Impact of land cover on soil properties and termite activity

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MAKERERE UNIVERSITY
COLLEGE OF AGRICULTURAL AND ENVIRONMENTALSCIENCES
SCHOOL OF AGRICULTURE
DEPARTMENT OF AGRICULTURAL PRODUCTION
IMPACT OF COVER ON SOIL PROPERTIES AND ON TERMITE ACTIVITY IN
KAMULI DISTRICT-EASTERN UGANDA
BY
KOJO ROBINAH
Reg. No. 12/U/478
Student No. 212000438
A RESEARCH PROJECT SUBMITTED TO THE SCHOOL OF AGRICULTURAL
SCIENCES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
AWARD OF THE DEGREE OF BACHELOR OF SCIENCE IN AGRICULTURAL
LAND USE AND MANAGEMENT OF MAKERERE UNIVERSITY (KAMPALA)
2015

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DECLARATION
I KOJO ROBINAH, do declare to the best of my knowledge that this research, titled ‘Impact
of cover on soil properties and on termite activity in Kamuli District-Eastern Uganda’
in partial fulfilment of the requirements for the award of the degree of Bachelor of Science in
Agricultural Land Use and Management of Makerere University (Kampala), is fully the
works of my efforts and that it has not been produced elsewhere for the same or related
award.
Signature..................................... Date................................................
CERTIFICATE
This is to certify that the project work titled ‘Impact of land cover and termite activity on soil
biological and chemical properties in Kamuli District-Eastern Uganda’, is a true record of the
original study conducted by KOJO ROBINAH (12/U/478), in partial fulfilment of the
requirements for the award of the degree of Bachelor of Science in Agricultural Land Use
and Management of Makerere University (Kampala) during the academic year 2014/2015,
with my guidance as the academic supervisor.
Academic Supervisor..................................................
Dr. OLUPOT GIREGON
Submitted on..........................................
Signature.................................................

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DEDICATION
I dedicate this research to my parents Mr and Mrs Zamba, and my two sisters Juan
Emmanuela and Poni Christine, who with their support and words of wisdom have opened up
my eyes to see beyond what they can see, and to my siblings, I hope this research acts as an
inspiration to you that no matter your sex, you can achieve what others can in this competing
world.

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ACKNOWLEDGEMENT
I acknowledge the works of this research to the ALMIGHTY GOD who has made wonders in
my life and filled my life with abundant pleasures, MAKERERE UNIVERSITY, COLLEGE
OF AGRICULTURAL AND ENVIRONMENTAL SCIENCES, SCHOOL OF
AGRICULTURAL SCIENCES AND DEPARTMENT OF AGRICULTURAL
PRODUCTION for recruiting me into this great institution of Higher Education; THE
GOVERNMENT OF THE REPUBLIC OF UGANDA for the Government Scholarship
through my years of education at the university; IOWA STATE UNIVERSITY UGANDA
PROGRAM(ISUUP) for allowing me to conduct my research from their Fields in Kamuli
district and for the accommodation, Dr. OLUPOT GIREGON for being a very supportive
supervisor throughout my research; Dr. DONALD KUGONZA and PROF. TWAHA ALI
BASAMBA for guiding me through the first stages of my study; Dr. ZZIWA EMMANUEL
for guiding me through my research; Mr. BONNY BALIKUDDEMBE for assisting and
guiding me through the laboratory analyses; NAMUWONGE IMMACULATE for being a
very helpful friend during this work and the INCREDIBLE CLASS OF BACHELORS OF
AGRICULTURAL LAND USE AND MANAGEMENT 2012 -2015 for being there through
all the years of study and the discussions and assistance rendered throughout our three years
of University.

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TABLE OF CONTENTS
DECLARATION ....................................................................................................................... ii
CERTIFICATE .......................................................................................................................... ii
DEDICATION........................................................................................................................... ii
ACKNOWLEDGEMENT .........................................................................................................iii
TABLE OF CONTENTS.......................................................................................................... iv
LIST OF ACRONYMS ............................................................................................................ vi
LIST OF FIGURES ..................................................................................................................vii
LIST OF TABLES...................................................................................................................viii
ABSTRACT.............................................................................................................................. ix
CHAPTER ONE ......................................................................................................................10
INTRODUCTION ...................................................................................................................10
1.1 Background ...................................................................................................................10
1.2 Problem statement.........................................................................................................12
1.3 Significance of the study............................................... Error! Bookmark not defined.
1.4 Objectives......................................................................................................................13
1.4.1 General Objectives ....................................................................................................13
1.4.2 Specific Objectives....................................................................................................13
1.5 Hypotheses.................................................................... Error! Bookmark not defined.
CHAPTER TWO .....................................................................................................................14
LITERATURE REVIEW ........................................................................................................14
2.1 Incidence of termites in Africa......................................................................................14
2.2 Agro ecology of termites............................................... Error! Bookmark not defined.
2.3 Factors that affect termite abundance ........................... Error! Bookmark not defined.

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2.4 Termite damage and yield losses .................................. Error! Bookmark not defined.
2.6 Control strategies for termites....................................... Error! Bookmark not defined.
2.6.1 Chemical control ....................................................... Error! Bookmark not defined.
2.6.2 Cultural control ......................................................... Error! Bookmark not defined.
2.6.2.1 Deep ploughing............................................................ Error! Bookmark not defined.
2.6.2.2 Crop rotation................................................................ Error! Bookmark not defined.
2.6.2.3 Use of cow dung ...................................................... Error! Bookmark not defined.
2.6.2.4 Intercropping............................................................ Error! Bookmark not defined.
2.6.2.5 Weeding and tillage practices .................................. Error! Bookmark not defined.
2.6.3 Biological control...................................................... Error! Bookmark not defined.
CHAPTER THREE .................................................................................................................17
MATERIALS AND METHODS.............................................................................................17
3.1 Location of the study area.............................................................................................17
3.2 Soils and vegetation......................................................................................................19
3.3 Materials and methods ..................................................................................................21
3.3.1 Materials.................................................................... Error! Bookmark not defined.
3.3.2 Methods used............................................................. Error! Bookmark not defined.
3.3.2.1 Reconnaissance survey and land mapping...................................................................21
3.3.2.2Initial data collection..................................................... Error! Bookmark not defined.
3.3.2.3 Soil testing. .................................................................. Error! Bookmark not defined.
3.3.2.4 Laboratory analysis of the soil.....................................................................................23
CHAPTER FOUR....................................................................................................................26
RESULTS AND DISCUSSIONS............................................................................................26
4.1 Soil properties influencing termite................................................................................26
4.2 Soil Biological properties influencing termites. ...........................................................29
4.2.1 Soil organic matter ....................................................................................................29
4.2.2 Carbon stocks ............................................................ Error! Bookmark not defined.
4.3 Soil Chemical Properties...............................................................................................30
4.3.1 Nitrogen.....................................................................................................................30
4.3.2 Calcium and Magnesium...........................................................................................32
4.3.3 Potassium ..................................................................................................................34
CHAPTER FIVE .....................................................................................................................37
CONCLUSIONS AND RECOMMENDATIONS ..................................................................37
5.1 Conclusions...................................................................................................................37

