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UNIVERSITY OF GHANA 
COLLEGE OF BASIC AND APPLIED SCIENCES 
 
 
 
 
 
 
 
VULNERABILITY AND ADAPTATION OF SMALLHOLDER 
FARMERS TO CHANGES IN AGROECOSYSTEM SERVICES 
AND CLIMATE IN SEMI-ARID REGIONS: THE CASE OF 
NANDOM DISTRICT OF GHANA 
 
ISHMAEL LENTE 
 
 
 
 
 
INSTITUTE FOR ENVIRONMENT AND SANITATION STUDIES 
(IESS) 
 
 
 
 
 
JULY 2017 
  
University of Ghana  http://ugspace.ug.edu.gh
UNIVERSITY OF GHANA, LEGON 
COLLEGE OF BASIC AND APPLIED SCIENCES 
 
VULNERABILITY AND ADAPTATION OF SMALLHOLDER 
FARMERS TO CHANGES IN AGROECOSYSTEM SERVICES 
AND CLIMATE IN SEMI-ARID REGIONS: THE CASE OF 
NANDOM DISTRICT OF GHANA 
 
BY 
ISHMAEL LENTE 
(ID NO. 10328204) 
 
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF 
GHANA, LEGON IN PARTIAL FULFILMENT OF THE 
REQUIREMENT FOR THE AWARD OF PHD 
ENVIRONMENTAL SCIENCE DEGREE 
 
 
INSTITUTE FOR ENVIRONMENT AND SANITATION STUDIES 
(IESS) 
 
 
 
JULY 2017 
 
 
 
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DECLARATION 
 
I, Ishmael Lente, a student in the Environmental Science Programme, University of 
Ghana, Legon, hereby declare that this thesis is the result of my own research work 
carried out in Nandom District in the Upper West Region of Ghana. The analysis of all 
environmental samples was done at Ghana Atomic Energy Commission (GAEC), 
Department of Chemistry Laboratory and Ecological Laboratory of the University of 
Ghana under the watch of the Chief Technologist, Mr. Nash Owusu Bentil and Mr. 
Emmanuel Ansah, respectively. Prof. Christopher Gordon, Dr. Opoku Pabi and Dr. 
Daniel Nukpezah supervised the study. 
 
Ishmael Lente, Doctoral Candidate 
……………………………………….  Date…………........................... 
3. Professor Christopher Gordon, Principal Supervisor 
………………………………………  Date…………………………... 
4. Dr. Opoku Pabi, Supervisor 
………………………………………  Date………………………… 
5. Dr. Daniel Nukpezah, Supervisor 
………………………………………  Date………………………… 
  
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ABSTRACT 
Significant proportion of people in semi-arid regions depend on climate variables and 
agroecosystem services to sustain their livelihoods. These livelihoods are threatened by 
the deterioration of agroecosystem services and climate variability. Recently, many 
communities exhibit low levels of adaptive capacity due to maladaptation and the lack 
of understanding of the patterns of vulnerabilities. This study examined the scope and 
nature of vulnerability and adaptation strategies of smallholder households to recent 
changes in agroecosystem services and climate in semi-arid Upper West Region of 
Ghana, specifically in the Nandom District.  
To achieve this goal, the study employed a participatory rural research design to collect 
and analyze the perceptions of smallholder farmers on vulnerability and adaptation 
options to the changes in agroecosystems and climate in the study area. Mixed methods 
involving qualitative and quantitative techniques were used to collect and analyze these 
data. The field work was undertaken between January 2016 and March, 2017 in four 
communities, namely, Billengangn, Gengenkpe, Naapaal and Ketuo using a 
questionnaire made up of semi-structured interviews with 194 households, focus group 
discussions with 125 farmers and key informant interviews with 10 relevant 
stakeholders drawn from government and non-government institutions. Soil and water 
samples were collected from selected farms, the Black Volta River, and selected 
boreholes and analyzed to assess the contribution of soil and water quality to livelihood 
strategies. Rainfall and temperature data spanning January 1984 to December 2014 
were collected from the Ghana Meteorological Agency (GMet) to investigate the trends 
and variability. The livelihood vulnerability index (LVI) was employed to investigate 
the level of vulnerability of households in the study communities. A regression analysis 
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was performed to investigate the determinants of adaptation strategies of smallholder 
farmers. 
The results of soil tests indicated that the soil was generally suitable for growth of crops 
in the study area. The levels of soil parameters ranged from marginal to adequate for 
crop production. The microbial analysis of the river water samples indicated high 
counts of total coliform and faecal coliforms. However, the results of the analysis of 
physico-chemical and trace metal parameters of river water samples with the exception 
of turbidity were within the TWQRs guidelines relating to domestic water use. 
The results revealed that rainfall over the period showed much variability and did not 
show any visible trend with an average annual total rainfall of 981.2 mm. The analysis 
depicted that both annual mean minimum and maximum temperatures were increasing 
and the area was becoming warmer during the day and the night. The annual mean 
maximum and minimum temperatures were 34.7 ℃ and 22.1 ℃. 
Findings further showed that thirteen (13) key agroecosystem services underpinned the 
agricultural livelihoods of smallholder farmers in semi-arid Ghana. These included 
bushmeat, freshwater, fruits, soil fertility, fuelwood, fish, mushroom, non-timber forest 
products (NTFP), cropland, vegetation, leaves, honey, and animal products. These were 
perceived to have changed over the past 5 years, driven mainly by low rainfall, bush 
burning, and tree cutting. Access to climate information, acceptance of the current 
climate and agroecosystem services change and availability of agroecosystem services 
were significant determinants of adaptation strategies in the study area. The study has 
identified a number of issues which can help to improve the adaptability of smallholder 
farmers to changes in agroecosystem services and. climate in this semi-arid region. 
  
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DEDICATION 
 
This thesis is dedicated to my late Father, Mr. William Kofi Mabre, my Sister, Mrs 
Edith Uncle-Sam, and mother, Mad. Janet Adwoa Fordjour. Also to my wife, Mrs. 
Linda Delali Kpogli Lente and children, Mishael Nana Kofi Lente and Joel 
Anyorbukorni Lente, as well as Mr. Seth A. Larmie and Mr. Emmanuel Acquah. 
 
  
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ACKNOWLEDGEMENTS 
In carrying out this thesis work, many people have assisted me in diverse ways and I 
owe them a lot of gratitude and appreciation. I wish therefore to express my warmest 
gratitude to my supervisors; Prof. Christopher Gordon, Dr. Opoku Pabi, and Dr. Daniel 
Nukpezah for their guidance and useful suggestions during every stage of this study. 
This work was supported under the PhD Students Research Support Grant by the 
Adaptation at Scale in Semi-Arid Regions (ASSAR) project. The five-year ASSAR 
project is funded by International Development Research Centre (IDRC) and 
Department for International Development (DfID) through the University of Ghana, 
Legon. I wish to express special thanks to the above Institutions for the support. 
I must sincerely thank the Institute for Environment and Sanitation Studies (IESS) of 
the University of Ghana, Legon for considering me to undertake this study and for the 
support.  
Special thanks must go to Mr. Seth A. Larmie, the CEO of SAL Consult Limited for 
his support and encouragement as well as Mr Emmanuel Kofi Acquah, Nana Yaw Otu 
Ansah and all staff of SAL Consult Limited. 
In addition, I thank Mrs. Linda Delali Kpogli Lente, my wife and my Sister Mrs. Edith 
Uncle-Sam. My gratitude goes to Mr. Amin Sulemana and Healther Nana Ama for their 
support. To all others who contributed in one way or the other to make this work a 
reality. I am most grateful and God richly bless you. 
GLORY BE TO GOD 
  
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Table of Contents 
DECLARATION ........................................................................................................... ii 
ABSTRACT .................................................................................................................. iii 
DEDICATION ............................................................................................................... v 
ACKNOWLEDGEMENTS .......................................................................................... vi 
Table of Contents ....................................................................................................... vii 
List of Figures ............................................................................................................... xi 
List of Tables .............................................................................................................. xiii 
List of Abbreviations .................................................................................................. xiv 
CHAPTER ONE: INTRODUCTION ........................................................................ 1 
1.1 Background ............................................................................................... 1 
1.2 Research Problem ..................................................................................... 5 
1.3 Research Questions ................................................................................... 8 
1.4 Objectives of the Study ............................................................................. 9 
1.5 Justification of the Study .......................................................................... 9 
1.6 Organization of the Thesis ...................................................................... 13 
CHAPTER TWO: REVIEW OF LITERATURE .................................................. 14 
2.1 Definition of Terms................................................................................. 14 
2.2 Climate Change and Climate Variability ................................................ 16 
2.3 Vulnerability to Climate Change ............................................................ 19 
2.4 Adaptation and Adaptive Capacity ......................................................... 22 
2.5 Agroecosystem Services Change ............................................................ 26 
2.5.1 Soil Quality and fertility ......................................................................... 28 
2.5.2 Quality of Water Resources .................................................................... 33 
2.5.3 Conclusions on Literature Review .......................................................... 35 
2.6 Conceptual framework ............................................................................ 37 
CHAPTER THREE: MATERIALS AND METHODS ......................................... 41 
3.1 Study Area .............................................................................................. 41 
3.1.1 Site Selection .......................................................................................... 41 
3.1.2 Socioeconomic Environment .................................................................. 43 
3.1.3 Climate and Vegetation........................................................................... 43 
3.1.4 Relief and Drainage ................................................................................ 44 
3.1.5 Soils 45 
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3.2 Methodology ........................................................................................... 46 
3.3 Reconnaissance Studies .......................................................................... 46 
3.4 Data Collection ....................................................................................... 47 
3.5 Participatory Rural Appraisal (PRA) ...................................................... 48 
3.5.1 Focus Group Discussion (FGD).............................................................. 48 
3.5.2 Key Informant Interviews (KII) .............................................................. 50 
3.5.3 Questionnaire .......................................................................................... 51 
3.6 Population and Sample Size.................................................................... 53 
3.7 Sampling ................................................................................................. 55 
3.7.1 Household Sampling and Data Collection .............................................. 55 
3.7.2 Soil Sampling .......................................................................................... 55 
3.7.3 Water Sampling ...................................................................................... 56 
3.7.4 Rainfall and Temperature Data Collection ............................................. 58 
3.8 Method of Analysis ................................................................................. 59 
3.8.1 Soil Fertility and Quality Analysis ......................................................... 59 
3.8.2 Water Quality Analysis ........................................................................... 60 
3.8.3 Observed Rainfall and Temperature Data Validation and Analysis ....... 61 
3.8.4 Perception of Climate Change and Weather Events by Smallholder 
Farmers ................................................................................................... 63 
3.8.5 Availability, Utilization and Changes in Agroecosystem Services in 
Study Area .............................................................................................. 63 
3.8.6 Vulnerability to Recent Agroecosystem and Climate Change ............... 64 
3.8.7 Assessing the Level of Vulnerability of Smallholder Farmers ............... 68 
3.8.8 Analysis of Perceptions of Smallholder Farmers on Adaptation 
Strategies ................................................................................................. 71 
3.8.10 Analysis of Knowledge and Information Sources on Adaptation 
Strategies ................................................................................................. 72 
3.9 Limitations of the Study.......................................................................... 72 
CHAPTER FOUR: RESULTS ................................................................................. 74 
4.1 Household Profile of Respondents.......................................................... 74 
4.1.1 Major Crops Cultivated........................................................................... 77 
4.1.2 Crop Utilization by Respondents ............................................................ 77 
4.1.3 Access to Credit ...................................................................................... 78 
4.2 Soil Fertility Status ................................................................................. 81 
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4.3 Water Quality .......................................................................................... 83 
4.3.1 Physico-Chemical Parameters in Water Samples ................................... 83 
4.3.2 Major Ions in River Water Samples from Study Communities .............. 84 
4.3.3 Trace Metals Results ............................................................................... 87 
4.3.4 Bacteriological Water Quality ................................................................ 87 
4.4 Rainfall and Temperature Trends in the Study Communities ................ 89 
4.4.1 Gauge and Observed Data ...................................................................... 89 
4.4.2 Perceptions of Rainfall and Temperature Trends in Study Communities .. 
   ...................................................................................................... 96 
4.5 Availability, Utilization and Changes in Agroecosystem Services ...... 104 
4.5.1 Agroecosystem Services in the Study Area .......................................... 104 
4.5.2 Perceptions of Utilization of Agroecosystem Services by Smallholder 
Farmers ................................................................................................. 107 
4.5.3 Perceptions of Changes in Agroecosystem Services ............................ 108 
4.5.4 How Long Smallholder Farmers Noticed Changes in Agroecosystem 
Services ................................................................................................. 109 
4.5.5 Perceived Drivers of Agroecosystem Services Change ........................ 110 
4.5.6 Perceptions on Trends in Changes in Agroecosystem Services ........... 113 
4.5.7 Perceptions on Impacts of Changes in Agroecosystem Services on 
Livelihood Strategies of Smallholder Farmers ..................................... 114 
4.6 Vulnerability to Changes in Agroecosystems and Climate .................. 116 
4.6.1 Exposures and Sensitivities of Respondents (Smallholder Farmers) ... 116 
4.6.2 Causes of Major Challenges of Smallholder Farmers .......................... 117 
4.6.3 Perceptions of Social Groups’ Vulnerability to Agroecosystems Change . 
   .................................................................................................... 118 
4.6.4 Dealing with Vulnerability to Agroecosystem Service Change ........... 120 
4.6.5 Perceptions of the Level of Satisfaction with Life by Respondents ..... 121 
4.6.6 Estimated Vulnerability of the Study Communities ............................. 124 
4.7 Adaptation Options of Smallholder Farmers ........................................ 128 
4.7.1 Perceptions of Farmers on Determinants of Adaptation Strategies ...... 130 
4.7.2 Determinants of Adaptation Strategies for Smallholder Farmers ......... 131 
4.8 Knowledge and Information Sources on Adaptation Options .............. 133 
4.8.1 Trustworthiness of Knowledge and Information Sources .................... 134 
4.8.2 Sufficiency of Knowledge and Information Sources ............................ 135 
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4.9 Managing Changes in Agroecosystem Service and Climate ................ 136 
4.9.1 Institutional Support in Managing Changes in Agroecosystem and 
Climate .................................................................................................. 136 
4.9.2 Types of Training on Agroecosystems Change Management .............. 137 
CHAPTER FIVE: DISCUSSION ........................................................................... 139 
5.1 Household Profile and Farm Characteristics ........................................ 139 
5.2 Soil Fertility Status ............................................................................... 143 
5.3 Water Quality ........................................................................................ 145 
5.4 Rainfall and Temperature Trends and Variability ................................ 149 
5.5 Perceived Trends in Availability, Utilization, and Agroecosystem 
Services Change .................................................................................... 152 
5.6 Level of Vulnerability of Smallholder Farmers .................................... 160 
5.7 Adaptation Strategies of Smallholder Farmers ..................................... 164 
5.8 Knowledge and Information Sources of Adaptation Strategies ............ 165 
CHAPTER SIX: CONCLUSION AND RECOMMENDATIONS ..................... 167 
2.4 Conclusion ............................................................................................ 167 
2.5 Recommendations ................................................................................. 170 
REFERENCE ........................................................................................................... 173 
APPENDICES .......................................................................................................... 185 
Appendix 1: Graphical Representation of Trends & Projections of Temperature 
& Rainfall for the Ecological Zones of Ghana Until 2080 ......... 186 
Appendix 2: Ethical Consent Letter ................................................................ 190 
Appendix 3: Focus Group Discussion Guide .................................................. 192 
Appendix 4: Key Informant Interview Guide .................................................. 194 
Appendix 5: Household Questionnaire ............................................................ 195 
Appendix 6: Pictures on Field Work-2016/2017 ............................................. 202 
Appendix 7: LVI Indices & Indicators ............................................................ 204 
Appendix 8: Soil and Water Quality Results ................................................... 207 
Appendix 9: Durbin-Watson Statistic Output .................................................. 213 
  
