
Contaminated Soil
Uncontaminated Soil
Treatment
2006
2007
2008
Treatment
2006
2007
2008
O
1.70
1.73
1.76
C
1.61
1.69
1.69
OB
1.57
1.60
1.63
B
1.54
1.56
1.56
OU
1.61
1.66
1.67
U
1.58
1.59
1.59
OS
1.64
1.67
1.68
S
1.59
1.59
1.59
LSD(0.05)
0.03
0.02
0.03
LSD(0.05)
0.02
0.02
0.02
O – oil contamination, OB – oil contamination treated with burnt rice husk dust, OU – oil
contamination treated with unburnt rice husk dust, OS – oil contamination treated with saw dust,
C – control not treated with oil or organic wastes, B – burnt rice husk treatment, U – unburnt
rice husk dust treatment, S – saw dust treatment.
41
Furthermore, significantly higher bulk density was obtained in residual year in spent
automobile oil treated plots relative to oil contaminated plots amended with organic wastes. The
bulk density was significantly higher in the control plots compared to other treatments. There
were significant differences in bulk density ( P<0.05) between the oil treated plots and
uncontaminated ones amended with unburnt rice husk dust and saw dust when compared with
the one amended with burnt rice husk dust in the residual study year. The observed high bulk
densities in oil contaminated plots suggest that contamination of soil with spent automobile oil
caused soil compaction through reduction of soil pores (Table 7) and thereby increased bulk
density. This is in line with Mbah et al. (2009) observation of increase in bulk density of spent
automobile oil contaminated soil in their study area. Similarly, Ogboghodo et al. (2001) reported
that soil degradation due to oil contamination caused soil compaction.
The observed bulk density values in oil contaminated soil for 2006 and 2007 cropping
seasons were above the critical limit of 1.63 Mgm-3 recommended for root penetration in sandy
loam according to Arshad et al. (1996), Grossman and Berdanier (1982) and Grossman (1981).
The increase in bulk densities after the first cropping season could be due to continuous
cropping. This agrees with the report of Mbah et al. (2009) that bulk density values increased
after first year of cropping due to continuous cropping. Anikwe et al. (2003) observed that soil
dry bulk density increased with time after tillage as a result of trafficking during field operations
and other natural forces like alternate wetting and drying circles that caused large effective stress
in tropical climates. Mbah (2004) and Mbagwu (1992b) also reported increased soil bulk density
after tillage. According to Mbagwu (1992b), the major physical constraint to high crop
production on degraded tropical soils was high bulk density. The bulk densities of the soil
amended with organic wastes were within non-limiting values for root penetration and
proliferation except for saw dust treated plots for 2006 and 2007, unburnt rice treated soil for
2007 in contaminated soil and control in 2007 season (Grossman,1981; Grossman and
Berdanier,1982).
The significantly lower bulk densities in burnt rice husk dust treatment suggest that the
organic waste contributed more in bulk density reduction than other wastes. On the other hand,
burnt rice husk dust could have long residual effect on bulk density of soil. This study
demonstrates that amendment of spent automobile oil contaminated and uncontaminated soils
with organic wastes is beneficial, as it will reduce soil bulk density. Low bulk density would
enhance plant root proliferation and aeration. According to Anikwe et al. (2007) and Kooistra
and Tovey (1994) high bulk density decreases soil pore volume and water available to crops.
42
4.3.4 Total Porosity and Pore Size Distribution
The effect of amendments on total porosity and pore size distribution for three cropping
seasons is shown in Table 7. The total porosity was significantly (P<0.05) lower in soils
contaminated with spent automobile oil relative to counterpart soils amended with organic
wastes during 2006 and 2007 cropping seasons. The total porosity values ranged from 35.9440.76 and 34.90 – 41.97% in spent automobile oil contaminated soil for the two seasons
respectively. Total porosity was significantly higher ( P<0.05) in oil contaminated soil amended
with burnt rice husk dust and unburnt rice husk dust in 2006 and the oil contaminated plots
amended with burnt rice husk dust compared with saw dust treated plots in the two seasons.
Significantly lower ( P<0.05) total porosities were obtained in the control relative to
plots amended with different organic wastes in 2006 and 2007 seasons. The total porosities
ranged from 39.34 – 42.07 and 37.16 – 41.23% in uncontaminated soil for the two seasons
respectively. The plots treated with burnt rice husk dust were significantly (P<0.05) higher in
total porosity than the other treatments in the two seasons. Total porosities of organic wastes
amended plots were significantly ( P<0.05) higher when compared with the control in 2006 and
2007 cropping seasons.
The total porosities were significantly lower (P<0.05) in oil contaminated soil and
control when compared with organic wastes amended plots in the residual season. Total
porosities of uncontaminated soil were generally higher relative to those of oil contaminated soil
in the study seasons.
Water filled porosity (micro-porosity) was significantly lower ( P<0.05) in the oil treated
plots in 2006 and 2007 cropping seasons relative to oil contaminated soil amended with organic
wastes. The water filled porosity was significantly higher in oil contaminated soil treated with
burnt rice husk dust and oil contaminated treated with unburnt rice husk dust in the 2006 season
compared with oil contaminated amended with sawdust plots. Treatment with organic wastes did
not significantly (P<0.05) influence macroporosity of oil contaminated soil in 2006 and 2007
cropping seasons.
The water filled porosity was significantly lower in the control relative to other
treatments except for oil contaminated and amended with unburnt rice husk dust and sawdust
plots in 2006 and 2007 study seasons. Water filled porosities were not statistically different in
plots amended with different organic wastes in two seasons. Significantly higher air filled
porosities were obtained in oil contaminated soil treated with burnt rice husk dust and unburnt
43
Table 7. Effect of amendments on total porosity and pore size distribution (%)
Contaminated Soil
Trt
2006
2007
2008
Total porosity
34.90
2006
Mip
2007
Mip
Map
2008
Mip
Trt
Map
2006
2007
2008
Total porosity
2006
Mip
Map
2007
Mip
Map
2008
Mip
Map
O
35.94
C
39.34 37.16 37.16 27.34 12.00 21.43 15.73 21.43 15.73
OB
40.76 41.97 38.66 28.38 12.36 25.62 14.10 21.34 14.72 B
42.07 41.23 41.23 29.38 12.72 24.23 17.00 24.23 17.00
OU
39.60 38.30 38.00 28.59 10.12 23.95 14.35 23.58 14.35 U
40.56 40.09 40.09 28.21 12.31 24.34 15.75 24.24 15.75
OS
38.21 37.77 37.36 26.90 11.30 23.98 13.85 23.77 13.70 S
40.19 39.91 39.91 29.45 10.70 24.16 15.75 24.16 15.75
LSD(0.05)
1.08
1.95
33.77
Map
Uncontaminated Soil
1.24
24.13 12.10 22.58 12.32 23.59 2.84
2.61
NS
2.33
NS
1.84
1.40
LSD(0.05)
0.92
1.54
1.54
1.58
1.60
1.49
2.49
1.49
Trt – treatment, mip – micro porosity, O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended with
unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt rice husk dust, U-unburnt rice husk dust, S-saw dust
2.49
44
rice husk dust in 2006 and in oil contaminated soil amended with burnt rice husk dust in 2007
cropping season relative to other treatments.
Generally, spent automobile oil treatment decreased total porosity, water and air-filled
porosities compared to organic wastes amended soil for the 2006 and 2007 cropping seasons.
The soil total porosity and micro porosity were lower after first cropping season in both soils.
Furthermore, significantly lower water filled porosities were obtained in control relative
to other treatments in the residual year. The oil treated plots amended with unburnt rice husk
dust and sawdust were significantly higher in water filled porosity than oil contaminated plots
amended with burnt rice husk dust in third season. Furthermore, the water filled porosity was not
significantly (P<0.05) different among the plots amended with organic wastes in
uncontaminated soil. Macro porosity was significantly lower ( P<0.05) under oil treatment and
control in residual year compared to oil contaminated plots amended with burnt rice husk dust
and unburnt rice husk dust.
Low total porosity observed in soil contaminated with spent automobile oil could be
attributed to coating of oil in the pores. Mbah et al. (2009), reported that spent automobile oil
contaminated soil gave lower total porosity compared with the control. Low soil total porosity
could result in poor aeration and build up of Carbon IV Oxide which is detrimental to soil
productivity. Vuto et al. (2005) noted that soil porosity was one of the fertility problems of
automobile oil contaminated soil. Lack of oxygen has been observed to impose limitation on
productivity of oil degraded soil (Leahy and Colwell, 1990; Ladousse and Tramier, 1991). Total
porosity values in spent automobile oil contaminated soil are not limiting to water and nutrient
storage (Karlen et al., 1994).
The significant increase in soil total porosity of organic wastes amended soil is in
agreement with the reports of Adeleye et al. (2010) and Asadu et al. (2008). The incorporation
of organic wastes into the soil to increase total porosity is an ameliorative measure. Anikwe
(2000) and Nnabude and Mbagwu (2001) worked with unburnt and burnt rice husk dust and
reported increased total porosity. According to Marshal et al. (1996), high values of total
porosity are beneficial as they improve aeration and create healthier environment for root
elongation, expansion and nutrient uptake. Nicou and Chopart (1979) reported that maize root
density increased by over 300 percent in a total porosity range of 43–48% which resulted in
doubling of grain yield of maize.
45
4.3.5 Aggregate Stability
Table 8 shows changes in aggregate stability following different amendments in the soil
for three study seasons. The values of aggregate stability ranged from 54.5 – 69.0 % and 50.3 64.8% in the spent automobile oil contaminated soil for the 2006 and 2007 cropping seasons
respectivelys. Aggregate stability was significantly (P<0.05) lower in the unamended oil
contaminated s oil compared to oil contaminated soil amended with different organic wastes of
burnt rice husk dust, unburnt rice husk dust and sawdust in the two seasons. The aggregate
stability in the oil contaminated soil amended with different organic wastes was not significantly
different in 2006 and 2007 cropping seasons. Furthermore, the aggregate stability of control was
significantly lower when compared with plots amended with different organic wastes in 2006
study season. Aggregate stability was not affected by amendments in 2007 cropping season.
The aggregate stability ranged from 65.8 – 72.0 % and 66.0 – 70.3% for 2006 and 2007
cropping seasons respectively in the uncontaminated soil.
The aggregate stability of oil treated plots was lower relative to control and organic
wastes amended soil of burnt rice husk dust, unburnt rice husk dust and sawdust. . However,
aggregate stability was higher in uncontaminated soil compared to oil contaminated one. It was
observed that continuous cultivation of soil decreased aggregate stability.
The aggregate stability was significantly lower under oil contamination and control
compared to organic wastes amended plots in residual year. Significantly higher aggregate
stability was obtained in oil treated plots amended with sawdust relative to other organic wastes
amendment in the contaminated soil in 2008 cropping season. The oil contaminated plots
treated with burnt rice husk dust and unburnt rice husk dust were higher in aggregate stability
compared to the one amended with sawdust in uncontaminated soil. The decrease in aggregate
stability in residual year could be attributed to continuous cultivation without amendment. The
organic wastes provided long residual positive impact on aggregate stability of soil.
The reduction in aggregate stability in spent automobile oil contaminated soil is
supported by the findings of Mbah et al. (2009) which indicated 9% reduction in aggregate
stability due to spent automobile oil contamination. According to Ogboghodo et al. (2001), land
degradation due to oil contamination could lead to alteration of soil physical properties such as
structure. The aggregate stability values are below 60% recommended as ideal (Anikwe, 2006).
46
Table 8.
Effect of amendments on aggregate stability (%) of contaminated and
uncontaminated soils
Contaminated Soil
Uncontaminated Soil
Treatment
2006
2007
2008
Treatment
2006
2007
2008
O
54.5
50.3
43.0
C
65.8
66.0
48.5
OB
69.0
64.8
46.0
B
72.0
70.3
55.4
OU
66.4
61.4
45.0
U
70.9
69.7
56.8
OS
62.7
58.4
55.1
S
70.2
69.5
49.2
LSD(0.05)
4.8
7.8
1.6
LSD(0.05)
2.9
Ns
3.0
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt
rice husk dust, U-unburnt rice husk dust, S-saw dust
47
Similarly, the increase in aggregate stability in organic wastes amended plots relative to
other amendments could be as a result of positive influence of organic matter build up in the
soil. Mbagwu et al. (1991) observed that organic matter from organic wastes bound smaller
aggregates into larger ones. Also, Harris et al. (1966) noted that organic matter from organic
wastes was essential for the production of a good soil tilth. Webber (1978) observed that
incorporation of organic materials into soil played an important role in the formation and
stabilization of soil aggregate. Asadu et al. (2008) attributed improvement in aggregate stability
of organic wastes amended soil to binding effect of organic components of the material.
Accordingly, this is a positive impact on soil as it would be less prone to mechanical breakdown
and erosion by water.
4.3.6
Mean Weight Diameter
The mean weight diameter under different treatments is shown in Table 9 for three
cropping seasons. The mean weight diameter (MWD) ranged from 2.52 – 3.10 mm and 2.40 –
3.04 mm in spent automobile oil contaminated soil for the 2006 and 2007 cropping seasons
respectively. The MWD of oil contaminated plots was significantly (P< 0.05) lower relative to
oil contaminated plots amended with different organic wastes for the seasons.
The MWD of oil contaminated plots amended with burnt rice husk dust and unburnt rice husk
dust was significantly higher than that of oil contaminated plots treated with sawdust except in
2006 cropping season for oil contaminated plots amended with unburnt rice husk dust. However,
the MWD of oil contaminated soil amended with burnt rice husk dust waste was significantly
higher relative to other organic wastes treatments in 2007 season.
Mean weight diameter ranged from 2.64 – 3.22 mm and 2.67 – 3.16 mm in
uncontaminated soil for the 2006 and 2007 cropping seasons respectively. The observed values
were significantly lower in control relative to organic wastes amendments in 2006 season.
Significantly higher MWD was obtained in burnt rice husk dust treated plots relative to other
organic wastes treatments in 2007 cropping season. The MWD of burnt rice husk dust amended
plots was significantly (P<0.05) higher than those of unburnt rice husk dust and sawdust
amended soil in 2006 and 2997 seasons.
48
Table 9.
Effect of amendments on mean weight diameter (mm) of contaminated and
uncontaminated soils
Contaminated Soil
Uncontaminated Soil
Treatment
2006
2007
2008
Treatment
2006
2007
2008
O
2.52
2.40
2.21
C
2.67
2.67
2.64
OB
3.10
3.04
2.81
B
3.22
3.16
3.16
OU
2.87
2.80
2.75
U
2.86
2.86
2.80
OS
2.89
2.65
2.57
S
2.80
2.71
2.71
LSD(0.05)
0.15
0.10
0.14
LSD(0.05)
0.15
0.41
0.41
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt
rice husk dust, U-unburnt rice husk dust, S-saw dust
49
The MWD of oil treated plots was significantly lower ( P<0.05) compared to control.
The MWD decreased after first cropping season. Continuous cultivation of soil was observed to
have degraded soil mean weight diameter.
The MWD was significantly (P< 0.05) lower under oil contamination and control relative
to organic wastes amendments in both oil contaminated and uncontaminated soils in the residual
year. The MWD of oil contaminated plots amended with burnt rice husk dust was significantly
higher than that of oil contaminated plots treated with sawdust in the contaminated soil.
However, MWD of burnt rice husk dust treated plots for uncontaminated soil was significantly
(P<0.05) higher than those of unburnt rice husk dust and sawdust treatments.