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5.2 Recommendations.........................................................................................................37
REFERENCES ........................................................................................................................39
ANNEX: ANOVA tables for the parameters investigated ......................................................44
LIST OF ACRONYMS
ASARECA - Association for Strengthening Agricultural Research in Eastern and
Central Africa.
FAO - Food and Agricultural Organisation
GPS - Global Positioning System
MAAIF - Ministry of Agriculture, Animal Industry and Fisheries
MT - Metric Tonnes
NEMA - National Environmental Management Authority
UBOS - Uganda Bureau of Statistics
UNEP - United Nations Environment Program
USAID - United States Agency for International Development
WFP - World Food Program
N - Nitrogen
P - Phosphorous
K - Potassium
C - Carbon
G - Grassland
M - Maize field
S - Shrub land
Mg - Mega grams

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LIST OF FIGURES
Figure 3.1: A map of Kamuli district showing its villages and the neighbouring districts .....17
Figure3. 2: A climate graph indicating the distribution of rainfall and temperatures from
January to December in Kamuli District..................................................................................18
Figure 3.3: Collecting bulk soil samples from the field using a bucket auger.........................22
Figure 4.1: Impact of cover and soil depth on % soil organic matter ......................................29
Figure 4.2: Impact of land cover type on % nitrogen concentration .......................................31
Figure 4.3: Impact of soil depth on soil nitrogen stock ...........................................................31
Figure 4.4: Impact on land cover type on exchangeable calcium ............................................33
Figure 4.5: Impact of land cover type on exchangeable magnesium.......................................34
Figure 4.6: Impact of land cover type on exchangeable potassium.........................................35

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LIST OF TABLES
Table 3.1: Agro ecological characteristics of the study site .... Error! Bookmark not defined.
Table 4.1: ANOVA table .........................................................................................................28
Table 4.2: Soil parameters and their means including some standard errors with reference to
land uses...................................................................................................................................36

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ABSTRACT
This study was conducted in Kamuli District-Eastern Uganda to assess the impact of cover on
soil properties and on termite activity. Three different land cover types were involved which
included; shrubs, maize and grassland. . In each cover, four core soil samples at a depth of 0 –
0.05m and four bulk soil samples at 0 – 0.15m were randomly collected. . The soil samples
were taken to the laboratory from where they were air- dried, crushed and sieved through a
two millimetre sieve before they were taken for routine analysis. Bulk soil samples were
analyzed for texture and chemical properties, including: N, P, K soil pH, soil organic matter
and core samples used to test for physical properties such as bulk density and saturated
hydraulic conductivity. Data was analyzed using the Genstat Statistical package14th edition.
The data was used to obtain an ANOVA table showing the significant parameters and the
parameters that were not significant in relation to cover, depth and the interaction between
cover and depth. Graphs showing the results from the statistical analyses were also obtained.
There was a significant effect of cover on Cstock, Nstock, soil organic matter and % Nitrogen
concentration; for example, the highest organic matter was observed under shrubs
(2.78±0.12) and the lowest amounts under grass (2.11±0.26). The highest %nitrogen
concentration was observed under shrubs (0.189±0.009), and the lowest %Nitrogen
concentration under grasslands (0.14±0.012). In contrast, there was no significant effect of
cover on K+, Ca2+ and Mg2+, but the highest calcium and magnesium levels were under the
maize fields, Ca2+ (6.01±0.91) ;( Fig 4.4) and Mg2+ (2.74±0.38); and the lowest levels were
under the grasslands Ca2+ (5.53±0.71) and Mg2+ (2.50±0.30). The only significant effect on
depth was only observed on Cstock. There was no significant effect of the interaction
between cover and depth on all the parameters studied, which indicated that that the effect of

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the parameters was consistent when averaged over the effect of interaction between cover and
depth.
Further studies are however needed to establish the impact of cover on soil properties and on
termite activity.
CHAPTER ONE
INTRODUCTION
Background
Land cover is a major factor influencing soil properties and termite activity in most soils of
tropical Africa. Moreover, soil productivity and characteristic vary depending on the
dominant vegetation in that area (Kumhálová1 et al., 2008). Physical, biological and
chemical properties vary greatly with landscape, land use system and management so much
that even on a seemingly uniform site, from the physiographic point of view, similarities
cannot be expected (Gruhn et al., 2000). For example, grass cover can reduce sediment
export by 30% to 100%, and most of the time by more than 90% runoff is significantly
reduced downstream of the grass strip and there is selectivity of sediment transport resulting
in an enrichment in soil organic carbon and litter. Adam (2010) reported that grasses increase
soil carbon levels. However, despite the numerous studies done on the different importance
of cover and their influence on soil properties, this study focused on how each of the cover
types, basically maize, shrubs and grasslands influence soil properties and termite activity.
Maize
Maize (Zea mays L) is one of the world’s important cereal crops (Agona et al., 2001). Among
the cereals in Uganda, maize registered the highest increment from 2,355 MT in 2009 to
2,374 MT in 2010, followed by Sorghum from 374 MT to 391MT, Millet 250 MT in 2009 to
268 MT in 2010, and least is rice at 206 MT in 2009 to 218 MT in 2010(MAAIF,2011).