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List of Figures 
Figure 1: Conceptual Framework for the Assessment of Vulnerability and Adaptation 
Options. ....................................................................................................... 40 
Figure 2: A map of Nandom district showing the study communities. ...................... 42 
Figure 3: A sketch of random soil sampling of farmland. .......................................... 56 
Figure 4: Crop utilization by respondents. .................................................................. 78 
Figure 5: Access to credit by respondents. ................................................................. 78 
Figure 6: Amount of credit received by respondents (1USD:4.7GHS). ..................... 79 
Figure 7: Sources of credit for respondents. ............................................................... 80 
Figure 8: Reasons why respondents (%) do not access credit. ................................... 81 
Figure 9: Major ions in borehole water from the study communities. ........................ 86 
Figure 10: Annual rainfall data-1984-2014 (Source: GMet). ....................................... 89 
Figure 11: Annual rainfall anomaly plot for 1984-2014 (Source: GMet) .................... 90 
Figure 12: Seasonal rainfall cycle (1984-2014). ........................................................... 91 
Figure 13: Annual mean maximum temperature for Babile (1984-2014). ................... 92 
Figure 14: Annual maximum temperature anomaly for Babile (1984-2014). .............. 92 
Figure 15: Seasonal maximum temperature cycle (1984-2014). .................................. 93 
Figure 16: Annual minimum temperature for Babile (1984-2014). ............................. 94 
Figure 17: Annual minimum temperature anomaly for Babile (1984-2014). ............... 95 
Figure 18: Seasonal minimum temperature cycle (1984-2014). ................................... 95 
Figure 19: Perception of increased rainfall by respondents (Field work, 2016/2017). . 96 
Figure 20: Perception of change of start of planting season by respondents. ............... 97 
Figure 21: Perception of increased temperature by respondents. ................................. 97 
Figure 22: Perception of severe drought by respondents. ............................................. 98 
Figure 23: Perception of severe floods by respondents. ............................................... 99 
Figure 24: Perception of drought situation by respondents. ....................................... 101 
Figure 25: Perception of flood situation by respondents. ........................................... 101 
Figure 26: Available agroecosystem services in the study area (Source: Field Survey 
2016/2017). ................................................................................................ 106 
Figure 27: Extent of household’s access to agroecosystem services. ......................... 107 
Figure 28: Household’s perceptions of relative access to agroecosystem services. ... 108 
Figure 29: Perceptions of respondents on agroecosystem services change in the study 
area. ........................................................................................................... 109 
Figure 30: Perceived period of observed agroecosystem services change. ................ 109 
Figure 31: Drivers of agroecosystem services change identified by respondents in study 
area. ........................................................................................................... 110 
Figure 32: Perceived changing trends in specific agroecosystem services. ................ 113 
Figure 33: Extent of agroecosystem service impact on smallholder livelihood strategies.
 ................................................................................................................... 114 
Figure 34: Major exposures of respondents. ............................................................... 116 
Figure 35: Causes of major challenges/risks/shocks (sensitivities) of respondents. .. 118 
Figure 36: Perceived most vulnerable group to changes in agroecosystem services and 
climate. ...................................................................................................... 119 
Figure 37: Responsibility for dealing with changes in the agroecosystem services and 
climate. ...................................................................................................... 121 
Figure 38: Subjective well-being of respondents in the study area. ........................... 122 
Figure 39: Subjective well-being of the study communities. ..................................... 123 
Figure 40: Vulnerability spider diagram of major components of LVI. ..................... 124 
Figure 41: Estimated overall livelihood vulnerability indices of respondents. .......... 126 
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Figure 42: Vulnerability triangle diagram for IPCC contributing factor. ................... 127 
Figure 43: LVI-IPCC value for the study communities. ............................................. 128 
Figure 44: Adaptation options and strategies of respondents. .................................... 129 
Figure 45: Source of knowledge and climate information for respondents. ............... 133 
Figure 46: Trustworthiness of sources of knowledge and climate information. ......... 135 
Figure 47: Percentage of respondents on whether source of knowledge and climate 
information is sufficient. ........................................................................... 136 
Figure 48: Support institutions/organizations identified by respondents. .................. 137 
Figure 49: Type of training received by respondents. ................................................ 138 
 
  
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List of Tables 
Table 1: Description of pH value range in soils. ....................................................... 32 
Table 2: Number of Focus Group Discussions in Respective Study Communities. . 50 
Table 3: GPS coordinates for water and soil sampling location. ............................... 57 
Table 4: Contributing factors to LVI per IPCC approach, ........................................ 70 
Table 5: Household characteristics of respondents. .................................................. 74 
Table 6: Major crops cultivated by respondents. ....................................................... 77 
Table 7: Physical and chemical properties of farm soil samples. .............................. 82 
Table 8: Physico-chemical parameters of Black Volta River samples. ..................... 83 
Table 9: Major ion in the Black Volta River water samples. .................................... 84 
Table 10: Mean concentrations of trace metals in river water. ................................... 87 
Table 11: Levels of microbial contamination in River water. ..................................... 88 
Table 12: Annual rainfall for Babile and the Study Communities (1984-2014). ........ 89 
Table 13: Summary of the maximum temperature for Babile and Study Communities 
(1984-2014). ................................................................................................ 91 
Table 14: Summary of the minimum temperature for Babile and Study Communities 
(1984-2014). ................................................................................................ 94 
Table 15: Perception of climate change and variability by respondents. .................. 100 
Table 16: Perceived drivers of change in specific agroecosystem services. ............. 111 
Table 17: Factors that affect utilization of available adaptation strategies. .............. 131 
Table 18: Significant determinants of adaptation strategies. ..................................... 132 
 
 
  
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List of Abbreviations 
ASSAR   Adaptation at Scale in Semi-Arid Regions 
ASSAR-RDS Adaptation at Scale in Semi-Arid Regions-Regional Diagnostic Study 
CSIR   Council for Scientific and Industrial Research 
EPA   Environmental Protection Agency 
FAO   Food and Agricultural Organization 
FGD   Focus Group Discussion 
GMet   Ghana Meteorological Agency 
GPS   Global Positioning System 
GSS   Ghana Statistical Service 
KII    Key Informant Interviews 
ICARDA  International Centre for Agricultural Research in Dry Areas 
IPCC   Intergovernmental Panel on Climate Change 
LVI   Livelihood Vulnerability Index 
SSA   Sub-Saharan Africa 
SPSS   Statistical Package for Social Sciences 
TAMSAT Tropical Application of Meteorological Satellite 
THBC   Total Heterotrophic Bacteria Count 
TWQRs  Target Water Quality Ranges 
UNFCCC  United Nations Framework Convention on Climate Change 
USAID   United States Agency for International Development 
WB   World Bank 
WHO   World Health Organization 
WMO   World Meteorological Organization 
WRC   Water Resources Commission 
WRI   Water Research Institute 
 