The low values of MWD observed in contaminated soil indicate that spent automobile oil
contamination has negative influence on soil structural stability. Ogboghodo et al. (2001) and
Udo (1984) reported changes including loss of soil structure following oil contamination.
Contamination of soil with spent automobile oil would make it vulnerable to degrading forces of
wind erosion contributing to low productivity. The decrease in MWD in the third season of
cropping could be attributed to continuous cropping without amendment. Since MWD is an
index of soil stability; low values would lead to poor stabilization and loss in productivity. Obi
(2000) pointed out that high values of MWD would enhance soil productivity. The significant
increase of MWD in unburnt rice husk dust and burnt rice husk dust amendments implies that
the organic wastes could have the potential for restoration of structurally degraded soil. Using
unburnt rice husk dust and burnt rice husk dust as soil amendment, Nnabude and Mbagwu
(2001) reported increases in MWD relative to control.
4.3.7
Water Retained at 60 cm tension
The effect of different amendments on percentage of water retained at 60 cm tension is
presented in Table 10 for the three cropping seasons. The values of water retained at 60 cm
tension ranged from 19.94 – 25.04, 18.38 – 24.90, 23.04 – 28.41 and 20.90 – 26.88 respectively
for oil contaminated and uncontaminated soils for the 2006 and 2007 cropping seasons. Water
retained at 60 cm tension was significantly (P<0.05) lower under oil contamination treatment
and control relative to amendments of different organic wastes in two seasons. Generally, the
observed values of water retained at 60 cm tension in two soils were significantly higher in oil
contaminated plots amended with burnt rice husk dust and burnt rice husk dust amended plots in
the uncontaminated soil compared to other organic wastes treatments in two cropping seasons.
Table 10. Effect of amendments on percentage of water retained at 60 cm tension ( dry mass
basis)
50
Contaminated Soil
Uncontaminated Soil
Treatment
2006
2007
2008
Treatment
2006
2007
2008
O
19.94
18.38
18.04
C
23.04
20.90
20.90
OB
25.04
24.90
24.87
B
28.41
26.88
26.88
OU
22.54
22.02
21.39
U
25.66
24.65
OS
22.04
21.29
20.81
S
25.95
25.10
23.10
LSD(0.05)
1.65
2.46
1.14
LSD(0.05)
1.74
1.73
1.72
27.66
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt
rice husk dust, U-unburnt rice husk dust, S-saw dust
Water retained at 60 cm tension was higher in plots under different organic wastes
treatment in the uncontaminated soil relative to those of oil contaminated plots supplemented
with organic wastes. Continuous cropping reduced water retained at 60 cm tension.
51
Significantly lower water retained at 60 cm tension was obtained in oil treated soil and
control compared to plots under different organic wastes amendments in the contaminated and
uncontaminated soils in 2008 cropping season. Amendment of contaminated and
uncontaminated soils with burnt rice husk dust significantly increased water retained at 60 cm
tension compared to amendment with other organic wastes in the two soils in third cropping
season. This observation suggests that burnt rice husk dust could have positive residual effect on
water retained at 60 cm tension in soil.
Spent automobile oil contamination of soil has negative influence on water retained at 60
cm tension. Lower water retention at 60 cm tension in contaminated soil compared to
uncontaminated soil could be attributed to displacement action of oil in the soil pores and this
would mean that crops growing in such soil would exert great suction pressure in order to
extract water for their use. This situation would cause water stress and limit soil productivity.
Retention of more water in the soil at 60 cm tension could be attributed to increase in water
storage pores in line with the observation of Obi and Asiegbu (1980).
Improvements in soil moisture retention due to organic wastes amendment were reported
by Nnabude and Mbagwu (2001), Mbah (2004), Mbagwu and Ekwealor (1990) and Mbagwu
(1989b). Retention of more water at 60 cm tension in the soil would mean more water to crops at
low tension. Besides, crop growing in such soil would be subjected to less “stress”.
4.3.8
Available Water Capacity
The values of available water capacity(AWC) after different treatments are shown in
Table 11 for three seasons. The observed values of AWC ranged from 0.14–0.19 cm/cm and
0.13–0.18 cm/cm for the treatments in oil contaminated soil for the 2006 and 2007 cropping
seasons. Significantly (P<0.05) lower AWC was obtained under oil contamination relative to
organic wastes treatments. The AWC for oil contaminated plots supplemented with burnt rice
husk dust was significantly ( P<0.05) higher than those of other organic wastes treatment.
Furthermore, AWC for oil contaminated plots amended with unburnt rice husk dusts was
significantly higher than that of sawdust treated one.
Table 11. Effect of amendments on available water capacity (cm/cm)
Contaminated Soil
Uncontaminated Soil
52
Treatment
2006
2007
2008
Treatment
2006
2007
2008
O
0.14
0.13
0.12
C
0.17
0.16
0.16
OB
0.19
0.18
0.17
B
0.21
0.20
0.20
OU
0.18
0.17
0.16
U
0.20
0.19
0.19
OS
0.17
0.17
0.16
S
0.19
0.18
0.18
LSD(0.05)
0.01
0.01
0.02
LSD(0.05)
0.02
0.02
0.02
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt
rice husk dust, U-unburnt rice husk dust, S-saw dust
53
The AWC values ranged from 0.17-0.21cm/cm and 0.16 – 0.20 cm/cm in
uncontaminated soil for two seasons. The AWC was significantly lower in control compared to
different organic wastes treatments for the two planting seasons. There were no significant
differences in AWC among the different organic wastes amendments for two seasons except for
burnt rice husk dust and sawdust amended plots in 2007 study year.
The values of AWC in oil contaminated soil were significantly lower (P<0.05) for 2006
and 2007 seasons relative to those of uncontaminated soil for the two cropping seasons. These
values in oil treated soil and control were also significantly lower compared to organic wastes
amended plots in both soils. Available water capacity was reduced after first cropping season in
the two soils. The available water capacity values were higher in uncontaminated soils than in
those of contaminated soils for two seasons.
Significantly lower AWC was obtained under oil contamination and control compared to
different organic wastes amendment in the contaminated and uncontaminated soils in the
residual year. Burnt rice husk dust and sawdust amendment significantly increased AWC in
uncontaminated soil relative to sawdust amended one. The amendment of burnt rice husk dust
and unburnt rice husk dust could have positive residual effect on available water capacity of soil.
The values of AWC in contaminated soil suggest that contamination of soil with spent
automobile oil could reduce available water capacity. Vuoto et al. (2005) observed that oil
contamination of soil affected its moisture content while Amadi et al. (1996) noted that oil
contamination would lead to less water availability. This observation disagrees with the report of
Udo (1984) that pollution of soil with oil increased its water holding capacity. Low available
water capacity is detrimental to soil and crop productivity. This is because it would retard
physiological processes and potential yield of crops.
Nevertheless, available water capacity in the contaminated soil and soil under other
treatments is within the non-limiting values considered not to impede soil productivity and
potential rooting depth (Grossman and Berdanier, 1982; Grossman, 1981). Improvements in
available water capacity in organic wastes amended plots could be attributed to specific surface
and colloidal nature of material released to the soil as well as to the hydrophilic nature of the
wastes. Nwite et al.(2011) and Nyamagara et al. (2001) reported improved available water
capacity in plots treated with organic wastes relative to the control. According to Khaleel et al.
(1981), enhancement in available water capacity due to organic wastes addition could be
attributed in part to structural modifications and increase in total porosity.
54
4.3.9
Hydraulic Conductivity
Table 12 shows effect of different treatments on hydraulic conductivity for the three
cropping seasons. The values of hydraulic conductivity ranged from 54.72 – 63.09 cmhr-1 and
53.59 – 61.84 cmhr-1 for spent automobile oil contaminated soil for the 2006 and 2007 cropping
seasons. The hydraulic conductivity values were observed to be significantly (P<0.05) lower in
oil treated plots relative to oil contaminated plots supplemented with burnt rice husk dust and
unburnt rice husk dust in the two seasons. The amendment of oil contaminated soil with organic
wastes did not significantly affect hydraulic conductivity for the two cropping seasons.
Similarly, the hydraulic conductivity values ranged from 65.90 – 72.50 cmhr-1 and 61.26
– 72.44 cmhr-1 in the uncontaminated soil for two years of application of treatments.
Significantly lower hydraulic conductivity was obtained in the control relative to amendments of
different organic wastes in 2007 cropping season. The amendments using different organic
wastes did not statistically affect hydraulic conductivity in the uncontaminated soil for two
seasons. The hydraulic conductivity values observed in oil treated plots were lower than those
obtained in control for the 2006 and 2007 cropping seasons. However, hydraulic conductivity of
different organic wastes treated plots in uncontaminated soil was higher compared to those
under oil treatment amended with organic wastes for the two seasons.
There was general decrease in hydraulic conductivity values after the first cropping
season. The hydraulic conductivity was significantly lower in oil treated plots and control
relative to plots under different organic wastes treatment. Hydraulic conductivity of organic
wastes amended plots in the contaminated and uncontaminated soils did not statistically vary in
the 2008 season.
The result observed in contaminated soil suggests that contamination of soil with spent
automobile oil has negative influence on hydraulic conductivity. Amadi et al. (1996) noted that
oil contamination would cause formation of oily scum, which could impede water availability
and transmission. Reduction of hydraulic conductivity would affect water transmission in the
soil column and, therefore, the amount of water storage. This, in effect, would impact on the
fertility status of the soil. Vuoto et al. (2005) observed that hydraulic conductivity contributed to
serious fertility problem in automobile oil contaminated soil.
55
Table 12. Effect of amendments on hydraulic conductivity (cmhr-1)
Contaminated Soil
Uncontaminated Soil
Treatment
2006
2007
2008
Treatment
2006
2007
2008
O
54.97
54.72
53.59
C
65.90
63.26
62.24
OB
63.09
61.84
60.83
B
72.50
72.40
72.38
OU
62.05
61.81
59.91
U
71.80
69.56
69.56
OS
59.36
57.88
57.23
S
66.50
69.16
69.16
LSD(0.05)
4.79
4.55
4.07
LSD(0.05)
NS
3.92
3.92
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt
rice husk dust, U-unburnt rice husk dust, S-saw dust
56
Besides, Mbagwu (1992b) in his study on improving the productivity of a degraded
ultisol in Nigeria with organic wastes reported that low hydraulic conductivity was one of the
major physical constraints to high-level crop production on degraded tropical soils.
Nevertheless, the values of hydraulic conductivity (Table 12) indicate that the soils were of high
permeability (Soil Survey Staff, 1993).This is expected since the soils are medium textured (
sandy loam). Mbah et al. (2009) noted that hydraulic conductivity values were highest in
organic wastes amended soil compared to the control. Ezeaku and Anikwe (2006) attributed
high hydraulic conductivity to bioactivity leading to creation of channel of flow of water.
Anikwe (2000) also reported that rice husk dust amendment of clay soil significantly improved
hydraulic conductivity and consequently water transmission. Durner et al. (2008) and Alvarez et
al. (2007) reported high values of hydraulic conductivity in sandy loam soil amended with
organic waste.
4.4
Chemical Properties
4.4.1
Effects of amendments on available P, total N, OC ( %) and pH (KCL)
Table 13 shows the effects of different treatments on available phosphorus ( P), total
nitrogen (N), soil organic carbon (OC) and pH for three cropping seasons. Available P, total N,
soil OC and pH were significantly (P<0.05) lower in oil treated soil relative to oil contaminated
soil amended with different organic wastes in 2006 and 2007 cropping seasons. The Table 13
further shows that the highest available P, total N, OC and pH occurred in oil contaminated plots
treated with burnt rice husk dust for the two seasons relative to other organic wastes amended
plots. Significantly higher available P, total N and pH were obtained for oil contaminated plots
amended with burnt rice husk dust compared to other organic wastes treated plots in 2006 and
2007 cropping seasons. The pH of oil contaminated soil amended with unburnt rice husk dust
was significantly higher than oil contaminated plots treated with sawdust in 2007 season.
Furthermore, significantly lower available P, total N, soil OC and pH were obtained in
control plots in 2006 and 2007 planting seasons. The values of available P, total N, OC and pH
were highest for the burnt rice husk dust amended plots relative to unburnt rice husk dust and
sawdust treated plots in the uncontaminated soil. The amendment of burnt rice husk dust
significantly increased available P, total N, soil OC and pH in the two seasons. On the other
hand, s ignificantly higher pH was obtained in unburnt rice husk dust amended plots compared
to sawdust treated plots for the seasons in the uncontaminated soil.
57
Table 13. Effect of amendments on available P, Total N, OC (%) and pH ( kcl ) of contaminated and uncontaminated soils.
Trt
Contaminated soil
2006
-1
P (Mgkg )
N(%)
Trt
2007
OC(%)
pH(kcl)
P
N(%)
OC(%)
Uncontaminated soil
2008
pH(kcl)
(Mgkg-1)
P
N(%)
OC(%)
2006
1
pH(kcl)
P (Mgkg )
N(%)
OC(%)
2007
pH(kcl)
(Mgkg1)
P
N(%)
OC(%)
2008
pH(kcl)
(Mgkg1)
P
N(%)
OC(%)
pH(kcl)
(Mgkg1)
O
23.04
0.17
1.18
3.7
23.39
0.14
1.18
3.5
22.05
0.14
0.74
3.4
C
27.16
0.79
1.23
4.7
26.67
0.76
1.20
4..7
26..65
0..74
1..18
4..5
OB
45.19
0.96
1.33
5.1
43.67
0.78
1.26
5.0
38.09
0.52
1.19
5.0
B
56.44
1.16
1.43
5.4
48.26
1.07
1.35
5..1
48..25
1..06
1..34
5..1
OU
37.41
0.80
1.31
4.9
35.04
0.76
1.22
4.9
33.56
0.49
1.18
4.6
U
50.10
1.06
1.36
5.1
47.88
1..01
1.31
5..0
47..87
1..00
1..30
5..0
OS
37.74
0.71
1.26
4.8
32.03
0.76
1.23
4.7
32.67
0.45
1.17
4.6
S
49.38
1.02
1.33
5.0
55.95
0..99
1.29
4..9
45..93
0..98
1..28
4..8
LSD(0.05)
4.35
0.15
0.06
0.2
4.03
0.05
0.01
0.2
5.20
0.06
0.11
0.6
LSD(0.05)
3.19
0.11
0..01
0.2
3..00
0..05
0..09
0.1
3..00
0..09
0..05
0..1
Trt-treatment, P – phosphorus (mgkg-1), N – nitrogen (%), OC – organic carbon(%), pH in kcl, O-oil, OB-oil contaminated amended with burnt
rice husk dust, OU-oil contaminated amended with unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt rice
husk dust, U-unburnt rice husk dust, S-saw dust.
.
58
Generally, the values of available P, total N, soil OC and pH were lower in oil treated
soil when compared to the control for the two seasons. Available P, soil OC, total N and pH
were higher by 15, 78, 21% and 12, 82, 27, 26%, respectively in control relative to oil
contaminated soil in 2006 and 2007 cropping seasons. Again, the values of these parameters
were higher in uncontaminated soil than in those of the contaminated soil for two cropping
seasons. The soil parameters were generally reduced by continuous cropping in both soils.
The available P, total N, soil OC and pH were significantly lower in the oil treated plots
and control compared to different treatments of organic wastes in the residual year.
