LAND USE EFFECTS ON SOIL QUALITY AND PRODUCTIVITY IN THE LAKE VICTORIA BASIN OF UGANDA

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the degree Doctor of Philosophy in the Graduate School of the Ohio State University

By

Lukman Nagaya Mulumba

*****

The Ohio State University

2004

Dissertation Committee Approved by Professor Rattan Lal, Adviser Professor Norman Fausey Professor Fred Hitzhusen ______Professor Frank Calhoun Advisor Soil Science Graduate Program ABSTRACT

Soil quality indices are useful tools for assessing agronomic/ biomass productivity and ascertaining temporal changes in soil properties in relation to land use and management. This study was conducted in the Lake Victoria region in Masaka, Uganda to: (a) identify key soil properties that impact soil quality and agronomic productivity; (b) evaluate soil quality-management inter-relationships; (c) evaluate the use of soil reflectance as a soil quality indicator, and (d) determine the cost and returns of different cropping systems.

Bulk and core soil samples were collected from the 0-20 and 20 – 50 cm depths, from the farmers’ fields, in order to determine soil organic carbon, nitrogen, calcium, phosphorous, magnesium, pH, _13C, _15N, coarse fragments, soil bulk density and soil texture. Saturated hydraulic conductivity (Ks) was determined in the field using a tension infiltrometer and soil depth using an auger. The soil degradation rating was assessed by assigning parametric values to levels of SOC, soil bulk density, Ks, soil texture, soil pH, soil depth and the proportion of coarse fragments in the top soil and these parameters were utilized to develop a single index. Air dry samples were scanned using a spectrometer and the first derivative of the spectral data was calibrated against the measured soil properties. Results indicated that soil quality was affected by SOC, soil depth and Ks. No direct effects of management on soil quality were discerned. Good predictions of several soil properties were obtained using the spectral data. Although a majority of farmers planted bananas as the first choice crop, the highest net returns were obtained from coffee while the highest costs were

ii measured for bananas implying that food self sufficiency was the major determinant of the choice of crop to be grown. It was recommended that grasslands must not be converted to agricultural land use because of their high susceptibility to soil degradation and that farmers be sensitized to think beyond food-self sufficiency, a goal that could also be achieved through strategies which increase farm income.

iii Dedicated to my family for all the sacrifices they made for me

iv ACKNOWLEDGMENTS

I wish to extend my gratitude to the Rockefeller Foundation whose fellowship enabled me to pursue a doctorate degree at the Ohio State University. I also wish to thank my advisor, Dr. Rattan Lal, for providing advice and support through the academic program and research.

Special thanks go to Dr. Keith Shepherd, of ICRAF, for his guidance and support when I was during the field work and data analysis. I also wish to acknowledge the other dissertation committee members, Professors Norman Fausey, Fred Hitzhusen, and Frank Calhoun for the time they took out of their schedules to serve on my committee and provide professional input throughout the duration of my study. Without their help, this dissertation would not have been completed.

I recognize the contribution of Dr. Moses Tenywa of Makerere University, Mr. Kabango Fred of Masaka, Richard Coe of ICRAF and Andrew Sila of ICRAF for the support and assistance that they rendered to me. I’m also thankful to Mr. Kalule, Mukiibi, Meddy, and Emma for their perseverance and assistance during data collection. Other thanks go to the staff of ICRAF and Makerere University for their cooperation and support in one way or the other. Special thanks are also due to Dr. Harriet Nannyonga for all the assistance she rendered to my family during data collection and while writing up this dissertation.

I greatly indebted to my parents, Haji Badru Mulumba and Mrs Hasifa Mulumba for all the support and sacrifice that they had to make for me to get a decent education.

v Last but not least, I’m thankful to my wife Birungi, my daughter Mariam and my son Nasser for their inspiration and moral support and long patience as I undertook my studies.

vi VITA

January 20, 1967 ………… Born – Jinja, Uganda. 1990 ……………………… B.Sc. Agriculture, Makerere University, Uganda. 1993 ……………………… M.Sc. Land and Water Management, University of Nairobi, Kenya. 1993- 1994 ……………… Lecturer, Makerere University 1994- 1996 ……………… Soil Scientist, Farmer Participatory Research Unit, ActionAid. 1995- 1999 ……………… Agriculture Officer, Ministry of Agriculture, Animal Industry and Fisheries. 1999 - Present …………… Graduate Student, The Ohio State University, Columbus, OH.

PUBLICATIONS

Tenywa, M.M., L.M.Nagaya and D.Bwamiki. 1995. Soil Sampling Criteria for Spatially Variable Soils. SSSEA, Conference held November 21-25, 1994, in Mbarara, Uganda. In. M.C. Rwakaikara, M.A Bekunda and J.S. Tenywa. (eds.). Proceedings of the Soil Science Society of East Africa conference, pp 229-235.

Nagaya L.M (1995): Participatory Evaluation of On-Farm Soil trials, a paper presented at the 14th conference of the Soil Science Society of East Africa, Mbarara

vii FIELDS OF STUDY

Major Field: Soil Science Minor Field: Environmental Economics

viii TABLE OF CONTENTS

Abstract...... ii Acknowledgments...... v Vita...... vii List of Tables ...... xi List of Figures...... xiv

Chapter 1 ...... 1

Introduction...... 1 1.1 Background and Setting ...... 1 1.2 Statement of the Problem ...... 5 1.3 Objectives of the Study ...... 6 1.4 Hypotheses to be tested...... 7 1.5 Goals and Significance of the study...... 8 1.6 Study Area...... 8

Chapter 2 ...... 16

Review of Literature...... 16 2.1 Soil Erosion ...... 16 2.2 Soil Quality...... 24 2.3 The Banana-Coffee System...... 35 2.4 Modeling Soil Quality, Erosion and Productivity ...... 36 2.5 The Diffuse Reflectance Spectroscopy ...... 40 2.6 Soil Organic Carbon ...... 42 2.7 Exchangeable Potassium...... 48 2.8 Magnesium ...... 48 2.9 Calcium ...... 49 2.10 Land use and Management Practices...... 49 2.11 Soils of the Tropics ...... 55

Chapter 3 ...... 59

Methodology...... 59 3.1 Research Design ...... 59 3.2 Methodology...... 655

ix 3.3 Laboratory Measurements...... 69 3.4 Mini Survey...... 75 3.5 Data Analysis...... 76

Chapter 4 ...... 78

Results and Discussion...... 78 4.1 Soil Physical Properties...... 78 4.2 Soil Chemical Properties...... 84 4.3 Soil Degradation Rating...... 101 4.4 Soil Productivity ...... 106 4.5 Calibration of Spectra with Soil Nutrients ...... 108 4.6 Delta 13 Carbon...... 113 4.7 Soil Erosion Index as Related to Land Use...... 117 4.8 Soil Quality-Management Relationship...... 118 4.9 Soil Quality Indicators ...... 120 4.10 Demographics, Costs and returns of different cropping systems ...... 121 4.11 Impact of Net returns to Cropping Systems in Determining Choice of Crop ...... 125 4.12 Maintenance of Soil Quality as a Function of Profitability of Cropping Systems ...... 127

Chapter 5 ...... 128

Conclusion and Recommendations...... 128 5.1 Conclusions ...... 128 5.2 Recommendations...... 131 5.3 Research Priorities ...... 133

Bibliography ...... 134

Appendices ...... 155 Appendix A: Soil Profile Description and Analytical data ……….…………………..156 Appendix B: Questionnaire ...... 157

x LIST OF TABLES Table Page

1.1 Trends in Land use and Productivity in the tropics – 1970 to 1999....2

2.1 Estimated annual economic and energetic costs (per ton) of soil and water loss from conventional corn assuming a water and wind erosion of 17 tons ha-1 year-1 over 20 years ...... 18

2.2 Soil attributes and standard measurement methodologies to be included in a minimum data set for monitoring soil quality ...... 27

2.3 The minimum data set needed for soil quality assessment for principal eco-regions in the tropics...... 30

2.4 Suggested critical levels of soil strength and structural indicator .....31

2.5 Criteria limits for soil mechanical properties...... 31

2.6 Criteria limits for soil Chemical properties ...... 32

2.7 Criteria level soil organic carbon content ...... 32

2.8 Proposed minimum data set of physical, chemical and biological indicators for screening the condition, quality and health of soil ...... 33

3.1 Natural Drainage Classes...... 62

4.1 Saturated Hydraulic Conductivity Classes...... 79

4. 2 Variation of Bulk density with Land Use at the Three Slope Positions.....81

4.3 Variation of SOC (g/Kg) with slope position ...... 86

4.4 Variation of Soil Organic Nitrogen (g/kg) with slope position among different land use...... 88

4.5 Variation of Soil Organic Nitrogen (g/kg) with Land Use at Different slopes...... 89

xi 4.6 Comparison of Exchangeable Calcium as Influenced by Slope Position and Level of Management...... 93

4.7 Comparison of Exchangeable Calcium Among Different Land Use Types ...... 94

4.8 Relationship Between Exchangeable Potassium with Land Use .....96

4.9 Relationship Between the Exchangeable Magnesium (cmolc/kg), Land Use and Slope Position...... 97

4.10 Variation of Exchangeable Magnesium with Slope Position under Different Land Use ...... 98

4.11 Variation of Extractable Phosphorous from Soils Under Different Land Uses...... 101

4.12 Rating Scheme for Soil Degradation Rating...... 102

4.13 Critical limits of soil properties for selected crops...... 103

4.14 Mean Soil Degradation Rating for the Measured Soil Properties ...103

4.15 Soil Degradation Rating under Different Land Use Types...... 104

4.16 Variation of Soil Degradation Rating with slope position...... 106

4.17 Shoot to Root Ratio as Influenced by Land Use, Slope position, SOC, management and days to germination ...... 107

4.18 Variation of Shoot:Root Ratio with Land Use...... 107

4.19 Summary for Calibrated Models...... 109

4.20 Comparison of Influence of Current and Former Land Use on _13 C.115

4.21 Delta 13 Carbon Under Different Land Uses...... 115

4.22 Variation of Erosion and Sediment Index with Land Use ...... 117

4.23 Variation of Soil Degradation Rating across Different Land Uses....119

4.24 Variation of Soil Quality Rating Across Different Slope Positions ...... 120

4.25 Age Distribution in the Study Area...... 122

xii 4.26 Household size in the study Kabonera sub county...... 123

4.27 Variation of Seasonal Costs between the Different Crops ...... 125

4.28 Gross Returns among the various crops per season ...... 125

4.29 Relative Importance of the Various Crops to the Farmers...... 126

4.30 Attributes that Make Selected Crops Important to Farmers...... 126

xiii LIST OF FIGURES

Figure Page 1.1 Population and land use changes...... 4

1.2 Location of Uganda...... 9

1.3 The Lake Victoria basin...... 10

2.1 An outline of the diffuse reflectance spectroscopy method...... 41

2.2 Factors that affect soil carbon concentration ...... 43

3.1 A Schematic Catena Sequence in Kabonera Sub-County, Masaka District.60

3.2 Delineation of Study Area and Sampling Sites in Kabonera Sub County…63

3.3 Sampling points and their relation to soil type...... 64

3.4 Plot of sampling design...... 65

3.5 Topsoil organic carbon concentration as a function of a spectral index of erosion-deposition status for C3, C4 and transitional land use systems.....68

3.6 The Tension Infiltrometer...... 73

4.1 Variation of Saturated hydraulic conductivity with slope position ………….80

4.2 Variation of Clay content with slope position ………………………………… 82

4.3 Variation of Silt content with slope position ………………….……………… 83

4.4 Variation of Soil pH with Land Use ...... 85

xiv 4.5 Variation of Exchangeable Calcium with slope position and Level of Management...... 91

4.6 Variation of Exchangeable Calcium among the different land use types .....92

4.7 Cation Exchange Capacity as influenced by Land use ...... 99

4.8 Calibration and Validation models for Soil Organic carbon …………….. 110

4.9 Calibration and Validation models for Delta 13 carbon ………………….. 113

4.10 Calibration and Validation model for Soil Nitrogen ………………………. 111

4.11 Calibration and Validation model for exchangeable calcium …………… 112

4.12 Variation of delta 13 carbon between former and current C3 and C4 vegetation …………………………………………………………………….. 114

4.13 Comparison of delta 13 carbon between former and current land use … 114

4.14 Variation of the square of delta 13 carbon with land use ………………… 116

xv CHAPTER 1

INTRODUCTION

1.1 Background and Setting

Soil degradation has severe negative impacts on food security, environment quality and standard of living. Land misuse and soil mismanagement, resulting from a desperate attempt by farmers to increase production of food, fiber, fuel wood and feeds for the growing population, exacerbate soil degradation. The damage manifests itself differently in developing compared to the developed countries. While developed countries have experienced some over-application of fertilizers and pesticides, developing countries suffer from nutrient mining of soil as a result of erosion, slash and burn agriculture, leaching, and lack of or inadequate use of farm inputs. Soil degradation remains a serious problem in most developing countries especially in sub-Saharan Africa because of the mining of soil fertility and the negative nutrient balance exacerbated by soil erosion.

A continued and ever increasing threat to productivity, food security and environmental quality in the tropics, especially in ecologically-sensitive ecoregions characterized by fragile soils still prevails. Reduced crop yields are a major concern in some of these regions, especially in sub-Saharan Africa, where the attainment of food security is closely linked with soil degradation due to nutrient mining.

1 Agricultural expansion into tropical forest areas has been responsible for at least 50% of deforestation since 1970 (Myers, 1991). From 1970 to 1999, land under arable and permanent crops increased by 1.9 million hectares, (Mha) while the areas under forests decreased by 3.1 Mha over the same time (FAO production year book (1970, 1999). Table 1.1 demonstrates the challenges that have been reported.

Area (Mha) Yield (Kg/ha) Land use 1970 1999 1970 1999 Arable & 4.9 6.80 - - Permanent crops Forests 9.2 6.10 - -

Coffee 0.34 0.64 1225 1230 Maize 0.26 0.27 821 747

Table 1.1: Trends in Land use and Productivity in the tropics – 1970 to 1999.

The major problem has been a failure of farmers to adopt improved and proven farming practices. These areas, particularly in sub-saharan Africa, missed out on the Green Revolution technologies as a result of lack of political will, rapid population growth and bad governance. Land related difficulties were exacerbated by corrupt and oversized governments offering poor crop prices, restricting marketing and mis-spending revenue. As a result, they still predominantly use traditional farming practices such as slash and burn, burning

2 of crop residues, overgrazing, continuous cultivation without rest to native fallowing, and excessive plowing using manual or mechanized equipment which drastically disturbs the soil. Such farming practices predisposes the soil to agents of erosion and other degradative processes. The loss of soil degrades arable land and eventually renders it unproductive.

National crop yield averages in tropical Africa rarely show an upward trend despite availability of improved cultivars and agrochemical inputs because of severe soil-related constraints. The low yields have also been attributed to high incidence of pests and diseases and inadequate management of soil and water resources (Lal, 1987).

Concerns about rising atmospheric levels of carbon dioxide (CO2) have stimulated interest in carbon (C) flow in terrestrial ecosystems and the potential for increasing C sequestration. Studies show that increase in human population puts increased pressure on cropland, wood and grazing land resources and promotes encroachment on forest areas (Mudimu, 1998; Jiung-Dong-Chu, 1996; Coppock, 1989). Hence, increased population is likely to result in reduced agricultural land area due to urbanization and conversion to other land uses and hence increased pressure on the existing land. Land use pressure in turn results in increased emissions of greenhouse gases from soil. Inappropriate land use practices coupled with a decline in soil quality, accelerated erosion and nutrient depletion exacerbate increasing gaseous emissions from soils. Figure 1.1 depicts the inter-relationship between changes in human population, changes in land use patterns and the productivity trends resulting from soil degradation.

Lal (2000) observed that about 25 percent of global emissions are from agricultural activities. Some soils have lost 30 to 50 Mg C/ha since conversion of natural ecosystem to agricultural land use (Lal, 1999). Nevertheless, agricultural soils represent a potential C sink through the use of improved management

3 practices such as reduced tillage, residue management, proper crop rotations and judicious use of fertilizers through integrated nutrient management (INM).

Increasing Population

• Age/sex profile • Demographic transition • Income changes

Changing land use and deforestation

Increasing Soil Degradation

Declining in Productivity

Figure 1.1: Population and land use changes

Increases in soil C (organic matter) result in improved soil physical properties and ultimately better crop performance. Therefore, the goal of sustainable management is to enhance the soil C pool, increase productivity, improve the environment, soil structure and the overall soil quality. Soil structure and its stability govern soil-water relationships, aeration, crusting, infiltration, 4 permeability, runoff, interflow, root penetration, leaching losses of plant nutrients and therefore the productive potential of a soil (Lal, 1979). The totality of existing studies demonstrate a need to provide further empirical depictions of relationships between land use patterns, soil characteristics and productivity of specific farming systems. Research included in this study was in a bid to contribute to the quest within one of Uganda’s farming systems.

A number of projects have been done in the study area to address the major constraints to crop production, including nutrient loss through crop mining, pest damage and soil erosion. In a survey by Tenywa et al. (1999), soil erosion was reported as one of the major constraints to crop production, the severity of which is influenced by management practices, soil type and cropping system. This was collaborated by Lufafa (2000), who observed very high erosion rates under annual crops, followed by rangelands, banana-coffee and banana alone.

1.2 Statement of the Problem

The Lake Victoria basin has experienced severe soil degradation resulting into low crop yields due to erosion and nutrient depletion. Within the farming unit, decisions on soil quality improvements are not based on sound scientific recommendations but are rather performed depending on level of education, experience, wealth and financial costs & returns as opposed to social benefits and costs. Although majority of farmers possess a general understanding of soil degradation impacts, they are not aware of its scope or magnitude (Ssali, 2001; Tenywa et al., 1999).

Choice to address this could either focus on crop productivity improvement or improving soil quality through management practices. However, the benefits

5 accruing from the latter are limited by genetic factors, taste & preferences of consumers and soil status.

This necessitates focus on soil quality improvement and productivity improvement as a first coarse of action. The sustainable use of soil is of fundamental importance to improve farmers’ quality of life. Although vast amounts of resources and efforts have been made to preserve natural resources, inappropriate soil management continues to be a major concern.

Due to scarcity of evidence relating management techniques and land use decisions, there is need for research at the farm level in order to fill gaps in knowledge, hence the study design and objectives

It is postulated that awareness of the implications of the farming decisions in terms of yield loss and economic damage (now and in the future) would greatly help farmers to make more informed decisions. Such information is especially important for resource-poor farmers of the tropics to whom off-farm inputs are either not available, or are expensive or inaccessible. Thus, there is a strong need to determine the broader social benefits of adopting improved management practices on soil quality of small holder agriculture in Masaka..

1.3 Objectives of the Study

1.3.1 Overall objective

Identify land use and management systems that enhance productivity and soil quality for small holder farmers in Masaka, Uganda.

6 1.3.2 Specific Objectives

The specific objectives of the study were to: (i). Identify key soil properties that impact soil quality and agronomic productivity , (ii). Examine soil quality -management inter-relationships, (iii). Evaluate the use of soil reflectance as a soil quality indicator, and (iv). Determine the cost and returns of different cropping systems

1.4 Hypotheses to be tested

Research involved testing the following hypotheses:

(i). Integrated indices of soil quality (available water capacity (AWC), infiltration capacity, SOC, N, P, CEC, bulk density, soil texture and top soil depth (TSD)) can be used to assess the effects of land use change; (ii). Historical land use changes and soil erosion have greater effects on soil quality than current land use changes; (iii). Soil reflectance is related to differences in soil quality as determined by soil physical and chemical properties; (iv). Net returns are an important determinant of choice of cropping systems; and (v). Maintenance of soil quality is determined by the profitability of a cropping system.

7 1.5 Goals and Significance of the study

The primary goal of this study was to identify land use and management practices that maintain soil quality without compromising crop net returns to farmers. The study will provide vital information that is needed to enhance the farmers’ decision making process in adoption of conservation-effective measures. Farmers will be able to decide whether it is cost-effective to invest more time or other resources/inputs in their farming operations. This information will be of significance to farmers, policy makers, scientists, environmentalists and extension agents particularly in Uganda..

1.6 Study Area

The study was carried out in Uganda’s Lake Victoria basin. Uganda is located in eastern Africa between latitude 1o30’ south and 4oNorth and longitude 29o30’East and 35oEast (Figure 1.2). The country has a land area of 241,500 km2 of which 15.3% is open water, 3% permanent wetlands and 9.4% seasonal wetlands.

Lake Victoria, with a surface area of 68,800 km2 and second only to Lake Superior in size has an adjoining catchment of 184,000 km2. It is the world's second largest body of fresh water, and the largest in the developing world, (Figure 1.3). Kenya, Tanzania and Uganda control 6, 49, and 45 percent of the lake surface, respectively.

8 Uganda

Figure 1.2: Location of Uganda.

9 Figure 1.3: The Lake Victoria basin.

10 1.6.1 Agriculture Systems

Agriculture in Uganda is practiced by 2.5 million small land holders, 80% of whom have less than 2.5 hectares of land each. Farming is largely subsistence and based on using simple tools such as the hand hoe and pangas (matchets). For the majority of these farmers, maintaining soil quality to produce enough food is a daily struggle. Over the years, significant changes in land use have taken place. Many people have encroached on swamps and forests for settlement and agricultural fields. Consequently, infertility patches are increasing throughout the country.

Much of the forests was initially converted to perennial banana (musa spp)-coffee (Coffea robusta) systems, which are close to natural forests in ground protection, soil and water conservation, C-sequestration and cover about 1/3 of the land area. However, due to widespread soil degradation, (e.g. nutrient depletion), disease and pest build up, significant portions of the banana-coffee cropping systems have reverted to annual cropping systems. Owing to continuous cultivation and sometimes poor soil protection, this system is prone to a high risk of degradation.