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Maize is the number-one staple food for the urban poor, in institutions such as schools,
hospitals and the military. Also, the crop is the number-one source of income for most
farmers in Eastern, Northern and North-Western Uganda (Ferris et al.,2006).The importance
of maize is centred on the large quantity of carbohydrates, proteins, vitamins and fats,
contained in the kernels, making it compare favourably as an energy source with root and
tuber crops ( Agona et al.,2001). Unlike in neighbouring countries (Kenya, Tanzania among
others), maize does not form a major part of the population’s traditional diet, but is grown
primarily for income generation, rather than for food security(USAID/COMP.E.T.E, 2010).
In Uganda, an average of 1.5 tonnes of maize per hectare is produced. In addition to being
eaten directly as food, it supports the local brewery industry. Maize is eaten on cobs, which
are either cooked or roasted. Maize flour is also used to prepare a local paste called posho
(UBOS, 2010).
Since 2000, annual WFP food procurement in Uganda has increased from the initial 28,000 to
121,000 tons (UBOS,2010).Over 90% of Uganda’s maize is produced by smallholders of
which about 60% of the annual maize output is consumed on the farm(Kaizzi,2014).These
developments are a ray of hope for the Ugandan economy which has enormous capacity to
produce increasing amounts of maize grain to supply the WFP and to sell to regional
countries and beyond if production and marketing constraints were dealt with (Kiiza,
agricultural economist, Makerere University, Pers. Comm.). The annual maize grain
production in Uganda has been steadily rising, according to the Uganda Bureau of Statistics
(2010) and is currently estimated to be between 500,000 and 750,000 metric tons.
Maize hence comprises a significant part of the diet of many of the region’s inhabitants. The
per capita total maize consumption is at about 28 kilograms a year in Uganda (Agona et al.,
2001). However, the yields remain low, fluctuating around 1.5tonnes per hectare (Kaizzi,
2014).
Improving the productivity of maize-based farming could significantly reduce hunger,
enhance food security and alleviate poverty through increasing the purchasing power of the
farmers. Given the large area on which maize is planted and its importance as a food and cash
crop, it was earmarked as priority crop for the regional research by ASARECA (AgriForum,
2001).
Grasslands

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Grasslands account for approximately 44% of Uganda’s total land surface and 80% of
agriculturally productive land (NEMA, 2007; Boval and Dixon,2012).They comprise open
savannah where the soil and average rainfall are not conducive for arable farming (Sabiiti,
2004).They are an important source of food for both humans and animals by being biological
factories, incorporating all nutrients from the soil and the gases in the atmosphere
(Sabiiti,2004).In addition, grasslands provide important services and roles including water
catchment, biodiversity reserves, for cultural and recreational needs, and potentially a carbon
sink to alleviate green house gases (Boval and Dixon, 2012; Sabiiti,2004). They also help in
the reduction of the rate of erosion and improving in soil properties such as organic matter
content. They intercept rainfall, to keep the soil covered with litter, to maintain soil structure
and pore space, and to create openings and cavities by root penetration (Mukankomeje,
2010).
Shrubs
Shrubs are one of the most invasive vegetation cover in the savannah biome in Africa (Lina
and Ephrime, 2011).They provide a primary source of cellulose to termites in deserts and
most ecosystems (Arizona-sonora desert museum, 2015).They are characterized by shallow
coarse soils that do not retain the below canopy litter layer that is suitable for termite activity
(Maliha, et al.,1999).Shrub encroachment is often associated with alteration of above and
below ground productivity, litter quality and organic matter levels (Zziwa et al., 2012).This is
because shrubs are invasive species which are known to establish in degraded areas, which
are characterised by low soil fertility, low organic matter levels and low
productivity(Mugerwa et al.,2012)
1.2 Problem statement
There is limited information regarding to which cover, depth and the interaction between
cover and depth, impact soil properties and termite activity in Uganda. Yet, most agricultural
activities are influenced by the soil properties on land. Soil manipulations have a lot of
influence on soil properties, for example addition of manures, mulching and fallowing
increase soil organic matter and therefore the soil structure. This in turn impacts on soil
aeration, porosity, nitrogen stock and texture.

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Poor agricultural practices and inappropriate soil and water conservation practices can
accelerate soil degradation through processes like erosion and subsequently, degeneration of
soil properties and loss of soil productivity. Reduction in soil productivity leads to low crop
yields which can severely impact livelihoods. Therefore, to prevent soil degradation, there is
need to promote proper soil conservation measures which maintain cover on the soil and
reduce the rates of soil loss through run off, reduce termite damage on useful vegetation,
preserve soil moisture, improve soil structure and also promote soil productivity. This can be
made easier if more studies are done on the impact of cover on soil properties and on termite
activity.
1.3 Objectives
1.3.1 General Objectives
The general objective of the study was to assess the impact of cover on soil properties and on
termite activity in Kamuli District-Eastern Uganda.
1.3.2 Specific Objectives
The specific objectives of this study were;
 To assess the different soil properties and how they are influenced by cover.
 To assess and discuss how the different cover types influence soil properties and to what
level of significance.
 To determine the extent to which cover influences termite activity and explain how the
different cover types impact termite activity

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CHAPTER TWO
LITERATURE REVIEW
2.1 Land cover and its composition
Land cover refers to all the physical material at the surface of the earth (Wikipedia). It ranges
from grass, water, trees, shrubs and planted vegetation. Cover has a significant effect on soil
properties and in most cases it acts as a barrier to water and wind erosion (Okon & Babalola,
2005). Agronomic or biological measures utilise the role of vegetation in helping to minimise
erosion. They are usually less expensive and deal directly with reducing raindrop impact,
increasing infiltration, reducing runoff volumes and decreasing water velocities
(Mukankomeje, 2010).
They are also important in improving soil properties such as organic matter content, soil
moisture, water infiltration, soil structure and bulk density (Mukankomeje, 2010).Different
cover types however impact soil properties differently depending on their thickness, densities
and C:N ratios (Brady,2008). According to Dhembare (2013) soil organic matter increases,
due to reduced erosion by help of soil conservation measures like use of grass strips. Le
bissonnais et al. (2004) reported that soil cover can reduce sediment export by 30% to 100%,
and most of the time by more than 90% runoff is significantly reduced downstream of the 6-
m grass strip and there is selectivity of sediment transport as a result of a grass strip resulting
in an enrichment in elementary clay and fine silt texture.