Significantly higher available P, total N, soil OC and pH were obtained in oil contaminated soil
amended with burnt rice husk dust relative to those under other organic wastes treatments in the
third cropping season. However, soil OC and pH were significantly higher in burnt rice husk
dust treated plots than in those of unburnt rice husk dust and sawdust amendments. Similarly,
significantly higher pH was obtained in unburnt rice husk dust amended plots relative to the one
under sawdust treatment in uncontaminated soil for the third season.
The significantly lower values of available P, total N and OC and pH in contaminated
soil suggest that spent automobile oil contamination negatively influenced these parameters.
Agbogidi et al. (2007) and Abii and Nwosu (2009) reported low total N, available P and OC in
crude oil contaminated soil due to spillage. Diana et al. (2004) and Amadi et al. (1996) reported
low total N, available P and OC in automobile oil contaminated soil relative to uncontaminated
soil. According to Vuoto et al. (2005) automobile oil contaminated soil has serious fertility
problem, as it would partly affect soil pH and nutrient content. The percentage organic carbon
and total N were of low values in 2006 and 2007 cropping seasons according to the ratings of
Enwezor et al. (1981), Metson (1961) and Landon (1991). The pH of soil was observed to be
strongly acidic for the two seasons of treatment application . Amadi et al. (1996) had noted that
pH was extremely acidic in oil contaminated soil for the two seasons. The result of pH values
differs from report of Diana et al. (2004) which indicated that hydrocarbon oil contaminated soil
significantly increased pH.
The generally low values of OC and total N is supported by Asadu (1990) finding that
the organic matter contents of the soils of the tropics are low because of high temperatures
which cause rapid mineralization. However, the improvements recorded in available P, total N,
OC and pH suggest that these nutrients sequestered in the organic wastes were released to the
soil during decomposition. This is consistent with the findings of Nwite et al. (2005) who noted
that available P, total N, OC and pH increased following organic waste amendment of soil.
According to Okonkwo et al. (2011) incorporation of organic wastes in soil resulted to increase
59
in organic carbon. Studies by Okonkwo and Ogu (2002) and Nnabude and Mbagwu (2001)
showed increases in available P, total N, OC and pH of organic wastes amended plots.
Similarly, Bengtson and Cornette (1973), Sanderson et al. (1969) and Trenel (1961) reported
increase in available P following addition of wastes as soil amendment. Although, pH increased
it was still strongly acidic after first cropping season following amendment of organic wastes.
Nnabude and Mbagwu (2001) noted
that amendment of soil with U and B failed to improve
strongly acidic soil and attributed it to organic acids as well as CO2 produced during the
processes of organic wastes decomposition. The result also agrees with Mbah (2004) who
pointed out that pH remained low following organic wastes amendment. Arya et al. (1991) and
Opara Nnadi et al. (1987) observed significant pH increases following organic wastes
amendment of soil. Wahab and Lupez (1980) noted that one of the basic functions of organic
wastes was to neutralize the soil pH.
4.4.2 Effect of Amendments on Exchangeable cations, CEC, ECEC (cmolkg-1) and BS(%)
The effect of different amendments on sodium (Na), potassium (K), calcium (Ca),
magnesium (Mg), cation exchange capacity (CEC), effective cation exchange capacity (ECEC)
and percent base saturation (% BS) is shown in Table 14 for three study seasons. These soil
properties except for Na and ECEC were significantly (P<0.05) lower in spent automobile oil
contaminated soil relative to the oil contaminated plots supplemented with different organic
wastes in 2006 and 2007 cropping seasons. The percent base saturation was not statistically
affected in the 2007 planting season. There were significantly higher K, Ca, CEC and ECEC in
oil contaminated plots amended with burnt rice husk dust compared to same soil with unburnt
rice husk dust and sawdust amendments in the two seasons. Exchangeable Mg and Ca were
significantly higher in oil contaminated plots treated with unburnt rice husk dust relative to same
soil with burnt rice husk dust and sawdust treatments for the two seasons.
Similarly, except for %BS and ECEC in 2006 and 2007 cropping seasons, the studied
soil properties were significantly lower in control compared to those under different organic
wastes treatments for two cropping seasons. Significantly higher Na and %BS were obtained in
burnt rice husk dust amended plots relative to unburnt rice husk dust and sawdust amendments.
The plots amended with unburnt rice husk dust and sawdust recorded significantly higher Na
and Ca in 2007 cropping season compared to the one treated with burnt rice husk dust.
60
Table 14a.
Effect of amendments on exchangeable cations, CEC, ECEC (cmolkg-1) and BS%
Contaminated Soil
Trt
2007
2008
comlkg-1
comlkg-1
comlkg-1
Na
K
O
0.17
0.63 2.25 1.34 5.45
7.71
81.2
0.16
0.58 2.05 1.26 5.10
4.96
79.8
0.15
0.54 2.04 1.25 4.82
4.58
80.0
OB
0.25
1.21 4.84 2.37 9.89
9.91
93.5
0.26
1.21 4.44 2.34 9.62
9.18
88.8
0.24
1.16 4.25 2.45 9.15
8.53
88.2
OU
0.24
1.18 4.38 2.70 8.75
9.11
93.2
0.23
1.16 4.50 2.35 9.12
8.98
90.0
0.23
1.15 3.71 2.32 8.21
7.86
91.2
OS
0.22
1.14 4.04 2.41 8.32
8.04
93.0
0.22
1.14 3.78 2.28 8.65
8.18
86.5
0.21
1.04 3.76 2.22 8.35
7.70
88.5
0.06 0.64 0.18 0.93
NS
7.9
0.01
0.09 0.63 0.15 1.25
0.80
NS
0.01
0.10 0.71 0.09 0.85
0.74
8.7
LSD(0.05) NS
Ca
2006
Mg
CEC ECEC BS% Na
K
Ca
Mg
CEC ECEC BS% Na
K
Ca
Mg
CEC ECEC BS%
CEC-cation exchangeable capacity, ECEC-effective cation exchange capacity, %BS-percent base saturation, NS-not significant.
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended with unburnt rice husk dust, OS-oil contaminated amended
with saw dust, Trt-treatment
61
Table 14b.
Effect of amendments on exchangeable cations, CEC, ECEC (cmolkg-1) and BS%
Uncontaminated Soil
Trt
Ca
2006
2007
2008
comlkg-1
comlkg-1
comlkg-1
Na
K
Mg
CEC
C
0.19
0.83 3.78 2.27 8.11
B
0.34
U
S
ECEC BS% Na
K
8.83
Ca
Mg
CEC
87.0
0.18
0.72 3.32 2.23 7.93
1.28 5.47 2.82 10.78 11.21
94.8
0.35
0.27
1.16 5.15 3.12 10.00 10.51
95.0
0.26
1.24 5.16 2.84 10.39 10.95
0.04 0.55 0.43 0.79
LSD(0.05) 0.02
0.80
ECEC BS% Na
K
7.22
Ca
Mg
CEC
ECEC BS%
81.3
0.17
0.72 3.32 2.23 7.93
1.30 5.28 2.97 10.19 10.43
97.5
0.33
1.30 5.28 2.97 10.19 10.43
97.5
0.31
1.26 5.10 2.74 9.93
93.3
0.31
1.26 5.10 2.74 9.93
93.3
92.2
0.25
1.23 5.10 2.92 10.52 10.11
90.0
0.25
1.23 4.10 2.90 10.50 10.10
90.0
NS
0.02
0.09 0.43 0.30 0.49
4.9
0.02
0.09 0.43 0.30 0.49
4.9
7.58
3.29
CEC-cation exchangeable capacity, ECEC-effective cation exchange capacity, %BS-percent base saturation, NS-not significant. C-control, Bburnt rice husk dust, U-unburnt rice husk dust, S-saw dust, Trt-treatment
7.22
7.58
NS
81.3
62
Lower exchangeable cations, CEC, ECEC and %BS were obtained in oil contaminated
soil and control relative to those under different organic wastes treatments in the two soils.
However, the values of these soil properties were generally higher in uncontaminated soil
compared to the contaminated one for the two seasons. These properties had significantly
(P<0.05) lower values in oil contaminated soil relative to control. The observed general
decreased levels of these soil properties after first cropping season could be attributed to
exploitation by the maize crops due to continuous cropping.
Furthermore, exchangeable cations, CEC, ECEC and %BS were significantly lower in
oil treated plots and control compared to those under the different organic wastes treatment in
residual year. Significantly higher Na, Mg, CEC and ECEC were obtained in oil contaminated
soil amended with burnt rice husk dust when compared to those of same soil with unburnt rice
husk dust and sawdust amendments in the residual year. The amendment of oil contaminated
soil with unburnt rice husk dust significantly increased Na and Mg compared to those of oil
contaminated plots amended with sawdust and burnt rice husk dust in third cropping season.
There were significantly higher Na and %BS in burnt rice husk dust amended plots relative to
sawdust and unburnt rice husk dust amendments in uncontaminated soil for the residual year.
Plots treated with unburnt rice husk dust and sawdust significantly increased Na and Ca relative
to the plots amended with burnt rice husk dust in the uncontaminated soil. This suggests that
amendment of soil with these organic wastes could have positive residual effect on
exchangeable cations of Na, K, Mg, Ca, CEC, ECEC and %BS.
The values of exchangeable cations of Na, K, Ca and Mg were low in spent automobile
oil contaminated soil. Odu (1980) observed decreases in exchangeable cations of oilcontaminated soil. Calcium dominated the exchange complex of the soil. This is a characteristic
of strongly weathered tropical soil (Abii and Nwosu, 2009). Agbogidi et al. (2007) reported
similar findings in evaluation of oil contaminated soil at Delta state. The values of Na and Mg
are low (Howeler, 1996; Landon, 1991) in contaminated soil. Low CEC, ECEC and %BS is in
agreement with the findings of Amadi et al. (1996) and Abii and Nwosu (2009) who particularly
reported that CEC was lower in oil contaminated soil. Low cation exchange capacity affects soil
productivity and crop growth (Greenland and Hayes, 1978) and would have significant effect on
crop yield and land productivity (Ihejiamaizu, 1996). Anikwe (2006) noted that low CEC among
others was unhealthy to sustained productivity of soil. Cation exchange capacity has been noted
63
to be one of the soil quality indicators (Anikwe, 2006). The values of CEC and ECEC in
contaminated soil are low (Landon,1991; Asadu and Nweke, 1999).
The improvements in exchangeable cations of organic wastes amended plots showed that
these cations were added to soil upon their mineralization. Okonkwo and Ogu (2002) and
Strongard (1984) observed increases in exchangeable cations of Na, K, Ca and Mg following
organic wastes amendment. The highest values of exchangeable cations obtained under the OB,
OU, U and B treatments could be attributed to the exposure of more surfaces for microbial
action.
Agbogidi et al. (2007) reported that reduction in Na and K could be as a result of nutrient
immobilization which resulted from formation of complexes after degradation and uptake by
crops. The exchangeable cations of Ca and Mg are of high to medium rating (Howeler, 1996;
Landon, 1991) in the amended soil. Sodium is of low rating (Asadu and Nweke, 1999).
Improved CEC, ECEC and %BS could be a reflection of increased OC and N content
particularly in organic wastes amended plots relative to control plots (Table 13). Mbagwu et al.
(1991) reported that organic matter contributed to CEC of soil of low activity clays. Asadu and
Akamigbo (1990) reported that organic matter contributed an average of 70% of CEC of Ultisols
and Oxisols in the tropics. The amendment of soil with organic wastes improved the quality of
the soil and, hence, its productivity. The cation exchange capacity and ECEC are low (Landon
1991; Asadu and Nweke, 1999) in OS and S amendments in two cropping seasons. The OB, B,
OU and U amendments compared to OS and S contributed more to improving soil quality and
productivity.
4.4.3 Effect of amendments on AI3+ Saturation and exchangeable acidity (EA)
Table 15 shows changes in AI3+ Saturation and exchangeable acidity (EA) after different
treatments for three cropping seasons. The values of AI3+ saturation ranged from 2.32 -3.35 %
and 2.49 -3.35 % and EA from 0.53 – 0.79 cmolkg-1 and 0.70 – 0.92 cmolkg-1 for the treatments
in oil contaminated soil in 2006 and 2007 cropping seasons respectively. The values of AI3+
saturation and EA were significantly (P<0.05) higher in unamended oil treated plots compared
to oil treated plots amended with the different organic wastes in the seasons. The observed
values of Al3+ saturation and EA in 2006 were significantly higher in oil treated plots amended
with sawdust relative to amendments of same soil with burnt rice husk dust and unburnt rice
husk dust treatments in the two cropping seasons. The Al3+ saturation in oil treated plots
64
Table 15. Effect of amendments on Al3+ saturation% and EA (cmolkg-1)
Contaminated soil
Trt
2006
2007
-1
2008
-1
cmolkg
Uncontaminated soil
Trt
2006
-1
cmolkg
cmolkg
Al3+%
EA
Al3+%
EA
Al3+%
EA
O
3.35
0.79
3.35
0.90
2.44
0.78
OB
2.32
0.53
2.49
0.70
2.27
OU
2.46
0.59
2.60
0.75
OS
2.51
0.61
2.69
LSD(0.05)
0.16
0.06
0.17
2007
-1
2008
-1
cmolkg
cmolkg-1
cmolkg
Al3+%
EA
Al3+%
EA
Al3+%
EA
C
3.04
0.73
3.05
0.78
3.00
0.60
0.55
B
2.17
0.43
2.27
0.55
1.51
0.42
2.39
0.57
U
2.27
0.51
2.39
0.57
1.71
0.46
0.75
2.42
0.62
S
2.33
0.56
2.44
0.62
1.74
0.45
0.16
NS
0.09
LSD(0.05)
0.14
0.09
0.14
0.09
0.10
0.10
Trt – treatment, Al3+% - Percent aluminium, saturation, EA – exchangeable acidity, O-oil, OB-oil contaminated amended with burnt rice husk
dust, OU-oil contaminated amended with unburnt rice husk dust, OS-oil contaminated amended with saw dust, C-control, B-burnt rice husk dust,
U-unburnt rice husk dust, S-saw dust.
65
amended with unburnt rice husk dust was significantly higher than the one amended with burnt
rice husk dust in 2007 season. There was not significant ( P<0.05) differences in exchangeable
acidity between plots contaminated with oil and amended with different organic wastes in the
second cropping season.
Furthermore, Al3+ saturation and EA were significantly higher in control compared to
other amendments in 2006 and 2007 cropping seasons. Significantly higher Al3+ saturation and
EA were obtained in plots treated with sawdust relative to plots amended with unburnt rice husk
dust and burnt rice husk dust in 2006 cropping season.
The values of Al3+ saturation were higher in oil treated plots relative to control in 2006 and
2007 cropping seasons. The AI3+ and EA were generally higher in spent automobile oil treated
plots amended with different organic wastes when compared to those of uncontaminated soil for
the two seasons. The values of these parameters increased following second application of
amendments.
The AI3+ saturation and EA were significantly higher in the oil treated plots and control
relative to different amendments of organic wastes in residual year. The AI3+ saturation was not
significantly affected by treatments in contaminated soil. Exchangeable acidity did not
significantly differ in residual year in both soils. This could be attributed to residual effect of
organic wastes treatments.
Aluminium (AI3+) saturation and EA relate to pH of soil (Asadu and Nweke, 1999). High
AI3+ saturation and EA could affect soil quality and lead to loss in soil productivity. This could
affect microbial activity, availability of plant nutrients, pesticide and heavy metals mobility
(Anikwe, 2006). Asadu and Nweke (1999) reported that EA in particular affected availability of
plant nutrients. Essentially, lime application is to prevent Al toxicity in soil (Pearson, 1975).