Agriculture is rainfed and dependent on mining the existing natural soil fertility. The low levels of inputs utilized result in low output and yield. Generally farmers rely on manual cultivation methods and on family labour. Nutrient depletion due to crop removal, erosion, leaching and volatilisation have all contributed to a severe decline in soil fertility.

11 1.6.2 Climate

The climate of Uganda is influenced by the Inter Tropical Convergence Zone (ITCZ) and air currents such as the southeast and northeast monsoons. In most of the country, the seasons are fairly distinct as rainy and dry seasons. The Lake Victoria Basin experiences a bimodal type of rainfall distribution with the short rains starting in March and ending in May. The long rains start in September and end in November.

The dry areas, in the north, usually receive about 900 mm (40 in) of rainfall annually, while the wet, in the south, receives more than 1,500 mm (60 in). The rainfall events are intense and occur over short durations. A small number of rain days account for most of the annual rainfall (Lal, 1987).

The success of rainfed agriculture is dependent on the reliability of the rains. Rainfall varies greatly, and local droughts are not uncommon. The effectiveness of rainfall in tropical environments is influenced by the prevalence of high temperature, which causes considerable losses through evaporation, and transpiration throughout the year. Similarly food production is constrained due to rainfall unreliability in terms of timing, distribution and amounts.

Considering the moisture regime, the radiation levels and the length of the growing seasons, the agriculture potential of the tropics can be even greater than that of the temperate regions (Lal, 1987). Potentially high output is because it is possible to grow crops through out the year. However, this potential is determined by the variability and distribution of rainfall (Lal, 1987), and by the level of management.

The temperatures are moderate throughout the year. In Kampala, near Lake Victoria, average daily temperatures range from 18° to 28°C (65° to 83°F) in

12 January and 17° to 25°C (62° to 77°F) in July; in Kabale, in the highlands of the southwest, temperature range from 9° to 24°C (49° to 75°F) in January and 8° to 23°C (47° to 74°F) in July.

Geological studies reveal that the predominant rocks found in Uganda were formed between 3000 and 600 million years ago (Precambrian era). In the eastern and western parts of the country, there are young rocks, either sedimentary or volcanic in origin, formed from about 135 million years ago (Cretaceous period) to the present.

The dominant soils are Luvisols, ferralitic (FAO: ferralsol), which are highly weathered with little weatherable mineral reserves. In general, soil nutrients are concentrated mainly in the top 30 cm of soil and are hence susceptible to loss through erosion (Sali, 2001). Agronomic or biomass productivity, therefore, depends on the delicate balance of nutrient recycling propagated by dense vegetation cover with deep rooting systems. Other soil types richer in mineral reserves such as volcanic soils also exist and most of them are highly productive. Gleysols occur in many places associated with present or past drainage systems. At the study site, there were variations in soil with slope position. The backslopes were dominated by Chromic Luvisols while slope shoulders were largely petroferric luvisols. Mollic gleysols were found in the valley bottoms (Appendix A).

Soil degradation and its attendant decline in productivity have been attributed to increasing population, use of inappropriate farming practices and changing land use resulting in over-cultivation and absence of fallow periods. Although there is evidence of declining soil productivity, especially in the fragile ecosystems, information on the extent of soil degradation is sketchy and fragmented (National Environment Authority, 1996).

13 Two strategies are available for reversing the trend of decline in per capita crop yield, namely: (i) crop productivity improvement, and (ii) soil productivity improvement. Qualitative information on crop productivity effects on erosion is not available for most soils, crops and ecoregions of the tropics (Lal, 1994; 1998). Uganda also faces a lack of information on these effects. Field measurements of erosion effects on productivity are required for principal soils and crops of the tropics for different management systems and input levels. Use of simulation models is a viable shortcut only if appropriate parameters are known. Furthermore models may need to be validated for each area where applied. Benefits accruing through the first strategy are limited because improvements in crop yields are genetically limited; high yielding cultivars may not necessarily fit the taste preferences. Similarly, benefits in crop yields are limited by soil status.

The management of soil quality results into both short term and long term changes. The short term changes include plant response to inputs while the long term changes result in changes in soil quality. For example clay content and CEC may change in the long run. Therefore, in addition to crop improvement, increasing soil quality can be a major avenue for attaining increased soil productivity. Unfortunately, soil quality is yet to receive its due position in the crop development economy. There are several reasons for the lack of prioritization of this topic in Africa:

• Soil quality is a function of a multiplicity of factors; • Soil quality is not easily quantified; • The yield-soil quality relationship is not clearly defined; • The soil quality -management inter-relationship is not well understood; and • Soil quality is not clearly articulated as an economic concept.

14 Lack of this information has had significant management implications particularly for small farm holdings. Thus soil productivity improvement has not received the emphasis it deserves.

Several methods have been developed for estimating soil quality. Most of them consider soil’s physical, chemical and biological characteristics, some of which are affected by management practices. The USDA Soil Quality Kit, developed by USDA-ARS, is one on-farm tool farmers can use to measure soil characteristics. Shepherd and Walsh (2002) have developed a method for the rapid assessment of soil quality. Using diffuse reflectance spectroscopy, they demonstrated that light reflectance could detect changes in soil quality. The method gives an integrated measure of soil quality that is sensitive to changes due to short-term management.

15 CHAPTER 2

REVIEW OF LITERATURE

2.1 Soil Erosion

Soil erosion, the physical displacement of soil by wind, water, snowfall and gravity, can have severe adverse economic and environmental impacts. The economic impacts on productivity arise as a result of the direct effect on crops, both onsite and offsite. The Environmental consequences are mainly off-site due to either pollution of water sources (lakes and rivers) or adverse effects on air quality (Lal, 1998). The effects of erosion are most severe in shallow soils or where there is a root restrictive layer at a shallow depth. Erosion by water and wind adversely affects soil quality and productivity by reducing infiltration rates, water-holding capacity, nutrients, organic matter, soil biota and soil depth (El- Swaify et al., 1985; Troeh et al. 1991). Each of these factors influences soil productivity individually but also interacts with the other factors, making assessment of the impacts of soil erosion on productivity difficult.

The use of inappropriate agricultural practices and subsequent soil and water loss are responsible for significant economic and environmental on-site costs. The on-site costs are expended to replace the lost nutrients and water. The loss of nutrients and water can account as much as 90% of the loss in productivity (Pimental et al., 1995). A ton of fertile agricultural topsoil typically contains 1 to

16 6kg of nitrogen, 1 to 3 kg of phosphorous, and 2 to 30 kg of potassium whereas a severely eroded soil may have considerably low levels of these nutrients (Pimentel, 1995).

Pimental (1995) estimated the on-site costs of erosion to be $5/ha/yr while Crosson (1997) estimated it at $0.60/ha/yr per year. den Biggelaar et al. (2001) estimated the on-site loss at $0.39 - 0.95/ha/yr for corn; $0.12 – 0.88 ha/year for wheat and $0.28-0.8 ha/ year for cotton. The differences in estimate are due to the assumptions made by the different authors. For example, Crosson (1983) assumed that the soil deposited would increase crop production at the site of deposition and hence the loss of production in one area could be offset by increased productivity in another area. However, deposition has other negative effects such as water logging, loss of water and nutrients. Furthermore in the process of erosion, soil’s characteristics are radically altered and hence affecting productivity. While Pimental (1995) considered the value of nutrients and water that are lost due to erosion, Crosson (1993) and den Biggelaar (2001) considered the relative decline in yield. Negative effects can also be masked by technological advances for example use of improved cultivars, chemicals, energy inputs and improved management practices.

An example of the yield loss of corn in the U.S. caused by erosion on 5% slope, and 700 mm of rainfall is presented in Table 2.1. Yield losses due to erosion can be much higher for Uganda since rainfall is higher in volume, more intense, and causes higher soil losses on steep slopes. Furthermore the subsistence nature of agriculture in Uganda would preclude the masking effects of chemical fertilizers.

17 Energy costs Quantities Yield Loss Cost of replacement Factor ton -1 Lost ton-1 % US$ ton-1 (103 kcal) Water runoff 75mm 7 1.76 41.18 Nitrogen 0.88kg 29.41 Phosphorous 0.04 kg 8 5.88 0.18 Potassium 7.23 kg 15.29 Soil depth 1.4 mm 7 0.94 - Organic Matter 0.12 tons 4 - - Water holding 0.1mm 2 - - capacity Soil biota - 1 - - Total on-site 8 8.58 86.00 Total off-site 2.94 5.88 11.53 91.88

Table 2.1: Estimated annual economic and energetic costs (per ton) of soil and water loss from conventional corn assuming a water and wind erosion of 17 tons ha-1 year-1 over 20 years (adapted from Pimentel et al.,1995)

Off-site effects occur when eroded soil and runoff originating from one area end up in another area. While on-site effects impact individuals, the off-site or down stream damages affect communities. Hence the cost of off-site damage in terms of eroded fields is generally much higher, and the effects much more spectacular. Off-site effects involve changes in soil quality, water quality and possible effects on the water cycle (Lal, 1998). They also include crop damage by runoff, sediment deposition and introduction of weeds and pathogens. The relative magnitude of on-site and off-site costs depends on eco-regional characteristics (Holmes, 1988).

On a global scale, Pimentel et al. (1995) estimated the off-site costs to be $3 per ton of soil eroded. Crosson (1968) contends that the off-site costs of erosion are much higher than the on-site costs of erosion. In Uganda, the offsite effects are 18 usually a result of sedimentation and siltation of water reservoirs, resulting in reduced carrying capacity. As a result, people may have to walk long distances in search of water. This study focused on the onsite effects that affected the farmers directly.

The factors responsible for erosion-induced soil degradation in the tropics may be grouped as exogenous and endogenous. The important exogenous factors affecting soil erosion are climate and soil characteristics. Tropical rains are characterized by high intensities partly due to relatively big drop size (Lal, 1999). The exogenous factors include the high climatic erosivity and high erodibility of some soils. The ability of a soil surface to accept a continuous heavy rainfall is a critical factor in the prevention of accelerated erosion of soils in the tropics. Earlier work on rainfall acceptance, the proportion of simulated rain transmitted from the surface into the subsurface horizons, was done in East Africa by Pereira (1955) and Rose (1960). They observed that fallowing with elephant grass (Pennisetum sp) and Paspalum improved rainfall acceptance more than fallowing with legumes such as Pueraria. Peraira et al. (1964) also reported that fallowing with grasses and leguminous cover crops improved the rainfall acceptance compared with soils under arable crops.

The low input agricultural systems with little or no investment in conservation- effective measures, and removal of crop residues from farmlands constitute the endogenous factors. The most important endogenous factors include deforestation, over exploitation and excessive grazing. Soil management is probably the most important endogenous factor that determines erosion induced changes in soil quality. It is important not only because of its impact on productivity and environmental quality but also because it can be regulated through judicious input and appropriate systems of soil and crop management (Lal, 1999).

19 Subsistence agriculture, based on little or no input leads to depletion of soil fertility, decline in SOM, deterioration of soil structure, low crop stand, low canopy cover, lack of crop residue mulch, and high susceptibility to wind and water erosion (Lal, 1999). Pereira et al. (1967) reported that continuous cultivation with plowing and harrowing decreased rainfall acceptance from 94% in the first year to 75% in the fourth year. Similarly, the rainfall acceptance of clean weeded plots was only 80% of that of unweeded plots (Pereira et al., 1964). The magnitude of soil erosion and runoff losses are also influenced by soil type, terrain, land use and farming system.

Accelerated soil erosion influences agronomic productivity through its impact on soil quality. It is more serious and its effects on crop growth more drastic in the tropics than in the temperate regions. Available data for tropical Africa indicate drastic yield reductions due to erosion, especially in the traditional agricultural systems based on low external inputs (Lal, 1998). In Tanzania, Kilasara et al (1995) observed that grain yields of corn (Zea mays ) and cowpeas (Vigna unguiculata) were generally 30% lower in the severely eroded as compared with the slightly eroded class. At one site, the reduction in crop yield was as much as 75%. Dregne (1990) reported irreversible soil productivity losses from water erosion in several countries including Uganda. The reduction in total production in 1989 for sub-Saharan Africa was estimated 3.6 million Mg for cereals, 6.5 million Mg for roots and tubers and 0.36 million Mg for pulses (Lal, 1995).

The direct effects of accelerated erosion on crop yields arise as a result of a reduction in the effective rooting depth, loss of plant nutrients, loss of plant available water (AWC) and loss of land area due to formation of gullies and reduced use efficiency of external inputs (Lal et al., 1999). Loss of water can lead to a reduction in crop yield due to accentuated drought stress on-site and inundation and the attendant anaerobiosis in depressional areas and deposition sites. Other soil factors affected by erosion include water infiltration capacity and

20 aggregation. Soil quality indicator in relation to erosion may be based on key properties as follows (Lal, 1998):

Sq = f(Rd, LLWR, Nc, SOC, Sc, Bd) ...... (2.1)

Where Rd = depth to root or water restricting layer LLWR = least limiting water range Nc = Nutrient content SOC = Soil organic carbon Sc = Structural characteristics (aggregation, water retention, transmission pores) Bd = Soil biodiversity (biomass C)

As erosion of top soil progresses, less fertile and acid subsoils are exposed, resulting in a degradation of the chemical status of the topsoil. This results in a reduction in the productive potential of these soils. Resource-poor farmers are not able to buy fertilizers and the result is less food production. Furthermore, as topsoil is eroded, total soil organic matter (SOM) is significantly reduced because in most soils, it is primarily located in the upper horizon. After removing the topsoil, subsequent tillage incorporates subsoil into the plow layer. Consequently, the properties of the surface soil are altered. Often, soil quality is negatively impacted by increasing crusting, and surface sealing, reducing water infiltration and AWC, and decreasing of nutrients. Root restricting layers limit the amount of water available to plants because erosion reduces the thickness of the rooting zone. Soil aggregation is also negatively affected where there is no adequate soil cover and hence erosion. Martin (1944) reported from 10 year’s work in Uganda that the percentage of water-stable aggregates >0.5 mm was more under grass cover (52.9%) as compared to that under cultivation (30.5%).

21 Reduced rooting depth makes plants susceptible to drought and nutrients deficiency since these can only be extracted from a small fraction of the top soil. Erosion also results in an uneven soil fertility due to movement of surface soil by runoff. This ultimately results in uneven crop development. Agricultural production on eroded soils may therefore not be sustainable because of the additional costs involved and the cumulative nature of the yield decline due to the progressive increase in soil erosion (Pagiola, 1992). While plant nutrients can be replenished by applying fertilizers and manures, drastic adverse changes in soil physical quality and water retention capacity cannot be easily restored by routine soil management options (Lal, 1998).

Several methods have been developed to assess the on-site effects of erosion. These have been broadly categorized as agronomic/soil quality evaluation, economic assessment and knowledge surveys (Lal, 1998). Using the agronomic methods, erosion induced changes in soil quality in relation to productivity are assessed.

Economic impacts can be evaluated by assessing the losses of plant available water, nutrients and other additional inputs needed due to erosion (Lal, 1998). Where no-input agriculture is practiced, loss in yields due to erosion can be used to estimate the economic impact of erosion.

The effects of future erosion on productivity can be simulated by varying top soil depth (TSD) and monitoring yield. Several studies have shown a yield decline when TSD is reduced. In some cases, the relationship has been exponential (Masse, 1990), quadratic (Larney et al., 1995) or linear (Thompson et al., 1991; Rose and Dalal, 1988). In the semi arid regions of Australia, Rose and Dalal (1988) observed a total yield loss of wheat (Triticum aestivum) when 160 mm of soil loss occurred. For soils with medium to deep profiles, a yield decline of 25%

22 occurred with a soils loss of 200mm. On the other hand, depositional sites may produce significantly more yields than slightly eroded sites especially in seasons with below normal rains (Ebeid et al., 1995).

Although the procedure of removing and adding top soil is rapid, it does not reflect the actual erosion process which occurs over a long period of time. Furthermore, erosion is a selective process since it preferentially removes humus and clay fraction leaving the inert and coarse fragments behind. For a gravelly Alfisol with shallow rooting depth in Nigeria, Lal (1985) observed that the decline in rate of corn grain yield caused by natural soil erosion was 16 times greater than that caused by artificially removing the top soil. On the contrary, Cihacek and Swan (1994) found corn yield reductions by natural erosion to be 11% compared with 18% by desurfacing thus the effects may be soil specific.

Crop yields can also be related to erosion-induced differences in TSD for the same soil series under natural field conditions, on similar landscape positions (Lal, 1998). However, this requires extreme care to ensure that there are no differences in the landscape position since crop yields can also be affected by the topographic position (Halvorson and Doll, 1991). Landscape position has a strong confounding effect on crop yield regardless of the severity of erosion (Afyuni et al., 1993). The effect of TSD on crop yield is also confounded by the tillage methods (Hairston et al., 1988).

Survey techniques have also been used to study natural erosion (Lal, 1998). These involve selecting eroded phases of the same soil series within the same landscape unit. Crops are grown on these phases with the same management. Uncultivated or virgin soils in comparable landscape are used as a control. The cultivated soils are classified into different erosion phases as slight, moderate or severe depending on TSD. Using this approach, erosion-crop yield relationships

23 can be assessed on farmers’ fields. Criteria for site selection and using this technique have been outlined by Schertz (1989).

Comparisons can also be made between different land management systems that may have contrasting effects on soil erosion and erosion related changes in soil properties (Lal, 1998). For this method to be valid, the management effects on soil properties should primarily be due to erosion. The effects of erosion on soil productivity can further be determined by assessing erosion-induced differences in soil quality and relating them to crop yield.

Simulation models have been used widely to predict erosion impact on productivity. An example of these is the Erosion Productivity Index Calculator (EPIC) model (Williams, 1995). Basically, the models look at TSD and crop yield, soil quality and productivity, nutrient budgeting, water balance and soil/ crop/climate interaction (Lal, 1998).

The relationship between soil productivity and erosion has been studied for more than six decades (Olson et al., 1999). However, results prior to 1950 differ from current findings as a consequence of technology advances. More efficient agronomic practices frequently mask the effects of erosion on yield as a result of new crop varieties, management practices, and fertilizer technology. Nevertheless, the assessment of soil quality may serve to provide an opportunity to evaluate and redesign soil and land management for sustainability.

2.2 Soil Quality

Soil serves as a medium for plant growth by providing physical support, water, essential nutrients and oxygen for the roots. The suitability of soil for sustaining

24 plant growth and biological activity is a function of physical and chemical properties, many of which are a function of SOM. Soil quality affects three essential facets of sustainable land management; productivity of crops and livestock, quality of natural resources and environment, and health of plants, animals and humans (Doran and Jones, 1996). It provides nutrients for plant growth that are essential for animal and human nutrition. Soil also provides a medium for the recycling and detoxification of organic materials and for the recycling of many nutrients and sequestering C.

Soil quality is not synonymous with productivity. While productivity can be measured by evaluating yield, soil quality is assessed by evaluating inherent soil characteristics. It is largely defined by soil function and represents a composite of its physical, chemical and biological properties.

Several definitions of soil quality have been suggested by different authors. While Lal (1997) defined it as the productivity and environmental moderation capacity of the soil, Doran and Parkin (1994) defined it as a measure of the condition of the soil in relation to its ability to perform functions of value to humans. Doran et al. (1996) defined soil quality as the continued capacity of the soil to function as a vital living system within ecosystem and land use boundaries to sustain biological productivity, promote the quality of air and water environments and maintain plant, animal and human health.

For optimum function, there is a need to have a balance between its major components namely sustainable productivity, plant and animal health, and environmental quality. The suitability of soil for sustaining plant growth and biological activity is a function of physical properties such as porosity, water holding capacity, structure and tilth, chemical properties such as nutrient supplying ability and pH. Many of soil’s biological, physical and chemical

25 properties are a function of organic matter content. The quality and health of soils determine agricultural sustainability, environmental quality (Pierzynski et al., 1994) and as a consequence of both, plant, animal and human health (Haberern, 1992).

Conditions of the native soil can serve as reference criteria for assessing soil quality. A basic set of soil quality indicators to assess soil quality in various agricultural management systems has been proposed by Doran and Parkin (1994). It includes physical (texture, depth of soil, rooting depth, soil bulk density, infiltration, water content and temperature), chemical (SOM, total C and N, pH, electrical conductivity, extractable N,P and K) and biological (microbial biomass C and N, potentially mineralized N, soil respiration) soil attributes.

SOM content and nutrient reserves are the basis of inherent soil fertility. The former plays an important role in the sustainability of agricultural systems (Swift and Woomer, 1993). The importance of organic matter in the minimum data set (MDS) has also been highlighted by Fenton et al. (1999). Similarly, Larson and Pierce (1994) have emphasized that SOM is the single most important indicator of soil quality and that soil in the upper few centimeters of the profile is the most important determinant of soil quality.

The dynamics of soil quality can be quantified by expressing soil quality as a function of measurable soil attributes, assessing the variation of these attributes over time and evaluating the dynamics of soil quality. Owing to the impossibility of describing soil quality in terms of all soil attributes, Larson and Pierce (1991) proposed that MDS be designed to monitor changes in soil quality in combination with pedotransfer functions. The MDS must include soil attributes which can be quantified and measured in a short time span to be useful in land use or management decisions. A consensus on what the MDS for soil quality should

26 contain is yet to be reached. The MDS described by Larson and Pierce (1991) is given in Table 2.2. They suggested that all indicators measurements could be combined to produce an overall measurement of soil quality or a change in soil quality in response to alternate management practices.

Doran and Parkin (1994) also developed a list of MDS which was reviewed and revised by the North Central Region 59 Technical committee on SOM and the working group of the USDA..

Soil attribute Methodology Nutrient availability for region Analytical soil test Total organic C Dry or wet combustion Labile organic C Digestion with KCl Texture Pipette or hydrometer method Plant-available water capacity Determined in field best or from water desorption curve Structure Bulk density from intact cores field measured, permeability or Ksat Strength Bulk density or penetration resistance Maximum rooting depth Crop specific – depth of common roots or standard pH Glass electrode, pH meter Electrical conductivity Conductivity meter

Table 2.2: Soil attributes and standard measurement methodologies to be included in a minimum data set for monitoring soil quality (adapted from Larson and Pierce, 1991)

27 Changes in soil quality can be assessed through the use of computer models to determine how the changes in the MDS impact the important functions of the soil such as productivity.