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CHAPTER ONE: INTRODUCTION 
1.1 Background 
In semi-arid regions, communities and households face two key challenges related to 
the concept of “double exposure” (O’Brien & Leichenko, 2000; Leichenko & O’Brien, 
2008). The first is the direct impact of climate change and extreme weather events and 
the second is the indirect effect of agroecosystem services change (Boon & Ahenkan, 
2011; Boafo et al., 2014; Egyir et al., 2015; Boafo et al.,2016). 
According to Baird & Cann (2012), climate change refers to the continuous increases 
in average air temperatures and changing weather patterns including rainfall, humidity 
and wind resulting in drought for some areas and flooding for others. This may be 
attributed directly or indirectly to anthropogenic activities that increase the levels of 
carbon dioxide and other greenhouse gases in the atmosphere. 
Globally, climate change or short-term fluctuations in weather conditions, pose the 
greatest threat to the livelihood systems of smallholder farmers in dryland environments 
like semi-arid regions (IPCC, 2007a). These threats have become widespread through 
the continuous alteration of agroecosystem services in these areas (Boon & Ahenkan, 
2011; Egyir et al., 2015). In most cases, smallholder households in Sub-Saharan Africa 
bear the greatest adverse impacts of these changes (Rurinda et al., 2014; Adenle et al., 
2015; Adu-prah & Ko, 2015; Apurv et al., 2015; Nyantakyi-Frimpong & Bezner-Kerr, 
2015; Vignola et al., 2015) due to over-dependence on climate variables and 
agroecosystem services. Semi-arid regions or dry sub-humid bands form one of the sub-
classes of drylands in the world supporting hundreds of millions of farmers (Morten, 
2007). Many farmers in semi-arid regions of Africa and Asia depend heavily on the 
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climate and agroecosystem services for their livelihoods and well-being (Boon & 
Ahenkan, 2011; Burkhard et al., 2012; Smith & Sullivan, 2014; Egyir et al., 2015; Notte 
et al., 2017). As such these farmers are particularly vulnerable to climate-related 
impacts, especially changes in rainfall and temperature as well as agroecosystem 
services change. Agriculture within Africa and Asia is usually practised under 
smallholder farming systems, where the farmers’ farmland holdings are relatively 
limited in size, not more than 2 hectares (Rurinda et al., 2014; Vignola et al., 2015). 
Farmlands constitute important entitlements that determine the endowments and wealth 
of the smallholder farmers. The cropland is the major asset of the farmers and serves as 
the source of food and income (Rurinda et al., 2014). 
Agriculture is largely the backbone of economies within Sub-Saharan Africa (SSA), 
providing employment and food for the growing population. However, it is noted to be 
dependent on climate variables and agroecosystem services (Boon & Ahenkan, 2011) 
and therefore, any change in these variables and services negatively effects food 
production systems and economies within these countries and the world at large. The 
changes in agroecosystem services have resulted in many negative transformations for 
smallholder farmers in Sub-Saharan Africa (Codjoe & Owusu, 2011; Sinare et al., 
2016). Harvey et al. (2014) supported this claim and noted that the poor smallholder 
farmers are those most vulnerable to the adverse impacts of climate change and 
variability. 
Several studies carried out in semi-arid areas of northern Ghana indicated that 
smallholder farmers contribute immensely to food production (Morten, 2007; World 
Bank, 2007; Antwi-Agyei et al., 2011; Antwi et al., 2015; Nyantakyi-Frimpong & 
Bezner-Kerr, 2015). In addition, households lack social safety nets and possess low 
adaptive capacities to climate-related risks and shocks (Etwire et al., 2013; Antwi et al., 
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2015; ASSAR-RDS, 2015; Nyantakyi-Frimpong & Bezner-Kerr, 2015). Nyantakyi-
Frimpong & Bezner-Kerr (2015) further observe that farmers in northern Ghana are 
restricted in the benefit of agroecosystem services besides their financial, political and 
social constraints. In a particular district, community or household, individuals 
experience varied impacts of climate change and related events due to differences in 
access to livelihood assets in respect of biophysical, socioeconomic and institutional 
factors (Nyantakyi-Frimpong & Bezner-Kerr, 2015). Several studies have assessed the 
impacts of climate change on smallholder farmers and attributed their vulnerability 
largely to socioeconomic factors such as access to credit and markets without due 
consideration of the contribution of agroecosystem services to enhancing the livelihood 
strategies and well-being in semi-arid areas (Boon & Ahenkan, 2011; Arnall & Kothari, 
2015; Egyir et al., 2015; Nyantakyi-Frimpong & Bezner-Kerr, 2015). 
Despite these observations, most climate change literature have concentrated on 
scenario-based and sectoral impacts of climate change to the detriment of enhancing 
understanding of the main drivers that undermine adaptive capacities and exacerbate 
vulnerability of smallholder farmers at local communities (Nyantakyi-Frimpong & 
Bezner-Kerr, 2015). Many adaptation strategies are not widespread, hence farmers are 
unable to adopt most of the introduced strategies and available adaptation options 
(Conde et al., 2007; Codjoe & Owusu, 2011). Adaptation is the process of adjusting 
and responding to actual or potential effects of climate change and variability so as to 
ameliorate the risks and take advantage of opportunities emanating from these changes 
(IPCC, 2007b). Recent changes in agroecosystem services has further limited the ability 
of farmers to adapt to climate change (De Fraiture et al., 2007; Smith & Sullivan, 214; 
Egyir et al., 2015). For instance, the reported low yield of cereals in semi-arid areas 
may be due to lack of pollinators and not necessarily climate change and variability or 
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the absence of improved seeds (Smith & Sullivan, 2014). Usually, climate change 
studies place little emphasis on the consequences of agroecosystem services change on 
the vulnerability and well-being of smallholder farmers. 
In semi-arid areas of Ghana, smallholder agricultural production will be compromised 
because of the changes in agroecosystem services and climate change (Boafo et al., 
2014). Smallholder farmers depend solely upon agroecosystem services and rainfall for 
their farming activities (Antwi et al., 2015). The two main agroecosystem services that 
influence agricultural livelihoods in dryland areas are soil fertility and water quality 
(Boon & Ahenkan, 2011; Egyir et al., 2015). Studies suggest that the synergistic 
impacts of ongoing agroecosystem services change and climate change will pose 
several threats to smallholder farming households by decreasing their adaptive 
capacities and increasing exposure and sensitivity (Turner II et al, 2003; Luers, 2005; 
Smit & Wandel, 2006; Smith &Sullivan, 2014; Egyir et al., 2015). 
Disaster risk assessment conducted by the Ghana National Disaster and Mobilization 
Organization (NADMO) and the United Nations Development Programme (UNDP) in 
2015 classified the study District as drought prone with about thirty communities cited 
as being flood risk areas. Furthermore, due to the extent of climate variability and the 
underlying landscape vulnerability within different social groups, farmers in these areas 
are unable to predict the onset of the rainy season to assist in the planning of their 
farming activities and adopt appropriate adaptation strategies (Antwi-Agyei et al., 
2012). 
  
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1.2 Research Problem 
In semi-arid regions, significant populations depend on agroecosystem services for 
sustaining their livelihoods (smallholder agricultural livelihoods) (Boon & Ahenkan, 
2011; Smith & Sullivan, 2014). However, with the increasing adverse effects of climate 
change and variability on agroecosystem services (Swinton et al., 2007; Zhang et al., 
2007; Power, 2010; Rockstrom et al., 2010; IPCC, 2014; Egyir et al., 2015; Sinare et 
al., 2016), there will be negative livelihood transformations for smallholder agriculture 
and households in these areas (Boon & Ahenkan, 2011; Smith & Sullivan, 2014). 
Largely, agricultural livelihoods and economies of the world, and particularly 
developing countries, depend heavily on the reliable flow and interactions of multiple 
agroecosystem services (MEA, 2005; Smith & Sullivan, 2014). Agroecosystem 
services form the foundation of agricultural livelihoods of semi-arid farmers, who are 
mostly smallholders (i.e. hold small plots of not more than 2 hectares, use family labour, 
low technology and low capitalization) (World Bank, 2007; FAO, 2010; Boon & 
Ahenkan, 2011). 
Changes in agroecosystem services directly affect crop production and the extent of 
vulnerability and well-being of smallholder farming households in dryland regions 
(Conde et al., 2007; De Fraiture et al., 2007). Loss of these services greatly influences 
livelihood strategies and development (Costanza et al., 1998; MEA, 2005; Boon & 
Ahenkan, 2011; Smith & Sullivan, 2014; Egyir et al., 2015) under the changing climate. 
Notte et al. (2017) note that greater than two-thirds of the world’s agroecosystem 
services is being degraded and altered leading to the reduction of future human well-
being through scarcity of basic materials for a good life and low biological production. 
Consequently, good social relations for semi-arid farmers are compromised by a 
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situation described as environmental refugees where people, especially the youth (aged 
between 18 and 25 years) migrate to larger cities due to changes in agroecosystem 
services and lack of viable alternative livelihoods aside farming (Antwi-Agyei et al., 
2012; Etwire et al., 2013). 
In addition, there is a shift in the rain season towards later rainfall while rainfall 
projections also vary considerably for various regions including semi-arid (Ghana 
Climate Change Report, 2015; Kankam-Yeboah et al., 2013; ASSAR-RDS, 2015). 
Available agroecosystem services, including forest products and non-timber forest 
products, soil and surface water supply are being threatened by rising temperature, 
extreme rainfall variability, pollution from agricultural activities and climate-related 
extreme events (Rockstrom et al., 2010; Boon & Ahenkan, 2011; Egyir et al., 2015). 
Climate change and extreme weather events such as drought, floods, prolonged dry 
spells, inter-decadal, interannual rainfall variability have devastated agroecosystem 
services and food production systems (Antwi-Agyei et al., 2012; Antwi et al., 2015; 
ASSAR-RDS, 2015). Semi-arid areas have been the most affected areas where rain-fed 
agriculture is most sensitive and vulnerable to extreme weather events (ASSAR-RDS, 
2015) as is the case of semi-arid Ghana (Etwire et al., 2013; ASSAR-RDS, 2015). 
Sub-Saharan Africa (SSA) and Asia have experienced the worst of the extreme weather 
events; meanwhile, these regions are projected to witness further severe food shortages 
because of the changing climate (IPCC, 2007a; 2014). Consequently, smallholder 
farmers have over the years tried on their own to adapt to these global processes (i.e. 
agroecosystem services change and climate change) by the adoption of both indigenous 
(traditional) and introduced strategies (Maddison, 2006; Antwi-Agyei et al., 2012; 
Etwire et al., 2013) to improve their well-being. However, the current adaptation 
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options available to farmers are not widespread to improve their agricultural livelihoods 
and well-being and may be due to the growing evidence of agroecosystem services 
deterioration (ASSAR-RDS, 2015). 
In Ghana, and the Upper West Region, Antwi-Agyei et al. (2012), Etwire et al. (2013), 
Antwi et al. (2015), and Nyantakyi-Frimpong & Bezner-Kerr (2015) among others 
report that farmers in northern Ghana can no longer predict the onset of the rainy season, 
hence are unable to plan their farming activities. This situation has led to farmers 
extending farming onto flood prone lands and increasingly marginal lands thereby 
making them more vulnerable to extreme weather events. 
Furthermore, household-level primary data on the nature of vulnerability of households 
to changes in agroecosystem services and climate change and variability in semi-arid 
Ghana is inadequate. Hence, the adaptation strategies of these farmers and their 
households have not been effective in reducing the actual or potential negative impacts 
of climate change and improve the adaptive capacities of smallholder farmers. (Boafo 
et al., 2014; Nyantakyi-Frimpong & Bezner-Kerr, 2015). The level of vulnerability 
depends upon availability and utilization of agroecosystem services (soil fertility, 
freshwater, food, bushmeat, medicinal plants, and fuelwood) that underpin the 
livelihoods strategies of these farmers (Boon & Ahenkan, 2011; Smith & Sullivan, 
2014) as well as climate variables (Boafo et al., 2014; Boafo et al., 2016). 
In addition, studies show that responses and adaptation strategies to climate threats are 
not well understood and inadequate in addressing the vulnerabilities of smallholder 
farmers and their households (Etwire et al, 2013; ASSAR-RDS, 2015; Nyantakyi-
Frimpong & Bezner-Kerr, 2015). This is because most of these strategies are not 
developed from empirical household-level data from the local communities. Moreover, 
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in developing adaptation strategies for farmers, often little or no attention is paid to the 
role of agroecosystem services to increasing adaptive capacities at the local scale (Boon 
& Ahenkan, 2011; Smith & Sullivan, 2014; Nyantakyi-Frimpong & Bezner-Kerr, 
2015). 
1.3 Research Questions 
The main research question: ‘How are smallholder farmers’ vulnerabilities and 
adaptation strategies shifting due to changing agroecosystem services and climate in 
semi-arid areas in the Upper West Region of Ghana?” and is answered by addressing 
the following specific questions: 
1. How does soil and water quality contribute to livelihood strategies of smallholder 
farmers in the study communities? 
2. How do smallholder farmers perceive rainfall and temperature trends and variability 
in study communities and the effects on their livelihood strategies? 
3. How do smallholder farmers perceive trends in the availability, utilization and 
changes in agroecosystem services in the study communities? 
4. What is the level of vulnerability of smallholder farmers to recent agroecosystem 
services change and climate variability in the study communities? 
5. How do smallholder farmers perceive adaptation options and knowledge sources on 
adaptation available for managing agroecosystem services and climate change? 
  
