MAKERERE UNIVERSITY

COLLEGE OF AGRICULTURAL AND ENVIRONMENTAL SCIENCES

SCHOOL OF AGRICULTUREDEPARTMENT 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........................................................................................................................i

CERTIFICATE..........................................................................................................................ii

DEDICATION..........................................................................................................................iii

ACKNOWLEDGEMENT........................................................................................................iv

TABLE OF CONTENTS...........................................................................................................v

LIST OF ACRONYMS...........................................................................................................vii

LIST OF FIGURES................................................................................................................viii

LIST OF TABLES....................................................................................................................ix

ABSTRACT...............................................................................................................................x

CHAPTER ONE......................................................................................................................11

INTRODUCTION....................................................................................................................11

1.1 Background...................................................................................................................11

1.2 Problem statement.........................................................................................................14

1.3 Significance of the study...............................................................................................14

1.4 Objectives......................................................................................................................15

1.4.1 General Objectives....................................................................................................15

1.4.2 Specific Objectives....................................................................................................15

1.5 Hypotheses....................................................................................................................15

CHAPTER TWO.....................................................................................................................16

LITERATURE REVIEW.........................................................................................................16

2.1 Incidence of termites in Africa......................................................................................16

2.2 Agro ecology of termites...............................................................................................16

2.3 Factors that affect termite abundance...........................................................................17

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2.4 Termite damage and yield losses..................................................................................18

2.6 Control strategies for termites.......................................................................................19

2.6.1 Chemical control.......................................................................................................19

2.6.2 Cultural control..........................................................................................................20

2.6.2.1 Deep ploughing............................................................................................................20

2.6.2.2 Crop rotation................................................................................................................20

2.6.2.3 Use of cow dung.......................................................................................................20

2.6.2.4 Intercropping............................................................................................................20

2.6.2.5 Weeding and tillage practices..................................................................................21

2.6.3 Biological control......................................................................................................21

CHAPTER THREE..................................................................................................................23

MATERIALS AND METHODS.............................................................................................23

3.1 Location of the study area.............................................................................................23

3.2 Soils and vegetation......................................................................................................24

3.3 Materials and methods..................................................................................................24

3.3.1 Materials....................................................................................................................24

3.3.2 Methods used.............................................................................................................25

3.3.2.1 Reconnaissance survey and land mapping...................................................................25

3.3.2.2Initial data collection.....................................................................................................25

3.3.2.3 Soil testing....................................................................................................................25

3.3.2.4 Laboratory analysis of the soil.....................................................................................28

CHAPTER FOUR....................................................................................................................35

RESULTS AND DISCUSSIONS............................................................................................35

4.1 Soil properties influencing termite................................................................................35

4.2 Soil Biological properties influencing termites.............................................................38

4.2.1 Soil organic matter....................................................................................................38

4.2.2 Carbon stocks............................................................................................................40

4.3 Soil Chemical Properties...............................................................................................41

4.3.1 Nitrogen.....................................................................................................................41

4.3.2 Calcium and Magnesium...........................................................................................44

4.3.3 Potassium...................................................................................................................45

CHAPTER FIVE......................................................................................................................48

CONCLUSIONS AND RECOMMENDATIONS..................................................................48

5.1 Conclusions...................................................................................................................48

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5.2 Recommendations.........................................................................................................49

REFERENCES.........................................................................................................................50

ANNEX: ANOVA tables for the parameters investigated......................................................57

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......26

Figure3. 2: A climate graph indicating the distribution of rainfall and temperatures from

January to December in Kamuli District..................................................................................27

Figure 3.3: Collecting bulk soil samples from the field using a bucket auger.........................32

Figure 4.1: Impact of cover and soil depth on % soil organic matter......................................39

Figure 4.2: Impact of land cover type on % nitrogen concentration........................................42

Figure 4.3: Impact of soil depth on soil nitrogen stock............................................................42

Figure 4.4: Impact on land cover type on exchangeable calcium............................................45

Figure 4.5: Impact of land cover type on exchangeable magnesium.......................................45

Figure 4.6: Impact of land cover type on exchangeable potassium.........................................46

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LIST OF TABLES

Table 3.1: Agro ecological characteristics of the study site.....................................................27

Table 4.1: ANOVA table.........................................................................................................41

Table 4.2: Soil parameters and their means including some standard errors with reference to

land uses...................................................................................................................................52

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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

20

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

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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∗( 5100 )∗( 50

wt )∗(15.20.2 )∗( 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. 5sampleweight 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).

27

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

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

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

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)

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).

Soil depth (m)

Soi

l org

anic

mat

ter (

%)

1.0

1.5

2.0

2.5

3.0

3.5

4.0

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.

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

Land cover type

Soi

l nitr

ogen

con

cent

ratio

n (%

)

0.10

0.15

0.20

GRASS MAIZE SHRUB

Figure 4.2: Impact of land cover type on % nitrogen concentration

Soil depth (m)

Soi

l nitr

ogen

sto

ck (M

g

ha1 )

0.2

0.3

0.4

0.5

GRASS MAIZE SHRUB

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

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.