An index of soil quality with adequate sensitivity may help farmers to evaluate the economic potential of new options and their impact on the soil resource (Bezdicek et al., 1996). It can also be useful in identifying problem production areas, making realistic estimates of food production and monitoring changes in sustainability and environmental quality as related to agricultural management and land use policies (Granatstein and Bezdicek, 1992).

Infiltration, hydraulic conductivity, shear strength and aggregate stability were also reported to be among the most critical physical factors of soil quality with regard to erosion (Karlen and Stott, 1994). Physical factors such as bulk density and chemical characteristics including nutrient availability, acidity, electrical conductivity, and salinity can also be considered important with regard to sustaining plant growth.

Chengere and Lal (1995) used stepwise regression analysis to relate crop yield to soil properties. Corn grain yield for a Miamian soil was shown to correlate highly (R2 = 0.97) with SOC, mean weight diameter, bulk density, cumulative infiltration and clay content.

The, USDA (1992) proposed a soil quality rating index based on crop residue management shown in equation 2.2

SqR = SOM + TP + E …………………..……………………………………(2.2)

Where SqR is the soil quality rating, SOM is the soil organic matter that must be returned to the soil to maintain the desired level of SOC content, TP is

28 the subfactor related to all field operations which break down residues and aerate the soil and E is the erosion subfactor which relates productivity decline to soil erosion as predicted by the USLE.

Lal (1994) suggested that soil degradation, caused by land misuse and soil mismanagement, should be quantified by measuring management-induced changes in soil properties or processes and their impacts on actual and potential productivity. Establishment of the cause-effect relationship between soil properties and processes on the one hand and crop productivity and environmental moderating functions on the other is crucial to enhancing soil productivity, restoring degraded lands and improving environmental quality. The MDS for three ecoregions have been summarized by Lal (1994) in Table 2.3

The MDS for different ecoregions are based on:

i) Soil fertility and nutritional constraints for the humid tropics, ii) Poor soil structure and adverse physical conditions leading to soil erosion, degradation and salinization for semi-humid and semi-arid tropics, and iii) Drought stress, wind erosion and sand dune migration for the arid regions (Lal, 1994).

The assessment of the potential and constraints for different land uses is based on the knowledge of critical level of soil and water indicators. The lower limit of critical level is one at which degradation rate is high but the trend can be reversed while the upper limit of the critical level refers to the point of no return or irreversible damage (Lal, 1994).

29 Humid Semi-humid/Semi arid Arid pH, acidity, base Soil structure, bulk Plant available water, saturation density, compaction, soil rooting depth, soil tilth, tilth soil compaction, crusting, Bulk density, soil tilth, pH, soil organic matter Soil and air infiltration rate, available content, plant available temperatures, water capacity, runoff nutrients evaporative demand, rate and amount, water wind velocity erosion, rainfall amount Erosion by wind and Surface and ground and intensity, net water, gully erosion, water and water quality radiation, soil water balance, runoff temperature Total salinity, and types pH, plant available of salts nutrients Soil & air temperatures, wind velocity, growing season

Table 2.3:The minimum data set needed for soil quality assessment for principal eco-regions in the tropics (adapted from Lal, 1994)

At the upper limit of the critical level, productive capacity and soil quality cannot be enhanced with adoption of improved management system and with additional inputs, although there may be other land uses that can produce some useful goods and services. Although critical level of indicators has to be decided on the basis of soil specific situations, some arbitrary guidelines and their rating factors have been suggested by Lal (1994). These are shown in tables 2.4 – 2.7

30 Limitation Relative Effective Penetration Soil bulk density Soil structure weighting rooting resistance (Mg/m3) factor depth (cm) (mPa) Light Heavy Morphology WSA% MWD texture texture mm

None 1 >150 <1.0 <1.3 <1.2 Subangular >75 >2.5 blocky to crumb Slight 2 100-150 1.0-1.5 1.3-1.4 1.2-1.3 Subangular 50-75 2-2.5 blocky Moderate 3 50-100 1.5-2.0 1.4-1.5 1.3-1.6 Moderate 25-50 1.0-2.0 subangular blocky Severe 4 25-50 2.0-2.5 1.5-1.6 1.4-1.5 Weak 5-25 0.5-1.0 subangular blocky Extreme 5 <25 >2.5 >1.6 >1.5 Massive or <5 <0.5 single grain

Table 2.4: Suggested critical levels of soil strength and structural indicator (adapted from Lal, 1994)

Limitation Relative Consistency Texture Coarse Penetration weighting Fragment in resistance factor Surface (%) None 1 Loose Loam <10 <1.0 Slight 2 Very friable Silt loam, silt, 10-20 1.0-1.5 silty clay loam Moderate 3 Friable Clay loam, 20-40 1.5-2.0 sandy loam Severe 4 Hard Silty clay, 40-60 2.0-2.5 loamy sand Extreme 5 Harsh or Clay, sand >60 >2.5 extremely hard

Table 2.5: Criteria limits for soil mechanical properties (adapted from Lal, 1994)

31 Limitation Relative pH Sodium Electrical weighting absorption Conductivity factor ratio (ds/m) None 1 6.0-7.0 <10 <3 Slight 2 5.8-6.0 and 7.0-7.4 10-12 3-5 Moderate 3 5.4-5.8 and 7.4-7.8 12-15 5-7 Severe 4 5.0-5.4 and 7.8-8.2 15-20 7-10 Extreme 5 <5.0 and 8.2> >20 >10

Table 2.6: Criteria limits for soil Chemical properties (adapted from Lal, 1994)

Limitation Relative Soil Organic Carbon Content Biomass carbon weighting of the surface horizon (%) (% of total) factor None 1 5-10 >25 Slight 2 3-5 20-25 Moderate 3 1-3 10-20 Severe 4 0.5-1 5-10 Extreme 5 <0.5 <5

Table 2.7: Criteria level soil organic carbon content (adapted from Lal, 1994)

Doran and Parkin (1994) and Larson and Pierce (1994) also proposed a minimum data set presented in Table 2.8.

32 Indicators of soil Relationship to soil condition and Ecologically relevant values condition function; rationale as a priority or units; comparisons for measurement evaluation I. Physical Texture Retention and transport of water and %sand, silt & clay; less chemicals; modeling use, soil eroded sites or landscape erosion, and variability estimate position Depth of, topsoil and Estimate of productivity potential and Cm or m; non cultivated rooting erosion; normalizes landscape and sites or varying landscape geographic variability positions Infiltration and soil Potential for leaching, productivity, Minutes/2.5 cm of water and 3 bulk density (ρb) and erosivity; ρb needed to adjust Mg/m row and/or landscape analyses to volumetric basis positions Water holding Related to water retention, transport, %(cm3/cm3), cm of available capacity (water and erosivity; available H2O: H2O/30cm; precipitation retention calculate from ρb, texture and SOM intensity characteristic) II. Chemical Soil organic matter Defines soil fertility, stability, and Kg C or N/ha-30 cm; non (OM) (total organic C erosion extent; use in process cultivated or native control and N) models and for site normalization pH Defines biological and chemical Compared with upper and activity thresholds, essential to lower limits for plant and process modeling microbial activity Electrical conductivity Defines plant and microbial activity Ds/m1; compared with upper thresholds; presently lacking in most and lower limits for plant and process models microbial activity Extractable N, P, and Plant available nutrients and Kg/ha-30cm; seasonal K potential for N loss; productivity and sufficiency levels for crop environmental quality indicators growth III. Biological Microbial biomass C Microbial catalytic potential and Kg N or C/ha-30cm; relative and N repository for C and N; modeling; to total C and N or CO2 Early warning of management produced effects on OM Potentially Soil productivity and N supplying Kg/ha-30cm/d; relative to mineralizable N potential; Process modeling total C or total N contents (anaerobic incubation (surrogate indicator of biomass) Soil respiration, water Kg C/Ha/d; relative microbial Microbial activity measure content and biomass activity, C loss vs. inputs (in some cases plants) temperature and total C pool modeling; estimate of biomass activity

Table 2.8: Proposed minimum data set of physical, chemical and biological indicators for screening the condition, quality and health of soil (after Doran and Parkin, 1994; Larson and Pierce, 1994).

33 The results of soil quality tests should be presented on volumetric basis. The relationship between soil quality indicators and various soil functions does not always comply to a single relationship, increasing linearly with magnitude of the indicator as is commonly thought (Janzen et al., 1992). In other words bigger is not always necessarily better.

Assessments must consider specific functions being evaluated in their land use and societal context and threshold values for key indicators must be established. Comparison of more than a few indicators becomes complicated without a framework for determining the relative importance of each indicator, and without evaluating these indicators within the context of site and climatic characteristics. Consequently assessment of soil quality and health is most efficiently achieved using a modeling framework that is based upon collecting and synthesizing an array of soil quality and health indicators and site or climatic characteristics.

Shepherd and Walsh (2002) developed a scheme for the development and use of soil spectral libraries for rapid nondestructive estimation of soil properties based on the analysis of diffuse reflectance spectroscopy. The soil properties were calibrated with data of soil reflectance with high correlation coefficients.

Several indices of sustainability have been developed by Lal (1994), the choice of which depends on the objectives of the assessment, land characteristics and land use. Indicators of a resource base include land resilience, which dwindles when degradative processes are set in motion. A common indicator of land resilience is the type and intensity of degradative processes such as compaction, hard setting and gullying, fertility depletion, nutrient imbalance and lack of biodiversity (Lal, 1994).

34 2.3 The Banana-Coffee System

A number of projects have been done in the study area to address the major constraints to crop production, including nutrient loss through crop mining, pest damage and soil erosion. In a survey by Tenywa et al. (1999), soil erosion was reported as one of the major constraints to crop production, the severity of which is influenced by management practices, soil type and cropping system. This was collaborated by Lufafa et al. (2000), who observed very high erosion rates under annual crops, followed by rangelands, banana-coffee and banana alone. The annual cropping system due to continuous cultivation and poor protection, especially at the beginning of the rainy season when there is minimal cover, exhibits a high degree of degradation.

Soil degradation due to nutrient mining and erosion has resulted into a severe decline in crop yield. Currently, banana yields range between 6 and 7 Mg/ha compared to the estimated potential of 60 – 90 Mg/ha (Stover and Simmonds, 1987). This decline is a serious concern among farmers and agricultural planners alike. In addition to reduction in yield, some data show that quality of the crop produced is also affected by accelerated soil erosion. Aswathanarayana and Cothern (1994) observed that food crops and vegetables grown in eroded soils tend to be deficient in some essential elements.

Characterization of the severely degraded lands cultivated for over 30 years has revealed no significant changes in SOM in 7 out of 50 sites that were sampled in the 1960s. The SOM content of the unproductive soil is as high as or higher than 3% (Lynam, 2002), thus the correlation that was there between yields and SOM has disappeared. This raises several questions such as the past and present quality of SOM. One also needs to know the physical conditions of the soil since

35 factors such as compaction may hinder mineralization of even the active fraction (labile) of SOM.

Soil fertility depletion has been said to contribute 50 – 73% of the decline in banana yield. Consequently, hardy and low soil fertility tolerant crops such as cassava (Monihot esculanta), sweet potatoes (Ipomoea batatas) have increasingly become important in the system.

2.4 Modeling Soil Quality, Erosion and Productivity

Several models have been developed to predict soil loss and its effects on productivity. By using appropriate models, it is possible to test soil management scenarios in relation to future impact on productivity. Three of the models, the Erosion Productivity Index Calculator (EPIC) (William, 1990), the Productivity Index (PI) (Pierce et al., 1983) and Century model (Parton et al., 1994) are discussed below. Using this approach, erosion induced differences in soil quality are estimated. The productivity index (PI) and EPIC are some of the widely used models.

2.4.1 Erosion Productivity Index Calculator

This is a process based model. Its strength lies in its ability to quantify the effects of soil quality indicator properties on crop productivity, erosion or water quality in the context of landscape and hydrologic characteristics. No empirical weighting factors are needed. In addition to predicting the effects of erosion on soil productivity, the EPIC model also predicts the effects of management decisions on soil, water, nutrient, and pesticide movements and their combined impact on

36 soil loss, water quality and crop yields for areas with homogeneous soils and management. It is also capable of dealing with decisions involving drainage, irrigation, fertilizer and lime application, planting dates, tillage and residue management (William, 1990). The drainage area considered by EPIC is generally field sized area up to 100 ha.

EPIC uses information on weather, hydrology, soils, nutrients, topography, tillage and other site characteristics to estimate crop growth and yields. Although the model was designed to measure long term impacts, it can also estimate production response within a single growing season (Benson, 1989). Results by De-Guzman (1997) in the Philippines showed that the model can predict soil erosion and yield data that are consistent with or not statistically different from measured data based on plot level experiments. Using the model, it is possible to come up with a function that relates yield to soil loss. Notwithstanding, others have found the model to either underestimate yield (Lacko et al., 1998) or to overestimate crop production (Cabelguenne et al., 1999) under conditions of severe moisture stress. It appears that some of the shortcomings of the model arise as a result of its assumptions. For example it assumes homogeneity, ignoring complexities such as slope variation. While estimates of long-term average yields are accurate for most purposes, the model is poor in predicting year-to-year variability. To meet the site specific requirements, several modifications have been proposed.

Just like other process based models, the EPIC model requires extensive amounts of quantitative data and does not evaluate all of the functions for soil quality in a comprehensive fashion.

37 2.4.2 Productivity Index

The PI is a function based model in which Pedo Transfer Functions (PTF) are used to estimate the sufficiency of bulk density, pH and available water capacity from a MDS of soil quality indicator properties and soil quality index scoring functions. The PI model combines TSD and soil quality (Pierce et al., 1983). It can be represented as:

PI = (Ai x Bi x Ci x Di x Ei x WFi) where:

Ai = sufficiency of AWC of soil layer i

Bi = sufficiency of aeration in soil layer i

Ci = sufficiency of bulk density of soil layer i

Di = sufficiency of soil pH of soil layer i

Ei = sufficiency of electrical conductivity of soil layer i

WFi = Weighing factor of soil layer i

It was modified by Pierce et al. (1983) to include sufficiency of available water capacity (AWC), bulk density, soil pH and a weighing factor for electrical conductivity. An idealized rooting depth of 1 meter was used. The model estimates crop productivity by characterization of the soil rooting environment and evaluates a soil’s vulnerability by simulated removal of surface soil and consideration of available water holding capacity, rooting depth, SOC, and pH (Wilson et al., 1991). Other parameters considered include concentrations of clay and coarse fragments. The model analyzes the relationship between soil properties affected by erosion, and soil productivity. Changes in PI on soil mapping units should be considered in their proper positions on a soil landscape else they can give misleading information (Onstad et al., 1985). However, application of this relative and parametric technique may be questionable for several soils of the tropics (Lal, 1994).

38 Since the model was developed to study the productivity effects of accelerated soil erosion in the US Corn Belt, it is mostly applicable for soils of the mid western U.S for which sufficiency functions and other limitations are known (Lal, 1998). It can give misleading information if landscape position is not considered. In some cases, an increase in PI with erosion has been observed (Lal, 1984).

Mulengera and Payton (1999) have come up with a soil erosion-productivity model which considers the effect of soil water storage capacity, crop evapotranspiration, soil chemical and physical properties important for crop growth. The model has been shown to give good predictions and is considered to be an improvement of the PI model.

2.4.3 The Century Model

The model simulates plant production and SOC dynamics based on the organic matter functional pool. Plant productivity is simulated via user-defined algorithms appropriate to a particular ecosystem and climate. The model is able to simulate simultaneously flows of carbon, nitrogen, phosphorous and sulfur in the plant soil system.

The century computer program consists of three major components names INITPAR, CENTURY and VIEW. INITPAR is used to develop site data files that initialize the simulation and may also be used to view model input and output definitions. The CENTURY component runs the environmental simulation, while VIEW allows for the output to be plotted, printed or saved.

Input parameters include soil temperature and soil water regimes, texture effects on SOM dynamics and detailed plant nutrient routines for N, P and S. Using the 39 century model, it is possible to get an insight into the effects of different land management practices. Similarly it can be used as a planning tool to predict the outcome of management changes (Parton et al., 1994). While validating the century model for tropical ecosystems, Woomer et al. (1994) found significant correlations between observed and simulated above-ground plant productivity, total soil nitrogen, total soil organic carbon and nitrogen mineralization. Although the model was found to underestimate soil carbon and nitrogen, the simulation of plant productivity was more accurate. Woomer and Swift (1994) found the model prediction to hold across a wide range of managed and natural ecosystems.

2.5 The Diffuse Reflectance Spectroscopy

The diffuse reflectance spectroscopy is a new method for the rapid assessment of soil quality. It is based on the premise that components of complex material mixtures such as those contained in the soil can be distinguished on the basis of their spectral signatures in the solar reflective region. The spectral signatures are defined by their reflectance, or absorbance, as a function of wavelength (Shepherd and Walsh, 2002). Figure 2.1 shows an outline of the diffuse reflectance spectroscopy method.

40 Spectral library building Spectral library use

Widely sample soil variability Measure soil reflectance within a target area spectra

Build a soil reflectance spectral library

Sample spectral data space

Acquire soil attribute data on Library selected soil/sites outlier

Calibrate soil attribute to spectra

NO Accuracy Yes Predict soil attribute acceptable? from spectra

Figure 2.1: An outline of the diffuse reflectance spectroscopy method (adapted from Shepherd and Walsh, 2002).

41 2.6 Soil Organic Carbon

The SOC concentration affects productivity through its effects on soil structure, plant AWC, as a source or sink of plant nutrients, and as a buffer against sudden fluctuations in soil characteristics. It is the active or biomass C which is more sensitive to differences in management and land use systems.

A number of fractionation approaches have been developed to separate and isolate various SOC fractions. They can be broadly categorized as physical, chemical and biochemical separation methods (Cheng and Kimble, 2001). The physical methods attempt to separate soil organic components by their particle size or density (Swift, 1996) while chemical separation methods are mostly based on the solubility and affinity of certain SOC in different solvents or extracting solutions (Swift, 1996).

The principal source of soil C is soil organic matter (SOM). It comprises of living and dead biomass (microorganisms, roots), and dissolved molecules exuded by living organism and transformation products of biological processes. Soil carbon is influenced by management, landscape variability, weather patterns, soil deposition/or erosion and environmental changes. The factors that affect soil carbon pool are summarized in Figure 2.2.

Although Woomer et al. (1994) found that biomass production is independent of SOC, the latter can indirectly influence crop production by altering soil bulk density, increasing water-holding capacity, infiltration rate and root penetration. Interactions between the clay fraction and SOM lead to the protection of SOC against mineralization and conversion to CO2.

42 Soil Carbon

Endogenous Exogenous

• Parent Landscape Climate Material Land use Tillage Soil fertility Cropping • Slope • Potential • Clay intensity mgt system characteristic ET • Silvicultur content • Tillage • Fertilizer • IPM s • Length of e • Clay method use • cropping • Drainage rainy • Pastoral minerals • Residue • Manure intensity density season • Arable • Exchange mgt application • Cultivation • Plantation cations Vehicular Green • fallowing Mixed • • • traffic manuring farming • Develop ment

Figure 2.2: Factors that affect soil carbon concentration (Tenywa, 2001)

2.6.1 Soil Organic Carbon Storage and dynamics

The SOC exists in different fractions or pools. These include:

1. Microbial biomass carbon (MBC), 2. Labile organic matter: easily decomposable SOM, 3. Recalcitrant or stabilized organic matter, resistant to microbial decomposition (N’emeth et al., 1998).

The residence period of SOC is influenced by practices such as tillage, biomass burning, fertilizer use, and land clearing. Since changes in SOC affect degree of 43 aggregation, it is important to conserve it in the soil. However, this does not always happen because of competing uses. Other cropping and management practices are known to increase the SOC level. These include use of grass/legume cover crops, manure application, elimination of bare fallow, increased residue return and reduced or minimum tillage (Lal, 1991; Paustian, 1997).

Soils with low SOC pool are susceptible to erosion and formation of surface crusts which further enhance erosion. The SOC concentration also influences plant available water reserves in the soil. Lal (1985) observed that SOC concentration may have more beneficial effects on the available water holding capacity than clay content. In the tropics, there is a rapid decline in the SOC concentration of cultivated soils due to the continuously high temperatures throughout the year (Calhoun, Pers. com).

Materials with high lignin content may result in increased formation of SOC pool due to their slow decomposition rate. However, this may be associated with a decline in productivity due to nitrogen immobilization, signifying the importance of litter quality. Therefore, SOC is a factor associated with the improvement or decline in soil fertility. The relationship is complex and rarely proportional (Brown et al., 1994).

The SOC pool can be separated into materials recognizably of cellular origin (light fraction) and non-cellular humus (heavy fraction). The heavy fraction is associated with clay minerals and may constitute up to 80% of C in soils (Brown et al., 1994). The heavy fraction consists of both “slow” and passive pools with a turnover time of decades to centuries, respectively (Parton et al., 1989). The SOC fractions contribute to soil aggregation thereby improving root penetration, resistance to erosion and water relations. They also increase the exchange

44 capacity in acid soils, reduce P fixation and Al toxicity (Brown et al., 1994). In Uganda, high correlations were obtained between SOC and soil moisture retention and plant available water reserves (Pidgeon, 1972).

Generally, there is more yield with mulching especially when the mulching material is from leguminous plants. The use of organic mulches as fertilizers is important especially for small holders who neither can afford commercial fertilizers nor are sure of their effectiveness. Leguminous mulch materials can supply some nitrogen and other nutrients needed to boost yield levels. Mulches with low C:N ratio were found to increase maize yields more than those with high C:N ratios on an Oxisol in Brazil (Schöningh and Alkämper, 1984). A reverse trend was observed in cowpeas (Vigna spp).