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1.4 Objectives of the Study 
The overarching goal is to examine the scope and nature of vulnerability and adaptation 
strategies of smallholder farming households to changes in agroecosystem services and 
climate in semi-arid areas of the Upper West Region of Ghana. 
The specific objectives of the study are to: 
1. Assess the contribution of soil and water quality to livelihood strategies of the study 
communities. 
2. Examine rainfall and temperature trends and variability and how these are perceived 
by the study communities in their selection of livelihood strategies. 
3. Investigate smallholder farmer perceptions on trends in the availability, utilization 
and changes in agroecosystem services in study communities. 
4. Assess the level of vulnerability of smallholder farmers stemming from recent 
changes in agroecosystem services and climate variability. 
5. Investigate how smallholder farmers perceive adaptation strategies and knowledge 
sources on adaptation available for managing agroecosystem services and climate 
change. 
1.5 Justification of the Study 
Semi-arid regions are marked by rain-fed agriculture and smallholder livelihood 
systems that depend heavily on reliable flow of agroecosystem services. According to 
Rockstrom et al. (2003), about 80 percent of the agricultural land worldwide is under 
rain-fed agriculture with generally low crop yield levels and high on-farm water and 
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soil health losses. Meanwhile, climate change and variability continue to affect the 
availability, quality and use of agroecosystem services. 
The IPCC in its Fifth Assessment Report, AR5 (IPCC, 2014), highlights the following 
key messages for Africa’s changing climate: 
 Africa’s climate is already changing and the impacts are already being felt; 
 Further climate change is inevitable in the coming decades; 
 Climate change poses challenges to growth and development in Africa; 
 Adaptation will bring immediate benefits and reduce the impacts of climate change 
in Africa; 
 Adaptation is fundamentally about risk management; 
 Adaptation experience in Africa is growing; 
 Some low-carbon development options may be less costly in the long run and could 
offer new economic opportunities for Africa; 
 Africa stands to benefit from integrated climate adaptation approaches; and  
 International cooperation is vital to avert dangerous climate change and African 
governments can promote ambitious global action. 
According to the report (AR5), enhancing understanding of the constraints and 
bottlenecks to adaptation and transformational change are critical knowledge gaps. 
Many climate studies in northern Ghana point out limits of adaptation at the household 
and community levels, yet the dynamic interactions between agroecosystem services, 
socioeconomic, and socio-political barriers remain poorly understood (Antwi-Agyei et 
al., 2012; Etwire et al., 2013; Nyantakyi-Frimpong & Bezner-Kerr, 2015). Social 
vulnerability analysis approaches alone have not improved the understanding of the 
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dynamics of smallholder vulnerability in the context of biophysical and relational 
factors that drive vulnerability and contribute to bridging this knowledge gap. 
Over the past five decades, semi-arid areas, especially, in West Africa are exposed to 
prolonged dry spells, drought and devastating floods (Rockstrom et al., 2003, 2010; 
Agrawal, 2008; Rockstrom et al., 2010; Codjoe & Owusu, 2011; Antwi-Agyei et al., 
2012; Komba & Muchapondwa, 2012; IPCC, 2014). The combined impacts of 
agroecosystem services deterioration and climate change continue to lower adaptive 
capacities and resilience of several local communities and make them more vulnerable 
(IPCC, 2007a, 2014). 
The impacts of climate change are observed through the occurrences of extreme 
weather events (Phillipo, Bushesha, & Mvena, 2015) that have affected crop production. 
These incidences have led to changes in the already limited agroecosystem services, 
especially soil fertility and freshwater supply and quality that form the base of the 
livelihoods strategies households in local communities (Boon & Ahenkan, 2011; Smith 
& Sullivan, 2014; Egyir et al., 2015). 
To address these risks and stresses, information on the patterns of vulnerability and 
adaptation strategies of smallholder farming households to agroecosystem services and 
climate change need to be acquired and better understood for policy decision-making; 
This will enable effective integration of outcomes into local and national policies and 
plans. Greater integration of climate and agroecosystem service information would help 
to prioritize other resources, strengthen institutional capacities and support responsive 
strategies for effective implementation of adaptation frameworks (Phillipo, Bushesha, 
& Mvena, 2015) at the local level. 
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There have been several studies conducted on climate change and smallholder farmers 
in Ghana, particularly in the Upper West Region (Antwi-Agyei et al., 2011; Antwi et 
al., 2014; Antwi et al., 2015; Nyantakyi-Frimpong & Bezner-Kerr, 2015), which 
indicate that adaptation strategies whether indigenous or introduced are not robust and 
farmers are unable to adapt for improving crop production. Therefore, research is 
strongly needed to explore the patterns of vulnerability to agroecosystem service and 
climate changes to improve the adaptation strategies of smallholder farmers in semi-
arid areas of West Africa and Ghana. Etwire et al. (2013) in a study conducted in the 
three regions of northern Ghana to assess vulnerability of smallholder farmers to 
climate change, conclude that the Upper West Region was most sensitive and 
vulnerable to climatic risks as a result of the region’s lack of access to food and water 
resources. 
There is also the need to explore and understand the agroecosystem services that 
support crop production and underpin the livelihood strategies of smallholder farmers 
in local communities. The sharply increasing pressure and demand placed on 
agroecosystem services in Sub-Saharan Africa, particularly northern Ghana because of 
the ongoing climate change and variability requires adaptation planning across scales. 
In addition, research is required to providing understanding of the factors that hinder 
adoption of adaptation strategies among smallholder farmers within the agricultural 
landscapes in arid and semi-arid areas. Only a few studies analyzed vulnerability of 
smallholder farmers using the agroecosystem services concept (Smith & Sullivan, 
2014). Understanding the impacts arising from agroecosystem services change and the 
patterns of vulnerability is required to identify feasible and practical adaptation 
strategies for different groups of smallholder farmers within households in local 
communities. 
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This study enhances awareness and understanding of vulnerability, adaptation options, 
and adaptive capacity of local communities to the impact of recent changes in 
agroecosystem services and climate, whilst providing relevant household-level baseline 
data for developing and implementing appropriate and resilient interventions by policy-
makers and other stakeholder groups. 
1.6 Organization of the Thesis 
The thesis consists of six chapters. Chapter 1 is the introduction and entails background, 
problem statement, research questions, objectives and the justification of the study. 
Chapter 2 consists of definition of terms, the literature review and research conceptual 
framework as well as limitations of the study, followed by Chapter 3, which comprises 
the materials and methods. Chapter 4 presents the results obtained from the research, 
whilst Chapter 5 provides the discussions of the results based on the objectives of the 
study. This discusses the results on soil and water quality analysis, rainfall and 
temperature trends and variability in study communities, smallholder perceptions on 
trends in the availability, utilization and changes in agroecosystem services, analysis of 
the level of smallholder farmer vulnerability to agroecosystem services and climate 
variability, adaptation strategies of smallholder farmers and the knowledge sources on 
adaptation available for managing recent changes in agroecosystem and climate. 
Finally, Chapter 6 presents the conclusions and the recommendations of the research 
findings. 
  
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CHAPTER TWO: REVIEW OF LITERATURE 
2.1 Definition of Terms 
Smallholder Farmers 
Smallholder farmers include farmers who possess or own relatively smaller (not more 
than 2 hectares) farmlands or plots for the cultivation of mainly subsistence crops, 
depending largely on household or family hands or labour (Morton, 2007; World Bank, 
2007). Smallholder farming systems are those characterized by small farmlands, low 
technology, no mechanization and low investments. Smallholder farming describes 
rural producers mostly existing in developing countries and for whom the farm forms 
their principal or only source of income. 
Livelihood 
Livelihood is the totality of the means by which households make a living from their 
engagement and lifetime activities (Chambers & Conway, 1992; Antwi-Agyei et al., 
2012). It is usually the means of securing the basic necessities of life such as food, water, 
fuelwood, shelter and clothing. 
Shocks 
Shocks refer to the sudden events that impact on livelihood security (Cooper et al., 
2008). These events include, for example, a fire that destroy grazing pasture and crops, 
outbreak of diseases such as Cerebrospinal Meningitis (CSM), floods, theft of livestock 
and communal conflicts. 
 
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Stresses 
Stresses refer to long-term patterns of events that sabotage normal livelihood efforts 
and adversely affect the survival of each member of the household (IPCC, 2001; Adger, 
2006; Smith & Wendel, 2006). Stresses include a steady decline in quality and quantity 
of agroecosystem services, climate change, seasonal variability, frequent cholera 
incidences and political instability. 
Coping Strategies 
These are the short-term responses households develop to specific shocks to contain 
challenging conditions until things normalize (Yohe & Tol, 2002; Dinar et al., 2008; 
Campbell et al., 2011; Engle, 2011). For instance, active members of the households 
may cope with drought or flood by migrating to other cities to work to remit their 
families back home. 
Adaptive Strategies 
These are long-term and permanent changes in strategy or behaviour and practices in 
response to continuing stresses (Fraser, 2007). Households respond to agroecosystem 
services change or climate change by altering their cropping systems or adopting new 
innovations and practices. Farmers may respond to the changing climate by establishing 
irrigation systems for dry season farming. Adaptive strategies are generally enabled by 
availability and access to assets such as fertile soils and forest products and the extent 
of adaptive capacities (Antwi-Agyei et al., 2012). 
 