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Adam (2010) reported that grass increases soil carbon levels. Several factors promote greater
soil carbon accumulation in grasses as compared to agricultural lands, including high density
of roots, root exudation, and, as stated previously, a lack of physical soil disturbance because
of the absence of tillage (Silveira et al., 2012). Grasslands contain a substantial amount of the
soil organic carbon.
2.2. Soil and its properties
Soil is a mixture of minerals, organic matter, gases, liquids and countless organism that
together support plant life (Wikipedia, the free encyclopaedia). The suitability of soil for crop
production is dependent on the quality of the soils’ physical, chemical and biological
characteristics. These soil properties are influenced by the activities of soil organism, human
manipulations of the soil and cover (Zhang et al., 2015).These soil properties therefore keep
changing from time to time depending on the different environmental and soil conditions.
2.2.1. Organic matter
Soil organic matter is derived from organic materials that are added to the soil, which
decompose upon break down by soil living organisms (Murphy et al., 2014). Organic
materials could be from both plants and animals. Soil organic matter makes up about 5% of
the soil mass, but its vita for the soil physical, chemical and biological properties (Brady,
2008; Hoyle, 2013). Organic matter is however different from organic carbon and humus in
that it includes all the elements that are components of organic compounds.
Importance of soil organic matter on soil properties
Soil organic matter improves the sol physical characteristics and enhances aggregate stability
which improves soil structure, water infiltration, soil aeration and also the activity of soil
organisms (Brady, 2008).
Soil organic matter also influences the soil cation exchange capacity by increasing nutrient
retention and release, acts as a buffer for soil pH due to its buffering capacity hence
increasing the availability of certain nutrients like phosphorous and also provides food to soil
biological living organisms(Murphy, 2014; Brady, 2008).

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2.2.2. Total Nitrogen
Nitrogen is the most abundant element in the atmosphere reaching to 79.9% (Lamb et al,
2014).Lamb explains that although unavailable to most plants, large amounts of atmospheric
nitrogen can be used by leguminous plants through biological fixation, where nodule forming
Rhizobium bacteria inhabit the roots of leguminous plants and through a symbiotic
relationship convert atmospheric nitrogen to forms of nitrogen that plats can use. It is also the
most essential element required by plants and used for vegetative growth, plant growth and
uptake of other nutrients (Brady, 2008; Zhang et al., 2015). Land use conversion is a major
factor affecting Nitrogen cycles (Bolin and Sukumar, 2000). The vegetation coverage
influences plant residue and organic matter input (Jonathan, 2006). Additionally,
mineralization in soil nitrogen is influenced by microclimate, soil conditions, land uses and
management practices (Burke et al., 1997). Soil Nitrogen is also influenced by manipulations
of the soil by the farmers through addition of fertilizers; both organic and inorganic.
2.2.8. Calcium and Magnesium
Concentrations of calcium and magnesium in the soil are dependent on the parent material
and addition of amendments to the soil. These components are normally directed to the soils
and not the plant since they are important liming material and hence the reason to why their
deficiencies are not seen in plants (Brady, 2008).Therefore, the concentration of these
components in the plants will depend on the soil conditions, land use and growth phase of the
plant.

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CHAPTER THREE
MATERIALS AND METHODS
3.1 Characterisation of the study area
3.1.1. Location of the study area
This study was conducted in the villages of Namasagali, Nakanyonyi and Naluwoli Village in
Kamuli district (00o55oN, 33o06oE), located in Eastern Uganda. It lies at average altitude of
1120m above sea level (Fig 3.2). Kamuli covers an area of 4,348km2 of which 3332km2 is
land and 1016km2 (23%) is water. The average land holding is 1.0 hectare per farm family.
Over 80% of the population depend on agriculture for their livelihood.
Kamuli district is bordered by Buyende District to the north, Luuka District to the east, Jinja
District to the south and Kayunga District to the west (Fig 3.1). The district headquarters at
Kamuli are located approximately 143 kilometres east of Kampala city, the capital city of
Uganda, by road.
Figure3. 1: A map of Kamuli district showing its villages and the neighbouring districts

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Figure3. 2: A climate graph indicating the distribution of rainfall (left-Y-axis) and
temperatures (right- Y-axis) from January (01- X-axis) to December (12-X-axis) in
Kamuli District for the year 2012.
Rainfall
Kamuli district receives bimodal rainfall, with one main dry season from December to
February and two rainy seasons, the heaviest rains in March to May and light rains from

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August to November (Kabbale et al., 2013;Fig3.2), with an average annual rainfall of
1298mm. The least amount of rainfall occurs in January, with an average of 34mm (Fig 3.2).
In April, the precipitation reaches its peak; with an average of 210mm.The variation in the
precipitation between the driest and the wettest months is 176mm.
Temperature
Kamuli is a warm district with average annual temperature range in most areas of 19o C to
25oC. Temperatures are a little higher in the southern than in the northern areas of the district.
The temperatures are highest on average in February, at around 23.5oC. At 21.3oC on
average, July is the coldest month of the year (Fig 3.2). The variation in annual temperature is
around 2.2oC.
3.1.3. Soils and vegetation
Soils
Most of the soils of Kamuli district are classified as deeply weathered red or yellow soils of
the humid tropics. They are dominated by low activity clays mainly kaolinite and
sesquioxides and referred to as rhodic oxisols by the USDA classification system. They are
formed from highly weathered parent materials on old, stable geomorphic surfaces. The soils
of Kamuli are characterised by low soil fertility, however, upon amendment through
application of organic fertilizers, their fertility can be restored. This is because, despite their
low fertility, they are well drained and have good physical characteristics. The pH of these
soils ranges from 5.0-6.5, with less than 5% organic matter in the surface horizon (FAO,
2000).
Vegetation
The dominant vegetation is forest remnants and savannah trees with grass and shrubs
(Sseguya et al., 2009). Much of it is secondary vegetation that has succeeded the original
forest cover as a result of farming, fuel wood harvesting, and other forms of land use. The
predominant vegetation cover in the district is the forest/ savannah type of mosaic consisting
of a mixture of forest remnants and savannah trees with grass and shrubs. Much of it is