The values of AI3+ saturation and EA in organic wastes amended soil suggest that the
amendment of soil could reduce AI3+ saturation and EA. This would improve soil quality and
productivity owing to removal of AI3+ from the exchange site or its neutralization by
decomposed organic wastes. Similar result was reported by Mbah et al. (2001) following
amendments of soil with organic wastes.
66
4.4.4 Heavy Metals
Changes in heavy metals contents following application of different amendments on soil
are shown in Table 16 for three cropping seasons. Heavy metal contents of zinc ( Zn), lead ( Pb)
and cadium ( Cd) of spent automobile oil contaminated soil were significantly (P<0.05) higher
than those of soil amended with burnt rice husk dust waste in 2006 and 2007 cropping seasons.
Significantly higher Pb and copper (Cu) were obtained in spent automobile oil treated plots
amended with sawdust compared to those treated with burnt rice husk dust and unburnt rice husk
dust in the two seasons. Cadium (Cd) and Cu were significantly higher in oil contaminated plots
amended with unburnt rice husk dust relative to those of same soil treated with burnt rice husk
dust and sawdust in 2007 season. The values of Cu and Pb in the control were significantly
lower in burnt rice husk dust and sawdust treated plots compared to the values of those amended
with unburnt rice husk dust in 2006 and 2007 cropping seasons.
Furthermore, significantly higher levels of Zn, Cu and Cd were obtained in sawdusts
amended plots when compared with the plots treated with burnt rice husk dust in the
uncontaminated soil. Significantly higher Pb and Cu were obtained in the plots treated with
sawdust for the uncontaminated soil relative to the one amended with burnt rice husk dust in the
three cropping seasons. The sawdust amended soil also increased significantly the levels of Zn,
Cu, Cd and Pb compared to the levels under burnt rice husk dust amendment for the
uncontaminated soil for three study seasons.
The heavy metals contents were generally higher in spent automobile oil contaminated
plots relative to control in two cropping seasons. The observed values of Zn, Cu, Pb and Cd
indicate that the application of oil and organic wastes increased their concentration in soil
compared to treatments of organic wastes alone. Oil contamination increased Zn, Cu, Pb and Cd
contents in the soil by 98, 72, 78, 15% and 97, 51, 71, 55%, respectively relative to control in
2006 and 2007 seasons respectively. Continuous amendment of these wastes also increased
heavy metals contents in the soil. Generally, the heavy metals contents of organic wastes
amended plots were higher compared to the control. The heavy metals contents of spent
automobile oil treated plots were higher compared to control and organic wastes amended
plots in 2006 and 2007 cropping seasons for the soils
67
Effect of amendments on heavy metals (mgkg-1)
Table 16a.
Contaminated Soil
Trt
Uncontaminated Soil
2006
2007
2008
-1
-1
-1
mgkg
mgkg
Trt
2006
-1
mgkg
mgkg
Zn
Cu
Pb
Cd
Zn
Cu
Pb
Cd
Zn
Cu
Pb
Cd
O
8.63
1.33
1.62
0.46
9.42
1.43
1.73
0.51
3.71
1.36
1.35
0.48
OB
8.02
1.20
0.72
0.36
8.11
0.83
0.79
0.39
2.87
0.75
0.69
OU
8.48
1.30
0.65
0.42
9.03
1.09
0.83
0.46
2.56
1.05
OS
8.51
1.31
0.98
0.39
8.89
1.35
0.94
0.47
2.90
LSD(0.05)
0.53
NS
0.26
0.08
0.59
0.17
0.10
0.05
0.63
2007
2008
-1
mgkg-1
mgkg
Zn
Cu
Pb
Cd
Zn
Cu
Pb
Cd
Zn
Cu
Pb
Cd
C
0.20
0.37
0.35
0.20
0.32
0.70
0.51
0.23
0.32
0.70
0.51
0.23
0.39
B
0.27
0.63
0.71
0.30
0.39
0.82
0.83
0.33
0.39
0.82
0.83
0.33
0.77
0.43
U
0.30
0.80
0.83
0.36
0.34
0.92
0.94
0.38
0.34
0.92
0.94
0.38
1.21
0.87
0.41
S
0.33
0.80
0.77
0.39
0.38
1.00
1.08
0.40
0.38
1.00
1.06
0.39
0.17
0.11
0.05
LSD(0.05)
0.05
0.16
0.16
0.06
0.06
NS
0.15
0.05
0.06
NS
0.15
0.05
Trt-treatment, O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended with unburnt rice husk dust, OS-oil
contaminated amended with saw dust, C-control, B-burnt rice husk dust, U-unburnt rice husk dust, S-saw dust
68
Table 16b.
Ratings of heavy metals (mgkg-1)
Metals
WHO(1996) Rating
LASEPA (2005)
Rating
Alloway(1990) Rating Bowen (1979,1977),
Kabata Pendias (1984)
Rating
Zn
0.0-<1.0
Low
0.0-3.0
High
1-900
Normal
1-900
Normal
Cu
0.0-5.0
High
0.0-2.0
Medium
2-1500
Normal
2-1500
Normal
Pb
0.0-5.0
High
0.0-01
Low
2-300
Normal
2-300
Normal
Cd
0.0-0.2
Medium 0.0-0.003
Low
0.01-20
Normal
0.01-20
Normal
WHO – World Health Organization, LASEPA – Lagos State Environmental Protection Agency
69
The values of Zn, Pb and Cd were significantly higher in oil contaminated plots
relative to amendments of same soil with burnt rice husk dust and unburnt rice husk dust in
residual year. Heavy metals of Cu and Pb were significantly higher in oil treated plots
supplemented with unburnt rice husk dust compared to the one under burnt rice husk dust
treatments in the third cropping season. The heavy metals were significantly lower in control
relative to the different amendments of organic wastes except for Zn in unburnt rice husk dust
treatment. The values of Pb and Cd were significantly higher in sawdust amended plots
compared to ones under burnt rice husk dusts treatment in the residual year.
Okonkwo and Ibaba (1999) reported high value of Cu (5.20 mg kg-1) for polluted soil
and low (1.9 mg kg-1) value for the unpolluted one. Increased heavy metal contamination of
soil is not only deleterious to soil productivity but harmful to both human beings and animals
which directly or indirectly depend on soil for their food. Langard (1999) reported that
automobile oil contained heavy metals that contaminated soil. Okonkwo and Ibaba (1999)
and Freedman (1989) corroborated that oil spillage caused heavy metals (Cu, Cd and Pb)
contamination of soil. Anikwe (2006) and Alloway (1990) noted that heavy metals degrade
soil quality and productivity. According to Akamigbo and Jidere (2002), quality of soil
resources is important index for its agricultural enterprise. Anon (1985) reported that spent
automobile oil was one of the common sources of soil contamination. Jones et al. (1991)
noted that heavy metals were absorbed at particle surface, bound to carbonates or occluded in
iron or manganese hydroxides, organic matter and sulphide. Zinc (Zn), Cu, Pb and Cd are of
high values in contaminated soil and low values (LASEPA, 2005 and WHO, 1996) in the
control. Copper (Cu), Zn, Cd and Pb could have the potential to build up rapidly in the soil
due to continuous application of wastes in the soil. Again, Department for Environment, Food
and Rural Affairs and Environment Agency (2002) reported renal damage, lung cancer and
kidney problem as being associated with Cd toxicity at critical level.
The high heavy metals contents in organic wastes amended soil show that amendment
of soil with organic wastes generally increased their content in the soil. Naidu et al. (1997)
observed that anthropogenic sources such as organic waste amendment were the greatest
threat to the environment as a result of surface input to soil system of heavy metals. Asadu et
al. (2008) and Nwite, et al. (2008) reported that anthropogenic activities such as amendment
of soil with agricultural wastes increased heavy metals concentration in the soil. Asadu et al.
(2008) noted that there were significant increases in the amount of Zn, Cu, Pb and Cd of
organic waste amended soil compared to the control. Gallardo - lara (1984) also reported
increases in Zn content following waste application in soil. Continuous amendment of soil
70
with organic wastes could impose the risk of heavy metal pollution on the soil with its health
implications such as ecotoxicology. In their studies on long-term effect of municipal waste
disposal on soil properties, Anikwe and Nwobodo (2000) reported similar observation on a
refuse dumpsite. Again, Asadu et al. (2008) pointed out that build up of these metals (Zn, Pb,
Cd and Cu) to critical levels could be phyto - toxic and might result in reduced plant growth
and increase within the food chain. The values of Cu and Pb are low (WHO, 1996; LASEPA,
2005) in the uncontaminated soil while cadium is of medium value (WHO, 1996; LASEPA,
2005).
4.5.1
Total Hydrocarbons at the end of harvest
Total hydrocarbons at the end of harvest are shown in Table 17 for three seasons. The
values ranged from 17.08 –28.51% and 13.38-15.26% for 2006 and 2007 cropping seasons
respectively. Significantly (P<0.05) higher total hydrocarbons were obtained in spent
automobile oil treated plots relative to other treatments for the two seasons. Total
hydrocarbons were significantly lower in oil contaminated plots supplemented with unburnt
rice husk dust and sawdust wastes compared to oil contaminated with burnt rice husk dust
amendment in 2006 cropping season. However, the amended organic wastes were not
significantly different in
71
Table 17.
Total hydrocarbons (%) at the end of harvest
Treatment
2006
2007
2008
O
28.51
15.26
2.50
OB
19.68
13.39
0.43
OU
16.68
13.49
0.07
OS
17.08
14.13
0.55
LSD (0.05)
1.65
1.02
0.43
O- spent automobile oil treatment, OB – oil treatment amended with burnt rice husk dust,
OU-oil treatment amended with unbrnt rice husk dust, OS- oil treatment amended with saw
dust.
72
4.5.2 Mineralization Rate constants and Half- life of organic wastes
Tables 18 and 19 show mineralization rate constants and half-lives for the different
organic wastes used for soil amendment. The mean mineralization rate constant was highest
in the spent automobile oil contaminated soil amended with burnt rice husk dust relative to
other wastes treatment. Lower mineralization rate constant was obtained in oil treatment
compared to oil contaminated plots amended with organic wastes. The same trend of higher
average mineralization rate constant in oil contaminated plots treated with burnt rice husk
dust was recorded in the one under burnt rice husk dust treatment in uncontaminated soil for
the cropping seasons. The average total hydrocarbons remaining at the end of harvest in 2007
season. Total hydrocarbons were reduced in second cropping season. In the residual year,
total hydrocarbons were significantly higher in spent automobile oil contaminated plots
compared to those amended with different organic wastes. Total hydrocarbons were not
significantly affected by organic wastes amendments in 2007 and 2008 cropping seasons.
The amendment of oil contaminated plots with burnt rice husk dust would have long positive
residual effect on total hydrocarbons in soil.
The high total hydrocarbons obtained in first cropping season could partly be
explained to be as result of low microbial activity due to initial shock before adaptation to the
applied spent automobile oil. Ladousse and Tramier (1991), Leahy and Colwell (1990)
reported that oil degradation was a natural process limited by pH, oxygen, scarcity of
nutrients such as nitrogen and phosphorus. Ogbo et al. (2009) reported high hydrocarbon
contents in the soil due to poor oil degradation as a result of lack of nutrients. Akpe (2003)
reported low biodiversity in oil polluted soil. This, Amadi et al. (1996), attributed to low N
and P which are limiting to degradation of hydrocarbons.
The organic wastes amendment probably was able to reduce hydrocarbons because
they provided more surface area for high microbial activity. Again, the result generally
suggests that organic wastes amendment could be used to attenuate the effect of spent
automobile oil contamination of soil. On the other hand these wastes could be used
in
bioremediation of spent automobile oil contaminated soil. According to Atlas et al. (1991),
bioremediation of organic wastes has become an increased important method of waste
treatment and commonly accepted as the most efficient, environmentally safe and costeffective means of treatment of hydrocarbon contaminated soils. Furthermore, Odokuma and
Dickson (2003) noted that bioremediation involved introduction of nutrients in form of
organic matter to the contaminated soil.
73
Table 18. Mineralization rate constants (k/ day)
Contaminated Soil
Trt
2006
2007
O
0.0145
OB
2008
Mean
Uncontaminated Soil
Trt
2006
2007
2008
Mean
0.0160 0.0150 0.0152
C
0.0180
0.0263 0.0234 0.0226
0.0335
0.0399 0.0377 0.0370
B
0.0461
0.0469 0.0364 0.0498
OU
0.315
0.0383 0.0359 0.0359
U
0.0451
0.0530 0.0480 0.0487
OS
0.0279
0.0380 0.0344 0.0334
S
0.0421
0.0569 0.0423 0.0471
Trt- treatment, O-oil treatment, OB – oil treated with burnt rice husk dust, OU – oil treated
with unburnt rice husk dust, OS – oil treated with saw dust C – control B – burnt rice husk
dust amendment, S – saw dust amendment, U – unburnt rice husk dust amendment
74
Table 19.
Half-life (T50)
Contaminated Soil
Uncontaminated Soil
Trt
2006
2007
2008
Mean
Trt
2006
2007
2008
Mean
O
48
43
46
46
C
39
26
30
32
OB
21
17
18
19
B
15
14
15
15
OU
22
18
18
19
U
15
13
14
14
OS
25
18
20
21
S
16
12
16
15
O – oil contamination, OB – oil contamination treated with burnt rice husk dust, OU – oil
contamination treated with unburnt rice husk dust, OS – oil contamination treated with saw
dust, C – control not treated with oil or organic wastes, B – burnt rice husk treatment, U –
unburnt rice husk dust treatment, S – saw dust treatment.
75
mineralization rate constants for amended plots were higher than those of oil treated
plots and control.
The mineralization rate constant was lower in the oil treated plots compared to the control.
In the residual year, mineralization rate constants were low in oil treated plots relative to oil
treated plots amended with different organic wastes. Again, low mineralization rate constant
was obtained in the control compared to plots amended with organic wastes. Okonkwo et al.
(2011) reported increased mineralization rate in organic wastes amended soil relative to
control.
The low mineralization rate constants in spent automobile oil contaminated soil could
be attributed to low content of nitrogen. This observation agrees with Macura and Kune
(1976) and LaRue (1977) who reported that, although, most microbes could metabolize a
wide range of C-compound, nonetheless, only certain N- fixers possessed the ability to grow
on media entirely free or very low in N- sources. Amadi et al. (1996) noted that low N and
P limited degradation of organic wastes by microbes in petroleum hydrocarbons. The faster
decomposition in the subsequent year after first cropping season could be due to adaptability
of micro organisms as well as readily available carbon and nitrogen. Similar observation was
made by Mbah and Mbagwu (2000), Saviozzi et al. (1994) and Jama- Adams (1993).
Generally, decomposition was faster at the early periods of decomposition of organic
wastes and subsequently declined. This may be as a result of increase of microbial activity
due to readily available C and low C: N ratio of these materials, which provided increased
surface area. Biswas and Mukherjee (2008) reported that C:N ratio was of great importance
in the decomposition of organic wastes. The authors also noted that low C:N ratio between
10 to 12 encouraged faster decomposition of organic wastes. Higher C:N ratio leads to loss of
carbon and immobilization of nitrogen (Biswas and Mukherjee, 2008). The faster rate of
decomposition observed in oil contaminated plots amended with burnt rice husk dust and
burnt rice husk dust amendment would be attributed to high surface area exposed for
microbial activity and low C: N ratio (23) compared to unburnt rice husk dust and sawdust
values ( 34 and 32, respectively ) in Table 3. This corroborates the findings of Mbah (2004)
and Enwezor (1976) who noted that an inverse relationship existed between C:N ratio and
amount of nitrogen mineralized from organic materials. Mbah (2004) and Biswas and
Mukerjee (2008) reported stabilization as carbon was depleted.