Cover type

Exc

hang

eabl

e ca

lciu

m (c

entim

oles

(+)

kg1

)

2

4

6

8

10

12

14

Figure 4.4: Impact on land cover type on exchangeable calcium

34

Cover type

Exc

hang

eabl

e m

agne

sium

(cen

timol

es(+

) kg

1)

1

2

3

4

5

6

GRASS MAIZE SHRUB

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)

35

Cover type

Exc

hang

eabl

e po

tass

ium

(cen

timol

es(+

) kg

1)

0.2

0.4

0.6

GRASS MAIZE SHRUB

Figure 4.6: Impact of land cover type on exchangeable potassium

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

REFERENCES

Adam, B. (2010).Farming carbon. Retrieved from Science Alert:

www.sciencealert.com.au/features/20102409-21355-2.html

Agona A., Nabawanuka J., Muyinza H., 2001. An overview of maize in Uganda,

Postharvest Programme, NARO Uganda

Agriforum, 2001. An overview of maize production in Uganda: No.15, April 2001.

Allen Julia Jones, 1985.Termite soil fertility and carbon cycling in Dry tropical Africa: A

hypothesis; Journal of Tropical Biology (1990):6:291-305

Arizona-Sonora, Desert Museum, 2015. Exhibits:Desert Grasslands.

Bahiigwa, G. B. A., 1999. Household food security in Uganda: An empirical analysis.

Kampala, Uganda: Economic Policy Research Centre.

Bolin. B., Sukumar. R.,2000. Global Perspective. In: Watson, R.T., Nobal, I.R.,Bolin.

B.,Racindranath. N.H.,Verardo, D.J., Dokken, D.J (Eds),Land Use, Land use change

and Forestry. Cambridge University press, Cambridge UK: pp23-51.

Boval M and Dixon RM, 2012.The importance of grasslands for animal production and

other functions: A review on management and methodological progress in the trpics:

6(5); 748-62

Brady N.C and Weil R.R., 2008.Ants and Termites: The nature and properties of soil,

revised fourteenth Edition. Pearson Education International. pp. 458-461.

Burkes, I.C, Lauenroth, W.K., Parton, W.J., 1997.Regional and temporal variation in net

primary production and nitrogen mineralization in grass lands. Ecology: 78: 1330-

1340

Chan, D. Y. (2008).Increasing soil organic carbon of agricultural land.State of New South

Wales.

40

Chan, D. Y., Cowie, A., Kelly, G., Singh, B., & Slavich, P. (2008).Soil Organic

CarbonSequestration Potential for Agriculture in NSW.NSW DPI Science & Research

Technical paper.

Dawes, T.Z, 2010. Impacts of habitat disturbance on termites and soil water storage in

southern Australian savannah, pedobiologia: 53:241-246

Dhembare, A. J. (2013). Physico-chemical properties of termite mound soil. Dept. of

Zoology.

FAO Coporate Document Repc, 2012. The importance of soil organic matter: Practices that

influence the amount of organic matter

Ferris, S., Engoru, P., Wood, M. and Kaganzi, E., 2006. Evaluation of the Market

Information Services in Uganda and Recommendations for the Next Five Years. PMA /ASPS

report, Kampala, Uganda.

Gruhn, P., Goletti, F., & Yudelman, M. (2000). Integrated Nutrient Management, Soil

Fertility,and Sustainable Agriculture:Current Issues and Future Challenges.

Washington, D.C. 20006: International Food Policy Research Institute.

Jonathan, D., 2006. Nitrogen mineralization potential in important agricultural soils of

Hawai, Soil crop: 15:1-5

Kabbale G.F, Akol A.M, Kaddu B.J and Onapa A.W, 2013.Bitting patterns and

seasonality of anopheles gambiae sense lato and anopheles funestus mosquitoes in

Kamuli District, Uganda. Parasites and Vectors, 2013.

Kaizzi K, 2014. Description of cropping systems, climate and soils in Uganda [online]

Kumhálová1, J., Matějková, Š., Fifernová, M., Lipavský, J., & Kumhála, F. (2008).

Topography impact on nutrition content in soil and yield. Plant Soil Environ., 54(6),

255–261.

41

Lamb. A Joh, Fabian G. Fernandez and Daniel E. Kaiser, 2004: Extension Specialists in

Nutrient management; University of Minnesota.

Le Bissonnais, Y., Lecomte, V., & Cerdan, ,. (2004). Grass Strip Effects On Runoff And

Soil Loss. Agronomie, 24, 129-136.

Lina T. Codilla and Ephrine B. Metillo, 2011.Distribution and abundance of the invasive

plant species ChromolaenaodorataL. In the Zamboanga Peninsula,

Philliines:International Journal of Environmental Science and development:2(5)

Liu, X., Herbert, v., HashemiX, .., & . Ding, Z. G. (2006). Effects of Agricultural

Management on Soil Organic Matter and Carbon transformation . Plant Soil Environ,

52(12), 531–543.