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secondary vegetation that has succeeded the original forest cover as a result of farming, fuel
harvesting and other forms of land use.
Although the acreage under cultivation has increased in the past 30 years (from 2 ha to 2.5 ha
on average), unit production has reportedly decreased. This is attributed to erratic and adverse
weather conditions, pests and diseases, and low adoption of agricultural technologies
(Bahiigwa, 1999).
3.1.4. Population and economic activities in the district
As of December 2002, Kamuli District had a population of about 712,000 (2002 population
census) /with a population density of 236 persons/km². Males comprise 40.5% of the
population and females make up 59.5%. The population growth rate is estimated at 5.1% per
year. The total population was projected to be 856,563 by 2015; 346,847 males and 365,232
females. The annual growth rate is 5.1%. Kamuli has a population density of 236 persons per
Km2. The education has high drop-out levels. The average house size for the district was 5.1
persons per household compared to the national average 4.7 persons as per 2002 census.
The literacy rates of the population are generally low at 61.8%, compared to the national rate
of 70%. A wide rate in literacy exists between males and females, with a very low literacy
rate of 54.6% for the females and a higher rate of 69.7% for the males. Education also has
high drop-out rates both for primary and secondary education. The human poverty indicator
is at 24.1% but higher in Budiope County because of the poor infrastructure.
The health sector is faced with low levels of health facility utilisation of 33%. Malaria and
acute respiratory infections are the most common diseases.
Kamuli District is a multi-ethnic and multi-cultural society, with the predominant ethnic
group being the Basoga who comprise 76% of the population. The Iteso make up 3.9% and
the Banyoro and Bagungu together make up 1.8%. Other Ugandan ethnicities make up the
rest (18.3%). The predominant language spoken in the District is Lusoga, with some Luganda
and English also spoken.

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Means of earning a livelihood in Kamuli District include: fishing which is a major economic
activity in the waters of L. Kyoga and River Nile. This would be a big potential for revenue
area for the district but there is still a lot of mismanagement. The fishermen entirely depend
on fishing with no alternative income generation projects. This has caused temptations and
use of unscrupulous methods of fishing such as use of undersized nets and smuggling
accelerating the depletion of the fish resource, which is a threat to tomorrow.
Most livestock kept in the district are the local breeds. There are very few cross breeds on
some of the fenced farms. There have been annual sporadic out breaks of livestock and crop
diseases. These could not fully be contained due to limited funding of the disease control
programmes. Livestock Statistics can be seen below; cattle are 160,000 heads, goats total to
148,000, fenced farms are up to 140, zero grazing units’ number to 150.
Upland rice has been selected as the strategic enterprise for development in the whole district
under the National Agricultural Advisory Services (NAADS) programme. Production is
predominantly small scale and subsistence with the hand hoe as the dominant tool and no
modern farming skills/ technology used such as irrigation. The commercial aspect of farming
has only been introduced through the NAADS programme which is one year old now in the
district.
3.2 Procedure for conducting the study
3.2.1 Reconnaissance survey and land mapping
This involved moving through my fields of study that is to say the maize fields, shrubs and
grasslands with a native of Kamuli district. During the reconnaissance survey, the boundaries
of the different fields were delineated to ensure that soil samples are collected from fields of
equal sizes.
3.2.2. Field Methods of Soil sampling
Before sampling, the entire residue was carefully removed plus any turf material away from
the sampling point

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3.4.1 Core sample collection
The core ring was driven at a depth of about 9cm by gently heating the flat top piece of wood
placed on top of the core. This was then gently removed from the soil, labelled and packed
into polyethene bags, fastened with rubber band to minimize moisture loss in the field. These
were then taken to the laboratory for analysis at Makerere University and were used to
analyze Bulk density and Saturated hydraulic conductivity
3.4.2 Bulk sample collection
Bulk samples were collected using an auger of 15cm in depth. They were collected adjacent
to the same sampling location from where the core soil samples had been removed. Soil from
auger collections were pooled together to constitute a composite sample through quarter
sampling and the required amount labelled and put into a polyethene bag. These were then
transported to the laboratory for analysis of chemical, biological and physical properties like
soil texture. These bulk soil samples were air-dried, pounded and passed through a 2 mm
sieve and the rest that could not pass through was discarded. The sieved soil samples were
then subjected to physical- chemical analysis following standard methods compiled by
Okalebo et al. (2002)
Figure 3.3: Collecting bulk soil samples from the field using a bucket auger

23
3.3. Laboratory analysis of the soil
The soil samples were taken to the laboratory air-dried and crushed further using a motor.
The crushed soil samples were then be passed through a 2mm sieve and the samples larger
than that were discarded. The remaining soil samples were then subjected to laboratory tests
for, soil organic matter, nitrogen, calcium, magnesium and potassium.
Total Nitrogen determination;
The Kjeldahl method was used to determine the nitrogen levels in the different manure
samples (Okalebo et al., 2002).
The total Nitrogen content of the soil samples was extracted using the acid digestion and then
determined by the distillation and titration method. One gram of soil was weighed and put
into a dry and clean digestion tube; a mixed catalyst was then added followed by 4 ml of
concentrated sulphuric acid. The tube contents were then inserted into a pre-heated block
digester. The temperature was raised to 3500C for an hour. The tubes were then removed and
the contents allowed to cool before 25 ml of distilled water was added and the contents mixed
thoroughly and further allowed to cool and settle so that a clear solution would form on top of
the tube for analysis. A steam distillation apparatus was then set up and 5 ml of the aliquot
from the sample solution were transferred to the reaction chamber of the distillation apparatus
followed by 10 ml of 1% NaOH. Steam distillation was started immediately and the ammonia
that evolved was trapped using 5 ml of 1% boric acid containing four drops of a mixed
indicator. The distillation continued for 2 minutes until the indicator solution turned green.
The distillate was then removed and titrated against a 0.005 M HCl for total organic nitrogen.
The titre volume was then determined and recorded as at the point when the solution changed
from green to pink. The same procedure was followed to determine the average titre volume
of the blank tube and the nitrogen content of the standards determined using the expression
below;
%N= [(V*M*14/1000)*40/5*100]/WT
Where;
V =volume of the acid