The
half-lives
of
the
organic
wastes are shown in Table 19. The half-lives
varied with the treatments in the two soils. It took an average of 19 days each for 50% of soil
under oil contamination amended with unburnt rice husk dust and burnt rice husk dust to be
76
decomposed relative to oil treated plots amended with sawdust. Furthermore, a period of 14
days was taken for 50% of unburnt rice husk dust to be decomposed relative to other
treatments in uncontaminated soil. On the average, it took more days for 50% of organic
wastes to be decomposed in oil treated plots when compared to the control and organic
wastes amended plots in the both soils.
There was observed decrease in number of days it took for 50% of organic wastes to
decompose after first cropping season in the two soils. The same trend of decomposition of
organic wastes observed in 2006 and 2007 cropping seasons was replicated in 2008 cropping
season. This could be attributed to increased activities of microorganisms. Hornick
Parr
(1987) noted that
and
organic wastes which were decomposed rapidly tended to release
their nutrients easily while those that were resistant to microbial attack released their
nutrients slowly. Accordingly, burnt rice husk dust when used as soil amendment decompose
easily to release their nutrients to the soil and the crop.
4.6.1
Grain yield of maize
Grain yield of maize following application of different treatments is presented in
Table 20 for three cropping seasons. The grain yield of maize ranged from 1.30-1.72 and
1.28-1.68 t ha-1 in oil contaminated soils for the 2006 and 2007 cropping seasons,
respectively. The grain yield of maize was significantly (P<0.05) lower in the spent
automobile oil contaminated plots compared to plots which received varying organic wastes
amendments. Significantly higher grain yield of maize was obtained in oil treated plots under
burnt rice husk dust amendments relative to saw dust treated plots for the two seasons.
Similarly, significantly lower grain yield of maize was recorded in the constrol
compared to different organic wastes treatments for 2006 and 2007 study seasons. There was
no significant difference in grain yield of maize among the different organic wastes
amendment in the uncontaminated soil for two seasons.
The grain yield of maize in spent automobile oil treated plots was generally lower
relative to the control and amendments of organic wastes in the two soils for the cropping
seasons. The grain yields of maize were higher by 38 and 36% in the control when compared
to the oil contaminated plots in 2006 and 2007 seasons respectively. Yields in oil treated plots
amended with different organic wastes were lower compared to yields in uncontaminated soil
for two seasons.
77
Table 20. Effect of amendments on grain yield of maize (t ha-1)
Contaminated soil
Uncontaminated soil
Trt
2006
2007
2008
Trt
2006
2007
2008
O
1.30
1.28
1.24
C
2.10
2.00
2.00
OB
1.72
1.68
1.62
B
2.28
2.25
2.24
OU
1.70
1.66
1.63
U
2.26
2.25
2.24
OS
1.62
1.62
1.52
S
2.22
2.21
2.20
LSD (0.05)
0.06
0.05
0.10
0.07
0.11
0.09
LSD (0.05)
O – oil contamination, OB – oil contamination treated with burnt rice husk dust, OU – oil
contamination treated with unburnt rice husk dust, OS – oil contamination treated with saw
dust, C – control not treated with oil or organic wastes, B – burnt rice husk treatment, U –
unburnt rice husk dust treatment, S – saw dust treatment, Trt-treatment
78
Grain yields increased by 25 and 24% for oil contamination under burnt rice husk dust and
unburnt rice husk dust amendments in the uncontaminated soil relative to contaminated one.
Continuous cropping reduced grain yield of maize in the two soils.
Grain yield of maize in oil treated plots and control were significantly lower than
yields under different amendments of organic wastes in the residual year. The oil treated plots
amended with burnt rice husk dust and unburnt rice husk dust recorded significantly higher
grain yields of maize compared to oil contamination under amendment in the 2008 cropping
season. The application of different organic wastes on uncontaminated soil did not result in
statistical variation in grain yields of maize. This could be attributed to comparable release of
nutrients by the organic wastes.
The low grain yield of maize in spent automobile oil contaminated soil could be
attributed to poor physical, chemical and biological properties. This implies low nutrient
utilization in the soil (Aulakh et al., 2007). High bulk density and low total porosity (Tables 6
and 7) particularly would reduce root penetration and feeding area to crops all contributing to
low grain yield of maize. Brady and Weil (2002) observed that contamination of soil with
toxic substances could degrade its productive capacity. Several researchers including
Odjegba and Sadiq (2002), Anoliefo and Vwoko (1995), Agbogidi and Nweke (2005),
Agbogidi et al. (2006) and Mbah et al. (2009) reported decreased yield of crops in oil
contaminated soil. Grain yields of maize in oil treated plots are low in contaminated soil.
The increase in yield of maize in organic wastes amended plots relative to the
contaminated soil could be due to improvements in physical and chemical properties (Tables
6 and 13). Similar result of increased crop yield following addition of wastes had been
reported by Schercham (1989), Hornick (1982) and Hortenstine and Rothwell (1973).
Okonkwo and Ogu (2002), Nnabude and Mbagwu(2001) and Anikwe (2000) reported similar
findings of significant increase in yield of maize in organic wastes amended plots relative to
control. Agbim (1981) and Nicolas (1990) reported significant increases in grain yield of
maize on plots under cassava peels amendments compared to the control. Nnabude and
Mbagwu (1999) and Anikwe (2000) reported that reduction in bulk density increased water
transmissivity, root penetration and cumulative feeding area of the crops, all of which
translated to better yield. The grain yields of maize in control and amended plots are
comparable to average global maize yields of 2.5 t ha-1 (Harper, 1999) and also ranged from
medium to high values of 2.0 -2.4 t ha-1 (NPAFS, 2010) as obtained in southeastern states of
Nigeria. The failure to sustain the increase of grain yield of maize recorded in second
cropping season in residual year could be attributed to low nutrient reserve (Table 13) as well
79
as continuous cropping. Mbah et al. (2009) noted that continuous cropping without
application of amendment reduced grain yield of maize. The highest grain yield of maize
obtained in burnt rice husk dust amended plots suggests that the amendment could sustain
grain yield of maize. This finding could be attributed to long residual effect of the organic
waste.
4.6.2
Percentage Relative Grain Yield of Maize
Table 21 shows the effect of different treatments on relative increase of grain yield of
maize for three cropping seasons. The mean yield of maize was generally higher in the
contaminated soil amended with organic wastes relative to the uncontaminated soil for two
cropping seasons. The oil contaminated plots amended with burnt rice husk dust and unburnt
rice husk dust gave the highest percentage yields in the contaminated soil relative to oil
contaminated plots amended with sawdust in the two cropping seasons. Similarly, percentage
grain yields of maize were higher in burnt rice husk dust and unburnt rice husk dust
amendments than sawdust treated plots for the 2006 and 2007 cropping seasons respectively
in the uncontaminated soil. Furthermore, the mean values of percentage maize grain yields
were higher in oil contaminated soil amended with burnt rice husk dust, unburnt rice husk
dust for the contaminated soil and also burnt rice husk dust and unburnt rice husk dust for the
uncontaminated one compared to sawdust amendment. The same trend observed in
percentage grain yield of maize in contaminated and uncontaminated soils for 2006 and 2007
seasons was obtained in the residual year in the two soils. The high percentage grain yield in
contaminated soil relative to uncontaminated one could be attributed to low grain yield of
maize in plots under oil contamination compared to control in the uncontaminated soil. Nwite
et al. (2009) noted
that automobile oil contamination of soil was capable of reducing its
productivity.
The high percentage grain yield of maize in oil contaminated soil amended with burnt
rice husk dust and unburnt rice husk dust for contaminated soil implies that the organic
wastes could increase productivity of oil contaminated soil. Anikwe (2001) in his work
pointed out that unburnt and burnt rice husk dust could be used for reclamation of marginal
soil. Mbah (2004) noted that wastes that had long residual effect could be used for the
reclamation of marginal soil because of their beneficial and long-term effect on sustaining
crop yield.
80
Table 21. Effect of amendments on percentage relative yield of maize (%)
Contaminated Soil
Uncontaminated Soil
Trt
2006
2007
2008
Mean
Trt
2006
2007
2008
Mean
OB
32
31
31
31
B
9
13
12
11
OU
31
30
32
31
U
8
13
12
11
OS
25
27
21
24
S
6
11
10
9
O – oil contamination, OB – oil contamination treated with burnt rice husk dust, OU – oil
contamination treated with unburnt rice husk dust, OS – oil contamination treated with saw
dust, C – control not treated with oil or organic wastes, B – burnt rice husk treatment, U –
unburnt rice husk dust treatment, S – saw dust treatment, Trt-treatment
81
4.6.3 Relationship between Selected soil properties and grain yield of maize
The relationships between selected soil properties and grain yield of maize grown in
contaminated and uncontaminated soils are shown in Table 22. Macro porosity, organic carbon
(OC), available P and Zn were highly correlated with grain yield of maize in contaminated soil.
The Table further indicates that total porosity, aggregate stability, Mg, %BS, CEC, Cd and Pb
had significant relationships with grain yield of maize in the soil. There were significant
relationships between macro porosity (r2 = 0.68 at P<0.05), available P (r2 = 0.67 at P<0.05)
and Zn (r2 = 0.52 at P<0.05) and grain yield of maize. There was also highly significant
relationship (r2 = 0.72 at P<0.01) between OC and grain yield of maize.
The result also shows highly significant relationship of total porosity, macro porosity,
micro porosity and CEC with grain yield of maize in the uncontaminated soil. Organic carbon,
available P, Mg, %BS, Al3+ saturation and Zn had significant relationships with grain yield of
maize in the uncontaminated soil.
Highly significant relationships were obtained with grain yield of maize and total
porosity (r2 = 0.82 at P<0.01) and macro porosity (r2 = 0.73 at P<0.01) in the soil. There were
significant relationships between micro porosity (r2 = 0.53 at P<0.05), CEC (r2 = 0.64 at
P<0.05) and grain yield of maize in uncontaminated soil.
The highly significant relationships obtained between total porosity, macro porosity, OC
and grain yield of maize indicate that these soil properties are productivity indicators. These
properties could positively influence nutrient and water availability as well as aeration status of
soil. These parameters need to be maintained in order to ensure soil productivity. Brechin and
McDonald (1994) noted that judicious management and conservation of soil increased crop
yield. Obi (2000) pointed out that total porosity, pore size distribution and generally soil
physical conditions improved soil productivity.
Asadu and Nweke (1999) reported that OC, available P, Mg and Zn were major
constituents of plant materials. This observation tends to support the significant relationships of
these soil properties with maize yield. The significant relationships obtained between grain
yield of maize and CEC, ECEC and %BS suggest that these parameters influenced soil
productivity. The cation exchange capacity, ECEC and %BS determine the extent of soil
fertility.
82
Table 22.
Relationship between selected soil properties and grain yield of maize
N=64
Parameter
Regression model
Coefficient Of Determination (r2)
TP Vs grain yield
AS Vs grain yield
MWD Vs grain yield
Macro p Vs grain yield
Micro p Vs grain yield
WRVS grain yield
Ks Vs grain yield
%OC Vs grain yield
%N Vs grain yield
P Vs grain yield
Ca Vs grain yield
K Vs grain yield
Mg Vs grain yield
Na Vs grain yield
BS Vs grain yield
CEC Vs grain yield
ECEC Vs grain yield
EA Vs grain yield
Al3+ Sat Vs grain yield
Cd Vs grain yield
Cu Vs grain yield
Zn Vs grain yield
Pb Vs grain yield
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
0.25
0.26
0.16
0.68*
0.07
0.08
0.05
0.72**
0.12
0.67*
0.08
0.01
0.35
0.15
0.36
0.28
0.12
0.43
0.28
0.48
0.07
0.52*
0.48
TP Vs grain yield
As Vs grain yield
MWD Vs grain yield
Macro P Vs grain yield
Micro P Vs grain yield
WR Vs grain yield
Ks Vs grain yield
%OC Vs grain yield
%N Vs grain yield
P Vs grain yield
Ca Vs grain yield
K Vs grain yield
Mg Vs grain yield
Na Vs grain yield
BS Vs grain yield
CEC Vs grain yield
ECEC Vs grain yield
EA Vs grain yield
Al3+ Sat Vs grain yield
Cd Vs grain yield
Cu Vs grain yield
Zn Vs grain yield
Pb Vs grain yield
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
y=
correlation coefficient (r)
Contaminated Soil
0.53x + 0.02
0.50*
1.59 x – 0.01
0.51*
1.73 x – 0.18
0.39
0.80 x + 0.03
0.83**
1.51 x – 0.01
0.27
1.52 x – 0.01
0.28
2.30x – 0.02
0.29
0.90 x+ 0.22
0.85**
1.06x + 1.32
0.35
1.00x + 0.01
0.82**
1.16x + 0.03
0.28
1.29x – 0.04
0.09
0.95x + 0.18
0.59*
0.80x + 1.35
0.39
0.61x + 0.01
0.60*
0.59x + 0.08
0.53*
1.58x – 0.04
0.35
1.58x – 0.55
0.66*
1.59x –0.11
0.53*
1.84x – 0.56
0.70*
0.99x + 0.20
0.26
1.64x – 0.05
0.72**
1.84x – 0.56
0.70*
Uncontaminated soil
0.07x – 0.85
0.90**
1.81x + 0.01
0.34
2.66x – 0.21
0.29
0.16x – 0.12
0.86**
0.05x + 0.08
0.74**
2.32x – 0.04
0.32
1.66 – 0.06
0.22
1.77x + 0.18
0.57*
2.09x + 0.05
0.13
1.78x + 0.01
0.63*
1.84x + 0.6
0.33
2.01x + 0.09
0.31
1.55x + 0.25
0.59*
2.40x – 1.10
0.25
1.05x + 0.01
0.51*
0.41x + 0.17
0.80**
1.39x +0.08
0.36
2.38x – 0.63
0.32
2.86x – 0.41
0.58*
2.13x – 0.20
0.01
1.97x + 0.20
0.22
1.90x + 0.15
0.56*
1.90x + 0.61
0.31
0.82**
0.11
0.08
0.73**
0.55*
0.10
0.09
0.32
0.02
0.39
0.11
0.09
0.35
0.06
0.26
0.64*
0.13
0.10
0.34
0.00
0.05
0.31
0.10
TP – total porosity, AS – Aggregate stability, MWD- Mean Weight diameter, Macro p – Macro porosity, Micro P- Micro
porosity, WR – water retention at 60 cm tension, Ks – saturated hydraulic conductivity, %OC – percent Organic carbon, %N –
percent total nitrogen, P – phosphorus, Ca – calcium, K – Potassium, Mg-magnesium Na-Sodium, BS – Base saturation, CEC –
cation exchange capacity, EA - Exchangeable acidity, Al3+ sat – Aluminum saturation, Cd – cadmium, Cu-copper, Zn – zinc,
Pb – lead, * -Significant, ** - highly significant, Ns -Non significant, N – number of samples the soil.
83
The significant relationships obtained between Al3+ saturation, EA and maize grain
yield indicate that these soil properties could influence the nutrient availability in the soil as
well as pH of soil. Besides, the pH of a medium is determined by the extent to which the
exchange complex is occupied by Mg and other metallic cations (Asadu and Nweke, 1999).