2.6.2 Natural vis-à-vis cultivated systems

Generally the SOC concentration of tropical soils under cultivation can decrease to as low as 30% of the original level but most commonly to about 30% of the corresponding values of soils under natural vegetation within 10 years or less (Brown and Lugo, 1990). The rate of SOC decline can be reduced by various management practices. Ayanaba et al. (1976) showed that SOC and nitrogen concentration declined rapidly after forest clearing on Alfsols in Nigeria, but the decline was slower, and in some cases SOC increased when maize residues were retained on site.

The SOC is an important regulator of numerous soil-related constraints to crop yield. For example SOC influences the available water holding capacity of the soil, porosity and nutrient availability. Regular and substantial additions of crop residue mulch left on the surface rather than incorporated into the soil have

45 proved to be beneficial for a wide range of soils and agro-ecological environments in the tropics (Lal, 1985). Mineralization of decomposing residues is a major source of plant nutrients in highly weathered soils with little inherent fertility (Sanchez et al., 1989). Other benefits include better soil and water conservation, improved soil moisture and temperature regimes and improvement of soil structure.

Climate is often the most critical factor in determining the sustainability and enhancement of SOM. Earlier studies by Birch and Friend (1956) revealed that the SOM status of the East African soils was relatively high and compared favorably with that of temperate countries. From their study, rainfall appeared to be the major factor governing the SOM and nitrogen contents of the East African soils. A later study by Ssali (2001) revealed that over the years there has been little change in SOM but a large decline in soil nutrients (Lynam, 2002). This is possibly because it is not the total SOC but the different carbon fraction that make a difference. For example although total SOC may remain unchanged, drastic changes may take place in the labile fraction. Shang and Tiessen (2000) observed a 28% reduction of forest-derived carbon, of which 59% was in the silts, 28% in the sand, after conversion to sorghum growing for 12 years.

2.6.3 _13 Carbon

13 C3, C4 and CAM plant have unique _ C values, which do not change significantly during decomposition and soil organic matter formation (Boutton et al., 1998). As a result _13C can be used to assess vegetation change and to quantify soil organic matter turn over. It has been demonstrated that _13 C values of soil organic matter can be used to reconstruct vegetation change (Boutton et al., 1998).

46 SOM studies capitalize on the fact that C3 and C4 plants have different isotopic signatures due to isotopic fractionation during photosynthesis. Fractionation occurs because of mass differences. Smaller masses have higher velocities and engage in more interactions, while heavy isotopes form stronger bonds and are 13 less reactive. During photosynthesis, plants discriminate against CO2 as a result of the biochemical properties of the primary carbon fixing enzymes and limitations to CO2 diffusion into the leaf. The extent of discrimination is a function of photosynthetic pathway type (O’Leary, 1988). In C3 plants (All trees and many 13 temperate grasses), CO2 is reduced to a three-carbon compound with _ C values ranging from -32 to -22‰ while C4 plants (some major crops and many 13 other grasses) reduce CO2 to a four-carbon compound with _ values ranging from -17 to -9‰ (Boutton et al., 1998).

Where vegetation has been compositionally stable, the _13 C value of soil organic carbon in the top soil is similar to that of the plant community (Stout and Rafter, 1978). This is because isotope fractionation is negligible during the early stages of organic matter decomposition in well-drained mineral soils (Melillo et al., 1989). The difference between the isotopic composition of the current plant community and soil organic matter are reflected by the relative proportions of C3,

C4 and or CAM plants (Boutton et al., 1998).

Mean age of soil organic matter increases with depth in the profile, ranging from less than 200 years near the surface to 2000-4000 years at a depth of 1 m

(Scharpenseel and Neue, 1984). Therefore, relative C3-C4-CAM productivity might only be evident in the _13 C of organic carbon near the soil surface where organic matter turnover is most rapid and current organic matter inputs are concentrated. Stable isotope ratios are measured by mass spectrometry (Schimel, 1993).

47 2.7 Exchangeable Potassium

Potassium is an osmotic regulator and hence maintains water/salt relationships in cells. It also functions as an enzyme co-factor to make some enzymes work. In electrochemical processes, it plays a key role in assimilation, phloem loading and long-distance assimilate transport, in nitrogen (N) metabolism and in storage processes (Krauss, 2002). Thus, K is indispensable for yield and quality in plants.

In its role as an osmotically active cation and in controlling the water relationships in plants, K has a vital function in the response of crops to adverse climatic and soil conditions such as drought, frost or salinity. Furthermore, potassium is very much involved in the mechanisms involved in plants resistance and tolerance to pathogens.

To fulfill these many roles in plants, K is absorbed in rather large quantities, even exceeding the amount of nitrogen (Krauss, 2002). Another unique feature is that the bulk of the uptake of K occurs within a short period of time in annual crops, for cereals usually before the onset of flowering.

K also influences the mode of leaf opening and closing, hence gaseous exchange. Under water stress, the stomata closes down. Similarly they close at night when they don’t need CO2 for photosynthesis

2.8 Magnesium

Magnesium is an essential element of chlorophyll, the green plant pigment that gives that gives leaves their color and enables them carry out photosynthesis. Magnesium is essential for enzymes in plants and especially in chlorophyll. High

48 levels of potassium suppress a plant’s uptake of magnesium. High magnesium content may result into deterioration of soil structure with consequent surface sealing, decreased infiltration, increased runoff and erosion during rainfall events (Dontsova and Darell, 2001).

2.9 Calcium

Calcium is important in maintaining membrane integrity and it is an essential constituent of cell walls (Eckert, 2001). Calcium is not very mobile like other nutrients hence deficiency symptoms occur first in the youngest leaves. Calcium is also important for cell division and elongation, permeability of the cell membranes and nitrogen metabolism. It is the predominant positively charged ion held on soil clay and organic matter particles because it is held more tightly than Mg2+ and K+ and other exchangeable cations (Kelling and Schulte,2004).

2.10 Land use and Management Practices

Predominant land uses include agriculture, industry, development, settlement and forestry. Agricultural practices can further be subdivided into pastoral, arable, plantation and mixed farming. Land use practices influence several soil properties such as SOC concentration and soil fertility. Collins et al.(1999) observed decline in SOC concentration in cultivated soils as compared to adjacent non-cultivated sites. Land use practices also affect emission of CO2 into the atmosphere. Soil disturbances, by mixing and inversion, are the primary mechanisms by which organic matter is mineralized and emitted into the atmosphere as CO2.

49 The influence of land use on soil properties is most drastic in the top few centimeters of soil. Chicacek and Ulmer (1999) observed that SOC concentration is little affected by agriculture in soil below 30 cm depth. Most of the SOC pool is concentrated in the upper 30cm layer, and is readily depleted by activities such as land use change and tillage.

In Canadian agroecosystems, intensified cropping systems and no tillage agriculture, improved crop nutrition, and SOC concentration. More extensive use of perennial crops also promoted C gains (Janzen et al., 1998). Boehm and Anderson (1998) observed an increase in soil quality (lower bulk density, more microbial biomass and greater soil aggregation) under continues cropping systems. This was attributed to the more frequent additions of crop residues in conjunction with nutrients such as N and P. However, it is unlikely that SOC concentration would increase where no fertilizers are applied or under continuous tillage. Similarly, the effect of soil erosion on soil quality varies with land use. Land recently converted from permanent vegetation usually has favorable soil structure, fine roots and high SOC content. Intense cultivation can cause adverse changes in these properties and reduce soil quality.

2.10.1 Agriculture

Agricultural management practices and cropping systems that don’t return crop residues to the soil lead to severe decline in SOC concentration. Similarly, increased aeration due to tillage enhances mineralization and hence increases emission of CO2 to the atmosphere (Lal, 1986; Lal et al.,1995). Intensive cultivation or tillage is one of the main land use practices which leads to loss of SOC pool (Post and Mann, 1990) and deterioration of soil structure and other soil

50 physical properties which results in decreased crop yields (Paustian et al., 1997) and release of CO2 to the atmosphere (Lal and Kimble, 1997). Improper land clearing and development can rapidly degrade soil quality and decrease yields (Lal, 1985). Use of heavy machinery results in increased bulk density, reduced porosity and reduced water infiltration (Lal and Cummings, 1979; Hulugale et al., 1984).

In Uganda, the traditional farming systems were stable under conditions of low population density because of their diversity and subsistence nature. The spatial and temporal arrangements of crops optimized the use of natural resources and ensured continuous food flow. However, with the advent of commercial agriculture, most of the soils’ fertility has been mined because of low external inputs. Consequently, crop yields have declined.

No-till or reduced tillage practices can increase SOM in the soil. The SOM concentration of the soil surface layer can be increased as a result of various interacting factors under the no-till system, such as increased residue return, less mixing and less soil disturbance, higher soil moisture content, reduced soil surface temperature, proliferation of root growth and biological activity, and decreased risk of soil erosion (Lal, 1989; Logan et al., 1991; Paustian, 1997).

Bajracharya et al.(1998) observed a 30-50% increase in SOC concentration within the Ap horizon under no-till plots (NT) compared to conventional till (CT) plots in a long-term tillage experiment in Ohio. They reported that much of SOC is concentrated within the macroaggregates particularly under reduced tillage systems. Similar results were observed by Beare et al. (1994). They found that in surface samples of both tillage treatments (no-till and conventional tillage), particulate organic matter (POM) in aggregate size range of 106 to 250µm was highest in NT than CT. Paustian et al. (1997) reported that on relative basis, most

51 sites showed 5 to 20% increases in SOC under NT vs. CT but pointed out that this could be an underestimate if sampling is restricted to the mineral soil because it does not account for the surface mulch which build up in NT soils. In all these studies SOC was only found to be higher in the topsoil. No- till alone will have less effect, therefore it must be practiced along with residue input from the crop.

2.10.2 Tillage

Tillage refers to the physical manipulation or perturbation of the soil in order to produce a seed bed. Most mechanical operations can damage soils physical properties such as structure, resulting into compaction or soil pulverization, hence deterioration of soil structure. Tillage also exposes new surfaces to microbial attack and changes the redox conditions within the profile (Brown et al., 1994). Therefore, tillage alters the pore structure of the topsoil in ways that have a direct impact on crop growth.

Experiments on no-till practices in the tropics have produced conflicting results. Whereas ploughing was found to be beneficial in Senegal (Charreau and Nicou, 1971; Chorpart and Nicou, 1976), research carried out in other semi-arid regions of west Africa recommends zero tillage (Hulugalle and Maurya, 1991). The hardsetting soils of the tropics may require some form of loosening in order to increase water infiltration and reduce runoff (Laryea et al., 1994). Crop establishment with no-till is generally unsatisfactory on soils that have compacted and crusted surface soil, uneven ground surface, poor seed-soil contact, and inadequate amounts of crop residue mulch (Lal, 1985). Deep tillage and soil inversion were found to increase plant-available water in the Sahel due to reduction in losses by water runoff and evaporation (Charreau, 1977). These

52 conditions occur in the semi arid tropics. A rating system for assessing the suitability of tillage for different soils in the tropics has been proposed by Lal (1985). The system considers soil and climatic factors such as erosivity, erodibility, soil tolerance, compaction, soil temperature regime, available water capacity, cation exchange capacity and SOC.

Tillage exposes the soil to raindrop impact, enhances mineralization of SOC and speeds the decline in soil structure. Therefore, erosion and erosion-induced changes in soil properties are more severe on plowed than on no till soil (Lal, 1983).

Intensification of tillage accelerates the mineralization of SOC. Dalal (1989) observed that a combination of no-tillage, crop residues retained and fertilizer N caused the highest concentration of SOC and N as compared to conventional tillage. Several studies have shown that tillage and crop residue management can substantially affect SOM and microbial activity in the surface layers, water relations and nutrient movement to at least 1.2m depth, even in a fine textured Vertisol (Dalal, 1989). In Senegal, Siband (1972) observed that cultivation caused marked deterioration of the surface soil with a rapid decline in SOM content, decrease in clay content and a reduction in the water holding capacity and CEC. The reduction in soil C pool by cultivation can be attributed to decrease in amount of plant residues returned to the soil and an increase in losses by mineralization, erosion and leaching. Tillage also makes soils more susceptible to surface sealing which results in increased runoff ( Norton and Dontsova, 1998). Nevertheless, the magnitude of tillage effects is sometimes highly variable. A study by Grace et al. (1998) noted that sequestration of SOC through reduced or no tillage practices gave inconsistent results.

53 On coarse-textured soils with good internal drainage, high biological activity and adequate quantity of crop residues, reduced tillage has been found to be effective (Aina, 1979; Lal, 1983). High SOM concentration and favorable soil structure allow the no-till soil to retain more available water in the root zone than soil repeatedly plowed. During periods of drought stress, crops in plowed plots wilted 2 to 3 days earlier than those in no-till plots (Lal, 1985). However, it is important to note that no-till systems based on mulch material of high C:N ratio may result into N immobilization during the initial stages of adopting the system.

No-till systems may not be effective where the supply of crop residue is limiting and are generally ineffective or less effective on soils with degraded antecedent soil physical conditions (Lal, 1985). With structurally-inert surface horizons, some form of mechanical tillage is necessary. For such soils, plowing increases total porosity and root growth (Nicou, 1974).

The ridge-furrow system has been found to be suitable for soil and water conservation in structurally unstable soils of the semiarid tropics (Lal, 1985). The ridge-furrow system conserves water, and to increases effective root volume on poorly drained soils and on nutrient deficient soils (Lal, 1985).

Generally crop response to different management practices is influenced by soil type, climate, type of crop and socioeconomic factors such as farm size and degree of mechanization. Other factors that may influence crop performance include timeliness of operations. In areas with short rainfall seasons, time of planting is very important. In Machackos, Kenya, reduction in maize grain yield of 4.7 to 6.3 % was observed for every day’s delay in planting (Dowker, 1971).

54 2.10.3 Pasture

Rangelands constitute 51% (6.7 billion ha) of the earth’s land surface (World Resources Institute and The International Institute for Environment and Development, 1986). In Africa, the land area under permanent pastures is 891 million ha, providing more than 82% of the animal feed requirements (UNEP/UNDP, 1993). In developing countries, rangelands supply more than 95% of total feed (World Resources Institute and The International Institute for Environment and Development, 1986). Pastures can potentially sequester significant amounts of atmospheric C in soils. However, this has a rapid SOM turnover, which characterizes the savanna (Trouve et al., 1994). A significant loss in SOC was observed at two plots in Australia where a grassland had been converted to cultivated cropland in 1925 (Li-changsheng et al., 1997). Garcia et al. (1994) also observed high rates of loss of remnant forest SOC when a tropical deciduous forest was converted to pasture. On the other hand, Grace et al. (1998) reported that legume pastures can lead to large increases in SOC even under significantly changed climatic conditions.

2.11 Soils of the Tropics

Tropical soils are predominantly Ultisols (11.2 %), Oxisols (22.5%), Aridsols (18.4%), Entisols (8.2%), Inceptisols (8.3%)and Alfisols (16.2%)(Van Wambeke,1992). The Oxisols are highly weathered with low cation exchange capacity (CEC), less than 40% base saturation, low nutrient reserve (Van-Ranst, 1994). They are characterized by low yields and lose their fertility rapidly when cultivated for food crops hence the need for management systems that may improve the continued productivity of these soils.

55 Generally, the water-intake rate of tropical soils under natural vegetation cover is high. However, the removal of vegetation and introduction of mechanized tillage operations result in disturbance and exposure of soil and cause a rapid decline in infiltration rate (Cunningham, 1963). Many tropical soils especially Oxisols, exhibit a phenomenon of microaggregation. The clay-sized particles are strongly aggregated to form silt and fine sand-sized aggregates (Lal, 1987). For all practical purposes, even the soils with high clay content behave as sand, with a high water intake and low retention capacity.

The Oxisols are the predominant soils in the study area. They are deep, permeable and well-drained and characterized by an Oxic horizon often underlain by plinthite. The Oxic horizon is defined as at least 30 cm thick, with the following properties (USDA, 1998):

a) Fine earth fraction that retains 10 cmols or less of NH4 ions/100g clay, b) Only traces of weatherable primary minerals, c) A texture of sandy loam or finer in the fine earth fraction, d) CEC of less that 16 cmols/100g clay, e) Small amounts of water dispersible clay, and f) Predominantly sandy loam with more than 15% clay.

The clay minerals are predominantly kaolinitic with varying amounts of iron and aluminum oxides. Soils derived from quartz parent material are also characterized by presence of stonelines (Lal, 1987). Although they are deeply weathered, the rooting depth of Oxisols may be severely restricted where you have plinthite, layers of argillic horizons and gravel horizons of various thickness (Lal, 1987). Root growth is also inhibited by aluminum toxicity, phosphorous deficiency and absence of calcium.

56 Most tropical soils have low inherent fertility (Sanchez, 1976) and several soil- related constraints to sustained intensive cropping such as nutrient imbalances, compaction and soil erosion (Lal et al., 1997). The frequent application of small amounts of fertilizers brings about a temporary improvement, but does not alter the fundamental problem, the bad fertilization status resulting from their low CEC. Similarly, liming results in yield decline due to formation of smaller aggregates, induced phosphorous deficiency, P sorption, Mn precipitation and reduced zinc availability (Kamprath, 1971).

Mean annual rainfall in the tropical regions varies widely. Even where it is adequate, problems to agriculture are caused by its variability, distribution and irregularities (Lal, 1985). Other constraints to intensive utilization of upland soils in the tropics include erosion, compaction, crusting, drought, shallow rooting depth, trafficability, soil temperature and soil fertility (Lal, 1985). Most of the available water is held at relatively low suctions. Pidgeon (1972) estimated the field capacities of some Ugandan soils to be 0.1 bar (0.01MPa) suction. He also observed significant correlations between particle size composition and field capacity of some soils in Uganda.

Increase in temperature results in increased SOM decomposition rates while decreases in precipitation decrease the potential for SOC sequestration (Stewart, 1993). In the humid tropics, many soils are dominated by low activity clays and are severely nutrient depleted (Sanchez, 1976) while in the dry tropics, with the exception of oxisols with ustic moisture regime, soils are relatively nutrient rich because they are less subject to weathering and leaching losses. Deficiency of nutrients, particularly, phosphorous and nitrogen also limit agricultural output (Dabin, 1980). Consequently, there is reduced biomass production and hence low SOC gains. Furthermore, erosion is a threat to the sustainable use of red and lateritic soils especially when they occur on sloping land or are subjected to

57 inappropriate land use (Swindale et al., 1998). The soils generally have low plant available water reserves and most of the plant available water is released at suctions between 0.1 and 0.3 bars (0.01-0.03MPa).

The processes and consequences of surface soil degradation of soils in the tropics were reviewed by Lal (1994) and he suggested the use of soil management technologies that minimize dependency on purchased inputs and sustain economic production without causing severe damage to the soil resources and environments. It is important to note that in tropical regions, additions of biosolids to maintain SOC are scarce and compete with needs for fuel or fodder. Therefore, knowledge of the precise amount of resources required to optimize the conditions for plant growth and yields during the cropping cycle becomes an important consideration in the development of more complex cropping systems.

In the semi-humid and semi arid tropics, soil physical constraints of poor soil structure, and drought stress are comparatively more severe problems than soil chemical and nutritional constraints (Lal, 1994). Drought stress and resource degradation are the predominant constraints to sustained use of soil and water resources in arid regions

58 CHAPTER 3

METHODOLOGY

3.1 Research Design

3.1.1 Site Characterization

The study was carried out in Bukoto county in Masaka district (31o39’E and 31o41’E ; 0o24’S and 0o27’E). The study covered an area of 40km2 and was initially surveyed at a semi detailed scale (1:50,000). Based on a soil map by the United Nations Environmental Program (UNEP), this area was found to be most susceptible to soil erosion. Consequently erosion studies were set up in this area. A land use map of the area extracted from satellite image (LandSat TM, 2000) using Normalized Difference Vegetation Index (NDVG) is available.

The study area was divided into one Km2 grids after which 30 grids were randomly chosen for soil sampling, making sure that the dominant land uses were covered. Areas under natural ecosystem served as a control.

Initial baseline characterization or assessment of antecedent conditions of the experimental site was done to objectively evaluate management induced changes in soil quality indicators. The characterization included information on

59 soil series, soil texture, signs of erosion, description of current land use and crop management, slope and topographical features, geographical location of the field and sampling areas, climatic information and location of environmentally fragile sites adjacent to the fields.

The percent slope at the mid slope position ranged from 3.5 to 29%, with a mean slope of 11% while that for the up slope ranged from 0 to 15%, with a mean of 6%. The bottom slope ranged from 1 to 4%. The relative positions of the profile pits and soil types along the transect are shown in Figure 3.1.

Soil type: Petroferic Luvisol Slope: 0-15% Microrelief: termite hills Soil type: Mollic gleysol Abundant plinthite Slope: 1 – 4% Soil type: Chromic luvisol Microrelief: None Slope: 3.5 – 29% Non stony Micro relief: termite hills Few plinthitic and quartzitic stones in top soil

Relative Distance

Figure 3.1: A Schematic Catena Sequence in Kabonera Sub-County, Masaka District.

60 Drainage classes were assigned as described in the USDA Hand Book 18 (1993). These are presented in Table 3.1:

Drainage Class Description Excessively drained Water is removed very rapidly. The occurrence of internal free water commonly is very rare or very deep. The soils are commonly coarse- textured and have very high hydraulic conductivity or are very shallow

Somewhat excessively Water is removed from the soil rapidly. Internal free water commonly drained coarse-textured and have high saturated hydraulic conductivity or are very shallow

Well drained Water is removed from the soil readily but not rapidly. Internal free water occurrence commonly is deep or very deep; annual duration is not specified. Water is available to plants throughout most of the growing season in humid regions. Wetness does not inhibit growth of roots for significant periods during most growing seasons. The soils are mainly free of the deep to redoximorphic features that are related to wetness

Moderately well drained Water is removed from the soil somewhat slowly during some periods of the year. Internal free water occurrence commonly is moderately deep and transitory through permanent. The soils are wet for only a short time within the rooting depth during the growing season, but long enough that most mesophytic crops are affected. They commonly have moderately low or lower saturated hydraulic conductivity in a layer within the upper 1 m. periodically receive high rainfall, or both.