24
M =0.05
WT= weight of the sample
Potassium determination
0.2 g of the soil sample was weighed and put into a dry and clean digestion tube; 4.4ml of
digestion mixture was added to the tubes. The tube contents were then inserted into a pre-
heated block digester. The temperature was raised to 3500C for 2hours. The tubes were then
removed and the contents allowed to cool. Then digest was transferred to a 50 ml volumetric
flask to which distilled water was added to the volume and the contents mixed thoroughly
and further allowed to cool and settle so that a clear solution would form on top of the tube
for analysis.
0.2ml of the digest was taken into a test-tube and 15ml of distilled water were added and the
samples allowed to stand .the solutions starting with the standards, the samples and the blank
were directly spread into flame of the flame photometer at a wave length of 766.5nm and
potassium was determined using the following method
%𝐾 = 𝑅 ∗ (
5
100
) ∗ (
50
𝑤𝑡
) ∗ (
15.2
0.2
) ∗ (
1
10000
)
R= emission
Wt. = weight of the sample
(Okalebo et al., 2002).
Organic matter determination
The Walkey-Black oxidation and titration method was used to determine organic carbon
(Okalebo et al., 2002). 0.5 grams of soil were weighed and put into a block digestion tube.
4ml of potassium dichromate were added using a pipette followed by 7.5ml of conc. H2SO4. 2
blank tubes were also prepared in the same way. The tubes were then placed in a pre-heated
block digester at 1500C for 30 minutes after which they were removed and allowed to cool
before transferring the contents into a 100 ml conical flask and adding three drops of the
indicator solution. Titration was then done on the digests using iron sulphate and the titre

25
volumes were determined and recorded. The same was done for the blank samples and a
mean titre volume for the two blanks determined. The organic carbon content of each soil
sample was then calculated using the expression below:
Organic carbon (%) =
𝑽×𝟎.𝟑×𝟎.𝟓
𝒔𝒂𝒎𝒑𝒍𝒆𝒘𝒆𝒊𝒈𝒉𝒕
x1.73
Where 0.8 = Molarity of iron sulphate used; V= blank titre volume – sample titre volume;
1.73=multiplication factor (Okalebo et al., 2002)
Exchangeable Calcium and Magnesium determination
Calcium and magnesium were extracted from the soil by mixing 10 millilitres of 1 normal,
pH7, ammonium acetate with a 10 gram scoop of air-dried soil and shaking for 5 minutes.
The filtered extract was analyzed with an inductively coupled plasma atomic emission
spectrometer for calcium and magnesium. The results were reported in parts per million
(ppm) calcium (Ca) and magnesium (Mg) in the soil.
3.4 Statistical Analysis of data
These comprised the soil analytical test results from physical, chemical and biological
properties under cover. The data from the experiment was first compiled entered into
Microsoft Excel spreadsheet and subjected to analysis of variance and means separated by
Fisher`s Protected Least Significant Difference at 5% probability level using Genstat version
14. The ANOVA constituted of the block, cover, depth and the interaction between cover and
depth (Table 4.1) and the graphical presentation of the results (Fig 4.1, 4.2, 4.3, 4.4, 4.5, 4.6
and 4.7). The statistical analysis also produced the means and standard errors of the
parameters, which were used to explain the results (Table 4.2).

26
CHAPTER FOUR
4.1. RESULTS AND DISCUSSIONS
Laboratory analysis of the soil samples obtained from the field indicates that soil properties
and termite activity is influenced by cover. These properties include; soil nitrogen, soil
organic matter, soil carbon stocks, Potassium, Calcium and magnesium and they vary across
all the land uses that is; shrubs, maize and grassland, meaning soil properties and termite
activity varies across all the land uses.
Variation of soil properties was observed in relation to cover, depth of the soil and the
interaction between the depth and the cover (Table 4.1; Annex, Table A1, A2, A3, A4, A5, A6
and A7), where A1 to A7 are the parameters investigated. There was a significant effect of
cover on soil organic matter, carbon stock, nitrogen stock, % nitrogen concentration, but
there was no significant effect of cover on Calcium, magnesium and potassium
concentrations (Table 4.1).
There was a significant effect of soil depth on soil carbon stock (Table 4.1; Annex, Table A1),
but there was no significant effect of depth of soil on the other parameters investigated (Table
4.1; Annex A1, A2, A3, A4, A5, A6, and A7).
There was however no significant effect of interaction between cover and depth on any of the
parameters (Table 4.1; Annex, Tables A1 to A5).
The soil properties investigated were found to be directly influenced by land cover for
example the soil organic matter was observed to vary depending on land cover type and
termite activity. This can be related to the termite feeding habits; for example; since termites

27
feed on litter and organic matter, the higher the concentrations of organic matter in the soil,
the lower the termite activity on land cover type because the termites will concentrate on the
organic matter and litter. Therefore, soil management practices that increase soil organic
matter are recommended to reduce termite damage on cover.

28
Table 4.1: ANOVA table
Parameters and significance levels
*stand s for (P<0.05), **Stands for (P<0.01), ***Stands for (P<0.001) and NS stands for Not Significant (P=0.05)
Treatment Df Cstock(
Mgha-1)
Nstock(Mgha-
1)
SOM (%) N concn (%) K+(Cmoles
Kg-1)
Ca2+(Cmole
Kg-1)
Mg2+(Cmole Kg-1)
Cover 2 * * * * NS NS NS
Depth 3 *** NS NS NS NS NS NS
Cover: Depth 6 NS NS NS NS NS NS NS

29
4.2 Soil Biological properties influencing termites.
4.2.1 Soil organic matter
There was a significant effect of cover on soil organic matter (Table 4.1; Annex, Table
A3).Soil organic matter was highest under shrubs (2.78±0.12) and lowest under grasslands
(2.11±0.26). There was no significant effect of soil depth and the interaction between cover
and depth on the soil organic matter, indicating that the effect cover on soil organic matter
was consistent when averaged over the effect of depth (Fig 4.1).
Figure 4.1: Impact of cover and soil depth on % soil organic matter
Soil organic matter under shrubs (2.78±0.12), was lower than 5%, which is a recommended
rate for crop production (O’kane, 2012).
Paustian et al., 1997 and Murphy et al., 1998 explained that C: N ratio may hinder soil
organic matter decomposition and therefore its percentages in the soil. Murphy further
explains that Shrubs have high C: N ratios and high lignin contents, which take long to be
broken down. The lower quality of shrub litter and roots (higher C: N ratio, lignified tissues,
secondary compounds) may therefore hinder soil organic matter decomposition (Montane et
al., 2007) which explains the low organic matter under shrubs.
Soil depth (m)
Soilorganicmatter(%)
1.0
1.5
2.0
2.5
3.0
3.5
4.0
GRASS
MAIZE
SHRUB