Maliha, S.Nash, Anderson.P.John and Whitford, G. Walter, 1999.Spartial and temporal

variability in relative abundance and foraging behaviour of subterranean termites in

desertified and relatively intact Chihuahuan Desert ecosystems.Applied soil Ecology,

12(2):149-157.

Ministry of Agriculture, Animal Industry and Fisheries, Statistical Abstract, 2011.

Agricultural Planning Department

Montane Francesc, Rovira Pere and Casals Pere, 2007.Shrub encroachment into mesic

mountain grasslands in the Iberian Peninsula: Effects of plant quality and temperature

on soil C and N stocks:21

Mugerwa S, Mpairwe D, Mutetikka D, Kiwuwa G, Zziwa E, Owoyesigire B, Peden D,

2012. Effect of cattle manure and reseeding on pasture productivity. Paper presented

in the Challenge Programme on Water and Food” workshop held between 10th- 15th

November 2008, Addis Ababa, Ethiopia.

Mukankomeje, R. (2010).Practical Tools on Soil and Water Conservation Measures .

Rwanda Environment Management Authority

42

Murphy K.L, J,M.Klopatek,1998.The effects of litter quality and climate on decomposition

along the elevation gradient, Ecol. Appl,.8:1061-1071

National Environment Management Authority (NEMA).2007. State of the Environment

Report for Uganda. NEMA, Kampala. 357pp.

Okalebo J.Robert, Keneth W. Gathua and Paul L. Woomer, 2002. Laboratory Mtehods

of soil and plant Analysis: A working manual: Second edition.

O’kane M.A., 2012.Efects of termites on soil coversystemperformance:S.LamoureuxO’kane

consultants Inc., C.O.Mineclosure,Brisbane Australia ;Australian Center of

Geomechanics.

Okon, P. B., & Babalola, O. (2005). General Variability Of Soils Under Vetiver Grass

Strips: Focus On Combating Land And Environmental Degradation. Sustainable

Agriculture , 27(3), 93-116.

Paustian. K.G.I.Agren and E. Bosatta,1997.Modelling litter quality effects on

decomposition and soilorganic matter dynamics,in Driven by Nature:Plant litter quality and

decomposition: pp313-335, CAB int ,Oxon,UK

Sabiiti E. N, 2004. Grasslands: A resource for humanity; Inaugural lecture

Shaefer G.R Carlos Ernesto, Leila de Sounza Lynch, Helga Dias Arato,Joao Herber M

Viano,Manouel Ricardo and Teresa Tells, 2014. Chemical, physical and

micromorphological properties of termite mounds and adjacent soils along a

toposequence in Zona da Mata, Minas Gerais State, Brazil: Catena 76:107-113

Silveira, M., Hanlon, E., Azenha, M., & Silva, H. M. (2012).Carbon Sequestration in

Grazing Land Ecosystems. Food and Agricultural Sciences, Soil and Water Science ,

Florida.

Sseguya Haroon, Mazur Experiences from a Livelihood Program in Rural Uganda 40(2): pp

123-138. Department of Sociology, Iowa State University.Robert .E, Masinde

Dorothy, 2009. Harnessing Community Capitals for Livelihood Enhancement:

43

Uganda Bureau of Statistics, 2010. Summary report on Uganda Census of Agriculture

2008/2009

USAID/COMP.E.TE,2010. Market Assessment and Baseline Study of Staple Foods,

Country Report-Uganda.

Xue Zhijing, Man Cheng,Shaoshan An,2013.Soil Nitrogen distribution for different land

uses and land scape positions in small watershed on Loess Plateau,China.Ecological

Engineering60:204-123

Zhang Wenju, Kailou Liu Jinzhou Wang , Xingfang Shao Minggang Xu Jianwei Li

Xiujun Wang Daniel V. Murphy, 2015.Relative Contribution of maize and external

manure amendment to soil carbon sequestrationin a long term intensive maize

cropping system.

Zziwa Emmanuel, Mpairwe Denis Geoffrey Kironchi1, Charles Gachene,

SwidiqMugerwa, 2012.The dynamics of land use and land cover change in

Nakasongola district, Journal of Biodiversity and Environmental Sciences

(JBES):2(5), pp 61-73

Zziwa E, Kironchi G, Gachene C, Mugerwa S, Mpairwe D, 2012.The dynamics of land

use and land cover change in Nakasongola district, Journal of Biodiversity and

Environmental Sciences: 2(5): 61-73.

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

Df Sum Sq Mean Sq F value Pr(>F)

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

Df Sum Sq Mean Sq F value Pr(>F)

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

Df SumSq Mean Sq F value Pr(>F)

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

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Cover:DepthF 6 1.8002 0.300028 1.1713 0.3570

Residuals 22 5.6355 0.256159

Table A7 Mg

Df SumSq Mean Sq F value Pr(>F)

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

47