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2.2 Climate Change and Climate Variability 
Climate change is the significant and persistent change in the mean state of the climate 
system as attributed to direct or indirect anthropogenic activities that emit greenhouse 
gases at high concentrations which alter the composition of the global atmosphere 
observed over long period of time, usually thirty years and above (IPCC, 2007a). The 
continuous increase in average global air temperature and changing weather patterns 
have resulted in extreme events such as drought and flooding in several areas of the 
world (Baird & Cann, 2012). Anthropogenic activities that result in excessive 
production of greenhouse gases such as Carbon (IV) Oxide, Methane, Ozone, 
Chlorofluorocarbons, Nitrous Oxide, and Water vapour as well as natural processes 
cause climate change (IPCC, 2001). 
Climate variability is an associated term that represents the temporary fluctuations of 
the weather that can occur without interference from human activities (Kalame et al., 
2009; Hameso, 2015). Climate change and climate variability are considered as climate 
change in most climate change literature in reference to the IPCC’s definition. 
Globally, climate change and variability poses the greatest threat to the livelihood 
systems of smallholder farmers in dryland environments (; IPCC, 2007b; FAO, 2010). 
In Africa, climate change and variability affect agriculture through shifts in rainfall 
trends and extreme weather events such as drought, floods and prolonged dry spells 
(Frazer et al., 2011). These climate events are pushing land systems to cross biophysical 
thresholds causing long-term drop in agricultural productivity, particularly in semi-arid 
regions. Reeds (2008) argued that drylands in SSA are vulnerable to climate change 
due to great uncertainty of climate change, which do not permit prediction of expected 
changes. These authors opined that research must focus on improving livelihood 
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security in drylands, particularly semi-arid areas. Asia and SSA (IPCC, 2014) are 
predicted to be hit by severe food shortage because most of the populations live in 
drylands that experience extreme weather events. The impacts of climate change on 
livelihoods of the population in SSA are already known, but there is limited information 
on effective ways to respond and adapt to these changes (ASSAR-RDS, 2015).  
In West Africa, the impacts of climate change and variability are visible, especially in 
semi-arid areas that host variety of biophysical environments intertwined with highly 
diverse socioeconomic, demographic and land use characteristics (ASSAR-RDS, 2015). 
Evidence of climate change is seen in temperature that has increased by 1 ℃ on average 
across the region over the past 50 years. Rainfall is highly variable and subject to 
uncertainty in the direction and magnitude of changes across the West African Region. 
Evidence from Burkina Faso and Ghana (Kalame et al., 2009) indicate that climate 
change adversely impacts on livelihoods. These authors noted that drought and high 
interannual rainfall variability contribute strongly to underdevelopment in West Africa 
and result in the observed high levels of poverty. 
Ghana’s Third National Communication to UNFCC (Climate Change Report, 2015) 
confirmed that various sectors of Ghana’s economy were being affected by climate 
risks and events such as increasing temperature, unpredicted rainfall, and floods. These 
impacts are expected to pose threat to livelihoods related sectors such as agriculture, 
forests, health, and water resources. 
It has been predicted that mean temperatures may rise by 3.8 % (1.02 ℃) in 2040, and 
slightly increase by 5.6 % (1.5 ℃) in 2060, and then by 6.9 % (1.8 ℃) thereafter. 
Average monthly maximum temperature is predicted to rise by 2040 and 2060 by 1.2 ℃ 
and 2.1 ℃, respectively as presented in the graphs in Appendix 1. 
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The climate change report (2015) further observed from rain gauge data spanning a 
period of twenty (20) years across the ecological regions that rainfall had become more 
variable. For the rate of change, the southern zones recorded 333 %, whereas the mid 
zones had 112 %, and 431 % for northern parts. Decadal rainfall change was negative 
for the middle part at -2.8%, but positive for southern (13 %) and northern (3.3 %). The 
changes are more intense towards the north than the south for both temperature and 
rainfall. Based on the historical rainfall patterns (1980-2010), rainfall across the country 
has been projected to decrease by 2.9 % in the near future (2040). This will be followed 
by a slight increase in the mid future (2060) by 1.1 % and later decrease in the far future 
(2080) by 1.7%. This observation is a reflection of the uncertainty associated with 
rainfall (see Appendix 1 for Graphs). 
In Ghana, other studies point out that reduction in rainfall in the main agricultural zones 
has resulted in crop failure during both the minor and major seasons (Egyir et al., 2015; 
Owusu & Waylen, 2012). Climate change and variability have increased the uncertainty 
of sustainability of the main traditional socioeconomic activities (i.e. crop production) 
of households in semi-arid areas of northern Ghana (; Maddison, 2006; Antwi-Agyei et 
al., 2012). These impacts will further increase hunger and poverty because of pressure 
on agroecosystem services that underpin agriculture in these areas (Cooper et al, 2008; 
Kalame et al., 2009). Consequently, these changing patterns of rainfall and temperature 
affect crop production, water availability, and agroecosystem service processes and 
functioning (IPCC, 2007a) as a whole (Nazari, et al., 2015). 
In addition, Nyantakyi-Frimpong & Bezner-Kerr (2015) reported on analysis of rainfall 
and temperature variables in the Upper West Region that there was wide temporal 
variability in rainfall over a 30-year period. These authors noted that the region 
recorded very low rainfall levels between 1981 and 1988 with the worst case in 1986 
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at 523.7 mm. The analysis reveals that the years between 1995 and 1997 recorded a 
sharp increase in rainfall. According to Nyantakyi-Frimpong & Bezner-Kerr (2015), 
the mean annual rainfall for the period 1953 and 2012 was 1036 mm with the peak 
amount at 1500 mm in 1963. Their findings further revealed that the start of the farming 
season delayed and only started either in the middle of April or May instead of February 
or March between 1980 and 1990. They also observed that the mean annual temperature 
was 33.7 ℃ for the period 1982 to 2012. 
2.3 Vulnerability to Climate Change 
Vulnerability refers to “the degree to which a system, subsystem, or system component 
is likely to experience harm due to exposure to a hazard, either a perturbation or 
stress/stressor” (Turner II et al., 2003, p.8074). Vulnerability to climate change is the 
extent to which a system is likely to experience harm due to exposure to climatic risk, 
stress or hazard. It depends on the duration and magnitude of the climate variation to 
which the system is exposed, its sensitivity and adaptive capacity (IPCC, 2001). 
Exposure depends on the nature, duration and magnitude of the exposed unit to climate 
change whereas sensitivity is the degree to which the exposed unit is affected, either 
negatively or positively by climate-related stresses or risks (Brooks, 2003). On the other 
hand, adaptive capacity is the capability of a system or exposed unit to respond or adjust 
to climate change to moderate actual or potential effects emanating from exposure and 
to cope with the harm and take advantage of any opportunity therein (McCarthy et al., 
2001; Adger, 2006). 
Vulnerability involves different disciplines with several conceptual approaches in its 
analysis and these give rise to issues of interpretation. Vulnerability to climate change 
can be treated based on two interpretations as opined by O’Brien et al. (2004). First, 
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vulnerability as a starting point, where the state of the exposed unit is attributed to 
biophysical, socioeconomic, socio-political factors but amplified by climate change, 
and second, vulnerability as an end point, where there is inability of the system to cope 
with changes. The end point considers vulnerability as a fallout of climate change 
impacts without adaptation (Gbetibouo & Ringler, 2009). 
Though vulnerability is subject to several interpretations, a number of studies agree that 
it is the most essential concept of human condition and well-being since both the rich 
and the poor could be vulnerable to climate change (Ghana’s third national climate 
change report, 2015; Hameso, 2015). It is also important because climate risks are 
inevitable whilst some communities or sectors are more vulnerable than others, as are 
smallholder farmers. Vulnerability to climate change is found to be moderated by 
household characteristics such as family size, farm size, level of education, access to 
credit, social safety nets, crop yield, other capital resources of local communities 
(Antwi-Agyei et al., 2012; Etwire et al., 2013). In this context, all smallholder farmers 
within the same space may be exposed to climate change but those less endowed with 
assets would be more sensitive to the impacts and be more vulnerable (Rurinda et al., 
2014; Nyantakyi-Frimpong & Bezner-Kerr, 2015). For example, Etwire et al. (2013) 
reported from a study conducted in northern Ghana that vulnerability to climate change 
was linked to large family size, no education, inadequate access to information and 
resources such as land and credit. Besides, the climate change report (2015) reported 
medium to extreme climate change risks to drought, floods, migration, unpredictable 
rainfall, and increasing temperature among respondents within Guinea and Sudan 
Savannah zones. 
It is evident that no one conceptualization of vulnerability is best for community 
assessment due to differentiated impacts of climate change stemming from the diversity 
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of societies, resources, and economies (Hahn et al., 2009). Many vulnerability studies 
have been based on entitlements models that comprise concepts such as human ecology, 
expanded entitlements, and political economy (Bohle et al., 1994), natural hazard or 
biophysical models such as the risk-hazard and pressure-and-release (Blaikie, Cannon 
& Wisner, 1994; Turner et al., 2010; Fussel, 2007), and recently the integrated 
approaches (Eakin, 2006). 
Bohle et al. (1994) opined that climate change will directly affect human ecology and 
resource endowments, especially for those in the tropics and semi-arid areas and result 
in low crop yields, increased flood hazards, and further land degradation. Strategies for 
responding to vulnerability must therefore reduce exposure to potentially harmful 
perturbations, increase ability to cope with stresses, shocks or crises and strengthen 
processes of recovery based on a sound understanding of the dynamics and relations 
that prescribe vulnerability for specific groups in the present and the future (Luers et 
al., 2003; Luers, 2005; Smit & Wandel, 2006). 
Bohle et al. (1994) contends that individuals and groups are vulnerable to climatic 
stresses and unable to cope with food entitlement decline because of endowment with 
environmentally degraded resources located in respect of the human ecology crises. 
Bohle et al. (1994) also observed that besides livelihoods, groups, and individuals such 
as widowed, divorced or separated women, malnourished children, the infirm, persons 
with disabilities (PWDs), and the elderly are most vulnerable to climate change impacts. 
These individuals or groups may be vulnerable to food entitlement because of poor 
resource or asset base within the human ecological space of vulnerability. 
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2.4 Adaptation and Adaptive Capacity 
Adaptation is said to have originated from the natural sciences, specifically, 
evolutionary biology (Futuyama, 1979; Winterhalder, 1980; Kitano, 2002). It mainly 
refers to the development of physiological and morphological traits and characteristics 
by an organism to cope with changes in the environment in order to survive and 
reproduce. Individual organisms develop adaptive features to ensure that each one 
survives in its environment in the event of unfavourable conditions (what is known as 
survivor of the fittest). 
Anthropologist and cultural ecologist, Julian Steward, is reported to be one of the 
earliest to apply adaptation to human systems (Smit & Wandel, 2006). He used the term 
“cultural adaptation” to describe adjustment of “culture core” to the natural 
environment through subsistence activities. Brook (2003, p.8) describes adaptation as 
“adjustments in a system’s attribute and characteristics to improve its ability to cope 
with external stress”. Adaptation is a concept that has been with societies and cultures 
and have allowed humanity and other organisms to survive to date. However, climate 
change and deterioration of the biophysical environments presents a new paradigm shift 
in terms of context for adaptation and poses several threats to societies, especially 
smallholder farmers within semi-arid regions. Since the beginning of the 1990s, many 
research studies have defined adaptation in various ways (Smit & Wandel, 2006). 
Several authors of climate change literature have described the concept of adaptation 
of smallholder farming communities and households to climate stresses and extreme 
weather events in different ways. For instance, Smit et al. (2000, p.225) defined 
adaptation as “adjustments in ecological-socio-economic systems in response to actual 
or potential climatic stress, their effects or impacts”. Adaptation to climate change 
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refers to the adjustment and response in natural or human systems to actual or potential 
risk, shock or stress from changing climatic conditions and their effects in a manner 
that moderates harm or capitalize on beneficial opportunities from climate change’ 
(Etwire et al., 2013; IPCC 2007b). 
Smit & Wandel (2006) opined that adaptation is directly associated with vulnerability 
and adaptive capacity. They consider adaptation as manifestations of adaptive capacity 
and means of reducing exposure and sensitivity of a system to a stress, shock or risk. 
Adaptations of a system are specific illustrations of the underlying adaptive capacity. 
The ability of a community to adapt is driven by factors such as managerial capability, 
access to credit, technological, and information resources, infrastructure, institutional 
arrangements, political will and social networks among others. 
Adaptive capacity refers to the ability of an exposed unit to adjust to climate change to 
overcome the potential hazards and take advantage of the opportunities and cope with 
the risks, shocks and stresses (McCarthy et al., 2001). People possess enhanced 
adaptive capacity if they are able to modify their attributes in order to cope better with 
changes emanating from external factors.  
Adaptation can take place at the individual, household or community level or beyond 
to ameliorate anticipated negative effects of climate change. Autonomous adaptation 
can also take place at household or farm level where both government and non-
government institutions provide anticipatory or planned adaptation (Maddison, 2007). 
Anticipatory or reactive adaptation is the action, process or outcome in the household, 
community, group or sector in terms of timing relative to risk, whereas planned 
adaptation is the result of an intent of a policy based on research or information that 
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conditions have changed or are about to change which requires actions to curtail 
damage (Fankhauser et al., 1999; Smit et al., 2000; Smit & Wandel, 2006). 
Adaptation is a common phenomenon among smallholder farming communities in 
dryland regions of the world (Vogel, 2005; Antwi-Agyei et al., 2012). Many of these 
communities have adapted to the changing climate and its variability for so long and 
do not consider climate change as a major challenge in relation to other stresses 
(Nyantakyi-Frimpong & Bezner-Kerr, 2015). However, changes in agroecosystem 
services in semi-arid areas of West Africa and Ghana add a new dimension to the 
existing stress (Boon & Ahenkan, 2011; Egyir et al., 2015). Adaptation in this context 
is the adjustment by smallholder farmers to reduce the impacts from the changes in 
agroecosystem services, climate and weather-related events and to take advantage of 
opportunities and cope with the associated damage. Adaptation develops the adaptive 
capacity and reduce vulnerability to these changes (Parry et al., 2007). Adaptation has 
several benefits for smallholder farmers (IPCC, 2014) in terms of increased crop yield, 
improved household incomes, enhanced agroecosystem services provisioning, and 
reduced vulnerability to climatic risks (IFAD, 2013). additionally, adaptation is 
beneficial to policy design, planning and decision making at international, regional, 
national, and local levels (Huq & Reid, 2004; Moser & Ekstrom, 2010). 
Empirical studies on adaptation from semi-arid Ghana, particularly in the northern part 
of the country conclude that smallholder farmers have low adaptive capacities to deal 
with the changing climatic conditions such as drought and floods (Antwi-Agyei et al., 
2012; ASSAR-RDS, 2015). Historically, these farmers have adopted many coping 
strategies to overcome the harsh environments. Recently, however, the unpredictable 
climate pattern inhibits adoption of either planned or anticipatory adaptation strategies 
of communities (Etwire et al., 2013; ASSAR-RDS, 2015). There is limited 
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understanding of the patterns of their vulnerability and adaptation options which is 
attributed to both changing climatic conditions and recent changes in agroecosystem 
services (Egyir et al., 2015). 
Furthermore, Gbetibouo (2009) and Maddison (2006) observed that without adaptation, 
climate change and climate-related events will cripple the agricultural sector but with 
adaptation, vulnerability is reduced significantly. This is because, adaptation moderates 
potential damage and takes advantage of opportunities associated with these changes 
and enable smallholder farmers to cope with consequences (IPCC, 2014). The 
adaptation measures of smallholder farmers are mainly anticipatory or reactive rather 
than actual ones (Etwire et al.,2013).  
Studies have identified several adaptation strategies to climate change and related risks. 
However, most of these are ineffective and not being used by smallholder farmers due 
to some unknown reasons. Adaptation strategies for smallholder farmers in semi-arid 
regions have remained the traditional ones that include terraces, small dams (dugouts), 
bonding or ridging among others (Etwire et al., 2013; Rurinda et al, 2014; Nyantakyi-
Frimpong & Bezner-Kerr, 2015). These strategies are mainly landscape management 
strategies used to slow down surface runoff and prevent flood as well as mulching to 
conserve soil water and maintain moisture of cropland. 
Deressa et al. (2009) in a study of farmer’s choice of adaptation methods in Ethiopia 
cited adaptation strategies among smallholder farmers to include the use of different 
crop varieties, tree planting, soil conservation, early and late planting and irrigation. 
Their study further identified lack of information on adaptation methods and financial 
constraints as bottlenecks to successful adaptation to climate change and related events.  
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In Tanzania, Komba & Muchapondwa (2012) identified actual adaptation options of 
smallholder farmers to include use of short season crops, drought resistant crops, 
irrigation and tree planting. A doctoral thesis by Hameso (2015) identified adaptation 
strategies among smallholder farmers in Sidama, Ethiopia as consumption of wild food, 
migration, consumption of food stock, sale of firewood, fewer meals per day, sale of 
assets, sale of livestock, and engaging in daily work. 
In a study undertaken in northern Ghana, Etwire et al. (2013) reported that adaptation 
strategies adapted by smallholder farmers were mainly crops and livestock related 
strategies. Other indigenous practices were also soil related strategies that involved 
landscape modifications such as bonding, and creating drainage channels. The third 
group, called cultural practice related strategies included mulching and non-burning of 
crop residues. The literature review on climate change and variability suggest that many 
communities exhibit low levels of adaptive capacity due recent agroecosystem services 
deterioration and more variable climatic factors. 
2.5 Agroecosystem Services Change 
Ecosystem services are the natural benefits of the landscape flowing to human society 
and agricultural production (Millennium Ecosystem Assessment, 2005; Smith & 
Sullivan, 2014). Generally, ecosystem goods and services are referred to as ecosystem 
services. According to the MEA (2005), provisioning ecosystem services include, food, 
freshwater, fibre and fuelwood. These services are linked directly to the constituents of 
human well-being (Notte et al., 2017), thus human security, basic material for good life, 
health, good social relations and freedom of choice and action (MEA, 2005). 
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Agroecosystem services are highly managed agricultural ecosystem services that 
provide rural society with food, forage, bioenergy, and medicinal materials that are 
essential to human well-being (Swinton et al., 2007; Power, 2010; Notte et al., 2017). 
According to Dawson & Martin (2015), agroecosystem service refers to ecosystem 
services within semi-arid agricultural landscapes that underpin the livelihoods of local 
communities (Xu & Mage, 2001; Freitas, Schutz, & Oliveira, 2007; Zhang et al., 2007; 
Muller & Burkhard, 2012). However, these services have recently come under threat 
from climatic and non-climatic stresses (Boon & Ahenkan, 2011; Smith & Sullivan, 
2014; Egyir et al., 2015). About two-thirds of agroecosystem services is being degraded 
and modified posing a major threat to future human well-being (Swinton et al., 2007; 
Power, 2010; Notte et al., 2017). 
A larger percentage of the world’s poor who live in semi-arid regions depend on 
multiple agroecosystem services to sustain their livelihoods (WRI et al., 2005), where 
agriculture is the major livelihood activity (Rockstrom et al., 2003). Attempts to 
improve food production have in most cases compromised agroecosystem services that 
underpin the livelihoods of rural people. Changes in the supply and function of these 
services have exposed local communities to multiple stresses, risks and shocks (Smith 
& Sullivan, 2014). There is an intimate link between ecological sensitivities and social 
vulnerability across all hot spots, particularly in semi-arid regions, due to the over-
dependence of livelihoods on agroecosystem services (Rockstrom et al., 2003; MEA, 
2005; Smith & Sullivan, 2014). 
Studies show that climate-related events such as drought and floods are pushing semi-
arid natural capital assets to cross-biophysical thresholds causing long-term drop in 
agricultural productivity thereby exposing the people to further risks of low crop yield 
and poverty (Egyir et al., 2015). Thompson & Scoones (2009) in their climatic risk 
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studies carried out in dry sub-humid areas of Ghana, recommended further research to 
better understand how the changes in agroecosystem services and institutional drivers 
are affecting the well-being of smallholder farmers in semi-arid areas of Ghana. Frazer 
(2011) argued that household’s access to different resources such as soil fertility, water 
regulation and provision, and social relations at the local scale provides greater capacity 
to adapt to a challenge. Productivity can be maintained if households have access to 
agroecosystem services without any external support in times of climate stresses or 
shocks. 
2.5.1 Soil Quality and fertility 
Crop production depends upon soil for plant growth and yield. Three key factors 
determine plant nutrition, thus, amount of available nutrients in the soil, environmental 
factors that affect nutrient availability, and plant nutrient uptake (Estefan et al., 2013). 
Studies indicate that soil organic matter for instance will decrease under climate change 
and variability because the soil will be a major source of atmospheric carbon. Climate 
change and its related events such as increasing temperature and variable rainfall 
patterns pose serious threat to soil properties and processes, particularly in semi-arid 
areas (Brevik, 2013). According to Johansen et al. (2012), smallholder farmers in rain-
fed agriculture consider the soil as a very important resource for crop growth and food 
production. But as cropping intensifies there is a general decline in the physical, 
chemical and biological properties of soil which limits crop yield. This is primarily 
caused by declining soil organic matter from accelerated oxidation through tillage and 
exacerbated by the continuous burning of crop residues that limit return of land surface 
biomass to the soil. 
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In addition, the inability of smallholder farmers to adopt cropping systems based on 
minimum tillage, crop residue retention, and appropriate crop rotations have resulted in 
the decline of soil quality in their fields. Soil fertility represents the inherent capacity 
of the soil to supply plant nutrients in adequate amounts, forms, and in soluble 
proportions required for maximum plant growth. 
Crop production in semi-arid regions heavily depends on two key agroecosystem 
services; soil fertility and water quality (Rockstrom et al., 2003). Soil is a non-
renewable agroecosystem service that forms the basis of agricultural livelihoods and 
well-being of human beings and livestock (Estefan et al., 2013). In addition to food 
production, soil provides solid foundation for material well-being of smallholder 
farmers through the provision of security (secure resources access as fertile soil is 
regarded as an entitlement for high biological production), basic materials for good life 
(sufficient nutritious food and shelter due to high biological production), health (high 
nutrition level and  access to clean water due to high biological production and water 
provisioning), good social relations (ability to increase crop yield and avoid out-
migration and be able to help others), and freedom of choice and action (ability to 
achieve goals by improved crop yield without limitations) (MEA, 2005; Gough, 
McGregor & Camfield, 2007; McGregor, 2006). This is because fertile soil as an 
agroecosystem services generally links the above components of human well-being to 
sustain the livelihoods of semi-arid people (MEA, 2005; White, 2010). Material well-
being refers to what people do to achieve their goals (McGregor, 2006). Soil is a 
resource that offers farmers in semi-arid regions opportunity to achieve all five 
components of human well-being, improve adaptive capacity (MEA, 2005), and reduce 
vulnerability of smallholder farmers. 
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Mary & Majule (2009) found that low soil fertility is caused by both poor farming 
practices and extreme climate and weather-related events including drought, erratic 
rainfall, prolonged dry spells and high temperatures. Low rainfall contributes to loss of 
soil water, whereas increasing temperatures lead to rapid decomposition of organic 
matter that hold the essential elements required by crops (Antwi-Agyei et al., 2012). 
Soils vary greatly throughout the world and have inherent weaknesses, primary 
deficiencies in nutrients that are essential to growing crops. Even when adequately 
supplied in the early stages of land cultivation, the nutrient-supplying capacity 
invariably diminishes with time. The mineral nutrients essential for plant growth are 
the primary macronutrients; carbon (C), hydrogen (H), oxygen (O), nitrogen (N), 
phosphorus (P), potassium (K) which are required by crops in larger amounts and 
therefore are most deficient in most farm soils. The secondary macronutrients like 
calcium (Ca), sulphur (S), magnesium (Mg) as well as the micronutrients such as boron 
(B), chlorine (Cl), manganese (Mn), iron (Fe), zinc (Zn), copper (Cu) and molybdenum 
(Mo) are equally essential for optimum crop growth and development.  
However, most soils are deficient in nitrogen (N), which is transient in nature and plants 
require a lot of it. In many cases, phosphorus (P) is just critical because soil chemical 
reactions reduce the effectiveness of P fertilizers. The soils of the Sub-Saharan Africa 
are generally well supplied with potassium (K), and usually do not need fertilization, 
especially for low-yielding rain-fed crops (Estefan et al., 2013). 
Soil depletion and degradation is a major drawback to crop production in Ghana, 
especially, in semi-arid areas. Continuous cropping without nutrient replenishment 
through fertilizer application and proper soil management practices is common among 
smallholder farmers in dryland and semi-arid areas in Northern Ghana (Antwi-Agyei 
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et al., 2012). Sanchez (2002) reported the rate of soil nutrient loss in Sub-Saharan 
Africa as 22 kg, 2.5 kg and 15 kg per hectare of cropped land per year for N, P and K, 
respectively (Cited in Okebalama, 2014). There are reports that both macronutrients 
and micronutrients are deficient in most semi-arid areas (Pinto et al., 2012; Etwire et 
al., 2013; Okebalama, 2014; Boafo et al., 2016). 
The quality and fertility of an agricultural soil are indicated by physical and chemical 
parameters such as pH, organic matter, organic carbon, electrical conductivity (EC), 
nitrogen (N), phosphorus (P), potassium (K) and trace metals (Cr, Fe, Mn, Cu, Zn, Cd, 
Pb, Ni, Co, and As), particle size, and Bulk density. The concentration levels of each 
of these parameters have implications for soil water, aeration, soil aggregate formation, 
nutrient reserves, moisture retention, biological activity, and nutrient availability to 
plants ((FAO, 1980; Soltanpour, 1985; Matar et al., 1988; Johnson & Fixen, 1990; 
Martins & Lindsay, 1990; Estefan et al., 2013). Table 1 shows pH value ranges in 
typical soils.  
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Table 1: Description of pH value range in soils. 
pH Range Description Indications Associated Conditions 
Below 5.0 Strongly Soil is deficient in Ca and/or Mg Poor crop growth because 
Acidic and requires liming. of low cation exchange 
capacity, possible Al3+ 
toxicity and probable P 
deficiency 
5.0-6.5 Moderately to Soil is without lime and should be Satisfactory for most crops 
Slightly closely monitored. 
Acidic 
6.5-7.5 Neutral Ideal range for crop production Soil cation exchange 
capacity is near 100 % base 
saturation 
7.5-8.5 Moderately Free lime (CaCO3) exists in soil Usually excellent filtration 
Alkaline and percolation of water 
because of high Ca content 
on clays. Both P and 
micronutrients are less 
available 
Above 8.5 Strongly An indication of sodic soil Poor physical conditions;-
Alkaline infiltration and percolation 
of soil water is low and may 
result in root deterioration 
and organic matter 
dissolution 
(Source: Estefan et al., 2013) 
 