The significant relationships of Zn, Cd and Pb with grain yield of maize in spent automobile oil
contaminated soil imply that these heavy metals were introduced into the soil. Mbah (2004),
Asadu et al. (2008) and Anikwe and Nwobodo (2002) noted that heavy metals of Zn, Cd and
Pb in the soil could be phyto-toxic to life. Mbah (2004) pointed out that a combination of all
essential plant nutrients contributed to the final grain yield of maize.
The significant relationship of cation exchange capacity with grain of maize suggests
that it contributed greatly to nutrient availability and soil productivity. Mbah (2004) noted
positive correlation of CEC with percent organic carbon. In addition, since CEC is positively
related to soil surface area and clay mineralogy (Biswas and Mukherjee, 2008), its significant
correlation implies increase and retention of other nutrients in the soil which gave rise to grain
yield of maize. Mbah (2004) observed a positive and significant correlation of CEC with grain
yield of maize in a soil amended with organic wastes. Biswas and Mukherjee (2008) noted that
crop yield increased with increase in level of nutrients supply. The nutrients except copper and
zinc fall below threshold concentration level considered to be toxic to crop growth and yield
(Biswas and Mukherjee, 2008).
4.7. 1
Soil properties, ascribed sufficiency values and calculated productivity index
Tables 23a – 25b show the soil properties, ascribed sufficiency values and calculated
productivity index (PI) under different treatments for three cropping seasons. The soil
properties and their individual sufficiency values were used in the computation of productivity
index (PI) in each study year. Productivity index is an algorithm which expresses relationship
between soil depth and crop yield. The PI for contaminated and uncontaminated soils amended
with different organic wastes was generally higher relative to spent automobile oil treatment
and control. Lowest PI was obtained in spent automobile oil treated plots compared to
amended plots in the both soils. The percentage increments of calculated PI in the
uncontaminated relative to the contaminated soil were 33, 70 and 30% for burnt rice husk dust,
unburnt rice husk dust and sawdust relative to oil contamination under burnt rice husk dust,
unburnt rice husk dust and sawdust amendments, respectively in 2007 season.
The PI decreased in the residual year in contaminated and uncontaminated soils. Highest PI
was recorded in burnt rice husk dust amended plots of the uncontaminated soil compared to oil
contaminated soil, control, unburnt rice husk dust, sawdust, oil contamination under burnt rice
84
Table 23 a. Soil properties, ascribed sufficiency values and calculated productivity
indices for contaminated soil
O
Soil property
2006
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL)
0-15
1.68
0.16
3.4
60
0.12
0.60
0.21
15-30
1.69
0.17
3.2
60
0.11
0.65
0.12
30-45
1.78
0.19
3.1
60
0.02
0.78
0.07
PI
0.24
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0-15
1.66
0.17
3.5
15-30
1.68
0.18
3.4
30-45
1.76
0.19
3.3
45 – 60
1.78
0.19
3.2
PI
RWF
(cm)
1.00
1.00
1.00
OU
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0-15
1.60
0.17
3.5
15-30
1.64
0.18
3.5
30-45
1.78
0.19
3.4
45 – 60
1.79
0.20
3.4
PI
RWF
(cm)
60
60
60
60
BD
(Mgm-3)
0.13
0.12
0.04
0.02
0.28
RWF
(cm)
60
60
60
60
BD
(Mgm-3)
0.20
0.16
0.04
0.01
0.28
Ascribed Sufficiency
AWC
pH
(cm-1)
(KCL)
0.65
0.25
0.70
0.21
0.78
0.16
0.78
0.12
RWF
(cm)
1.00
1.00
1.00
1.00
Ascribed Sufficiency
AWC
pH
-1
(cm )
(KCL)
0.65
0.25
0.70
0.25
0.78
0.21
0.79
0.21
RWF
(cm)
1.00
1.00
1.00
1.00
OB
OS
Soil property
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
(Mgm-3) (cm-1)
(KCsL) (cm)
(Mgm-3) (cm-1)
(KCL) (cm)
0-15
1.63
0.17
3.9
60
0.17
0.65
0.43
1.00
15-30
1.66
0.18
3.9
60
0.14
0.70
0.43
1.00
30-45
1.68
0.19
3.8
60
1.12
0.78
0.38
1.00
45 – 60
1.70
0.20
3.6
60
0.10
0.79
0.30
1.00
PI
0.31
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, PI-productivity
index
85
23 b. Soil properties, ascribed sufficiency
for uncontaminated soil
C
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0-15
1.66
0.17
3.5
15-30
1.68
0.18
3.6
30-45
1.70
0.19
3.3
45 – 60
1.78
0.20
3.0
PI
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0-15
1.51
0.18
4.0
15-30
1.62
0.19
3.7
30-45
1.64
0.20
3.5
45 – 60
1.66
0.21
3.5
PI
values and calculated productivity indices
RWF
(cm)
60
60
60
60
2006
Ascribed Sufficiency
BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0.13
0.65
0.25
0.11
0.70
0.21
0.09
0.78
0.16
0.02
0.79
0.14
0.28
RWF
(cm)
1.00
1.00
1.00
1.00
U
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0-15
1.62
0.18
3.7
15-30
1.64
0.18
3.6
30-45
1.65
0.19
3.5
45 – 60
1.66
0.20
3.4
PI
RWF
(cm)
60
60
60
60
BD
(Mgm-3)
1.00
0.96
0.92
0.81
0.36
Ascribed Sufficiency
AWC
pH
-1
(cm )
(KCL)
0.70
0.47
0.78
0.34
0.79
0.25
0.80
0.25
RWF
(cm)
1.00
1.00
1.00
1.00
B
S
RWF
(cm)
60
60
60
60
Ascribed Sufficiency
BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0.60
0.70
0.34
0.58
0.70
0.30
0.50
0.78
0.25
0.48
0.79
0.16
0.38
RWF
(cm)
1.00
1.00
1.00
1.00
Soil property
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL) (cm)
0-15
1.63
0.18
3.7
60
0.61
0.70
0.34
1.00
15-30
1.64
0.18
3.6
60
0.58
0.70
0.30
1.00
30-45
1.65
0.19
3.5
60
0.50
0.78
0.25
1.00
45 – 60
1.65
0.20
3.4
60
0.50
0.79
0.16
1.00
PI
0.39
C-control, B-burnt rice husk dust, U-unburnt rice husk dust, S-saw dust, PI-productivity index
86
Table 24 a.
Soil properties, ascribed sufficiency values and calculated productivity Indices
for contaminated soil
O
Soil property
2007
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL) (cm)
0-15
1.80
0.16
3.5
60
0.10
0.60
0.25
1.00
15-30
1.80
0.17
3.3
60
0.09
0.65
0.16
1.00
30-45
1.81
0.18
3.1
60
0.02
0.70
0.07
1.00
45 – 60
1.84
0.18
3.0
60
0.01
0.70
0.03
1.00
PI
0.20
OU
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0-15
1.67
0.18
3.5
15-30
1.79
0.19
3.5
30-45
1.80
0.19
3.4
45 – 60
1.80
0.21
3.3
PI
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0-15
1.66
0.18
3.5
15-30
1.68
0.18
3.5
30-45
1.80
0.19
3.5
45 – 60
1.83
0.19
3.4
PI
RWF
(cm)
60
60
60
60
BD
(Mgm-3)
0.30
0.03
0.04
0.04
0.27
Ascribed Sufficiency
AWC
pH
-1
(cm )
(KCL)
0.65
0.65
0.78
0.65
0.78
0.62
0.79
0.12
RWF
(cm)
1.00
1.00
1.00
1.00
OB
OS
RWF
(cm)
60
60
60
60
Ascribed Sufficiency
BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0.14
0.65
0.25
0.12
0.65
0.25
0.04
0.78
0.25
0.01
0.78
0.21
0.23
RWF
(cm)
1.00
1.00
1.00
1.00
Soil property
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL) (cm)
0-15
1.66
0.16
3.9
60
0.14
0.60
0.43
1.00
15-30
1.68
0.18
3.9
60
0.12
0.70
0.43
1.00
30-45
1.78
0.19
3.8
60
0.04
0.78
0.36
1.00
45 – 60
1.80
0.19
3.6
60
0.04
0.78
0.30
1.00
PI
0.27
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, PI-productivity
index
87
24 b. Soil properties, ascribed sufficiency values and
uncontaminated soil
C
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
RWF
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
0-15
1.67
0.16
3.5
60
15-30
1.66
0.18
3.5
60
30-45
1.80
0.19
3.3
60
45 – 60
1.80
0.20
3.0
60
PI
U
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0-15
1.78
0.16
4.3
15-30
1.79
0.18
4.0
30-45
1.80
0.18
4.0
45 – 60
1.83
0.18
3.7
PI
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0-15
1.78
0.17
4.4
15-30
1.78
0.19
4.1
30-45
1.84
0.20
4.0
45 – 60
1.80
0.20
4.0
PI
RWF
(cm)
60
60
60
60
calculated productivity Indices for
2007
Ascribed Sufficiency
BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0.12
0.60
0.25
0.05
0.70
0.25
0.04
0.78
0.16
0.04
0.79
0.14
0.26
2006
Ascribed Sufficiency
BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0.10
0.65
0.64
0.11
0.70
0.47
0.04
0.70
0.47
0.01
0.70
0.34
0.36
RWF
(cm)
1.00
1.00
1.00
1.00
RWF
(cm)
1.00
1.00
1.00
1.00
B
S
RWF
(cm)
60
60
60
60
Ascribed Sufficiency
BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0.10
0.65
0.62
0.10
0.78
0.47
0.01
0.79
0.47
0.01
0.79
0.47
0.39
RWF
(cm)
1.00
1.00
1.00
1.00
Soil property
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL) (cm)
0-15
1.74
0.18
3.7
60
0.10
0.70
0.34
1.00
15-30
1.78
0.19
3.6
60
0.80
0.78
0.30
1.00
30-45
0.80
0.19
3.5
60
0.08
0.78
0.25
1.00
45 – 60
1.80
0.20
3.5
60
0.07
0.80
0.25
1.00
PI
0.34
C-control, B-burnt rice husk dust, U-unburnt rice husk dust, S-saw dust, PI-Productivity index
88
Table 25 a.
Soil properties, ascribed sufficiency values and calculated productivity indices
for contaminated soil
O
Soil property
2008
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL) (cm)
0-15
1.85
0.15
3.6
60
0.05
0.55
0.30
1.00
15-30
1.89
0.16
3.5
60
0.02
0.60
0.25
1.00
30-45
1.92
0.17
3.5
60
0.01
0.65
0.25
1.00
45 – 60
1.92
0.18
3.4
60
0.01
0.70
0.21
1.00
PI
0.15
OU
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0-15
1.80
0.16
4.1
15-30
1.82
0.17
4.1
30-45
1.84
0.18
4.0
45 – 60
1.85
0.19
4.0
PI
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0-15
1.82
0.16
4.2
15-30
1.83
0.16
4.1
30-45
1.85
0.17
4.0
45 – 60
1.87
0.18
4.0
PI
RWF
(cm)
60
60
60
60
BD
(Mgm-3)
0.04
0.08
0.06
0.01
0.27
Ascribed Sufficiency
AWC
pH
-1
(cm )
(KCL)
0.60
0.52
0.65
0.52
0.70
0.47
0.78
0.47
RWF
(cm)
1.00
1.00
1.00
1.00
OB
OS
RWF
(cm)
60
60
60
60
Ascribed Sufficiency
BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0.08
0.60
0.56
0.07
0.60
0.52
0.03
0.65
0.47
0.03
0.70
0.47
0.23
RWF
(cm)
1.00
1.00
1.00
1.00
Soil property
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL) (cm)
0-15
1.81
0.17
4.1
60
0.09
0.65
0.52
1.00
15-30
1.82
0.17
4.1
60
0.08
0.65
0.52
1.00
30-45
1.84
0.18
4.0
60
0.06
0.70
0.47
1.00
45 – 60
1.86
0.18
3.8
60
0.04
0.70
0.38
1.00
PI
0.25
O-oil, OB-oil contaminated amended with burnt rice husk dust, OU-oil contaminated amended
with unburnt rice husk dust, OS-oil contaminated amended with saw dust, PI-Productivity
index
89
25 b. Soil properties, ascribed sufficiency values and calculated productivity indices for
uncontaminated soil
C
Soil property
2008
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
-3
-1
-3
-1
(Mgm ) (cm )
(KCL)
(cm)
(Mgm ) (cm )
(KCL) (cm)
0-15
1.80
0.16
4.3
60
0.08
0.60
0.61
1.00
15-30
1.82
0.17
4.0
60
0.05
0.65
0.47
1.00
30-45
1.85
0.17
3.8
60
0.04
0.65
0.38
1.00
45 – 60
1.87
0.18
3.6
60
0.03
0.70
0.30
1.00
PI
0.21
U
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
(Mgm-3) (cm-1)
(KCL)
0-15
1.78
0.17
4.3
15-30
1.79
0.18
4.3
30-45
1.80
0.19
4.0
45 – 60
1.81
0.19
3.8
PI
Soil property
Measured property
Soil depth (cm) BD
AWC
pH
-3
-1
(Mgm ) (cm )
(KCL)
0-15
1.80
0.17
4.4
15-30
1.82
0.18
4.3
30-45
1.83
0.18
4.3
45 – 60
1.85
0.19
4.0
PI
RWF
(cm)
60
60
60
60
BD
(Mgm-3)
0.10
0.11
0.08
0.07
0.35
RWF
(cm)
60
60
60
60
BD
(Mgm-3)
0.08
0.07
0.04
0.04
0.37
Ascribed Sufficiency
AWC
pH
(cm-1)
(KCL)
0.64
0.64
0.70
0.64
0.78
0.47
0.78
0.38
RWF
(cm)
1.00
1.00
1.00
1.00
Ascribed Sufficiency
AWC
pH
-1
(cm )
(KCL)
0.65
0.65
0.70
0.64
0.70
0.64
0.78
0.47
RWF
(cm)
1.00
1.00
1.00
1.00
B
S
Soil property
Measured property
Ascribed Sufficiency
Soil depth (cm) BD
AWC
pH
RWF
BD
AWC
pH
RWF
(Mgm-3) (cm-1)
(KCL)
(cm)
(Mgm-3) (cm-1)
(KCL) (cm)
0-15
1.75
0.17
4.4
60
0.12
0.65
0.65
1.00
15-30
1.78
0.17
4.3
60
0.10
0.65
0.64
1.00
30-45
1.80
0.18
4.2
60
0.08
0.70
0.65
1.00
45 – 60
1.82
0.18
4.0
60
0.06
0.70
0.47
1.00
PI
0.32
C-control, B-burnt rice husk dust, U-unburnt rice husk dust, S-saw dust, PI-Productivity index
90
husk dust, unburnt rice husk dust and sawdust amendments in the 2007 cropping season. The
sufficiency of soil depth shows that it is not limiting to soil productivity. The calculated PI of
the soils generally increased in second planting season. The low PI in the oil contaminated soil
could be attributed to poorer soil properties. Valimaki et al. (2005) noted that soil
contaminated with automobile oil had low pH, and tended to be strongly acidic. Leahy and
Colwell (1990) and Ladousse and Tramier (1991) in their observations noted that pH was
limiting in soil contaminated with spent automobile oil. On the other hand, the productivity
index in organic wastes amended soils suggests that the wastes improved the productivity of
the soil. This corroborates the report of Puget et al. (2000) that organic wastes contained
valuable materials that improved soil productivity. Other researchers (Tisdall, 1996; Nnabude
and Mbagwu, 2001; Mbagwu, 1992a; 1992b and Agbim, 1985) had earlier reported positive
effects of organic wastes in increasing soil productivity. The burnt rice husk dust that recorded
highest PI in the residual study year could sustain soil productivity.