Somewhat poorly drained Water is removed slowly so that the soil is wet at a shallow depth for significant periods during the growing season. The occurrence of internal free water commonly is shallow to moderately deep and transitory to permanent. Wetness markedly restricts the growth of mesophytic crops, unless artificial drainage is provided. The soils commonly have one or more of the following characteristics: low or very low saturated hydraulic conductivity, a high water table, additional water from seepage, or nearly continuous rainfall.

Continued

Table 3.1 Natural Drainage Classes. (adapted from the soil survey Manual, 1993)

61 Table 3.1 continued Poorly drained Water is removed so slowly that the soil is wet at shallow depths periodically during the growing season or remains wet for long periods. The occurrence of internal free water is shallow or very shallow and common or persistent. Free water is commonly at or near the surface long enough during the growing season so that the most mesophytic crops cannot be grown, unless the soil is artificially drained. The soil, however is not continuously wet directly below the plow-depth. Free water at shallow depth is usually present. This water table is commonly the result low or very low saturated hydraulic conductivity of nearly continuous rainfall, or of a combination of these

Very poorly drained Water is removed so slowly that free water remains at or very near the ground surface during much of the growing season. The occurrence of internal free water is very shallow and persistent or permanent. Unless the soil is artificially drained, most mesophytic crops cannot be grown. The soils are commonly level or depressed and frequently ponded. If rainfall is high or nearly continuous, slope gradient may be greater.

Table 3.1: Natural Drainage Classes. (adapted from the soil survey Manual, 1993)

3.1.2 Sampling Frame

Kabonera sub county constituted the sampling frame. The boundary of the study area was delineated and divided into grids as shown in Figure 3.2. Thirty grids were randomly chosen for soil sampling and determination of Ks.

62 9958000 24 25 9 26 27 28

19 20 21 22 23

9956000 15 16 17 29 18

14 13 9954000 10 11 12

5 6 30 7 8 9952000 1 2 4 3

9950000 350000 352000 354000 356000 358000 360000

Figure 3.2: Delineation of the study area and sampling sites in Kabonera sub-county

63 Within each numbered grid, sampling was conducted with techniques that ensured that soil types were comparable. All the soil samples were from the Mirambi catena (Figure 3.3). A Y sampling scheme was used. For each Y unit, samples were collected from point 0, 30, 90, and 270m (Figure 3.4). For each limb of the Y, giving 10 clusters per Y. The 30 Ys chosen to cover the different land use types were geo-referenced.

350000 355000

# # # # # # # # #### # # #### # ### ### # ##### # ## # # # # ## # #### # # # # # # # Sampled points

# # # # # # ### #### # # # ### # Masaka soils # # # # # # ### ## # ### # # # # # # BUGANDA CATENA KABIRA CATENA # # # # # # ## # # ## # #### # ##### # #### # ### KATERA SERIES # # # ## # # # # # # KATIKEKILE COMPLEX 9955000 9955000 KIFU SERIES # # # # # ## #### # # MIRAMBI CATENA # # # # # # # ### # # # # # # #### # # ##### # # #

# # # # # # # # # # # ### # #### # ## ## #### ## ## # # # # # # # # # # # # # # ### # # # ## # ### # #### # # #### # # # # N # #

W E 3 350000 0 3 355000 6 Kilometers S

Figure 3.3: Sampling points and their relation to soil type.

64 Plot sampling design Cluster location 0, 30, 90, 10 m 270 10 m

Figure 3.4: Plot of sampling design

3.2 Methodology

3.2.1 Soil sampling

Soil samples were collected from the 0- 20 and 20 -50cm depth for the different land uses. At each sampling point, information on elevation, slope position, slope shape, land use type, frequency of flooding, signs of erosion, thickness of mulch layer and auger depth and level of management were collected. The level of management was considered to be high if the field was mulched, weeded or pruned (in case of bananas and coffee); medium if weeded or mulched but not pruned; and poor if all the above were missing. It was based of a visual observation. A total of about 1800 bulk samples and 834 core samples were collected for analysis.

65 The samples were air dried and passed through a 2 mm sieve. They were then scanned through a spectrometer after which their spectral indices of soil fertility and erosion were calculated. A limited number of sites within these strata was chosen to systematically sample the spectral variation. Actual soil quality measurements were also carried out at these sites to confirm the spectral index and to evaluate what soil quality attributes are most important. The spectral index of erosion was used to control for the effects of erosion in the analysis.

All the chemical analysis was only done on the top soil (0-20 cm) since this is were most changes are expected to occur. The sub soil (20-50) was only in the spectral analysis.

3.2.2 Productivity

Productivity of the different land uses was compared using bio-assays. The process involved growing maize crop for two weeks in plastic tubes (11 cm diameter) with a soil depth of 10 cm. The soils used were obtained using an auger from areas with different land uses commonly found in the study area. Days to germination, total dry weight, shoot weight and root weight were recorded. The seeds planted were of equal weight (0.4gm). The performance of the seeds was used to determine the relative productivity of the soil under the different land use types.

3.2.3 Delta 13 Carbon

Stable isotope ratios of carbon and nitrogen were measured by continuous flow isotope ratio mass spectrometry (20-20 mass spectrometer, PDZEuropa, 66 Northwich, UK) after sample combustion to CO2 and N2 at 1000 C in an on-line elemental analyzer (PDZEuropa ANCA-GSL). The gases were separated on a Carbosieve G column (Supelco, Bellefonte, PA, USA) before introduction to the IRMS. Sample isotope ratios were compared to those of standard gases injected directly into the IRMS before and after the sample peaks and delta 15N (AIR) and delta 13C (PDB) values were calculated. Final isotope values were adjusted to bring the mean values of standard samples distributed at intervals in each analytical run to the ‘true’ values of the working standards. The working standards are periodically calibrated against international isotope standards.

By convention, stable 13C isotope abundance is commonly expressed as _13C with units of per thousand(‰) as: the relative ratio of the heavy isotope 13C to the light isotope 12C in a sample, relative to the Vienna-Pee Dee Belemnite (PBD) limestone standard;

13 13  C   C     −     12   12   C   C  13 0  sample  std ä C( 00) =  1000 13C       12C  std   

3.2.4 Land use history

Using calibrations for soil organic carbon, _13C, and the erosion-deposition index, empirical models of the effects of land use history on soil quality were constructed. Similar work has been done by Shepherd et al (2002) whose model explained 67% of the variation in topsoil organic carbon concentration over an area of about 5,000 km2 in the Kenya Lake Victoria Basin. Figure 3.5 shows top soil organic carbon as a function of spectral index of erosion-deposition. 67

70

C3 dominated 60 C3 dominated

) 50 -1

40 38.5 Transitional 30

20.9 Est. SOC (g kg 20 C4 dominated dominated 15.6 10

[SOC]half =0.4455 0 0.0 0.2 0.4 0.6 0.8 1.0 α sed

Figure 3.5: Topsoil organic carbon concentration as a function of a spectral index of erosion-deposition status for C3, C4 and transitional land use systems.

_sed is a measure of the fraction of sediment-like material in the soil, the erosion and sedimentation index (dsed). It was obtained by projecting soil spectra into the sediment principal component model. dsed is the Mahalombies distance from the sediment model center (ie. Number of standard deviations from the model center). Using CART analysis, 0.964 was calculated as the cutoff between deposition and erosion. Values above the cutoff point also known as fast sources indicated erosion while those less than the cutoff point reflected deposition.

68 3.3 Laboratory Measurements

Several soil properties were measured to establish the soil quality index as discussed below.

3.3.1 Soil Carbon and Nitrogen

Soil carbon was determined simultaneously with nitrogen using the dry combustion technique (Wright and Bailey, 2001). The samples were air-dried and finely ground into a flour-like texture which were then weighed and packaged into tin capsules for analysis.

3.3.2 Phosphorous and Potassium

Phosphorous and potassium were determined using Olsen’s method of bicarbonate extraction (Black, 1965). Many extraction techniques for plant- available phosphate have been developed. The modified Olsen extractant is convenient for routine use because inorganic P and exchangeable K can be determined from the same extract.

3.3.3 Magnesium and Calcium

Ca and Mg were extracted using 1 N KCl extractant at 10:1 soil:solution ratio. Analyses were conducted in batches of 33 (one tray of samples) with 30 soil samples, 2 blanks, and 1 standard soil.

69 3.3.4 Bulk density

Bulk density was determined using undisturbed soil core samples (BSI, 1975) of 5.7 cm diameter and 6cm in height, at 0-5 cm depth. Another soil sample was obtained from around each core to determine its moisture content (Blake and Hartge, 1986).

3.3.5 Soil texture

Soil texture was determined using the hydrometer method (Landon, 1984; Gee and Bauder, 1986,). The hydrometer method of silt and clay measurement relies on the effects of particle size on the differential vertical velocities of the particles through a water column (i.e., the sedimentation rate). Sedimentation rate is dependent upon liquid temperature, viscosity, and the diameter and specific gravity of the falling soil particles.

Soil was dispersed into individual particles after pretreatment with hydrogen peroxide to destroy organic matter, and addition of sodium hexametaphosphate to aid dispersion, then dispersed throughout a water column and allowed to settle. Hydrometer measurements quantified the amount of material remaining in suspension at specific time intervals. This was then related to the amounts of sand, silt and clay in the soil.

3.3.6 Soil pH

Soil pH was determined using a pH meter at a soil:water ratio of 1:2.5 (Landon, 1984). The soil-water mixture was stirred for 10 minutes, let to stand for 20

70 minutes and stirred again for 2 minutes. The pH meter was calibrated at pH7 and pH 4. Immediately before pH measurement of each sample, the sample was stirred for 5 seconds with a glass rod and allowed to settle for 30 seconds. The pH meter electrode was immersed to the same depth in the bottles, taking care not to strike the bottom of the sample bottle with the electrode tip. pH readings were recorded after the instrument had stabilized.

3.3.7 Soil depth

Soil depth was determined using an auger and a measuring tape.

3.3.8 Infiltration capacity

Infiltration capacity was determined using a tension infiltrometer (Ankeny et al., 1988). Tension infiltrometer readings were taken at the centre of each plot at two tensions levels, -3 and -7 cm

A Tension-Infiltrometer (Figure 3.6) can be a useful tool for quantifying macropore effects on infiltration in the field (Clothier and White, 1981; Ankeny et al., 1988). It offers a way to define some of the main characteristics of soil structure (Watson and Luxmore, 1986) and was designed to measure the water- flow parameters of soils in a way that excludes the effect of pores larger than 0.3 - 0.1 mm (Chong and Green, 1983).

By raising or lowering the tube in the bubble tower, the desired tension to be maintained at the bottom of the base plate was set. Water was allowed to infiltrate soil at a rate which was slower than when water is ponded on the soil surface. By setting the tension at or close to zero, it was possible to get 71 infiltration rates close to the saturated hydraulic conductivity of the soil.

Data was collected manually by recording the water level in the supply tower over time. There are a number of methods to calculate the hydraulic properties from the tension infiltration data. One method is based on the assumption of a log-linear relationship between tension and hydraulic conductivity, as first described by Gardner (1958). Other methods to determine the hydraulic properties use inverse parameter estimation methodology to calculate the van Genuchten parameters from the infiltration data (Simunek et al., 1994). Since it is widely accepted that the spatial distribution of hydraulic conductivity follows a lognormal distribution, the tests can be performed on log-transformed data.

72 Figure 3.6: The Tension Infiltrometer

3.3.9 Empirical Model

Hydraulic conductivity determination was based on Wooding’s work (1968).

2  4  Q = πr K 1+ ……………………………………………………..……………3.1  πrα  Where Q = volume of water entering the soil per unit time (cm3hr-1) K = hydraulic conductivity (cmhr-1) _ = is a parameter

73 It is assumed that unsaturated hydraulic conductivity of soil varies with matric potential h(cm) as proposed by Gardner (1958).

K(h) = K sat exp(αh) …………………………………………………..………………..3.2 where h = matric potential or tension at the water source.

Ksat = saturated hydraulic conductivity

K in equation 3.1 was replaced by K sat exp(αh) and substituting of h1 and h2, gave:

2  4  Q(h1 ) = πr K sat exp(αh1 ) 1+ ……………………..……………..……………..3.3  πrα 

2  4  Q(h2 ) = πr K sat exp(αh2 ) 1+ ……………………….………….…….………..3.4  πrα 

Dividing equation 3.4 by 3.3 and solving for _ yields:

ln[Q(h2 ) / Q(h1 )] α = …………………………………………………..…………….3.5 h2 − h1

Since Q(h1) and Q(h2) were measured, and h1 and h2 were known, _ was computed directly from equation 3.5. With _ known, it was possible to calculate Ksat from equation 3.3 or 3.4

3.3.10 Soil Degradation Rating

Critical levels of soil several soil properties as determined by Lal (1994) were used to develop a soil degradation rating. Changes in soil quality reflected the effect of management practices. High productivity (yield) was used as an indication of good soil quality. Hence the soil quality parameters measured

74 helped to determine the nature of the relationship between soil quality and productivity. The measured soil parameters included soil bulk density, infiltration capacity, soil depth, proportion of coarse fragments, SOC, soil texture and soil pH.

The values of the soil quality parameters from a native undisturbed area served as a standard measure of quality against which to assess fields to be measured. This also served as the control.

3.4 Mini Survey

A mini survey of the farmers in the micro-catchment area (Appendix B) was conducted to determine the level of management, yield levels achieved by farmers, and the costs and returns that accrue to farmers. Estimates of these variables were then related to the soil quality parameters outlined in order to achieve the first objective. The indices of management considered included: • Timeliness of operations • Use of recommended agronomic practices • Record keeping • Time spent on the farm • Use of commercial farm inputs • Participation in seminars/ field days

Efforts were made to get yield estimates for bananas, coffee, maize and beans during the survey. The survey also helped to establish the costs and returns that accrue to the farmers. The data were analyzed using SPSS.

75 3.5 Data Analysis

Data was analyzed using SPSS, unscrambler and GENSTAT statistical packages as described below.

3.5.1 Spectral data

Soil diffuse reflectance spectra were recorded for each library sample using a FieldSpec FR spectroradiometer (Analytical Spectral Devices Inc., Boulder, Colorado) at wavelengths from 0.35 to 2.5 µm with a spectral sampling interval of 1 nm.

The second derivatives of the spectras were obtained using the unscrambler softwarell and these were regressed against the soil properties measured in the laboratory. A random sample of one-third of the soils was withheld for validation purposes.

Data was analyzed using multiple regressions for example between yield and other variables. Regression analysis was also used to compare the two methods of analyzing soil physical and chemical properties. Correlation analysis was carried out to determine the associations between variables.

Data exploration revealed a plot and Y effects. Consequently, these were incorporated in the analysis as random factors. Data was analyzed using Mixed Effects Models, in GENSTAT with Y and Plot as random models and land use, management level, slope position and mulch cover as the fixed model. Mixed-

76 effects models provide a powerful and flexible tool for the analysis of balanced and unbalanced grouped data. These data arise in several areas of investigation and are characterized by the presence of correlation between observations within the same group. Some examples are repeated measures data, longitudinal studies, and nested designs. Classical modeling techniques which assume independence of the observations are not appropriate for grouped data. The analyses were based on a 5% level of significance (Hoshmand, 1994).

The least significant difference (LSD) was used to determine the differences between the different land use practices (Hoshmand, 1994) based on the several soil physical and chemical properties.

The assessment of the potential and constraints for different land uses was based on knowledge of critical level of soil and water indicators. Lal (1994) suggested that different critical levels should be assigned weighting factors, the relative significance of which is based on the productivity loss at that level of soil indicator.

77 CHAPTER 4

RESULTS AND DISCUSSION

4.1 Soil Physical Properties

4.1.1 Saturated hydraulic conductivity

Exploratory data analysis suggested fourth-root transformation of the saturated hydraulic conductivity data. A similar transformation was used by Elsenbeer et al. (1992) for top soils.

The data on saturated hydraulic conductivity (Ksat) presented in Figure 4.1 shows a range of 3.27 to 5.88 cmhr-1. For a tropical soil, this is moderately low (Table 4.2). Furthermore, Ksat was neither influenced by land use nor bulk density but varied among slope positions. The elimination of terms with the highest (non-significant) t probability resulted into slope position as the only significant factor affecting Ksat. On its own, land use did not explain the variation in Ksat.

The level of management (which considered pruning, weeding, erosion control and moisture conservation) was also not a significant factor. The Ksat was highest on the uplands and midslopes and least at the valley bottoms (see Figure 4.1). Elsenbeer et al. (1992) and Dunkerley (2002) also obtained higher Ksat values for the upper slope and mid slope positions relative to the foot slope.

78 Class Ksat cm/hr Very rapid >12.5 Rapid 8 - 12.5 Moderate rapid 6 - 8 Moderate 2 - 6 Slow 0.8 - 2 Very slow <0.8

Table 4.1: Saturated Hydraulic Conductivity Classes (adapted from Landon, 1984).

Similarly Needelman et al. (2004) observed high runoff rates at the foot of a colluvial hillslope implying low infiltration. The low water transmission at the bottom land areas could be a result of clay accumulation, siltation and presence of a high water table in these areas. The low Ksat makes these areas prone to flooding during the rainy season.

79 6 b b 5.5 5 4.5 4 3.5 a

(Ksat cm/hr ) 3 2.5 2 Foot slope Midslope Upland Slope position

Figure 4.1: Variation of Saturated Hydraulic Conductivity with Slope position

4.1.2 Soil bulk density

The data in Table 4.2 shows that soil bulk density ranged from 1.04 to 1.39 MgM- 3. Analysis of soil bulk density showed that land use (P <0.001) and slope position (P=0.029) had highly significant effects but not level of management (P = 0.112). However, when land use was dropped from the model, management level became highly significant (P <0.001) but not the slope position (P = 0.250). This implies a high interaction between variables including those that don’t seem to be significant. In this case management effects were masked by land use.

The highest bulk density was observed under the grasslands at all slope positions (Table 4.2). In contrast, Motavalli and McConnell (1998) reported increased bulk density due to continuous cultivation over a seven year period in a tropical pacific island environment. This is possibly due to differences in soil type and the cultivation methods employed. Critical limit of soil pH for maize and grain 80 legumes is 1.5 Mg m-3 .Beyond this value, yield can be depressed by 20% of the maximum

At the slope bottom and the uplands, there was no difference in bulk density between the cultivated crops and the forest. At the mid slope, the bulk density under annual crops was higher than that under forests but not different from the other crops. The high bulk density observed on grasslands could be a result of soil compaction due to animal trampling while grazing. Secondly, grasslands were often located on shallow and some times stony soils, which could not be used for other crops. Neill et al. (1997) and Pando et al. (2004) also found higher soil bulk density in grasslands and rangelands compared to agriculture sites.

For all land uses, bulk density was highest at the foot slope. In contrast, no differences were observed between the mid slope and the uplands. The higher bulk density a

Soil Bulk Density (MgM-3) Land use Foot slope Mid slope Up land Natural Forest 1.15a1 1.06a 1.04a Eucalyptus 1.17a 1.08ab 1.06a Bush fallow 1.25a 1.17ab 1.14a Coffee 1.26a 1.18ab 1.15a Banana-coffee 1.26a 1.18ab 1.16a Banana 1.29a 1.20ab 1.18a Annuals 1.30a 1.22b 1.19a Grassland 1.39b 1.31c 1.28b Note: Figures followed by the same letter within the same slope position are not statistically different

Table 4. 2: Variation of Bulk density with Land Use at the Three Slope Positions

81 4.1.3 Soil texture

Soil texture was influenced by slope position but not land use and management. The absence of land use and management effects could be due time frame involved. Changes are expected after very long periods of time. Furthermore, management levels may change from season to season or year to year depending of availability of labor, income and the prevailing market prices. On the basis of the exploratory analysis, clay content was transformed to the natural log in order to get a normal distribution for statistical analysis. The data presented in Figure 4.2 shows that clay content varied from 20.4% to 25.6%. Clay increased as one moved down the slope (Figure 4.2). The trend could be a result of deposition from the upland areas, resulting into enrichment of the low- lying areas.

27.0 a a 25.0

23.0 ab 21.0 b %Clay 19.0

17.0

15.0 Foot slope Midslope Upland Slope position

Figure 4.2: Variation of Clay content with slope position

82 The results in Figure 4.3 show that silt content varied from 14.6% at the foot slope to 21.6% in the upper slope. There was more silt up slope and less down slope, an opposite trend to that observed with respect to clay. Since upslope is a detachment area, this confirms that erosion is a selective process and preferentially removes humus and clay fraction leaving the inert and coarse fragments behind. Li and Lindstrom (2001) also observed a linear decrease in clay content and a corresponding increase in silt content due to the selective removal of finer particles by water. Similarly, in laboratory experiments, Sutherland et al. (1996) observed a preferential removal of fine material with splash and wash. Preferential removal may also explain the high clay content in the bottom lands (depositional areas) and low clay content upslope (detachment areas).

25 23 c 21 b 19 17 a 15 %Silt 13 11 9 7 5 Foot slope midslope upland Slope position

Figure 4.3: Variation of Silt Content with Slope Position

83 4.2 Soil Chemical Properties

4.2.1 Soil pH

Data presented in Table 4.4 shows that soil pH varied from 5.3 to 6.9. Soil bulk density was influenced by land use (P= 0.002) but neither slope position nor management level. It was the highest under banana-coffee intercrop and the lowest under the grasslands. Steenwerth et al. (2002) also found lower values of soil pH and higher values of total soil C, N, in the grassland than in cultivated soils. The low pH under grasslands could be a result of leaching. Some cropping systems may also have an acidifying effect on the soil that is related to the amount of materials removed at harvest, amount and type of fertilizers normally used and the amount of leaching that occurs. The high pH under the banana based cropping systems could be a result of returning crop residues after harvest.