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2.5.2 Quality of Water Resources 
The physical impacts of climate change on biophysical systems such as water resources 
and livelihoods are visible in many parts of the world (IPCC, 2007b). The IPCC 
estimates that by 2020, between 75 and 250 million people in developing countries, 
most of them in Africa would lack access to water due to water stress (UNEP-PROVIA, 
2013). 
Africa has significant water resources that include large lakes such as the Volta, Chad, 
rivers, streams, ocean, and groundwater that when properly managed can support her 
socio-economic growth. However, climate change and variability, and population 
explosions as well as resource deterioration are expected to have adverse effects on 
water availability, accessibility, and demand in Africa and add to the cost of managing 
water resources (Tingju & Ringer, 2010). According to Onuoha (2010), more than 600 
lakes in Africa are declining rapidly owing to the combined impact of climate change 
and resource overuse. For instance, Lake Chad lost over 50 % of its water between 1973 
and 2002. 
Studies show that the impacts of climate change in Africa would be greatly felt through 
water resources stresses (IPCC, 2007b). Climate change will create uncertainties, alter 
hydrology, weather factors, and cause the occurrence of more extreme events. This will 
put further pressure on water resources and sensitive sectors such as agriculture, 
livestock, and fisheries that depend mainly on water. In Africa, most households depend 
on these sectors for their socio-economic development. 
Increases in flood and drought events coupled with reduction in mean precipitation 
would impact water resources, and adversely affect agriculture and human health. This 
could come from sewage contamination during flood events (IPCC, 2007b). In Sub-
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Saharan Africa, where evidence of long-term declines in water availability exist, 
climate change may make it more difficult to access improved water needed for healthy 
livelihoods and well-being. 
In Ghana, the Volta River Basin covers about 70 % of the surface of the country and is 
shared by five other West Africa countries which include; Mali, Burkina Faso, Cote 
d’Ivoire, Togo and Benin. The Black Volta River Basin that transverse the study area 
of this research, which Ghana shares with Burkina Faso and Cote d’Ivoire has a total 
drainage area of about 149,015 km2 with only 38,107 km2 lying in Ghana. The main 
tributaries of the Black Volta River Basin are Benchi, Chuko, Laboni, Gbalon, Pale, 
Kamba and Tain rivers (Kankam-Yeboah et al., 2004). 
The crucial roles of water in food production in Ghana cannot be overemphasized 
because about 80 % of agriculture is rain-fed and depend upon rainfall to meet 
evapotranspiration demand and maintain soil moisture (Antwi-Agyei et al., 2012). In 
many parts of semi-arid Ghana, where precipitation is already limited, water stress had 
further made crop production vulnerable to climate change impacts (Codjoe et al., 
2012). Evidence of floods, droughts, warmer temperatures, decline precipitation, 
bushfires, over use of fertilizers, and water pollution from changes in crop production 
in semi-arid and sub-humid regions of Ghana have been reported (Codjoe & Owusu, 
2011). In its Fourth Assessment Report, AR5, the IPCC noted that climate change 
impacts on surface and groundwater supplies may adversely affect livelihood strategies 
in dry sub-humid areas (IPCC, 2007b). Water provisioning service is essential for 
smallholder farmers’ well-being and crop production in semi-arid regions (ASSAR-
RDS, 2015). 
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Freshwater supply as an agroecosystem service within an agricultural landscape is 
greatly influenced by water quality. Water quality indicators of concern for domestic 
use include physical, chemical, ions, trace metals, and microbiological. These 
parameters are pH, EC, TDS, TSS, BOD, Alkalinity, Turbidity, Salinity, Colour, 
Nitrate, Potassium, Sodium, Calcium, Magnesium, Fluoride, Chloride, Sulphate, 
Chromium, Iron, Manganese, Copper, Zinc, Lead, Nickel, Cadmium, and Arsenic. The 
WHO (2011) established guideline values for some physical and chemicals parameters 
in drinking water based on whether they are of health significance or not. These 
guidelines recommended that water should be aesthetically acceptable to consumers to 
avoid the usage of unsafe water. In this view, the appearance, taste, odour and other 
physical properties of drinking water must be acceptable to consumers at all times. 
The Water Resources Commission (WRC) of Ghana through the water research 
institute (WRI) of the Council for Scientific and Industrial Research (CSIR) established 
the raw water quality guidelines for the purposes of monitoring and ensuring the proper 
use of raw water. The Ghana raw water quality criteria and guidelines is called the 
Target Water Quality Ranges (TWQRs) and is referred to as “no effect range” (WRC, 
2003, p. 2). The volumes 1 and 4 (B) deals with the domestic water use and agricultural 
water use for irrigation, respectively. 
2.5.3 Conclusions on Literature Review 
The review of literature indicates that significant populations of people in semi-arid 
regions depend heavily on climate variables and agroecosystem services to sustain their 
livelihoods (IPCC, 2014; ASSAR-RDS, 2015; Nyantakyi-Frimpong & Bezner-Kerr, 
2015). It is evident that many communities, particularly in Sub-Saharan Africa exhibit 
low levels of adaptive capacity due to growing evidence of agroecosystem services 
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deterioration and increasing vulnerabilities of smallholder farmers. Climate change is 
expected to pose the greatest threat to populations in harsh environments where natural 
resources are overexploited. 
Climate projections in indicate that Ghana will experience warmer periods and by 2060, 
average temperature may rise form 1.0 ℃ to 3.0 ℃, whilst by 2090 from 1.5 ℃ to 
5.2 ℃ (Ghana Climate Change Report, 2015). The predicted percentage of warming 
will be highest in the northern regions in relation to the southern parts. Analysis of 
climate data indicate upsurge in temperature and more recorded warmer days and nights. 
It is also predicted that about sixty percent days may be warmer by 2060. Most 
projections indicate declining cold days and nights. 
Rainfall will continue to be uncertain and difficult to predict. Projections of mean 
annual rainfall from different models predict wide range of changes. About half of the 
models predict increases while the other half project decreases. The proportion of total 
annual rainfall that falls in heavy events tends towards an increase in the ensemble 
projections. Projected changes in 1and 5-day rainfall maxima trend towards increases, 
but projection ranges between both increase and decrease in all seasons. 
According to the Third Ghana Communication to the UNFCC, Climate Change Report 
(2105), agriculture is dominated by smallholder farmers who are sensitive to climate 
variables, particularly rainfall. Irrigation systems are non-existent for most of these 
farmers. 
In addition, Ghana will be adversely affected by climate change and variability through 
alteration of the agro-ecology and natural resources deterioration. The magnitude of 
climate change impact is determined by the extent of vulnerability and the level of 
adaptive capacity of smallholder farmers. Some adaptation efforts are in place to 
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safeguard the environment and human life, and improve agriculture. However, low 
adaptive capacities of households have led to maladaptation in most communities. 
Though Ghana is projected to be severely affected by climate change and variability, 
household-level case studies required to highlight the factors contributing to the level 
of vulnerability of livelihoods and ineffective adaptation are inadequate. This 
knowledge gap hinders understanding of the barriers and enablers of adaptation to 
climate change and variability in Ghana, particularly semi-arid areas. There is therefore 
the strong need for a specific study to explore the scope and nature of vulnerability and 
adaptations of smallholder farmers to recent changes in agroecosystem services and 
climate. This thesis employs an exploratory and descriptive research design through 
participatory rural appraisal methods to assess the level of vulnerability of households 
to changes in agroecosystem services and climate in semi-arid Ghana. Biophysical 
factors including rainfall, temperature, and soil and water quality as well as household 
primary data on socioeconomic and socio-political indicators were aggregated into 
vulnerability assessment of the study communities. 
2.6 Conceptual framework 
The aim of this study was to explore and understand the scope and nature of 
vulnerability and adaptive strategies of smallholder farmers to changing 
agroecosystems and climate in semi-arid areas of the Upper West Region of Ghana. In 
the study area, households contend with extreme natural resource depletion challenges, 
including poor soil fertility, pests, crop diseases, and lack of access to agricultural 
inputs. Added to these challenges are the effect of changes in agroecosystem and 
climate, manifesting in the form of prolonged droughts, floods, scarcity of fuelwood, 
water shortage among others (Armah, 2011; Boafo et al., 2014; Boafo et al., 2016). The 
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concepts and theories of vulnerability, adaptation, and agroecosystem services were 
employed to guide this research.  
The conceptual framework for the vulnerability analysis is adopted from Turner II et 
al. (2003). The framework explains the connections between the drivers of change and 
the vulnerability context in relation to responses and the extent of impact of key 
ecosystem services change and climate change on the agricultural livelihoods and well-
being of smallholder households in semi-arid areas of West Africa. The concept 
contends that vulnerability of the smallholder household arises mainly because of 
changes in biophysical and socioeconomic factors as well as socio-political 
(institutional) factors, internal and external to the smallholder farmer (Chambers, 1994; 
Turner II et al., 2003; Arnell, 2004; Eakin & Luers, 2006; Fussel, 2007, 2009;). The 
biophysical drivers are mostly natural and anthropogenic climate change and variability 
that include floods, droughts and fires on one hand and ecosystems change or changes 
in natural capital such as agroecosystem services on the other hand. The socioeconomic 
drivers most often consist of policy, demography, land tenure systems and economic 
(local markets) factors. The socio-political or institutional factors comprise governance 
structures and arrangements that affect the smallholder farmer livelihoods (Eakin & 
Luers, 2006; Fussel, 2007, 2009).  
The extent of impact on households from changes in agroecosystem services and 
climate in semi-arid areas is dependent on the vulnerability level of smallholder farmers, 
which is proportional to exposure level, sensitivity, and adaptive capacity as presented 
in the conceptual framework below (Figure 1). 
The level of exposure is dependent upon the frequency, intensity, and duration of the 
hazard or risk posed by a driver in this case agroecosystem services change, natural and 
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anthropogenic climate change and variability (IPCC, 2007a; Mbow et al., 2008). 
Sensitivity of smallholder farmers in dryland areas also depend on the cropping and 
livestock systems, socioeconomic, socio-political as well as human and ecological 
conditions (Luers, 2005). The third factor of vulnerability is the adaptive capacity, 
which assesses the traditional and introduced adaptation options, strategies and patterns 
of responses for improving the well-being of the smallholder farmer. To reduce 
vulnerability to agroecosystems and climate change and enhance adaptive capacity, 
smallholder farmers usually employ traditional or indigenous adaptation options 
(Etwire et al., 2013; Antwi et al., 2015; Nyantakyi-Frimpong & Bezner-Kerr, 2015). 
However, these traditional strategies have mainly been short-term solutions that have 
proven insufficient and ineffective for dealing with long-term hazards posed by the 
consequences of agroecosystem services and climate change in semi-arid areas 
(ASSAR-RDS, 2015).  
External institutions such as governments, non-government agencies, and research 
groups provide knowledge and information on agroecosystem management and climate 
change to smallholder farmers in semi-arid areas in northern Ghana. Furthermore, 
modern and scientific mechanisms for responding to agroecosystem services change 
and climate change have been introduced (Etwire et al., 2013; Antwi et al., 2015; 
Nyantakyi-Frimpong & Bezner-Kerr, 2015). The study (i) identifies key agroecosystem 
services, utilization, and changing trends; (ii) examines perceptions regarding rainfall 
and temperature variables and gauge data, (iii) measures the household and community 
vulnerability due to changes in agroecosystem services and climate variability, and (iv) 
investigates adaptation strategies available and knowledge and information sources of 
smallholder farming households in the local communities. 
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Figure 1: Conceptual Framework for the Assessment of Vulnerability and Adaptation 
Options. 
(Source: Modified from Tuner II et al., 2003)  
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CHAPTER THREE: MATERIALS AND METHODS 
3.1 Study Area 
3.1.1 Site Selection 
This research forms part of a larger study conducted by the Adaptation at Scale in Semi-
Arid Regions (ASSAR) Project in semi-arid areas of Africa and Asia. In West Africa, 
the project focused on the dry sub-humid band that extends from the Upper West 
Region of Northern Ghana through to Southern Mali called the Wa-Bobo-Sikasso 
transect. In Ghana, two main districts host the ASSAR project, thus, Lawra and 
Nandom districts both in the Upper West Region. 
This study was carried out in the Nandom district, which lies in the north western corner 
of the Upper West Region of Ghana between Longitudes 2°25 W and 2°45 W and 
Latitudes 10°20 N and 11°00 S. The Lambussie and Jirapa districts bound it to the East 
and South, respectively, and to the North and West by the Republic of Burkina Faso. 
The total area of the district is about 567.6 square kilometres and constitute about 3.1 % 
of the Region’s total land area. The district has 84 communities with 86 % of the 
inhabitants living in rural areas. The population density is about 89 per square 
kilometres (Ghana Statistical Service (GSS), 2014). It is the most densely populated 
district in the region (GSS, 2014). The selected communities for this study are 
Billengangn, Gengenkpe, Naapaal and Ketuo (Figure 2). 
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Figure 2: A map of Nandom district showing the study communities. 
 