30
In addition, the generally low organic matter levels in the soil can be attributed to excessive
cultivation using inappropriate implements could have resulted in the soils being over-
worked and the consequent loss which has caused many land degradation problems such as
erosion and soil structural decline (Chan, 2008). Chan continues and explains that tillage
operations expose the soil to high temperatures hence the rate of decomposition of organic
matter hence decline in soil organic matter content in soil. Liu et al. (2006) also reported that
tillage can reduce the distribution of soil organic matter while an appropriate crop rotation
can increase or maintain the quantity and quality of soil organic matter, and improve soil
chemical and physical properties.
Termite activity also significantly contributes to low organic matter content, which in turn
contributes to lack of aggregation, high erodibility, poor nutrient and water holding capacity
and hence the loss of soil carbon into the atmosphere, which reduces the soil carbon stocks
(Allen, 1985).
4.3 Soil Chemical Properties
4.3.1 Nitrogen
There was a significant effect of soil cover on both the % Nitrogen concentration (Table 4.1;
Annex, A4) and the soil Nitrogen stocks (Table 4.1).The highest %nitrogen concentration was
found under shrubs (0.189±0.009), and the lowest was under grasslands (0.14±0.012)
;(Fig.4.2; Table 4.2)
Also, the highest Nstock was found under shrub (0.369±0.019), and the lowest was observed
under grasslands (0.286±0.024); (Fig 4.3; Annex, Table A2), which indicates that %Nitrogen
concentration is directly proportional to Nstock
There was however no significant effect of depth and interaction between cover and depth on
the %nitrogen concentration and Nstock. implying that the effect of cover on %N and Nstock
was consistent when averaged over the effect of depth of soil.

31
Figure 4.2: Impact of land cover type on % nitrogen concentration
Figure 4.3: Impact of soil depth on soil nitrogen stock
Generally there is low nitrogen content especially under the grasslands (0.14±0.012) which is
below 0.2% according to Okalebo et al. (2002) and considered to be very low for crop
production. This can be explained by nutrient mining especially through burning of cover,
grazing and termite activity. Nutrients such as nitrogen are lost from the field through
Land cover type
Soilnitrogenconcentration(%)
0.10
0.15
0.20
GRASS MAIZE SHRUB
Soil depth (m)
Soilnitrogenstock(Mgha
1
)
0.2
0.3
0.4
0.5
GRASS MAIZE SHRUB

32
harvested crops and crop residues, as well as through leaching, atmospheric volatilization,
and erosion (Gruhn et al,2000).
Land use conversion is also a major factor affecting Nitrogen cycles (Bolin and Sukumar,
2000). The vegetation coverage also influences plant residue and organic matter input
(Jonathan, 2006). Additionally, mineralization of soil nitrogen is influenced by microclimate,
soil conditions, land uses and management practices (Burke et al., 1997). The shrub lands
accumulated large concentrations of soil nitrogen because of its abandoned nature. Shrubs
establish in formerly degraded areas (Zziwa et al., 2012), which are normally abandoned.
This accumulates large vegetation cover, during the restoration process, which promotes
organic matter accumulation, floral and fauna activity such as termite activity, soil structure
improvement and reduced soil erosion resulting to higher nitrogen concentrations compared
to the other land uses (Xue et al., 2013).
The nitrogen in the maize fields was significantly lower than that from the shrub lands
because the maize crop requires nitrogen for it to complete its life cycle and will therefore
utilise the soil nitrogen, which reduces its contents in the soil. Crops management practices
such as harvesting, weeding and tillage also contribute to losses in soil nitrogen through crop
residue and soil erosion.
The low concentration of nitrogen in grasslands is because of uncontrolled grazing and
increased termite damage. Soil nitrogen is related to the soil organic matter levels and litter
generated (Xue et al., 2013). This is because leaves of plants and organic matter are
known to contain up to about 98% nitrogen. Over grazing reduces litter generation and
therefore organic matter accumulation which also exposes the soil to nitrogen losses by
volatilisation and erosion.
The high Nitrogen content in the soils is also associated with organic matter incorporation by
termites, as faecal pellets mixed with saliva (Shaefer et al., 2014).
.
4.3.2 Calcium and Magnesium
Neither the main effect of cover and depth nor the effect of their interaction had a significant
effect on basic cations (Table 4.1; Annex; Table A6 and A7). Potential significant differences

33
existed between the effect of cover on basic cations when averaged over the effect of depth of
soil but which could have masked by the large variability. For example, the highest calcium
and magnesium levels were under the maize fields, Ca2+ (6.01±0.91) ;(Fig 4.4) and Mg2+
(2.74±0.38) ;(Fig 4.5) and the lowest levels were observed under the grasslands Ca2+
(5.53±0.71) and Mg2+ (2.50±0.30).
Calcium and Magnesium levels in the soil are directly affected by the soil pH such that at
high pH they are abundant (Ciolkosz 2001). Therefore the generally high pH of the soils of
Kamuli could have contributed to the large concentrations of theses ions in the soils.
Figure 4.4: Impact on land cover type on exchangeable calcium
Cover type
Exchangeablecalcium(centimoles(+)kg
1
)
2
4
6
8
10
12
14
GRASS
MAIZE
SHRUB

34
Figure 4.5: Impact of land cover type on exchangeable magnesium
4.3.3 Potassium
Neither the effects of cover, depth of soil or interaction between cover and depth had a
significant effect on potassium (K+) ion concentration (Table 4.1; Annex, Table A5). ).
Potential significant differences existed between the effect of cover on basic cations when
averaged over the effect of depth of soil but which could have masked by the large
variability. For example, potassium ion concentration was highest under maize (0.33±0.041)
and lowest under grasses (0.29±0.05) ;( Table 4.2)
Cover type
Exchangeablemagnesium(centimoles(+)kg
1
)
1
2
3
4
5
6
GRASS MAIZE SHRUB

35
Figure 4.6: Impact of land cover type on exchangeable potassium
Cover type
Exchangeablepotassium(centimoles(+)kg
1
)
0.2
0.4
0.6
GRASS MAIZE SHRUB