84 8.0 6.9 c 7.0 6.3bc 5.9ab 5.8 ab 6.0 5.3a 5.0 4.0

Soil pH 3.0 2.0 1.0 0.0 Annuals Bananas Banana- Coffee Grassland coffee Land use

Figure 4.4: Variation of Soil pH with Land Use

4.2.1 Soil Carbon Content

The data on soil organic carbon (SOC) presented in Table 4.3 shows that soil organic C ranged between 14.2 and 19.5gKg-1. SOC content less than 2% is considered very low (Landon, 1984). Furthermore, SOC was influenced by slope position (P <0.001) but not land use (P = 0.173) and management (P = 0.376). A comparison of native vegetation, pasture and plantations in Australia, also found no significant differences in soil C content between land-uses (Mendham et al., 2003).

Failure to observe a difference in SOC between land uses could be a result of the type of carbon stocks available and how it is stored in the soil. While total 85 SOC may not change due to land use, the labile C may change but the change may be masked when one looks at total SOC. For example, in Ethiopia, Solomon et al. (2002) observed a decline of up to 96% of forest-derived SOC in sand and up to 85% in the silt fraction.

SOC was highest on the upland and lowest at the foot slopes (Table 4.3) although the latter had more clay content. The uplands were usually shallow due to plinthite, covered with grass, and used for grazing. The cover provided by the grass could have protected loss of topsoil where most SOC is concentrated. A similar observation was made by Brubaker et al. (1993). Percival et al. (2000) found that soil clay content or concentration explained little of the variation in soil C. Therefore, high clay content does not necessarily reflect high SOC content.

Pulleman et al. (2000) also found high SOM level in grasslands relative to other land uses. Similarly, McGrath (2001) observed higher total C and N concentrations under pasture soils than under other land uses such as annual cropping and secondary forest fallow, indicating that soil C and N maintenance and/or accumulation following forest conversion may be greater in pastures than in these other two land uses.

Landscape Mean (SOC)-0.5 SE Mean difference LSD(.05) position SOC Foot slope 14.20 0.27a 0.0093 Foot slope-midslope 0.02 0.02 Midslope 17.05 0.24b 0.0034 Foot slope-upland 0.04 0.02 Upland 19.49 0.23c 0.0063 midslope-upland 0.02 0.01

Table 4.3: Variation of SOC (g/Kg) with slope position

86 In ecosystems dominated by grasses, only about 10% of total system carbon is in the above-ground plant biomass, with the majority of plant carbon below ground in the root system (50-90%). About 90% of the carbon in grassland ecosystems is stored in soil organic matter (Follett et al., 2001).Furthermore, while grazing, animal droppings are returned to the soil. This promotes recycling of organic matter in the uplands as opposed to cultivated areas where there is often a net decline in organic matter content due to crop and residue removal. In studying the effect of previous cropping history, Chotte et al. (1990) found that fallows/pasture led systematically to an increase of organic content in the soil. The uplands are also usually flat, a characteristic that may offer some natural protection from erosion and hence SOC accumulation. On the contrary, the valley bottoms are used to grow off-season crops during the dry season. Since no fertilizers are usually applied, this results into a depletion of soil nutrients and rapid oxidation of soil carbon.

No land use effect on soil organic carbon was observed probably because SOC was generally above the critical level of 1%. In the same area, Ssali 2001) found that SOC content had not changed after 20 years despite a decline in soil fertility.

The low SOC levels could be increased through mulching and returning crop residues to the gardens. This could ultimately be of benefit to the farmers. The World Bank (2002) launched a $100m Biocarbon Fund to provide finance to projects that store carbon in vegetation and soils while trying to reverse land degradation, conserve biodiversity and improve the livelihoods of local communities (Newcombe, 2003). Subsequently, The World Bank (2003) also launched the Community Development Carbon Fund to provide carbon finance to small scale projects in the least-developed countries. Local communities can potentially benefit from these funds by increasing their carbon stocks.

87 4.2.2 Nitrogen

Data in Table 4.4 shows that Soil Organic Nitrogen (SON) ranged from 0.93 g/kg to 2.47 g/kg. Analysis of SON data showed that it was highly influenced by slope position (P < 0.001) and land use (P = 0.008). Similar to SOC, SON content was highest on the uplands and lowest in the foot slope (Table 4.4). The uplands were usually flat and not very susceptible to soil erosion. On the other hand, the bottom lands are susceptible to flooding during the wet season. Because the uplands were generally used as pasture, soil nitrogen concentration was maintained. Decline in soil structure by tillage operations enhances mineralization of SON which would be otherwise protected against microbial processes (Kristensen et al., 2000). In Northwest Vietnam, Wezel et al. (2002) observed lower organic matter, nitrogen and phosphorus content at the lower mid slope. This accelerated soil degradation at lower slope positions was attributed to an enhanced mineralization and crop export.

Slope Position Land use Foot slope Midslope Upland Forest 1.55a2 2.13b 2.47c Banana-Coffee 1.16a 1.52b 1.72c Banana 1.14a 1.49b 1.68c Annuals 1.06a 1.37b 1.54c Coffee 1.01a 1.30b 1.46c Bush fallow 1.01a 1.30b 1.46c Grassland 0.97a 1.25b 1.40c Eucalyptus 0.93a 1.19b 1.32c Note: Figures followed by the same letter within the same land use are not significantly different

Table 4.4: Variation of Soil Organic Nitrogen (g/kg) with slope position among different land use.

88 The effect of slope position was more than that of land use. In contrast, the management effect (P = 0.159) was not significant.

Soil organic nitrogen varied with land use, with highest levels observed under forest, banana-coffee intercrop, bananas and annuals (Table 4.5). The high nitrogen content under forest could be a result of nutrient recycling since the amount extracted gets returned to the soil as leaf litter. Similarly, the relatively high soil organic nitrogen under the banana based cropping system may be attributed to mulching.

Soil under coffee had significantly less soil organic nitrogen than that under forests, banana-coffee intercrop and bananas. However, no difference was observed between coffee and the annual crops. The same patterns were observed at all slope positions. No differences in soil organic nitrogen were observed between coffee, bush fallow, grassland and eucalyptus.

Soil Organic Nitrogen (g/kg) Land use Foot slope Mid slope Upland Forest 1.55a3 2.13a 2.47a Banana-Coffee 1.16a 1.52a 1.72a Banana 1.14a 1.49a 1.68a Annuals 1.06ab 1.37ab 1.54ab Coffee 1.01b 1.30b 1.46b Bush fallow 1.01b 1.30b 1.46b Grassland 0.97b 1.25b 1.40b Eucalyptus 0.93b 1.19b 1.32b Note 3: Figures followed by the same letter within the same slope position are not significantly different

Table 4.5: Variation of Soil Organic Nitrogen (g/kg) with Land Use at Different slopes 89 4.2.3 Exchangeable Calcium

Data on exchangeable calcium presented in figure 4.5 shows a range of 2.7 to 6.1 cmol/kg. Ca2+ was influenced by slope position, management level and land use. It increased with increasing levels of management regardless of the slope position (Figure 4.5).

Contrary to Brubaker et al. (1993), for the same level of management, exchangeable calcium was highest for upslope and least for the foot slope position. Slope position had more influence on exchangeable calcium than management level. High levels of management on bottom lands were not significantly different from poor levels of management on the uplands and poor management on the mid-slope positions. The difference on the upslope and medium slopes were not statistically significant. For all slope positions the medium and high levels of management were not significantly different however,

90 7 6 5

4 3

2 1 0 Exchangeagle Calcium (cmolc/kg) High Medium Poor Level of Management

bottom midslope Upslope

Figure 4.5: Variation of Exchangeable Calcium with slope position and Level of Management.

they both had higher exchangeable calcium than the foot slopes implying even minimal levels of management could result in increased Ca levels. No significant differences were observed between foot slopes with high management and medium slopes or uplands with poor management (Table 4.6).

Exchangeable calcium varied with land use and was higher under the banana- coffee intercrop than under coffee alone. Similarly, exchangeable calcium was significantly higher under bananas than coffee (Table 4.7 and Figure 4.6). All cultivated areas had higher exchangeable calcium than the grasslands probably due to high extraction by the grasses. Ayuba (2001) also found reduced exchangeable Ca in grazed area.

91 Bananas and the Banana/coffee intercrop also had more exchangeable calcium than bush fallow. Differences between bush fallow and the other land uses were not significant. The lack of variation is possibly as a result of fallowing land after it is completely exhausted. On the contrary, mulching under bananas may help to enhance calcium content.

No significant differences were observed between the bananas, banana/coffee intercrop and the annuals implying that they had the same effects on soil calcium. Exchangeable calcium under all land use types did not vary significantly from the forested areas. This could be a result of the high variation observed under the forest areas.

6

5 4

3

(cmol/kg) 2 1 Exchangeable Calcium 0 Annuals Banana Babana- Bush fallow Coffee Eucalyptus Grassland Natural Coffee Forest Land use type

Figure 4.6: Variation of Exchangeable Calcium among the different land use types

92 Mean difference LSD (.05) Pair Compared (cmol/kg) Bottom/High Mgt: Bottom/Medium Mgt 0.484 0.658 ns Bottom/High Mgt:Bottom/Poor Mgt 1.015 0.579 * Bottom/High Mgt:Mid slope/High Mgt 1.791 1.147 * Bottom/High Mgt:Mid slope/Medium Mgt 1.308 1.316 ns Bottom/High Mgt:Midslope/Poor Mgt 0.777 1.265 ns Bottom/High Mgt:Upland/High Mgt 2.408 1.338 * Bottom/High Mgt:Upland/Medium Mgt 1.925 1.469 * Bottom/High Mgt:Upland/Poor Mgt 1.393 1.421 ns Bottom/Medium Mgt:Bottom/Poor Mgt 0.531 0.520 * Bottom/Medium Mgt:Midslope/High Mgt 2.275 1.328 * Bottom/Medium Mgt:Midslope/medium Mgt 1.792 1.147 * Bottom/Medium Mgt:Midslope/Poor Mgt 1.261 1.246 * Bottom/Medium Mgt:Upland/High Mgt 2.892 1.514 * Bottom/Medium Mgt:Upland/Medium Mgt 2.409 1.338 * Bottom/Medium Mgt:Upland/Poor Mgt 1.877 1.422 * Bottom/Poor Mgt:Midslope/High Mgt 2.806 1.304 * Bottom/Poor Mgt:Midslope/Medium Mgt 2.323 1.273 * Bottom/Poor Mgt:Midslope/Poor Mgt 1.792 1.147 * Bottom/Poor Mgt:Upland/High Mgt 3.423 1.494 * Bottom/Poor Mgt:Upland/Medium Mgt 2.940 1.450 * Bottom/Poor Mgt:Upland/Poor Mgt 2.408 1.338 * Midslope/High Mgt:Midslope/Medium Mgt 0.483 0.658 ns Midslope/High Mgt:Midslope/Poor Mgt 1.014 0.579 * Midslope/High Mgt:Upland/High Mgt 0.617 0.760 ns Midslope/High Mgt:Upland/Medium Mgt 0.409 0.979 ns Midslope/High Mgt:Upland/Poor Mgt 0.123 0.925 ns Midslope/Medium mgt:Midslope/Poor mgt 0.531 0.520 * Midslope/Medium mgt:Upland/High mgt 1.100 1.030 * Midslope/Medium mgt:Upland/Medium mgt 0.617 0.760 ns Midslope/Medium mgt:Upland/Poor mgt 0.085 0.918 ns Midslope/Poor mgt: Upland/High Mgt 1.631 0.984 * Midslope/Poor mgt: Upland/Medium Mgt 1.148 0.924 * Midslope/Poor mgt: Upland/Poor Mgt 1.616 0.760 * Upland/High Mgt:Upland/Medium Mgt 0.483 0.658 ns Upland/High Mgt:Upland/Poor Mgt 1.015 0.579 * Upland/Poor Mgt:Upland/Medium Mgt 0.532 0.520 * ns means not significant

Table 4.6: Comparison of Exchangeable Calcium as Influenced by Slope Position and Level of Management.

93 Pair compared Mean difference LSD Annuals-Bananas 0.343 0.644 ns Annuals-Banana/Coffee 0.737 0.774 ns Annuals-Bush fallow 1.404 1.784 ns Annuals-Coffee 0.813 0.774 * Annuals-Eucalyptus 1.295 2.908 ns Annuals-Grassland 2.407 1.048 * Annuals-Forest 0.662 2.908 ns Banana-Banana/Coffee 0.394 0.64 ns Banana-Bush fallow 1.747 1.742 * Banana-Coffee 1.156 0.608 * Banana-Eucalyptus 1.638 2.86 ns Banana-Grassland 2.75 0.944 * Banana-Forest 1.005 2.86 ns Bush fallow- Coffee 0.591 1.778 ns Bush fallow-Eucalyptus 0.109 3.322 ns Bush fallow/Coffee-Grassland 1.003 1.804 ns Bush fallow/Coffee-Forest 0.742 3.322 ns Banana/Coffee-Bush fallow 2.141 1.786 * Banana/Coffee-Coffee 1.55 0.776 * Banana /Coffee-Eucalyptus 2.032 2.908 ns Banana Coffee/grassland 3.144 1.052 * Banana coffee/Forest 1.399 2.908 ns Coffee- Eucalyptus 0.482 2.846 ns Coffee-Grassland 1.594 1.012 * Coffee-Forest 0.151 2.846 ns Eucalyptus-Grassland 1.112 2.982 ns Eucalyptus-Forest 0.633 3.744 ns Grassland-Forest 1.745 2.982 ns ns means not significant

Table 4.7: Comparison of Exchangeable Calcium Among Different Land Use Types

94 4.2.4 Exchangeable Potassium

Data on K+ shown in Table 4.8 shows that K+ varied between 0.19 to 0.50 cmolc/kg and was highly influenced by land use type. The management and slope position effects were not significant. Soils under all cultivated crops had more K+ than coffee and grassland. In turn there was more K+ under coffee than under the grasslands. It appears that coffee extracts a lot of K+ from the soil and hence without returning some of it through use of fertilizers or crop residues, it is bound to decline with time. In Tanganyika, Haarer (1962) reported partial mulching to give better returns for robusta coffee. Coffee has an extensive array of surface roots for moisture extraction. Coffee plants take up a lot of K+, which is later released when they die. Similarly, bananas also take up a lot of potassium while growing (Haarer, 1962). However it is returned to the soil when banana leaves and pseudo stems are used as mulching materials. Two thirds of the K+ removed is in the pseudo stem (Williams, 1975). Because bananas are shallow rooted and coffee is deep rooted, the two plants seem to compliment each other in the utilization of K+ resulting into overall increase of K+ in the soil in which they grow.

Nitrogen and K+ are the predominant elements in the nutrition of coffee trees (Coste, 1992). Roelofsen and Coolhaas (1940) observed that the fruit bearing branches, the coffee berry alone may contain 75% of the total K+ present in the branch. Much of the K+ is contained within the pericarp of the berry (Williams, 1975). Prolonged harvesting without return of crop residues or fertilizers, K+ is bound to reduce drastically over time.

Soils from grassland areas had significantly lower K+ than all the other land uses possibly because they were mostly located on shallow soils and often used for

95 communal grazing. In Nigeria, Huluggale (1994) reported depleted exchangeable potassium under grazed pasture. Leaching could also have contributed to the low K values.

Land Use Exchangeable Potassium(cmolc/kg) Natural Forest 0.50a Annuals 0.36a Bananas 0.35a Banana-Coffee 0.38a Bush fallow 0.23b Coffee 0.26ab Eucalyptus 0.29a Grassland 0.19c Note: Figures followed by the same letter are not significantly different

Table 4.8: Relationship Between Exchangeable Potassium with Land Use

4.2.5 Exchangeable Magnesium

Data on exchangeable magnesium shows that Mg2+ varied between 0.32 to 1.55 cmol/kg(Table 4.9). Mg2+ was influenced by both land use and slope position and was generally higher at the up slope positions and low in the bottom lands. The variation of Mg2+ content with land use type was similar at all slope positions. Mg2+ was highest under banana-coffee systems followed by bananas, annual crops, forest, grassland, and bush fallow respectively. The least Mg2+ was found under eucalyptus.

96 The high Mg content under the banana-coffee system could be a result of the increased litter under this system. Campo et al. (2000) found litter fall to account for as much as 84% in Mexico. With the exception of coffee, exchangeable magnesium was not significantly different among the cultivated crops. The low Mg content under the grasslands and bush fallow could be a result of leaching. While studying nutrient balance in Togo, Poss and Saragoni (1992) reported that leaching accounted practically for all the output in Magnesium.

Slope Position Land use Foot slope Mid Slope Up land Annuals 1.37a 1.88a 2.24a Banana 1.40a 1.90a 2.26a Banana-Coffee 1.55a 2.06a 2.41a Bush fallow 0.46cd 0.97cd 1.32cd Coffee 1.02bc 1.53bc 1.88bc Eucalyptus 0.33cd 0.84cd 1.19cd Grassland 0.49d 1.00d 1.35d Forest 1.14abc 1.65abc 2.01abc Note: Figures followed by the same letter within the same slope position are not statistically different

Table 4.9: Relationship Between the Exchangeable Magnesium (cmolc/kg), Land Use and Slope Position.

Contrary to Chen et al. (1997), Mg2+ in Masaka increased with slope position for all land use type and was highest on the uplands (Table 4.10). The Mg2+content at the mid slope and foot slope did not differ significantly.

97 Mean Exchangeable Magnesium Land use Slope position (cmolc/kg) Annuals bottom 1.371a Mid slope 1.881a Upland 2.236b Bananas bottom 1.395a Mid slope 1.904a Upland 2.260b Banana/Coffee bottom 1.545a Mid slope 2.055a Upland 2.410b Bush fallow bottom 0.455a Mid slope 0.965a Upland 1.321b Coffee bottom 1.016a Mid slope 1.526a Upland 1.882b Eucalyptus bottom 0.328a Mid slope 0.837a Upland 1.193b Grassland bottom 0.486a Mid slope 0.996a Upland 1.352b Natural Forest bottom 1.144a Mid slope 1.653a Upland 2.009b Note: Figures followed by the same letter within the same land use are not statistically different

Table 4.10: Variation of Exchangeable Magnesium with Slope Position under Different Land Use

4.2.6 Cation exchange capacity

Data presented in Figure 4.7 shows that the sum of cations (Ca2+, Mg2+ and K+) ranged from 96.7 in the Banana-coffee system to 33.8 cmol kg-1 under the grassland. The sum of cations was influenced by land use but not slope position 98 and management. It was the highest under Banana-coffee and the lowest under coffee and grasslands. The latter also had the lowest pH. The dominant cation was calcium.

The CEC of a soil generally increases with soil pH due to the greater negative charge that develops on organic matter and clay minerals due to deprotonation of functional groups as pH increases (Sparks, 1995). CEC values above 25 are considered high, and above 40 very high (Landon, 1984).

120 96.7a 100

80 69.5ab 68.4ab 60 43.8b 40 33.8b

20

Sum of cations (cmolc/Kg) 0 Annuals Bananas Banana- Coffee Grassland Coffee Land use

Figure 4.7: Cation Exchange Capacity as influenced by Land use

99 4.2.6 Phosphorous

Phosphorous occurs in soils in both organic and inorganic forms, the latter is usually more important for crop nutrition (Landon, 1984). Inorganic P can occur as various compounds of Ca, Fe, and Al or as exchangeable phosphate anions held by the positive charges on edges of clay plates (Landon, 1984).

Data for extractable inorganic P shows that P varied from 3.52 to 10.84 mg/Kg (Table 4.11). and was influenced by land use but not slope position and level of management. It was the highest under bananas and the banana-coffee intercrop and least under eucalyptus.

It was not possible to have a meaningful comparison between forests and the other land used due to the high standard error for forest owing to a small number of samples and a high variation among the few samples. All the banana based cropping systems had more extractable inorganic phosphorous than coffee, grasslands and bush fallow. Araujo (2004) also found no slope effect on inorganic P on a luvisol. Different crops have different P requirements. For coffee, P is important in the early stages of growth since in mature crops do not show marked response to P (Haarer, 1962). Similarly, bananas extract less P compared to other macro nutrients. A moderate crop of 16 tons removes 38Kg N, 285Kg K but only 9 Kg of P (Joseph, 1971).

100 Land use Mean Exchangeable P (mg/Kg) Standard error Annuals 9.31ab1 0.12 Bananas 9.93a 0.07 Banana-Coffee inter crop 10.84a 0.13 Fallow 5.13c 0.37 Coffee 7.76b 0.11 Eucalyptus 3.52c 0.63 Grassland 5.70c 0.17 Forest 7.24*2 0.64 Note 1: Figures followed by the same letter are not statistically different Note 2: A high standard error could not permit a meaningful comparison

Table 4.11: Variation of Extractable Phosphorous from Soils Under Different Land Uses l

Although coffee soils are often deficient in P, deficiency symptoms have been reported from only a few areas (Haarer, 1962). Nazareno et al. (2003) also found no growth response of coffee to P. The high amounts of crops residues often associated with banana farming could also have contributed to the high levels of P. In West Africa, Buerkert et al. (2000) also found an increased P availability due to the use of crop residues. Similarly, Silver (1994) found a high correlation between litter fall and soil phosphorous.

4.3 Soil Degradation Rating

Soil Degradation Rating (SDR) was determined as a function of SOC, soil bulk density, Ksat, soil texture, soil pH, proportion of coarse fragments in the top soil and rooting depth. The above parameters were weighted as proposed by Lal (1994) with a scale of 1 to 5 as shown in Table 4.12. A weight of 1 was given

101 when there was no limitation and five was given when the limitation was extreme. In this way, good soils had the least SDR and poor soils had the highest. In order to get a normal distribution, the SDR was transformed to the square of its reciprocal.