4.7.2
Productivity Index and Grain Yield of Maize
The calculated productivity index (PI) and grain yields of maize for the contaminated
and uncontaminated soils are presented in Table 26. The mean PI values were 0.25 and 0.42,
respectively for contaminated and the uncontaminated soils respectively. This was 64% lower
in contaminated soil when compared with the uncontaminated soil. Similarly, the mean grain
yields of maize at harvest were 1.62 and 2.21 t ha-1 for the contaminated and uncontaminated
soils. This shows that mean grain yield of maize was lower by 36% in contaminated soil
relative to the uncontaminated one. The highest average grain yields of maize of 1.72 and 2.30
t ha-1 for the contaminated and uncontaminated soils were obtained in oil contaminated plots
under sawdust and burnt rice husk dust amendments respectively.
The findings suggest that calculated PI increased or decreased with grain yields of
maize. These findings are consistent with the report of Nwite and Obi (2008), Nwite et al.
(2009), Nwite (2002) and Anikwe (2000) that grain yields of maize followed the trend of
increases or decreases in PI. There were generally low and high PI values in the contaminated
and uncontaminated plots (Table 26). According to Pierce et al . (1983), Anikwe (2000), Nwite
(2002) and Nwite and Obi (2008) productivity index is a veritable tool for predicting soil
productivity.
91
Table 26.
Productivity Index and Grain Yield of Maize
Contaminated soil
Uncontaminated soil
Trt
PI
Grain yield of maize (tha-1)
Trt
PI
Grain yield of maize (tha-1)
O
0.24
1.63
C
0.28
2.20
O
0.20
1.50
C
0.26
2.15
O
0.23
1.62
C
0.21
2.00
OB
0.23
1.62
B
0.38
2.26
OB
0.23
1.62
B
0.39
2.30
OB
0.27
1.68
B
0.37
2.25
OS
0.31
1.72
S
0.39
2.30
OS
0.27
1.68
S
0.34
2.22
OS
0.25
1.66
S
0.32
2.18
OU
0.28
170
U
0.36
2.24
OU
0.27
1.68
U
0.36
2.24
OU
0.27
1.68
U
0.35
2.23
Total
2.97
19.47
5.14
26.57
1.62
0.42
2.21
Means 0.25
PI– Productivity index, O – oil contamination, OB – oil contamination treated with burnt rice
husk dust, OU – oil contamination treated with unburnt rice husk dust, OS – oil contamination
treated with saw dust, C – control not treated with oil or organic wastes, B – burnt rice husk
treatment, U – unburnt rice husk dust treatment, S – saw dust treatment, Trt - treatment
92
4.7.3 Relationship between calculated PI and Grain Yield of Maize
The relationship between calculated productivity index (PI) and grain yield of maize
in the contaminated and uncontaminated soils is shown in Table 27. There were positive and
significant relationships between PI and grain yields of maize in the contaminated and
uncontaminated soils. There were highly significant relationships between PI and grain yield
of maize (r = 0.76 at P< 0.01) for contaminated soil and (r = 0.92 at P < 0.01) for the
uncontaminated one. Similarly, the R2 relationship between PI and grain yield of maize (r2 =
0.84 at P<0.01) for the uncontaminated soil was highly significant.
The relationships between PI and grain yield of maize in uncontaminated soil were
higher by 21 and 45% when compared to the contaminated soil. This could be attributed to
better soil properties of uncontaminated soil. Productivity index explained 84% of the total
variation observed in grain yield of maize in uncontaminated soil relative to contaminated one
indicating that the parameters used to compute the productivity index strongly influenced
grain yield of maize. The correlation between PI and grain yield of maize in plots amended
with burnt rice husk dust and unburnt rice husk dust were r =0.36 and 0.66 in uncontaminated
soil relative to oil contamination soil and control, respectively. On the other hand, the poor
relationships between PI and grain yield of maize in the oil treatment compared to organic
wastes amendment implies that spent automobile oil could impact negatively on soil
productivity. The result further suggests that organic wastes treatment of soil could
increase its productivity.
This observation corroborates Atlas et al. (1991) observation that bioremediation of oil
contaminated soil is effective and efficient means of treatment of hydrocarbon degradation.
The authors noted that the method had become increasingly important and commonly accepted
as the most environmentally safe. The amendment of oil contaminated soil with organic wastes
increases its productivity. The burnt rice husk dust and unburnt rice husk dust more than
sawdust amendment increased the productivity of the soil.
The observed poor relationships between PI and grain yield of maize in oil
contaminated soil could support the observation of Leahy and Colwell (1990) and Ladousse
and Tramier (1991) that pH and nutrients limited oil degradation and therefore soil
productivity. This study also showed that combined data for 2006, 2007 and 2008 for PI
explained 67% of the variations in grain yield of maize in the two soils. Gantzer and McCarthy
(1987) reported that their combined data for 1985 and 1986 for productivity index explained
about 82% of the variation in grain yield of maize.
93
Table 27. Relationship between Productivity Index and Grain Yield of Maize
Dependent crop Parameter
Regression
Model
R
r2
N =128
PI VS grain yield of maize
Y = 3.12x +0.77
0.76**
0.58*
Contaminated
PI VS grain yield of maize
Y = 1.68x+1.63
0.92**
0.84**
Uncontaminated
PI - Productivity index, ** - highly significant at P< 0.01, r -correlation coefficient,r2 coefficient of determination, *- significant at 5%, N-number of samples
94
4.7.4 Relationship between individual Productivity Indicators and Grain Yield of Maize
Table 28 shows the relationship between individual productivity indicators and grain yield of
maize in contaminated and uncontaminated soils. The result showed positive relationships
between individual productivity indicators and grain yields of maize in the soils. There were
highly significant (P<0.01) relationships between individual productivity indicators and grain
yield of maize in the contaminated and uncontaminated soils. The R2 relationship was
significant for available water capacity (r = 0.57 at P<0.05) and pH (r= 0.58 at P<0.05) with
grain yield of maize in the uncontaminated soil.
The highly significant relationship between bulk density and grain yield of maize in
the two soils suggests that it contributed greatly to soil productivity and influenced grain
yield of maize positively in the soils. Anikwe (2000) and Nwite et al.(2007) reported
significant correlation between available water capacity and grain yield of maize. The poor
relationship between pH and grain yield of maize in the uncontaminated soil tends to suggest
that this soil property was less critical in determining the productivity of the soil.
4.7.5 Relationship between calculated PI and Productivity Indicators
Table 29 shows that there were significant and positive relationships between
individual productivity indicators and calculated PI in the contaminated and uncontaminated
soils. There were highly significant (P<0.01) R2 relationships between individual
productivity indicators and calculated productivity index in the two soils. Except for AWC in
the uncontaminated soil, the R2 relationships between individual productivity indicators and
calculated productivity index were significant for both soils. The result suggests that there
was synergy among the individual productivity indicators in influencing the productivity of
the soils. This tends to suggest that although AWC was a major determining factor of soil
productivity, its influence on uncontaminated soil was minimal compared to contaminated
soil. Molua and Lambi (2006) reported that available water was the most critical factor
determining yield.
Table 28.
Relationship between Productivity Indicators and Grain Yield of Maize
95
Dependent crop Parameter
Regression
Correlation
Coefficient of
Model
Coefficient(r) determination
N=128
(r2)
0.91**
0.83*
0.94**
0.89**
Y = 5.64 x - 2.16
0. 95**
0.89**
Y = 5.18x + 1.24
0.75**
0.57*
Y = 0.34x + 0.51
0.76**
0.58*
Contaminated soil
BD VS grain yield of maize
AWCVS grain yield of maize
Y = 6.42x –2.94
Y = 7.74 + 0.30
Uncontaminated soil
BD VS grain yield of maize
AWCVS grain yield of maize
pH VS grain yield of maize
BD – Bulk density, AWC – Available water capacity, RWF - Root weighting factor, * significant at 5%, **-significant at 1%, vs – versus, N – number of samples.
96
Table 29. Relationship between calculated productivity Index and Grain Yield of Maize
Dependent crop Parameter
Regression
Correlation
Coefficient of
Model
Coefficient(r) determination
N=128
(r2)
Contaminated soil
BDVS Productivity index
Y = 1.30x - 0.64
0.81**
0663*
AWC VS Productivity index
Y = 1.50x+0.06
0.75**
0.56**
pH VS Productivity index
Y = 0.05x + 0.01
0.76**
0.58**
Uncontaminated soil
BD VS Productivity index
Y = 2.01x - 1.05
0. 83**
0.70**
AWCVS Productivity index
Y = 2.77x – 0.18
0.74**
0.55ns
pH VS grain yield of maize
Y = 0.18x - 0.57
0.75**
0.56*
BD – Bulk density, AWC – Available water capacity, RWF – Root Weighting
factor, * - significant at 5%,** highly significant at P > 0.01, VS – versus, N- number of
samples
97
CHAPTER FIVE
5.0
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1
Summary
This study was undertaken in order to evaluate the productivity of spent automobile oil
contaminated soil amended with organic wastes, namely burnt rice husk dust, unburnt (fresh)
rice husk dust and saw dust. The result indicates that spent automobile oil contamination
degraded soil physicochemical and biological properties, reduced its quality, productivity and
potential grain yield of maize.
On the other hand, organic wastes amendment generally improved soil productivity.
Due to their ability to improve soil physical and biological properties and generally enhance
the levels of soil essential plant nutrients, the organic wastes were able to improve essentially
the fertility status of the soil. However, it was discovered that the spent automobile oil
contamination and the addition of organic wastes increased levels of heavy metals (Cu, Pb, Zn
and Cd) concentrations in the soil.
Partially burnt rice husk dust was relatively decomposed fast to release its nutrients
while saw dust and unburnt (fresh) rice husk dust were slowly decomposed. The organic
wastes that were slowly decomposed provided long residual effect, which is good for
restoration of sustainable production.
There were highly significant and positive relationships between productivity index
(PI) and grain yield of maize in the uncontaminated and contaminated soils. There were
increased relationships of PI and grain yield of maize with organic wastes amendment in the
soils. There were significant and positive relationships of productivity indicators with grain
yield of maize in the soils.
5.2
Conclusion
The result of this study has shown that spent automobile oil contamination of soil can
degrade its physical, chemical and biological properties. The agronomic effects of this are
resultant low grain yield of maize, heavy metals contamination of soil and generally reduced
productivity.
The amendment of soil with organic wastes of burnt rice husk dust, unburnt (fresh) rice
husk dust and sawdust proved good source of nutrients. The agronomic potentials and effects
of these organic wastes as soil amendment can be summarized as follows:
98
1.
The productivity of spent automobile oil contaminated soil was improved by
organic wastes amendment.
2.
Organic wastes of burnt rice husk dust, unburnt rice husk dust and saw dust which
constitute environmental nuisance in Abakaliki (Ebonyi state) could be used in the
bioremediation of spent automobile oil contaminated soil.
3.
Even though, burnt rice husk dust, unburnt rice husk dust and sawdust could be
used for bioremediation of oil contaminated soil, there is the danger of it building
up heavy metals in the soil ecosystem.
4.
The organic wastes improve soil physical properties such as bulk density, total
porosity, pore size distribution, aggregate stability, available water capacity, water
retention and hydraulic conductivity all of which enhance crop yield.
5.
The wastes improve soil chemical properties particularly organic carbon, total
nitrogen, available phosphorus, exchangeable cations, cation exchange capacity,
effective cation exchange capacity, reduction of Al3+ saturation and exchangeable
acidity.
6.
These wastes have the potential to ameliorate effect of spent automobile oil
contamination of soil.
5.3
Recommendation
This study has demonstrated that organic wastes amendment of soil has the potential to
ameliorate the effect of spent automobile oil contamination of soil, improve physicochemical
and biological properties and generally enhance its productivity. Consequently, it may be
appropriate to recommend that burnt rice husk dust organic waste which provided long residual
treatment effect, generally improved overall physicochemical and biological properties and
resulted in better grain yield be used for bioremediation of spent automobile oil contaminated
soil among other organic wastes.
99
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Appendix
Effect of Amendments on bulk density for contaminated and uncontaminated soils in 2006
Factor Factor
A
B
I
II
III
IV
Total
Mean
a1
O
1.70
1.68
1.72
1.69
6.79
1.70
O+B
1.58
1.59
1.55
1.56
1.28
1.57
O+U
1.62
1.60
1.59
1.61
6.42
1.61
O+S
1.63
1.64
1.62
1.66
6.55
1.64
TOTAL
6.53
6.51
6.48
6.52
MEAN
1.63
1.63
1.62
1.63
C
1.60
1.58
1.63
1.62
6.43
1.61
B
1.55
1.52
1.54
1.53
6.14
1.54
U
1.58
1.59
1.57
1.56
6.30
1.58
S
1.59
1.60
1.58
1.57
6.34
1.59
TOTAL
6.32
6.29
6.32
6.28
MEAN
1.58
1.57
a2
a1
=
soil contaminated with oil
a2
=
soil not contaminated with oil
1.58
1.57
*** Analysis of variance ***
Variate: BD
Source of variation
d.f.
s.s.
m.s.
v.r.
F pr.
FactorB
3
0.352500
0.0117500
43.38
<.001
Residual
12
0.0032500
0.0002708
Total
15
0.0385000
***** Information summary *****
Aliased model terms
FactorA
FactorA. FactorB
***** Tables of means *****
Variate: BD
Grand mean 1.6275
FactorB
O
O+B
1.6975 1.5700 1.6375 1.6050
O+S
O+U
118
*** Least significant differences of means (5% level) ***
Table
factor B
rep.
4
d.f
12
1.s.d
0.02535
Analysis of variance
Variate: BD
Source of variation
d.f.
s.s.
m.s.
v.r.
Trt
3
0.0110187
0.0036729
14.82 <.001
12
0.0029750
0.0002479
15
0.0139937
***** Tables of means *****
Variate: BD
Grand mean 1.5756
Trt
B
C
S
1.5350 1.6075 1.5850 1.5750
*** least significant differences of means (5% level) ***
Table
Trt
rep.
4
d.f
12
1.s.d.
0.02426
U
F pr.
119
Effect of Amendments on bulk density for contaminated and uncontaminated soils in 2007
Factor
Factors
A
B
I
a1
O
II
III
IV
Total
Mean
1.72
1.73
1.74
1.71
6.90
1.73
OB
1.58
1.59
1.60
1.62
6.39
1.60
OU
1.64
1.65
1.66
1.67
6.62
1.66
OS
1.66
1.67
1.68
1.65
6.66
1.67
Total
6.60
6.64
6.68
6.65
Mean
1.65
1.66
1.67
1.66
C
1.70
1.68
1.69
1.67 6.74
1.69
B
1.55
1.56
1.55
1.57 6.23
1.56
U
1.59
1.60
1.58
1.58 6.35
1.59
S
1.60
1.58
1.59
1.60 6.37
1.59
Total
6.44
6.42
6.41
6.42
Mean
1.61
1.61
1,60
1.61
a2
***** Analysis of variance *****
Variate: BD
Source of variation
d.f.
s.s.
.s.
v.r.