The study communities were selected based on appropriate guidelines from experts and 
stakeholders. Discussions and interviews with experts from the EPA and the National 
Disaster and Mobilization Organization (NADMO) at the district during pre-study and 
reconnaissance visits in September, 2015 provided valuable information that was used 
to select these farming communities. The selection of a study community was based on 
NADMO’s classification of flood risk communities. According to drought and flood 
risk assessment conducted by NADMO and the UNDP (2015), the entire district is 
drought prone with about thirty communities considered as flood risk. A list of these 
communities was obtained from NADMO and the four communities were randomly 
selected. Billengangn, Gengenkpe, Naapaal, and Ketuo are about 12.55 km, 3.35 km, 
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3.95 km, and 2.87 km, respectively away from the Black Volta River. This River was 
cited as a major cause of the flood situation in the study area. 
3.1.2 Socioeconomic Environment 
The main livelihood activity in the selected communities is cereal crops production that 
is generally practised under rain-fed conditions. Livestock rearing in the study 
communities is an alternative livelihood activity. Agriculture is the major source of 
income for majority of smallholder farmers, which is limited to one main farming 
season due to a unimodal rainfall pattern from May/June to September/October 
(Dickson & Benneh, 1988; Antwi-Agyei et al., 2012). This situation results in out- 
migration of the youth to southern Ghana in search of greener pastures during the dry 
season. According to the 2010 population and housing census, 85.3 % of households 
are engaged in agriculture in the Nandom district, with 98.0 % involved in crop 
production. In terms of industry and commerce, the medium-term development 
programme reports that the main industries include flour milling, pito brewing, 
commercial food preparation, shea butter extraction, xylophone making, basket 
weaving and charcoal burning among others. The study area has three main markets 
located in Kuturu, Ko and Nandom. 
3.1.3 Climate and Vegetation 
The study area is tropical continental as is the case in semi-arid regions of Ghana. Thus, 
all twelve (12) months of the year have an average temperature of above 12 ℃ and 
situated inland than near the sea (GSS, 2014). Throughout the year, temperature is high 
with an average minimum of 22.1 ℃ at night and an average maximum of 34.8 ℃ 
during the day (i.e. GMet data, 1984-2014). The mean monthly temperature ranges 
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between 21 ℃ and 32 ℃. The highest monthly maximum temperature rises up to 
38.9 ℃ before the rainy season usually in May with lowest minimum temperature 
falling to about 16.9 ℃ in December when the Harmattan winds from the Sahara dry 
up the vegetation (i.e., GMet data, 1984-2014). The rainfall pattern of the study area is 
unimodal and restricts farming to only the rainy season. 
The study area lies within the Guinea Savannah vegetation belt which consist of grasses 
interspersed with scattered fire resistant trees such as the shea (Vitellaria paradoxa), 
dawadawa (Parkia biglobosa), and baobab (Adansonia digitata) trees (GSS, 2014). The 
trees have significant domestic uses such as fuelwood and charcoal, construction of 
houses, cattle kraals and fencing of gardens by smallholder households. The shorter 
shrubs and grasses in the vegetation provide fodder for livestock.  
The only cash crop trees found in the area are shea (Vitellaria paradoxa), “dawadawa” 
(Parkia biglobosa), mango (Mangnifera indica), and cashew (Anacardium occidentale). 
According to the District Medium Term Development Plan (DMTDP, 2015), the shea 
tree is one great economic asset district. These trees are however, threatened by 
degrading human activities such as bush burning, indiscriminate tree felling for fuel 
wood, charcoal production, poor agronomic and animal husbandry practices. This 
situation has been exacerbated by climate-related stresses and extreme weather events 
like floods and prolonged dry spells. These events decreased vegetation cover and 
increased soil water evaporation, soil erosion, and depletion of soil fertility (GSS, 2014). 
3.1.4 Relief and Drainage 
The topography of the district is gently undulating, located at about 180 meters above 
sea level with a few isolated hills (GSS, 2014). The relative plain topography is suitable 
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for road construction, distribution of utility lines, general construction work and crop 
production.  
The water table of the area is relatively low, resulting in limited surface water and 
groundwater resources during the dry season (GSS, 2014). The surface water bodies 
include a few interconnected streams that enter the Black Volta River, which forms the 
western boundary of the district. The Black Volta River presents an opportunity for the 
development of irrigation systems for dry season farming and aquaculture. 
The rivers and streams are perennial in nature and most become just intermittent pools 
in the dry season while others completely dry up. However, there are a number of dams 
and dugouts, which provide water for irrigation, domestic use, construction and 
watering of livestock. The interconnected water bodies in the district facilitate storm 
water drainage, thus, making the district relatively less floodable, except in few low-
lying areas and areas in close proximity to the Black Volta River. Though the water 
bodies in the district have potential for supporting agricultural activities in the dry 
season, they equally serve as constraints in road constructions and limit access to 
communities during the rainy season. 
3.1.5 Soils 
The main soil types in the district are sandstone, gravel, mudstone, alluvium, granite 
and shale that have weathered into different soil grades (GSS, 2014). Due to seasonal 
erosion, soil types emanating from this phenomenon are sand, clay and laterite ochrosol. 
The main textural classes of soil types include loamy-sand and sandy-loam (field work, 
2016). The soil pH (1:5) of the study area ranges between 5.6 and 8.0 (field work, 2016). 
The soil types in the study area can be described as suitable for normal crop growth, 
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especially, of cereals and root tuber crops such as millet, maize, sorghum and yam with 
respect to the pH and provide an advantage for the construction of housing units. The 
soil types also respond positively to the application of organic manure and commercial 
fertilizers to give high crop yields (GSS, 2014). 
3.2 Methodology 
The participatory rural household appraisal (PRA) research survey design, otherwise 
known as participatory rural household appraisal (PRHA) technique (Chambers, 1994) 
was used for the study through the mixed method approach (qualitative and 
quantitative). The study collected both qualitative and quantitative data from primary 
and secondary sources. The PRHA techniques used include; household survey 
questionnaire with semi-structured face-to-face interviews, key informant interviews 
and focus group discussions (FGDs). Sampling of farm soils and water were also 
carried out to assess their contribution to livelihood strategies in terms of quality. The 
research was conducted between September 2015 and July 2017. 
3.3 Reconnaissance Studies 
The research was preceded by reconnaissance studies in September 2015 with 
community meetings and engagement, with chiefs and relevant stakeholders, in 
addition to secondary data from extensive literature search. The community 
engagement helped in identifying and mapping out the study communities within the 
Nandom district. The preliminary study also informed the selection of variables to be 
investigated and methods of data collection. 
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3.4 Data Collection 
Primary data from the households in the study communities constituted the core of this 
research. This involved the administration of three data collection instruments to 
achieve the research objectives. These included standardized household survey 
(questionnaire), focus group discussions (FGDs) and semi-structured interviews with 
key informants at the household, community and district levels. The household survey 
method provided the study with quantitative data with relatively less qualitative data 
whereas the last two other methods generated purely qualitative data. Each survey 
instrument was accompanied with a consent letter as attached in Appendix 2. The 
questionnaire items were vetted by the supervisors of the study to ensure its face 
validity, completeness, accuracy and uniformity (Lokesh, 2002). The questionnaire was 
pre-tested on selected households in the study communities to ensure reliability of the 
of the research instrument. Gender balance was taken into consideration. The outcome 
of the pilot study helped the main study in several ways. First, it provided prior 
knowledge of the problems to be encountered during the conduct of the main study that 
included explanation of terminologies like agroecosystem services, unwillingness to 
respond to the questionnaire. Second, this ensured that the questionnaires were suitable 
for the chosen sample. Finally, it also ensured that less time was spent on each 
questionnaire during the actual data collection (Jack & Norman, 2000). 
Secondary data sources for the study included information from the District Medium-
Term Development Plan (DMTDP, 2015), Gauge climate data from the Ghana 
Meteorological Agency, drought and flood reports from the NADMO, project reports, 
journals, textbooks, internet searches and general literature search. 
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3.5 Participatory Rural Appraisal (PRA) 
The participatory rural appraisal method involves a tool kit of interactive and innovative 
techniques (Chambers, 1994) that allows for the incorporation of new techniques to 
tackle new research problems that may arise due to its flexibility. The method is 
interdisciplinary, which makes it useful for explaining complex concepts like 
agroecosystems, agroecosystem services change, vulnerability, adaptation and climate 
change. Besides, participants are given opportunity to contribute and exchange ideas 
freely in an informal setting. The discussions are guided by a checklist, which could be 
modified to attain the required responses as opposed to predetermined questionnaire. It 
is also community based and informative as learning takes place through exchange of 
ideas with field responses. PRA is a field survey method most often targeted at local 
communities. The specific data collection techniques employed under the participatory 
rural appraisal were the focus group discussions and the key informant interviews. 
3.5.1 Focus Group Discussion (FGD) 
The focus group discussion (FGD) is a type of in-depth interview undertaken by a group, 
whose meetings present characteristics defined with respect to the proposal, size and 
composition and interview procedures (Freitas et al., 1998: Jenkins et al., 1998). The 
FGD is a technique that allows for spontaneous expression of ideas and facilitates the 
interaction of everybody (Jenkins et al., 1998; Ayivor, 2012). 
In this study, discussions centred on local knowledge and experiences on four (4) main 
research themes that bordered on, (i) impacts of rainfall and temperature trends and 
variability on smallholder farmers livelihood strategies, (ii) trends in availability, 
utilization and changes in ecosystem services over time, (iii) the scope and nature of 
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exposure and sensitivity (degree of vulnerability) of smallholder households stemming 
from changes in agroecosystem and climate and (iv) the adaptation options, knowledge 
and information sources on adaptation available for managing and responding to 
agroecosystem and climate change. 
During the discussions, issues regarding the scope and nature of challenges confronting 
smallholder households, adaptive capacities and responses were explored. The 
discussions centred on major challenges and opportunities of smallholder households, 
adaptive capacities and responses and their knowledge and information sources for 
dealing with the identified challenges stemming from agroecosystem and climate 
change as well as taking advantage of opportunities to improve upon their well-being 
in the local communities. Four (4) focus group discussions were held with two (2) male 
and two (2) female groups in Billengangn, Gengenkpe, and Naapaal whilst two (2) 
focus group discussion comprising a male and a female group each in Ketuo. The 
number of FGDs held in each community depended on the total number of discussants 
available at the time of the study. The FGDs were held in the study communities 
between 5th and 24 April, 2017. The discussions, which were led by the researcher with 
the support of a moderator, was facilitated by a checklist of questions (see Appendix 
3). The discussion guide was made up of four main themes with at least three items 
each. The FGD involved separate groups of male and female participants who were 
either household heads or nominated members of the household. The opinions of the 
various groups assisted the researcher to sufficiently formulate vulnerability indicators 
for the vulnerability analysis. The FGDs were carried out in the local language 
“Dagaare”. 
Fourteen (14) FGDs were held in four communities with 125 participants comprising 
48 percent males and 52 percent females. The ages of the participants ranged between 
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20 and 82 years. Each FGD consisted of between seven (7) and eleven (11) individuals. 
Table 2 shows the breakdown of the number of communities, focus group discussions 
held as well as the total number of participants. 
Table 2: Number of Focus Group Discussions in Respective Study Communities. 
Study Community Number of Focus Group Discussions Number of Discussants 
  Male Female Total 
Billengangn 4 (2 male and 2 female groups each) 20 22 42 
Gengenkpe 4 (2 male and 2 female groups each) 20 18 38 
Naapaal 4 (2 male and 2 female groups each) 9 14 23 
Ketuo 2 (1 male and 1 female group each) 11 11 22 
Total 14 60 65 125 
Source: Field work 2016/17 
 