Limitation Relative weighting factor None 1 Slight 2 Moderate 3 Severe 4 Extreme 5

Table 4.12: Rating Scheme for Soil Degradation Rating

The critical limits of some soil properties where crop yield is 80% of the maximum yield were suggested by Aune and Lal (1997) and these are presented in Table 4.13. Data from East and Southern Africa confirm a critical level of nitrogen for increasing crop yield of nitrogen of 2.4%. (ICRISAT, 2000). Sufficiency levels for Magnesium are 0.2-0.5 (cmol kg-1) when CEC is less than 10 (cmol kg-1); and 0.98-2.4 (cmol kg-1) when CEC is greater than 20 cmol kg-1 (INMASP, 2002).

102 Crop Soil Property Maize Ground nut Cowpea Sweet potato Cassava Soya Bulk density Mg m-3 1.5 1.39 1.39 1.5 1.39 SOC % 1.08 pH 5 5.1 4.7 5.1 Al-saturation % 23.5 48.6 38.8 32.4 70.8 20.8 P (mg/kg) 7.6 8.6 10.6 K+ mmol/kg (cm) 0.83 0.7 0.7 Rooting depth (cm) 23 23 22 23 23 22

Table 4.13: Critical limits of soil properties for selected crops.

Table 4.14 shows the soil degradation rating for the measured soil properties. The limitations for all soil properties ranged from none to extreme, with the exception of SOC which ranged from none to severe. The mean limitation for SOC and soil texture was moderate (SDR = 3) and slight for pH (SDR = 2.1). Overall, soil depth and soil bulk density had a slight limitation but often yielded extreme limitations on hill tops.

Soil Property Mean Standard Median Mode Standard Minimum Maximum Error Deviation Ksat 1.2 0.017 1.0 1.0 0.47 1.0 5.0 Bulk Density 1.4 0.025 1.0 1.0 0.69 1.0 5.0 Soil Depth 1.3 0.022 1.0 1.0 0.63 1.0 5.0 Soil texture 3.0 0.016 3.0 3.0 0.43 1.0 5.0 pH 2.1 0.047 2.0 1.0 1.32 1.0 5.0 SOC 3.0 0.010 3.0 3.0 0.29 1.0 4.0 Coarse fragments 1.5 0.029 1.0 1.0 0.81 1.0 5.0 SDR 13.5 0.087 13.0 12.0 2.44 9.0 26.0

Table 4.14: Mean Soil Degradation Rating for the Measured Soil Properties

103 The data shows that SDR ranged from 10.7 to 19.7 (Table 4.15). The SDR was highly influenced by land use (P<0.001) and slope position but not management. The least SDR was observed under the bananas, annuals, banana-coffee intercrop and natural forest and highest under grasslands. The high SDR under bush fallow could be an indication that it takes a relatively long time to recover the nutrients that are lost during cultivation. Sánchez et al. (2002), also found high degradation levels under grassland and attributed it to soil compaction, which results into loss of soil depth and an increase in bulk density. Similarly, while comparing different land uses (Forest, grassland, shrubs, secondary forest, cultivated lands and reforested), Fu et al. (2003), got high soil quality index for forest land but low for grass, reforested areas and cultivated areas. McGrath et al. (2001) observed low concentrations of C and N in secondary forests regenerating from abandoned crop fields after many years, suggesting that the recuperation of soil losses of C and N resulting during no-input annual cropping is slower than previously thought.

Soil Degradation Rating Land use Bottom Midslope Upland Forest 11.9a 10.7a 10.8a Banana-Coffee 13.9a 12.1a 12. 2a Annuals 14.1ab 12.2ab 12.3ab Eucalyptus 14.4abc 12.3abc 12.5abc Banana 14.4abc 12.4abc 12.6abc Coffee 15.1bc 12.8bc 13.0bc Bush fallow 16.8c 13.8c 14.0c Grassland 19.7d 15.3d 15.6d Note: Figures followed by the same letter within the same slope position are not statistically different

Table 4.15: Soil Degradation Rating under Different Land Use Types

104 The low SDR under the banana based cropping systems can be attributed to mulching. Bananas are mulched more than any other crops. Further more, while the bananas have a superficial root system, and extract nutrients and water from the top soil layers, coffee has a deep root system which may further help to loosen up the soil. The deep root system also helps to promote recycling of nutrients which would otherwise have been lost through leaching. On the other hand coffee as a sole crop is not usually mulched, which may increase soil degradation due to exposure to agents of erosion. Secondly, some farmers grow coffee in places where bananas have failed to grow implying that the soils could be also inherently poor.

There was no significant difference between the annuals, bananas and coffee irrespective of slope position. High variability in SDR was observed under coffee in comparison to other land uses. The high SDR under grasslands indicates a low soil quality. This is corroborated by the low soil depth in these areas and high proportions of coarse aggregates in the top soil.

In order to examine how soil degradation was influenced by slope position irrespective of land use, the relationship between SDR and slope was evaluated. SDR was influenced by slope position being highest at the slope bottom and lowest at midslopes (Table 4.16). The SDR at the slope bottom was not significantly different from that at the uplands. The lack of variation between bottom and uplands could be a result of shallow soils on the uplands and poorly drained soils at the slope bottom. Indeed most of the farming activities take place on the mid slopes. In contrast, on a typically disturbed slope, Fu et al. (2004) observed higher soil quality levels on the upper slopes and foot slopes compared to the middle slopes and lower slopes. The difference could be a result of how soil quality was assessed. In their analysis of soil quality, they considered

105 soil nutrients and moisture distribution, while this study considered soil depth, coarse fragments, infiltration capacity, texture, pH as well selected soil nutrients.

Slope position Mean Soil Degradation Rating Foot slope 14.6a Mid slope 13.0b Up land 13.4ab Note: Figures followed by the same number are not statistically different

Table 4.16: Variation of Soil Degradation Rating with slope position.

4.4 Soil Productivity

The dry matter yield of maize was not affected by land use, slope positions, days to germination and management. However, the shoot to root ratio was highly influenced by land use (P <0.001) (Table 4.17). Although the other parameters don’t seem to be significant, they seem to be highly related to land use. For example when days to germination were dropped from the model, management became highly significant. Similarly, further dropping of management from the model resulted in significant effects of land use, SOC and days to germination. This could be a result of interdependence between the predictor variables.

For many tropical soils, Jens and Lal (1997) showed that the effect of SOC on productivity was weak, being more pronounced when SOC was below 1%. This tends to support the observations made since SOC ranged from 1.4 to 1.9%, and hence higher than the 1% threshold.

106 Fixed term Wald statistic df Chi-sq prob Days to germination 0.8 1 0.37 Position 0.1 1 0.76 SOC 0.4 1 0.54 Management 1.1 2 0.58 Land use 5134.8 5 <0.001

Table 4.17: Shoot to Root Ratio as Influenced by Land Use, Slope position, SOC, management and days to germination

Data on shoot to root ratio (SR) presented in Table 4.18 shows that SR ranged from 1.18 to 1.80. An increased fraction of the exported photosynthate goes to the shoot when supply is reduced (Minchin et al. 1994). The highest SR was observed under the annual crops, followed by banana-coffee intercrop, bananas, coffee, forest and grass respectively. There was no difference between coffee and forest. Although grass had a low SR ratio, it had a very big standard error making it difficult to make meaningful comparisons.

Land use Shoot:Root Ratio Standard Error Annual 1.80a1 0.2155 Banana 1.37c 0.2155 Banana-Coffee 1.49b 0.2167 coffee 1.18d 0.2168 Forest 1.18d 0.2171 Grass 1.148*2 0.6087 Note 1: Figures followed by the same letter are not statistically different Note 2: A high standard error could not permit a meaningful comparison

Table 4.18: Variation of Shoot:Root Ratio with Land Use.

107 4.5 Calibration of Spectra with Soil Nutrients

4.5.1 Soil Chemical Properties

Certain sections of the spectrum are more useful for discriminating among soil samples with differing characteristics (Thomasson, 2001). Spectral regions of high discriminatory power were 420 to 960 nm, 1020 to 1770 nm and 1830 to 2480 nm. Calibrations were based on principal component regression (PCR) using the first derivatives of the spectras in order to minimize variation among samples caused by variation in grinding and optical set-up (Marten and Naes, 1989).

Partial Least Square (PLS) regression calibration models were made for the measured variables using unscrambler. Two thirds of the sample were used to train the model and one third was used to validate it. The calibration results are shown in Table 4.18. The calibration adjusted R2 varied from 0.34 to 0.85.

From the models developed in table 4.19, predictions were made and these are discussed in the scatter plots that follow.

108 Attribute Slope Offset Correlation ln C g/Kg 0.74 0.76 0.85 _13 C 0.65 -6.67 0.79 ln N 0.67 0.13 0.8 _15N 0.35 1.24 0.56 Sqrt SDR 0.62 0.93 0.79 ln pH 0.47 0.95 0.67 Sqrt Exc. Mg 0.48 0.66 0.68 Sqrt Ex. Ca 0.58 0.58 0.78 ln Ex K 0.28 -0.93 0.47 ln Ex P 0.15 1.79 0.34 ln %Sand 0.56 1.77 0.69 Sqrt % silt 0.34 2.88 0.57 Sqrt % clay 0.40 2.90 0.62

Table 4.19: Summary for Calibrated Models.

Soil Organic Carbon

The predictions made gave a coefficient of determination (R2) of 0.79 (Figure 4.8), while the cross validated model had an R2 of 0.76. Others have also been able to successfully predict total carbon, total nitrogen and cation exchange capacity using Near-infrared reflectance spectra (Shepherd and Marcus, 2002;Cheng-Wen et al.,2001; Morra et al., 1991). The resulting model had a bias of -0.22 hence it tended to under-predict the SOC content.

109 Model Bias - C gKg C gKg Calibration Validation model - C g/kg 70 70 70 60 60 60y = 0.9341x + 1.4597 y = 0.9096x + 1.9229 Bias = -0.22 50 2 2 R = 0.79 50 50 R = 0.76 40 40 40 30 30 30

20 20 20

10 10 10

0 0 0 -10 10 30 50 70 0 10 20 30 40 50 60 70 -10 10 30 50 70 Measured C (g/Kg) Measured C (g/Kg) Measured C (g/Kg)

Figure 4.8: Calibration and Validation Models for Soil Organic Carbon.

Good predictions were also obtained for delta 13 carbon (Figure 4.9) and soil nitrogen (Figure 4.10) and Ca2+ (Figure 4.11). However, the predictions for exchangeable K (R2=0.18), Exchangeable Mg (R2 = 0.42), soil pH (R2=0.43 and extractable P (R2=0.18) were poor. While Shepherd and Marcus (2002) were able to get good predictions for Ca (R2 = 0.88) and Mg (R2 = 0.81), Cheng-Wen et al. (2001) also predicted them with less accuracy (R2 ~0.8-0.5).

110 Acidified d 13 C- Calibration Acidified d13 C validation Model Bias - acidified d13 C

0 -5 0 -30 -20 -10 0 -30 -25 -20 -15 -10 -5 -30 -20 -10 0 -5 -5 -10 y = 0.966x - 0.6726 Bias = 0.047 2 -10 y = x + 0.0005 R = 0.63 -10 2 -15 -15 R = 0.70 -15 -20 -20 -20 Predicted -25 -25 -25 Predicted d13 C Predicted d 13 C -30 -30 -30 Measured d13 C Measured d13 C Measured

Figure 4.9: Calibration and Validation Models for Delta 13 Carbon

N (g/Kg) Calibration) N (g/Kg) Validated Model Bias - N 6 6 6 5 y = 0.9136x + 0.1557 y = 0.8502x + 0.2554 5 Bias =-0.024 2 5 R = 0.71 R 2 = 0.63 4 4 4

3 3 3

2 2 2 1 1 1 Predicted N (g/Kg) Predicted N (g/Kg) Predicted N (g/Kg) 0 0 0 0 2 4 6 0 2 4 6 0 2 4 6 Measured N (g/Kg) Measured N (g/Kg) Measured N (g/Kg)

Figure 4.10: Calibration and Validation Models for Soil Nitrogen.

111 Calibration Model - Exch Ca Validation Model - Exch Ca Model Bias for Exch Ca

12

y = 0.9754x + 0.156 14 12 10 R 2 = 0.882 12 y = 0.853x + 0.7128 10 2 Bias = -0.05 8 10 R = 0.6506 8 8 6 6 6 4 4 4

Predicted Exch Ca 2 2

2 Predicted Exch Ca 0

Predicted Exch. Ca 0

0 2 4 6 8 10 12 -3 2 7 12 0 0 2 4 6 8 10 12 Measured Exch Ca Measured Exch Ca Measured Exch Ca

Figure 4.11 Calibration and Validation Models for Exchangeable Ca

4.5.2 Soil Texture

High correlations were obtained for sand (R2 = 0.69) and clay (R2 = 0.62) with the spectral data (Table 4.19). (Shepherd and Marcus, 2002; and Cheng-Wen et al. (2001) were also able to get good predictions for sand and clay. Therefore, spectroscopy can be a useful tool for giving an indication of soil texture.

4.5.3 Comparison of SDR and spectral data

A correlation coefficient of 0.79 was obtained between the spectras and the SDR (Table 4.19). This implies that using spectral data, it is possible to get an indication of the soil quality status of an area rapidly and economically. Better predictions can be made if more soil variables are included in the determination of SDR.

112 4.6 Delta 13 Carbon

Analysis of the plant litter confirmed that coffee (_13C = -28.5‰) and forest (_13C 13 = -28.22‰) had a C3 photosynthetic pathway while maize (corn) (_ C = -

12.01‰) had a C4 photosynthetic pathway. The bananas also behaved like C3 plants with a mean _13C value of -25.16‰. Therefore current vegetation was classified as follows:

• Presence of woody vegetation (C3)

• Bananas (C3)

• Presence of graminoid vegetation or grasslands (C4)

Former vegetation was classified as: 13 • _ C < -19.5‰ (C3) 13 • _ C ≥ -19.5‰ (C4)

The isotopic composition of SOC (_13C) was significantly different between the

C3 and C4 plants (P<0.001) (Figure 4.12 and Figure 4.13) for both former and current land use. However, former land use had more influence since it had a higher Wald statistic (Table 4.20). The Wald statistic is a measure of the contributions of individual terms in the fixed model. Furthermore, the difference between C3 and C4 was higher for former land use as compared to current land use implying that what is done on the land now, can have implications far in the future. Rowntree (2004) also found former land use to have a higher effect on soil quality than current land use. In a related study, Verheyen et al. (1999) demonstrated that historical land use, even in the distant past, can still influence present-day soil characteristics.

113 450 450 400 LSD =9.28 400 LSD = 14.1 350 350 300 300 Carbon 250 250 200 200 Square of Delta 13 C3 C4 C3 C4 Former Land use Current Land Use

Figure 4.12: Variation of Delta 13 Carbon Between Former and Current C3 and C4 Vegetation

-15 C3 C4 -16

-17 -17.12 -18 -17.79 Delta 13 C -19

-20 -19.57 -20.16 -21

Former land use Current land use

Figure 4.13: Comparison of Delta 13 Carbon between Former and Current Land Use

114 Fixed term Wald statistic df Chi-sq prob Current 89.29 1 <0.001 Former 585.77 1 <0.001

Table 4.20: Comparison of Influence of Current and Former Land Use on _13 C

13 There was significantly less _ C under C3 vegetation compared to C4. The isotopic composition of _13C under bananas and banana/coffee intercrop were not significantly different from that under forests (Table 4.21 and Figure 4.14). However, it was lower under the banana-coffee intercrop compared to the bananas. The highest isotopic composition of SOC (_13C) was found under the grasslands followed by, bush fallow and annuals.

Land use _ 13C Natural Forest -21.23a Banana-Coffee -20.28a Bananas -19.79a Coffee -19.17a Annuals -18.15b Bush fallow -17.03b Grassland -15.55c

Table 4.21: Delta 13 Carbon Under Different Land Uses

115 600

500

400

300 Square of delta 13C 200 Annuals Bananas Banana- Bush fallow Coffee Grassland Forest Coffee Land use

Figure 4.14: Variation of the Square of Delta 13 C with Land Use

Analyzing the data by current land use revealed that on its own, current land use (P= 0.25) did not explain the variation in SOC at _ = 0.05. On the contrary, former land use was highly significant (P= 0.004). A combination of both former and current land use in the model resulted in both current land use (P=0.068) and former land use (P=0.001) being significant. The increase in explanatory power points at an interaction between the former and current land uses in explaining the variation in SOC. The improvement in model also indicates that former land use explained more of the variation in SOC than present land use. Therefore land use effects continue to take place even long after the practices are discontinued.

116 4.7 Soil Erosion Index as Related to Land Use

An erosion and sedimentation index (dsed) was obtained by projecting soil spectra into the sediment principal component model. The dsed is the Mahalombies distance from the sediment model center (i.e. number of standard deviations from the model center). Using CART analysis, 0.964 was calculated as the cutoff between deposition and erosion. Values above the cutoff point also known as fast sources indicated erosion while those less than the cutoff point reflected deposition.

Data on dsed shows that it ranged from 0.33 to 1.71 (Table 4.22). Dsed was highly influenced by land use (P=0.007) and slope position (P = 0.048). Level of management (P = 0.074) was not significant at 5% level. Elimination of management effect from the model resulted into an increased significance for land use (P= 0.002) while making slope position (P=0.055) not significant at the 5% level.

Land use Mean Dsed Natural Forest 0.33a1 BC 0.79a Annuals 0.91ab B 0.93ab C 1.07b Bush fallow 1.55bc Grassland 1.71c Note: Figures with the same letter within the same slope position are not statistically different

Table 4.22: Variation of Erosion and Sediment Index with Land Use

117 The least dsed values were found under natural forest and banana coffee intercrop, indicating a net sediment deposition. High dsed values were observed under the grasslands and bush fallow implying a high susceptibility to erosion. Dsed values under the banana-coffee intercrop, annuals and bananas did not significantly differ from those in the natural forest. Similarly bananas and coffee were not significantly different although coffee tended to have higher dsed values than bananas. Lufafa (2000), also observed very high erosion rates under annual crops, followed by rangelands, banana-coffee and banana alone. The annual cropping system exhibits a high degree of degradation due to continuous cultivation and poor protection, especially at the beginning of the rainy season when there is minimal cover.

4.8 Soil Quality-Management Relationship.

A multiple linear regression of land use, slope position and management against SDR returned slope position (P=0.005) and land use (P<0.001) as highly significant factors. Management level (P=0.436) was not significant. The differences between land uses are shown in Table 4.23. SDR varied from 10.65 to 19.73

For all slope positions, the least soil degradation rating was found under the natural forests and banana coffee intercrop while the highest was found under the grasslands implying better soils under natural forests and banana-coffee intercrop but poor soils under the grasslands. Field observations indicated that these results were plausible. The soil under grassland were often stony and shallow and hard to auger. Wide variations were observed for bananas and eucalyptus. Annuals and bananas were not statistically different from the other crops with the exception of the grasslands.

118 Soil Degradation Rating Land use Foot slope Midslope Upland Natural Forest 11.86a1 10.65a 10.75a BC 13.90a 12.07a 12.22a Annuals 14.08ab 12.17ab 12.32ab Eucalyptus 14.37abc 12.30abc 12.51abc B 14.40abc 12.42abc 12.58abc C 15.08bc 12.80bc 12.97bc Bush fallow 16.80c 13.80c 14.02c Grassland 19.73d 15.29d 15.59d Note: Figures with the same letter within the same slope position are not statistically different

Table 4.23: Variation of Soil Degradation Rating across Different Land Uses

Soil quality rating reduced with increasing slope position (Table 4.24). The highest SDR values were observed at the foot slope implying that these soils are poorer than those found at the mid slope and upland. The greatest limitation for the foot slopes is poor drainage. These areas become inundated during the rainy season. The mid slope tended to be better than the upland although the two were not statistically different.

119 Soil Quality Rating Land use Bottom Midslope Upland Natural Forest 11.86a 10.65b 10.75b BC 13.90a 12.07b 12.22b Annuals 14.08a 12.17b 12.32b Eucalyptus 14.37a 12.30b 12.51b B 14.40a 12.42b 12.58b C 15.08a 12.80b 12.97b Bush fallow 16.80a 13.80b 14.02b Grassland 19.73a 15.29b 15.59b Note: Figure followed with the same letter for the same land use are not significantly different

Table 4.24: Variation of Soil Quality Rating Across Different Slope Positions

4.9 Soil Quality Indicators

Farmers need early warning signals and monitoring tools to help them assess the status of their soil because by the time degradation becomes visible, it might be too late or very expensive to reverse it. The soil quality indicators can be physical such as Physical indicators

• Soil depth

• Infiltration capacity (field capacity)

• Bulk density

• Water holding capacity

• Soil structure

• Aggregate stability

The composition and abundance of weed species growing on agricultural soils is also a useful indicator of soil condition frequently used by farmers (Barrios et al., 120 1994). Biological indicators can potentially be used to capture changes in soil quality since they can simultaneously reflect changes in the chemical, physical and biological properties of the soil.

Although there is hardly any documentation presenting scientifically supported relationships between soil quality problems and phytological indicators in Uganda, the following have been thought to reflect the soil status.

A rich mix of a variety of plants is an indicator of good soil productivity (the reverse is true as well). On the other hand, Spear grass (thatch grass) indicates low soil pH and low soil fertility. Similarly, tea is usually grows on acid soils and possibly high exchangeable aluminum. Elephant grass grows in areas with high exchangeable K (or bases) and generally fertile soils. Groundnut and legumes in general indicate sulphur presence and sufficiency, effective legume N fixation, and low levels of soil N. Cassava bitterness is an indicator of soil fertility depletion, high acidity and possible micronutrient toxicities while poor root development and brown lesions often indicate Aluminum toxicity.

4.10 Demographics, Costs and returns of different cropping systems

4.10.1 Age distribution and family size

The age distribution for the study area presented in Table 4.25 shows that more than 40% of the farmers were above 50 years while the 18-30 age range was constituted by less than 16% of the population. Therefore the proportion of the aging population involved in agriculture was quite high and this may undermine the agricultural potential since the farmers are less energetic

121 Age range % 18-20 2.8 21-30 12.8 31-40 21.3 41-50 22.7 51-60 14.2 60+ 26.2

Table 4.25: Age Distribution in the Study Area.