F pr.
FactorB
3
0.0327187
0.0109062
55.11
<.001
Residual
12
0.0023750
0.0001979
Total
15
0.0350937
***** Tables of means *****
Variate: BD
Grand mean 1.6606
FactorB
O
O+B
O+S
1.7250 1.5975 1.6650 1.6550
*** Least significant differences of means (5% level) ***
Table
factorB
rep.
4
d.f.
12
1.s.d.
0.02167
***** Analysis of variance *****
O+U
120
Variate: BD
Source of variation
d.f.
s.s.
m.s.
v.r.
FactorB
3
0.0363797
0.0121266
112.48 <.001
Residual
12
0.0012937
0.0001078
Total
15
0.0376734
***** Tables of means *****
Variate: BD
Grand mean 1.6053
FactorB
B
C
S
1.5588 1.6850 1.5925 1.5850
*** least significant differences of means (5% level) ***
Table
FactorB
Rep.
4
d.f.
12
1.s.d
0.01600
U
F.pr.
121
Effect of Amendments on bulk density for contaminated and uncontaminated soils in 2008
Factor Factor
A
B
I
a1
O
1.74
OB
1.60
OU
OS
III
IV
Total
Mean
1.76
1.80
7.02
1.76
1.62
1.63
1.65
6.50
1.63
1.66
1.63
1.64
1.64
6.57
1.64
1.65
1.66
1.65
1.67
6.63
1.66
Total
6.65
6.63
6.68
6.76
Mean
1.66
1.66
1.67
1.69
C
1.68
1.69
1.70
1.70
6.77
1.69
B
1.58
1.57
1.60
1.58
6.33
1.58
U
1.59
1.60
1.58
1.59
6.36
1.59
S
1.60
1.62
1.59
1.60
6.41
1.60
Total
6.45
6.48
6.47
6.47
Mean
1.61
1.62
1.62
1.62
a2
II
1.72
***** Analysis of variance *****
Variate: BD
Source of variation
d.f.
s.s.
m.s.
v.r.
F pr.
FactorB
3
0.0412250
0.0137417
30.82
<.001
Residual
12
0.0053500
0.0004458
Total
15
0.0465750
***** Tables of means *****
Variate: BD
Grand mean 1.6687
FactorsB
O
O+B
O+S
1.7550 1.6250 1.6525 1.6425
*** least significant differences of means (5% level) ***
Table
Factor B
Rep.
4
d.f.
12
1.s.d.
0.03253
Analysis of variance
O+U
122
Variate: BD
Source of variation
d.f.
s.s.
m.s.
v.r.
FactorB
3
0.0363797
0.0121266
112.48 <.001
Residual
12
0.0012937
0.0001078
Total
15
0.0376734
**** Tables of means ****
Variate:BD
Grand mean 1.6053
FactorB
B
C
S
U
1.5588
1.6850
1.5925
1.5850
*** least significant differences of means (5%level) ***
Table
FactorB
rep.
4
d.f.
12
1.s.d.
0.01600
Fpr.
123
Effect of Amendments on Relationship between Productivity Index and Grain Yield of Maize
for Contaminated and Uncontaminated Soils
Contaminated Soil
S/NO
PRODUCTIVITY INDEX
GRAIN YIELD OF MAIZE
1.
0.24
1.63
2.
0.20
1.50
3.
0.15
1.30
4
0.23
1.62
5
0.23
1.62
6
0.27
1.68
7
0.31
1.72
8
0.27
1.68
9
0.25
1.66
10
0.28
1.70
11
0.27
1.68
12
0.27
1.68
Total
2.97
19.47
Mean
0.25
1.62
UNCONTAMINATED SOIL
1
0.28
2.20
2
0.26
2.15
3
0.21
2.00
4
0.38
2.26
5
0.39
2.30
6
0.37
2.25
7
0.39
2.30
8
0.34
2.22
9
0.32
2.18
10
0.36
2.24
11
0.36
2.24
12
0.35
2.23
Total
5.14
26.57
Mean
0.42
2.21
124
Regression on Pl and Grain Yield of Contaminated Soil Descriptive Statistics
Mean
Grain yield of maize
Pl
1.5475
.2483
Std.
Deviation
.17462
.04260
N
12
12
Correlations
Pearson Correlation
Sig. (1-tailed)
N
Grain yield of maize
PI
Grain yield of maize
PI
Grain yield of maize
PI
Variables Entered/Removed
Variables
Variables
Model
Entered
Removed
1
PIa
Grain yield of maize
PI
1.000
.762
.002
.762
1.000
.002
12
12
12
12
Method
Enter
a. All request variables entered.
b. Dependent variables: Grain yield of maize
Model Summaryb
Model
R
R Square
Adjusted R
1
.762a
.580
.539
Std. Error of
The Estimate
.11863
Model Summaryb
Mode
l
1
a.
b.
R Square
Change
.580
Change Statistics
F change
Df1
df2
Sig. F Change
Durbin-Watson
13..836
1
10
Predictors. (Constant), PI
Dependent Variable: Grain yield of maize
.004
1.734
125
Model
Regression
Residual
Total
a.
b.
ANOVAb
Sum of
df
squares
.195
1
.141
10
.335
11
Mean square
.195
.014
F
Sig
13.836
.004a
predictors: (Constant), PI
Dependent Variable: Grain yield of maize
Coefficientsa
Unstandardize Coefficients
Model
1
a.
(Constant)
PI
B
Std. Error
.772
3.123
.211
.840
Standardized
Coefficients
Beta
.762
T
Sig.
3.654
3.720
.004
.004
Dependent Variable Grain yield of maize
Residuals Statisticsa
Predicted Value
Residual
Std. Predicted
Value
Minimum Maximum Mean
1.2404
1.7401
1.5475
-22128
.16975
.00000
-2.308
1.447
.000
-1.867
1.431
.000
Std. Deviation
.13304
.11311
1.000
.953
N
12
12
12
12
a. Dependent Variable: Grain yield of maize
Regression on PI and Grain Yield of Uncontaminated Soil
Descriptive Statistics
Mean
Std. Deviation
N
Grain yield of maize 2.1900
.10357
12
.3342
.05664
12
Correlations
Grain yield of maize
Pearson Correlations
Sig. (1-tailed)
N
Grain yield of maize
PI
Grain of maize
PI
Grain yield of maize
PI
1.000
.917
PI
.917
1.000
.000
.000
12
12
Variables Entered/Removedb
Model
Variables Entered
Variables Removed
a
1
PI
a.
All requested variables entered
b.
Dependent Variable: Grain yield of maize
12
12
Method
Enter
126
Model summaryb
Model
R
R Square
Adjusted R
Std. Error of the Estimate
1
.917a
.842
.826
.04324
Model summaryb
Change Statistics
R Square
Mode Change
F Change Df1
df2
l
1
.842
53.119
1
10
a.
Predictors: (Constant), PI
b.
Dependent Variable: Grain yield of maize
Model
1
Regression
Residual
Total
a.
b.
a.
1
10
11
.000
1.497
ANOVAb
Mean Square
F
.099
.002
Unstandardized
Coefficients
B
Std. Error
(Constant)
PI
Sig
.000a
53.119
1.629
1.677
Coefficientsa
Standardized
Coefficients
Beta
.078
.230
.917
T
Sig.
20.913
7.288
.000
.000
Dependent Variable: Grain yield of maize
Predicted Value
Residual
Std. Predicted Value
Std. Residual
a.
Df
Durbin-Watson
Predictors: (Constant), PI
Dependent Variable: Grain Yield of Maize
Model
1
Sum of
squares
.099
.019
118
Sig. F Change
Residuals Statisticsa
Minimum Maximum Mean
1.9817
2.2837
2.1900
-06559
.06376
.00000
-2.192
.986
.000
-1.517
1.475
.000
Std. Deviation
.09501
.04123
1.000
.953
N
12
12
12
12
Dependent Variable: Grain yield of maize
Effect of Amendments on Relationship between Total Porosity and Grain Yield of
Maize for Contaminated and Uncontaminated Soils
Contaminated Soil
127
S/N
TOTAL POROSITY
GRAIN YIELD OF
MAIZE
1
35.94
1.63
2
40.76
1.50
3
39.60
1.30
4
38.21
1.62
5
34.90
1.62
6
41.97
1.68
7
38.30
1.72
8
37.77
1.68
9
33.77
1.66
10
38.66
1.70
11
38.00
1.68
12
37.36
1.68
Total
455.24
19.47
Mean
37.94
1.62
Uncontaminated Soil
1
39.34
2.20
2
42.07
2.15
3
40.56
2.00
4
40.19
2.26
5
37.16
2.30
6
41.23
2.25
7
40.09
2.30
8
39.91
2.22
9
37.16
2.18
10
41.23
2.24
11
40.09
2.24
12
39.91
2.23
Total
478.94
Mean
39.91
26.57
2.21
Simple Regression Analysis on the Effect of Total Porosity of Contaminated Soil on
Maize Yield
Descriptive Statistics
128
Maize Yield
Total Porosity
Mean
Std. Deviation
1.2750
.20505
38.0833 5.29937
N
12
12
Correlations
Pearson Correlation
Sig. (1-Tailed)
N
Maize Yield
Maize Yield
1.000
Total Porosity
.504
Maize Yield
.047
Total Porosity
Maize Yield
12
Total Porosity
12
Variable Entered/Removedb
Variables
Model
Entered
1
Total Porositya
a.
All requested variables entered
b.
Dependent variable: Maize Yield
Total Porosity
504
1.000
.047
12
12
Variables Removed
Method
Enter
Model Summaryb
Model
R
.504a
1
Adjusted R
Square
.179
R Square
.254
Std. Error of
The Estimate
.18574
Model Summaryb
Change Statistics
Model
1
R Square
Change
.254
F change
3.406
df1
1
df2
10
Sig. F Change
.095
Predictors: (Constant), Total Porosity
Dependent Variable: Grain yield of maize
ANOVAb
Sum of Squares
Model
Df
Mean
Square
1
Regression
.118
1
.118
Residual
.345
10
.034
Total
.463
11
Durbin-Watson
.713
a.
b.
a.
b.
F
3.406
Sig
.095a
Predictors: (Constant), Total Porosity
Dependent Variable: Grain yield of maize
Coefficientsa
Unstandardized Coefficients
Standardized Coefficients
129
B
Std. Error
Model
1
(Constant)
.532
Total Porosity .020
1
T
1.311
1.846
.504
Dependent Variable: Grain yield of maize
Predicted Value
Residual
Std. Predicted Value
Std. Residual
a.
.406
.011
Coefficientsa
95% Confidence Interval for B
Lower Bound
Upper Bound
-.372
1.437
-.004
.043
Model
a.
Beta
Residuals Statisticsa
Minimum Maximum Mean
1.0588
1.3709
1.2750
-.21238
.38514
.00000
-2.091
.928
.000
-1.143
2.074
.000
Std. Deviation
.10336
.17710
1.000
.953
N
12
12
12
12
Dependent Variable: Grain yield of maize
Simple Regression Analysis on the Effect of Total Porosity of Uncontaminated Soil on
Maize Yield
Descriptive Statistics
Mean
Std. Deviation
N
Maize Yield
2.1250 .33337
12
Total Porosity
42.0833 4.25245
12
Correlations
Pearson Correlation Maize Yield
Total Porosity
Sig. (1-Tailed)
Maize Yield
Total Porosity
N
Maize Yield
Total Porosity
Total Porosity
903
12
12
Variables Entered/Removedb
Variables Entered
Variables Removed
a
Total Porosity
Model
1
a.
b.
Maize Yield
1.000
.903
.000
All requested variables entered
Dependent variable: Grain yield of maize
Model Summaryb
Model
R
R Square
Adjusted R
Std. Error of
.000
12
12
Method
Enter
Sig.
.219
.095
130
a
1
.903
Mode
l
1
c.
d.
.815
R Square
Change
.815
The Estimate
.15052
.796
Model Summaryb
Change Statistics
F change
Df1
df2
Sig. F Change
Durbin-Watson
43.955
1
10
.000
1.244
Predictors: (Constant), Total Porosity
Dependent Variable: Grain yield of maize
ANOVAb
Model
1
Regression
Residual
Total
a.
b.
Mean Square
.996
.023
Coefficientsa
Unstandardized
Standardized
Coefficients
Coefficients
B
Std. Error Beta
(Constant)
-.853
Total Porosity .071
Model
1
Df
1
10
11
F
43.955
Sig
.000a
T
Sig.
-1.890
6.630
.088
.000
Predictors: (Constant), Total Porosity
Dependent Variable: Grain yield of maize
Model
1
Sum of
Squares
.996
.227
1.222
.451
.011
.903
Coefficients
95% Confidence Interval for B
Lower Bound
Upper Bound
-1.858
.153
.047
.095
a.
Dependent Variable: Grain yield of maize
Residuals Statisticsa
Minimum Maximum Mean
Predicted Value
1.2700
2.4021
2.1250
Residual
-.30214
.21014
.00000
Std. Predicted Value
-2.841
.921
.000
Std. Residual
-2.007
1.396
.000
a.
Std. Deviation
.30090
.14352
1.000
.953
Dependent Variable: Grain yield of maize
EFFECT OF AMENDMENTS ON PRODUCTIVITY INDEX
N
12
12
12
12
131
Soil property
Measured property
Ascribed sufficiency
0-15
15-30
30-45
45-60
0-15
15-30
30-45
45-60
BD (Mgm-3)
1.68
1.69
1.78
1.80
0.12
0.11
0.02
0.01
AWC (cm/cn)
0.16
0.17
0.19
0.20
0.60
0.65
0.78
0.79
pH in Kcl
3.4
3.2
3.1
3.0
0.21
0.12
0.07
0.03
RWF (DRZ)cm
60
60
60
60
1.00
1.00
1.00
1.00
BD – bulk density, AWC – available water capacity, RWF – root weighting factor
PI = 0.12 x 0.11 x 0.02 x 0.01
=
0.000264
0.60 x 0.65 x 0.78 x 0.79
=
0.240318
0.21 x 0.12 x 0.07 x 0.03
=
0.0000529
=
0.2406349
=
0.24
Mineralization Rate constants (K/day) of burnt rice husk dust for Uncontaminated Soil
in 2006
t (days)
Cumulative CO2-C
Co-Ct
Co
Mgco2
7
30.1
173.8
2.2400
14
79.6
124.3
2.0945
21
103.8
100.1
2.0004
28
140.1
63.8
1.8048
Co
203.9
Y = 2.3849-0.0200x
-K = -0.0200
2.303
K = 0.0461
Log Co-Ct
132
Mineralization Rate Constants (K/day) of Saw dust for Contaminated Soil in 2007
t (days)
Cumulative CO2-C
Co-Ct
Log(Co-Ct)
Co
Mgco2
7
14.1
29.7
1.4728
14
18.9
24.9
1.3962
21
19.2
24.6
1.3909
28
31.5
12.3
1.0899
Co
43.8
Y = 1.62595-0.01649x
-k
2.303 =0.01649
K=0.0380/day
Mineralization Rate Constants (K/day) of unburnt rice husk dust for Uncontaminated
Soil in 2008
t (days)
Cumulative CO2-C
Co
Co-Ct
Log(Co-Ct)
Mgco2
7
39.6
129.8
2.1133
14
66.0
103.4
2.0145
21
83.6
85.8
1.9335
28
124.3
45.1
1.6542
Co
169.4
y = 2.2935 – 0.02083x
- K = - 0.02083
2.0303
k=0.0480/day

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