36
Table 4.2: Soil parameters and their means including some standard errors with reference to land uses
Statistic SOM (%) Cstock(mgha-1) Nstock(mgha-1) N (%) K(mgkg-1) Ca(mgkg-1)
Grass Maiz
e
Shru
b
Grass Maize Shru
b
Gras
s
Maize Shru
b
Gras
s
Maiz
e
Shru
b
Gras
s
Maiz
e
Shru
b
Grass Maize Shru
b
Grand
mean(±se
)
2.11±
0.26
2.69
±0.2
1
2.78
±0.1
3
5.96±
1.04
8.09±
1.38
7.68
±0.8
9
0.29
±0.0
2
0.32±
0.025
0.37
±0.0
2
0.14
±0.0
12
0.16
±0.0
13
0.18
5±0.
009
0.29
±0.0
5
0.33
±0.0
41
0.30
±0.0
49
5.53±
0.71
6.02±
0.91
5.72
±0.4
8

37
CHAPTER FIVE
CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
This study was aimed at assessing the impact of cover on soil properties and on termite
activities in Kamuli District-Eastern Uganda. The results indicated that shrubs were
accumulating more organic matter and %nitrogen than the maize and grasslands, as the maize
accumulated higher % Mg, Ca and K than the shrubs and grasslands, which deviated from
what would have been expected. The high organic matter concentration in shrubs is evidence
that the rate of soil organic matter decomposition into humus is influenced by C: N ratio of
the plant tissue. Shrubs with a higher C: N ratio tends to accumulate more organic matter
over time since they take longer to be broken down that for the maize and grasslands. Also ,
the generally low organic matter levels in the soils of Kamuli under all cover types can be
attributed to excessive cultivation using inappropriate implements could have resulted in the
soils being over-worked and the consequent loss which has caused many land degradation
problems such as erosion and soil structural decline These results also indicated that termite
activities are influenced by the presence of organic matter and the cover type. This is because
termites are on organic matter/litter feeders, and will prevail more in soils with higher organic
matter levels. However when there is little or no organic matter in the soil, they will divert
their feeding activities to the cover. Whereas the generally low nitrogen concentrations in the
soil are attributed to nutrient mining especially through burning of cover, grazing and termite
activity, since nutrients are lost from the field through harvested crops and crop residues, as
well as through leaching, atmospheric volatilization, and erosion.
5.2 Recommendations
The organic matter levels of the soils of Kamuli are generally below the recommended rates
of 5%. This calls for artificial manipulation of the soil organic matter levels by adding
manures and fallowing. This could be employed to reduce termite damage to the vegetation
and improve soil fertility. Organic matter or litter being a sole feed for termites, the farmers
should consider maintaining large concentrations of it in the soils. This prevents the termites
from diverting to vegetation as a source of feed, in case the organic matter in the soil is
depleted.

38
Organic matter also attracts natural predators of termites such as fungi and black ants, which
help to regulate the populations of termites in the soil naturally. By so doing, termite damage
on vegetation will be reduced and soil organic matter levels will be maintained at optimum
levels required for crop production.
Mulching of the gardens and fields can be employed as a cropping system by the farmers.
This is because the mulches which are grasses and dry leaves normally act as litter and
therefore in case of termites, the mulches will be attacked first before the crop, and by the
time the termites eat up all the mulches, the crop will have attained full maturity. Mulches are
also known to increase carbon stocks in the soil and upon decomposition, they form organic
matter which is important for improving soil properties such as water holding capacity and
soil structure.
The low nitrogen content observed could be overcome by practices such as fertilizer
application, minimum harvesting, green manuring and minimum tillage activities.

39
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44
ANNEX: ANOVA tables for the parameters investigated
Table A1 soil C stock
Df Sum Sq Mean Sq F value Pr(>F)
RepF 2 1.4635 0.7318 5.5813 0.01095 *
Cover 2 1.1766 0.5883 4.4872 0.02321 *
DepthF 3 12.8569 4.2856 32.6875 2.759e-08 ***
Cover:DepthF 6 0.6590 0.1098 0.8377 0.55415
Residuals 22 2.8844 0.1311
Table A2 nitrogen stock
RepF 2 0.020616 0.0103081 2.3690 0.11701
Cover 2 0.035269 0.0176343 4.0527 0.03174 *
DepthF 3 0.026635 0.0088782 2.0404 0.13753
Cover:DepthF 6 0.033867 0.0056445 1.2972 0.29943
Residuals 22 0.095727 0.0043512
Table A3 SOM

45
Df SumSq Mean Sq F value Pr(>F)
RepF 2 0.60446 0.302228 7.0332 0.00435 **
Cover 2 0.40149 0.200746 4.6716 0.02037 *
DepthF 3 0.04633 0.015443 0.3594 0.78288
Cover:DepthF 6 0.26600 0.044333 1.0317 0.43150
Residuals 22 0.94538 0.042972
Table A4 N
RepF 2 0.011654 0.0058269 2.7428 0.08640 .
Cover 2 0.017466 0.0087330 4.1107 0.03042 *
DepthF 3 0.013740 0.0045798 2.1558 0.12210
Cover:DepthF 6 0.018054 0.0030091 1.4164 0.25288
Residuals 22 0.046738 0.0021245
Table A5 K
RepF 2 0.06663 0.033315 0.7969 0.4633
Cover 2 0.02467 0.012335 0.2951 0.7474
DepthF 3 0.01773 0.005909 0.1414 0.9341
Cover:DepthF 6 0.24557 0.040929 0.9791 0.4626
Residuals 22 0.91969 0.041804
Table A6 Ca
Df Sum Sq MeanSq F value Pr(>F)
RepF 2 0.4262 0.213080 0.8318 0.4485
Cover 2 0.0426 0.021301 0.0832 0.9205
DepthF 3 0.4022 0.134058 0.5233 0.6707

46
Cover:DepthF 6 1.8002 0.300028 1.1713 0.3570
Residuals 22 5.6355 0.256159
Table A7 Mg
RepF2 1.6053 0.80266 0.7062 0.5044
Cover 2 0.3356 0.16781 0.1476 0.8636
DepthF3 1.6012 0.53374 0.4696 0.7065
Cover:DepthF 6 8.7841 1.46402 1.2880 0.3033
Residuals 22 25.0066 1.1366

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