Table 4.26 shows that family size ranged from 1 to 28 people per household, with an average of 7 people per household. Most these were school going children whose contribution to the day to day farming activities was minimal.

122 Family Size Frequency Valid Percent Cumulative Percent 1 2 1.4 1.4 2 4 2.8 4.3 3 9 6.4 10.6 4 18 12.8 23.4 5 20 14.2 37.6 6 18 12.8 50.4 7 20 14.2 64.5 8 14 9.9 74.5 9 11 7.8 82.3 10 11 7.8 90.1 11 5 3.5 93.6 12 3 2.1 95.7 13 4 2.8 98.6 21 1 .7 99.3 28 1 .7 100.0 Total 141 100.0

Table 4.26: Household size in the study Kabonera sub county

4.10.2 Level of Education .

The level of education in the study area varied from no education (21.4%) to post secondary education (5%). The greater part of the population (58.2%) had only basic primary education, having spent less than 7 years in elementary school. The relatively more educated people were often not involved in farming. A large proportion of the farming community (62%) had no other source of income other than farming.

123 4.10.3 Costs and Returns of Different Cropping Systems

More than 90% of the farmers interviewed were subsistence farmers growing crops mostly for home consumption. Excess produce is usually sold to any willing buyer who often gets it from the farmer’s garden. With regard to coffee, it is sold by the individual farmer to intermediaries, who in turn sell it to coffee dealers. There are no cooperatives or farmer groups to strengthen the farmers’ bargaining powers, and help stabilize prices. Therefore prices of agricultural commodities fluctuate and the farmers have no control.

The costs and returns of the various crop enterprises varied with soil quality based on the survey data (Table 4.27). In the banana cropping system, the highest costs were incurred on soils perceived to be of good quality while the least costs were incurred on the poor soils. For coffee and annual crops, the highest costs were incurred on medium quality soils, followed by the good quality soils. For all crops, the least costs were incurred on the poor quality soils. Furthermore, costs were also highest under bananas followed by coffee and annual crops. The trend is possibly due to the heavy mulching usually under bananas and the use of pesticides. All farmers used only hand labor for their farming activities. No machinery was use used.

On the contrary, the highest returns (Table 4.28) were obtained under coffee, followed by bananas, maize and beans. As expected, better returns were observed on good soils as opposed to the poor soils. For bananas and coffee, there was a drastic drop in returns when soil changed from good to fair quality. These results suggest that coffee, was the most profitable crop enterprise in the area while the annuals were the least profitable.

124 Soil Quality Seasonal Costs per Acre (Uganda Shillings) Banana Coffee Annual crops Good 32,969 17,544 7,956 Fair 24,593 19,490 13,419 Poor 15,536 9,984 5,093 Mean 24,717 16,366 10,111

Table 4.27: Variation of Seasonal Costs between the Different Crops

Soil Gross Returns per Acre (Uganda Shillings) Quality Bananas Coffee Maize Beans Good 223,712 254,587 74,463 46,108 Fair 99,685 110,135 42,705 41,509 Poor 38,656 85,441 21,050 31,665 Mean 112,660 139,404 41,589 39,821

Table 4.28: Gross Returns among the various crops per season

4.11 Impact of Net returns to Cropping Systems in Determining Choice of Crop

The most important factor influencing choice of crop to be grown in the area was food self-sufficiency followed by net income to be realized from crop/enterprise.

125 About 59% of the farmers chose bananas as their first crop (Table 4.29), grown mostly for consumption (Table 4.30) but with some farmers (11%) growing it as a source of income. Only 33% of the farmers interviewed had coffee as their first choice crop and this was largely grown for income (Table 4.30). Maize and beans were largely grown for food but were also regarded as cash crops that could bring additional income to the farmers.

Choice of crop Crop 1st 2nd 3rd 4th Banana 58.7% 29.8% 1.4% 2.2% Coffee 32.9% 34.0% 5.0% 5.9% Maize 1.4% 9.9% 19.1% 37.8% Beans 2.8% 9.9% 52.5% 28.1% Others 2.8% 16.3% 21.3% 25.9%

Table 4.29: Relative Importance of the Various Crops to the Farmers.

High Income Food Security Fast growth Banana 11% 96% Coffee 99% Maize 24% 87% Bean 18% 92% 5%

Table 4.30: Attributes that Make Selected Crops Important to Farmers.

126 4.12 Maintenance of Soil Quality as a Function of Profitability of Cropping Systems

While high income could be a motivating force to maintain high soil quality, food security seems to be the major driving force. Although the highest returns per hectare were obtained under coffee regardless of the soil quality, the highest costs were incurred under bananas. Expenses were incurred mainly on mulching materials, manure and erosion control. Over 70% of the farmers reported that they made efforts to control erosion under bananas as compared to 58% under coffee. Similarly, more mulching was undertaken under bananas (72%), compared to coffee (26%) and annuals (5%).

127 CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

5.1 Conclusions

Empirical research carried out to address the objectives and hypotheses specified at the beginning of study (see page 5 and 6) resulted in a number of conclusions and recommendations that are discussed in subsequent sections.

5.1.1 Key Properties that Impact Soil Quality in the Study Area

A single index of soil quality, the Soil Degradation Rating (SDR), was developed and found valid thereby proving the first hypothesis. The SDR was influenced by landscape position and land use. The most limiting soil properties affecting soil quality were soil organic carbon content and texture. The uplands, often used for grazing, were constrained by shallow soil depth and high proportions of coarse fragments, while the foot slopes often had low infiltration capacity and hence were potentially prone to flooding. All measured soil quality indicators in the study area varied from no limitation to extreme limitation.

The least SDR was obtained under forests and the banana-coffee intercrop while the highest was under the grasslands, despite their high carbon content. SDR 128 was also lowest on the mid slopes and highest at the foot slopes and the uplands. Therefore, grasslands in the study area are fragile and converting these to croplands would exacerbate soil degradation. When all empirical evidence is taken together, it indicates that soil quality was maintained under the banana- coffee intercrop system. These results therefore suggest that banana-coffee intercrop is the best system if one is to ensure maintaining soil quality.

5.1.2 Soil Quality and Management Inter-relationship

No direct relationship was observed between soil quality and management, possibly as a result of several other confounding factors such as weed cover which were not evaluated in isolation during the course of this study.

Based on the _13 C signatures, the historic land use had stronger effect on soil carbon than the current land use, hence proving the second hypothesis. The implication of these results is that land use effects may be observed long after the specific land use activity had occurred. Hence the effects of land use can be both immediate and long term and farmers should take into account the potential long-term effect of current land use decisions.

5.1.3 Reflectance as a Soil Quality Indicator

Using the soil data from the study area, soil reflectance proved to be a useful soil quality indicator with a reasonable degree of accuracy. In addition, a high correlation between the soil spectras and the SDR was observed. Soil reflectance was strongly related to soil quality as determined by soil physical and

129 chemical properties, thereby confirming the third hypothesis. Using spectral analysis enabled simultaneous determination of several soil physical and chemical properties notably soil organic carbon, Ca, N, soil degradation rating, and _13 carbon, with high precision (R2 ≥ 0.8). Acceptable levels of prediction were also obtained for soil pH, Mg, sand and clay contents. Therefore, reflectance spectroscopy can be used as a rapid analytical technique to simultaneously estimate several soil properties with acceptable accuracy, rapidly and cost-effectively.

5.1.4 Costs and Returns of Different Cropping Systems and Determinant of Crop Choice

The highest costs were incurred in the banana-based cropping system, followed by that in coffee and the annuals respectively. In contrast, the highest income was realized from coffee, followed by bananas and annuals. Net returns were also highest from coffee, followed by that in bananas and annuals respectively.

Under the banana cropping system, the highest costs were incurred on soils perceived to be of good quality, which also resulted into better yields. The least costs were incurred on soils of poor quality with a low yield potential. For coffee and annual crops, the highest costs were incurred on medium quality soils. A majority of farmers (59%) planted bananas as the first choice crop, grown mostly for home consumption and some (11%) as a source of income. Only 33% of the farmers interviewed planted coffee as their first choice crop, mostly for income. Annuals were used both for food and as cash crops. The results suggest that food self sufficiency was the major determinant of the choice of crop to be grown. While high income could be a motivating force to maintain high soil quality, food security seemed to be the major driving force based farmers’ responses, net

130 returns and acreage of the different crops. Availability of market information and easy access to these markets may help in transformation of farmers from subsistence to commercial farmers. This will in turn motivate farmers to conserve and increase the productivity of their soils.

5.2 Recommendations

Based on the discussion of results, the research supports the following recommendations:

• The generally low soil organic matter levels in the study area necessitates using crop residue mulch which may also contribute to the reduction of soil erosion and conserve soil moisture. By conserving the soil moisture, variability in crop yields owing to the unreliable rainfall would be reduced.

• Grasslands, mostly located on the uplands, must not be converted to agricultural land use because of a high susceptibility to soil degradation.

• Farmers need to be informed about the new ways of maintaining productivity of their soils because the long term adverse effects of poor management accrue on future productivity and soil quality.

• Poorly managed farms were observed to be either weedy or bare with poor crop stands. With a research focus, poorly managed systems may be separated into different classes in order to quantify the management effects.

• Farmers need to be sensitized to think beyond food self-sufficiency and enhance farm income through a judicious combination of both cash crops and

131 food crops. Traditional food crops have now become non-traditional cash crops from which farmers can earn additional income. Providing better information on markets and easy access to these markets will motivate farmers to change from subsistence to commercial farmers, which will ultimately result in increased soil conservation.

• Agriculture inputs need to be used even on good quality soils to avoid mining of soil fertility, further degrading soil quality and reducing long-term productivity.

Limitation of the Research

• Data collection was done in only one season and hence may have failed to capture some of the management dynamics that take place in different seasons of the year.

• The role of market imperfections, subsidies and tariffs on profitability (net returns) to the farmer was not addressed.

• Thirdly, off-site effects and economic costs of erosion, which can be even more important than the on-site effects, were out of the scope of this study and therefore were not addressed.

132 5.3 Research Priorities

On-farm research needs to be done to establish the relationship between soil productivity and soil quality especially to cover long-term trends.

There is a strong need for research to address management levels in more detail, encompassing weed cover, mulch cover, ground cover, and the crop stand (varieties). Poorly managed systems would be separated into different classes in order to quantify the management effects.

Management effects on soil quality need to be assessed from long-term experiments where the confounding effects of other variables (e.g. weed growth) can be accounted for.

Article 12 of the Kyoto protocol created a “Clean Development Mechanism” in which units of certified emissions reduction by developing countries can be traded and used with Annex I countries to comply with the Kyoto emission limits (Oberthur and ott, 1999; Toman, 2000). The Clean Development Mechanism can assist non Annex 1 countries achieving sustainable development. Temporal changes in soil properties need to be related to soil carbon sequestration for developing the database required to facilitate trading carbon credits.

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153 APPENDIX A SOIL PROFILE DESCRIPTION AND ANALYTICAL DATA

154 SOIL PROFILE DESCRIPTION AND ANALYTICAL DATA

Soil profile description for Petroferric Luvisol Location: Kasaali (353306; 9954887) Geomorphic position: Shoulder Micro-relief: Termite hills Slope: 18 % Precipitation: 1218 mm yr-1 Temperature regime: Ustic Permeability: Medium Drainage: Moderately well drained Current land use: Grassland Runoff: Rapid Liability to erosion: High Stoniness: Abundant plinthite Probable parent material: Quartz and iron stone Diagnostic horizons: Argillic A Classification (FAO): Petroferric Luvisol

Horizon Description Horizon Ap Bt Bv Depth 0-18 18-25 25-76+ Color 5YR3/3 (moist) 5YR4/4 (moist) 5YR5/4 (moist) Sharpness Clear Gradual - Boundary Regularity Wavy Irregular - Type Sub angular blocky Columnar - Structure Strength Strong Weak - Consistency (moist) Friable Friable Extremely Firm Stickiness Slightly sticky Slightly sticky Non-sticky Porosity Porous Fine porous - Flora Abundant small Frequent small Rare roots roots Remarks Coarse with Non-coherent Indurate plinthite abundant plinthite plinthite and quartz and quartz

155 Soil profile description for Chromic Luvisol Location: Nabinene (355031; 9955023) Geomorphic position: Backslope Micro-relief: Termite hills Slope: 20 % Precipitation: 1218 mm yr-1 Temperature regime: Ustic Permeability: Medium Drainage: Well drained Current land use: Annual crops Runoff: Rapid Liability to erosion: High Stoniness: Few plinthitic and quartzitic stones in topsoil Probable parent material: Quartz and iron stone Diagnostic horizons: Argillic A Classification (FAO): Chromic Luvisol

Horizon Description

Horizon Ap Bt1 Bt2 Depth 0-26 26-52 52-150+ Color 5YR3/4(moist): 5YR5/6 (moist) 5YR5/8 (moist) Sharpness Clear Gradual - Boundary Regularity Smooth Wavy - Type Granular Sub angular blocky Columnar Structure Strength Strong Weak Very weak Consistency (moist) Friable Moderately firm Firm Stickiness Slightly sticky Sticky Sticky Porosity Porous Fine porous Fine porous Flora Abundant small & Frequent medium Few large roots medium-sized sized roots roots Remarks Few plinthitic and Common plinthitic No impeding quartztic stones and quartzitic indurate layer of stones plinthite at shallow depth

156 Soil profile description for Mollic Gleysol Location: Kyawunyi (353768; 9955273) Geomorphic position: Valley Micro-relief: None Slope: 2 % Precipitation: 1218 mm yr-1 Temperature regime: Ustic Permeability: Poor Drainage: very poorly drained Current land use: Grazing/cultivation Runoff: low Liability to erosion: Slight Stoniness: Non-stony Probable parent material: Quartz Diagnostic horizons: Mollic A Classification (FAO): Mollic Gleysol

Horizon Description

Horizon Ap Bt1 Bt2 Depth 0-47 47-93 93-150+ Color 7.5YR3/2(moist): 7.5YR4/2 (moist) 7.5YR5/2 (moist) Sharpness Gradual Diffuse - Boundary Regularity Smooth Wavy - Type Fine granular Massive Massive Structure Strength Weak - - Consistency (moist) Moderately friable Moderately firm Firm Stickiness Moderately sticky Very sticky Very sticky Porosity Fine porous Non-porous Non-porous Flora Abundant fine, Frequent medium Rare small & medium- sized roots sized roots Remarks Clayey texture; no Clayey texture; no Silty clay texture; stones. stones. no stones.

157 APPENDIX B QUESTIONNAIRE

158 MINI SURVEY TO DETERMINE THE LEVEL OF MANAGEMENT, COSTS AND RETURNS.

INTERVIEWER’S GUIDE.

• Greet the respondent • Introduce your self and explain the purpose of the study as follows:

The purpose of the study is to determine the level of management so as to establish its relationship to soil quality/productivity. The study also is aimed at estimating the costs and returns that accrue to the farmers so as to determine if profitability as opposed to food security is an important determinant of choice.

Your active participation in the interview will provide information that will be used by various organizations whose interest is to promote improved land productivity within rural households. The information provided will be treated with complete confidentiality.

Thank you. ______

Farmer Number………………………………. Enumerator…………………………………… Date……………………………………………

Location characteristics Farmer’s name ……………………………………. Sub county ………………………………………… Parish ……………………………………………… Village ……………………………………………..

Enterprise selection

1. How long have you lived in this village?...... (yrs)

2. Rank four most important crop enterprises on your farm? Rank in order of priority. 1) ------2) ------3) ------4) ------

159 3. What attributes (qualities) do the above enterprises have that make them important (rank the attributes) Banana Coffee Maize Beans based based High net income Food security Resistance to diseases Fast growth Others (specify)

4. What factors do you consider in locating the different crops? Please rank them below.

1) Soil type/soil fertility…………. 2) Slope ……….. 3) Accessibility ……….. 4) Other specify)………………… 5. How are your crops grown? (tick what is applicable).

Bananas Coffee Annuals 1. Acreage 2. Intercropping 3. Mono cropping (pure stand)

6. Land ownership 1. Land owned ……………………(acres) 2. Land borrowed ………...………….(acres) ………% of total land 3. Land rented ……………………(acres) ………% of total land 4. Total Land utilized…………………….(acres) ……….% of total land 5. Total land available for Agric. ………….(acres)

6. If you rent land how much do you pay per acre per season------(shs)

160 Timeliness of Operations

7. If the main occupation is farming, and you had a working day of 10 hours approximately how much time would you spend on the following activities? Bananas Coffee Annuals Total (hrs) 1. Land preparation 10 2. Planting 10 3. Weeding 10 4. Harvesting (where 10 applicable)

8. Which of the above activities is more labor intensive under the different crops? Assuming you had 10 hrs how would do you allocate them under the different crops? Bananas Coffee Annuals Land preparation Planting Weeding Harvesting (where applicable) Total (hrs) 10 10 10

9) When do you usually begin preparing your fields for planting? (tick what applies) Bananas Coffee Annuals

10) When do you? (Please put the right number that corresponds to the response below) Bananas Coffee Annuals Plant Weed Harvest

161 11) What was the type of labor used during 1st season of 2003? Please indicate proportions (%) Bananas Coffee Maize Beans 1. Family labor 2. Hired labor 3. Other (specify)

12) For what operations did you hire labor? ( tick all that applies) Bananas Coffee Maize Beans 1. Plowing 2. Planting 3. Weeding 4. Harvesting 5. Others (specify)

13. If you were to hire labor, how much would you be willing to pay for the following activities per acre? Plowing (Cost (shs)/acre Planting Weeding Harvesting Banana Coffee Annuals Other

14. Do you experience labor shortages? 1) Yes 2) No

15. If yes, during what times in the cropping season do you experience labor shortages? (tick all that is applicable). Bananas Coffee Annuals 1) Land preparations 2) Planting 3) Weeding 4) Harvesting 5) Transporting/storing

162 Use of recommended agronomic practices.

16. Do you carry out the following activities to increase crop production? Tick what applies? Bananas Coffee Annuals 1. timely field operations 2. control soil erosion 3. control pests and diseases 4. mulching 5. planting improved seeds 6. crop rotation 7. fallowing 8. use of commercial fertilizers 9. use of compost 10. use organic manure 11. crop thinning 12. Pruning 13. others

17. What constraints do you encounter in implementing the practices in no. 16?(circle what applies) 1) Lack of labor 2) Lack of funds 3) Lack of agricultural guidance /knowledge 4) Others (specify)

18. If you applied commercial fertilizers (in no. 16(8)), what was the type applied? ……………………………………………………………………………………………… ……………………………………………………………………………………………… ……………………………………………………………………………………………… 19. If yes, cost incurred per acre during first season of 2003 Bananas Coffee Annuals

Cost/k Kg Total Cost Kg Total Cost Kg Total g used (shs) /kg used (shs) /kg used (shs) Commercial fertilizers Farmyard manure Mulch Compost

20. How do you rate the quality of soil on your major agricultural land with regard to fertility? 1) Very good (2) good (3) fair (4) poor

163 21. Is soil erosion a problem on your holding? 1) Yes 2) No

22. What practices do you use to control soil erosion? 1) bunds 2) grass strips 3) contours 4) ditches 5)others (specify)

23. What practices do you use to conserve water in the soil? 1) mulching 2) retention ditches 3) other (specify)

24. Estimation of Yield levels 1st season 2003 Bananas (Bunches) Coffee (bags) Annuals Acreage Total yield Quantity destroyed Estimated value of net harvest Yield per acre

Determination of Costs and Returns

25. What was the cost of seeds/planting materials during the 1st season of 2003 for the various crops? Crop Unit Cost Quantity bought/acre Total cost Bananas Coffee Annuals

26. What costs did you incur while controlling pests during the first season of 2003? Bananas Coffee Annual crops Type of pesticide Quantity used Estimated cost

27. What is the estimated cost of herbicide used during first season of 2003 Bananas Coffee Annuals 1. Type of herbicide 2.Quantity used 3. Estimated cost 28. Do you incur any post harvest storage costs? 1) Yes 2) No (if no, go to No. 30) 164 29. If yes, indicate the amount? Bananas Coffee Annuals 1. Storage costs 2. Pesticide

30. If you incurred machinery operational costs during the first season of 2003 Please indicate below.

Type of Machine Associated crop Total Cost

Access to agricultural information. (circle what applies)

31. Have you ever attended a field day? 1) Yes……… ..2) No…….…

32. Have you ever attended a demonstration trial? 1) Yes…………2) No………

33. Have you ever attended a farming training course? 1) Yes…………2) No………

34. Do you have a radio? 1) Yes …………2)………… 35. If yes, do you listen to any agricultural education programs? 1) Yes ………. 2) No……. . 36. Are you a member of the extension contact group 1) Yes ……………. 2) No

37. If you have questions about your farm operations, who do you ask? 1. Extension agent………………………………………….. 2. Research team……………………………………………. 3. Cooperative official………………………………………. 4. Contact/model farmer……………………………………… 5. Neighbour…………………………………………………. 6. NGO………………………………………………………. 7. Other (specify)……………………………………………..

165 38a . To be answered by the interviewer

Wealth category (relative to other house holds). Look at the house, general appearance of the compound etc) Circle the most appropriate. 1. Above average 2. Average 3. Below average

Social Demographic Information

38b . Age of respondent………………… 39. Marital status: 1) Single 2) Married 3) Widowed 4) Separated 5) divorced

40. No of years in school……... 41. Highest level of education 1) No education 4) Post secondary education. 2) Primary education 5) University 3) Secondary education

42. House hold composition: No. Adults …….. No .Children……….

43. Membership to agricultural groups: 1) Yes 2) No 44. If yes, name the agricultural group ______45. Experience in farming …………(yrs) 46. Do you have any non agricultural income generating activities? 1) Yes 2) No

47. If yes mention these activities------

Thank you

166