The FGDs were gender based to reflect the unique opinions of both male and female 
respondents. This is especially necessary since women in the local communities of the 
study area are traditionally restricted to expressing themselves freely in the presence of 
their male (husbands) counterparts. Youth participation was non-existent partly because 
of migration and for the purposes of receiving education in and out of the district. The 
FGDs were transcribed and analyzed qualitatively using descriptive methods in the 
context of major issues under investigation. The individual groups were used as units 
of measurement and analysis. 
3.5.2 Key Informant Interviews (KII) 
This technique involved interviewing individuals who were likely to provide expert 
views, information, ideas, and insights on a particular subject or topic. The key 
informant interviews obtained qualitative descriptions or experiences of knowledgeable 
individuals about the research questions (Kvale, 1996; Ayivor, 2012). In this study, key 
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informant interviews were undertaken involving knowledgeable and opinion leaders of 
the communities and heads of government and non-government organizations within 
the district. 
In all, ten (10) interviews were held with the relevant stakeholders drawn from 
government and non-government organizations as well as community opinion leaders 
between 5th and 24 April, 2017. The stakeholders consulted include NADMO, Nandom 
Deanery Integrated Rural Development Programme (NANDIRDEP), Department of 
Agriculture of the Ministry of Food and Agriculture (MOFA), Environmental 
Protection Agency (EPA), Nandom District Assembly, Community Development 
Department, Business Advisory Centre/Rural Enterprises Programme, Naapaal 
Community Opinion Leaders, Billengangn Traditional Authority and Naapaal 
Traditional Authority. A checklist of questions was used to guide the interview process 
(see Appendix 4). 
3.5.3 Questionnaire 
The questionnaire was also used to investigate the research problem (Kendall, 2008; 
Lai & Waltman, 2008; Harris et al., 2010) to provide evidence based household-level 
data. The questionnaire generated data generated using both closed-ended and open-
ended questions including the Likert scale (Harris et al., 2010). 
The questionnaire was found to be appropriate for the smallholder farming households 
because it allowed them to provide their own views and opinions. The questionnaire 
solicited information on the research questions of this study. In the closed-ended 
questions, predetermined answers expected from the respondents were provided and 
the respondents ticked in a box to make a choice whereas with the open-ended type, 
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spaces were provided for original answers or any unexpected answers from the 
respondents. The questionnaire was divided into six (6) sections lettered A to F (see 
Appendix 5). The first section (A) collected information on the socio-demographic 
background of respondents. Such information included gender, age group, level of 
education, occupation, etc. The section had ten (10) items in it.  
The second section (B) collected information on farm characteristics of the respondents 
and comprised seven (7) items. The third section (C) solicited information on the 
perception of smallholder farmers on historical and recent rainfall and temperature data 
and related events in the local communities and had one (1) item of seventeen (17) 
multiple options. The fourth section (D) collected data on the perception of smallholder 
farmers on trends in the availability, utilization and changes in ecosystem services over 
time in the study area and consisted of seven (7) items. The fifth section (E) collected 
information on the scope and nature of challenges, exposure and sensitivity (degree of 
vulnerability) of smallholder households due to agroecosystems and climate change and 
consisted of nine (9) items. Finally, the sixth section (F) collected data on the perception 
of smallholder farmers on the adaptation options and knowledge sources on adaptation 
available for managing agroecosystem and climate change. This section comprised 
fourteen (14) items. 
In all, the questionnaire comprised forty-nine (49) items. In addition to the content of 
the questionnaire, a consent letter was attached at the front page that spelt out the main 
goal of the study and appeal to the respondents for their cooperation. The consent was 
attached to certify ethical requirement of the College of Basic and Applied Sciences of 
the University of Ghana, Legon and encourage the respondents to respond to the 
questionnaire on schedule.  
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Six (6) enumerators comprising five (5) males and one (1) female selected from each 
of the study communities assisted in the administration of the questionnaire to the 
respondents. The administration of the questionnaire was preceded by a day’s workshop 
organized to explain the items on the survey instruments to the enumerators. The 
researcher accompanied the enumerators during administration of the instruments, 
which offered the opportunity to explain terminologies that were not familiar to the 
enumerators. The questionnaire was administered in the study communities between 
14th March and 14th April, 2016. Appendix 6 shows the picture of some events during 
the field work. 
3.6 Population and Sample Size 
Population 
The target population for this study was smallholder farmer households drawn from the 
four communities in Nandom district of the Upper West Region of Ghana. The ages of 
the smallholder participants ranged from 15 years and older and involved those engaged 
in crop production (the major agricultural activity). Ideally, the study should have 
covered more communities in the district to enrich the validity of the results obtained. 
However, the detailed nature of the research instruments and methods, the statistical 
models and participatory techniques used curtailed any errors about validity of the 
research findings. 
This study employed the probability proportional to size (PPS) technique (Yansaneh, 
2005; Antwi et al., 2015) for the determination of the study population and sample size. 
According to the 2010 population and housing census (GSS, 2014) analytical report, 
about 85.3 percent of households in the Nandom district of the Upper West Region of 
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Ghana are engaged in agriculture and mainly crop production. Based on the above 
percentage, the study population was derived from the number of households in each 
of the selected communities involved in agriculture or crop production. The 
communities selected for the study were Billengangn, Gengenkpe, Naapaal and Ketuo 
with number of households as 135, 109, 141, and 57, respectively. The number of 
households engaged in agriculture, that is crop production from each of these 
communities was calculated as 85.3 percent of their respective numbers of households 
indicated above. The exact number of households involved in agriculture estimated was 
115, 93, 120 and 47 for Billengangn, Gengenkpe, Naapaal and Ketuo, respectively, 
making an estimated total study population of 375 smallholder farmer households 
engaged in agriculture (crop production). 
Sample Size 
The sample size used for the survey questionnaire for each of the study communities 
was estimated using equation (3.1) (Yamane, 1967; Antwi et al., 2015), at 95% 
confidence level with degree of variability equal to 0.5. 
𝑁
𝑛 = 2         (3.1), 1+𝑁(𝑒)
where n is the sample size, N is the total population size (total household size) for all 
the four study communities and e is the sampling margin of error, which in this study 
is equal to 0.05. From equation (3.1), 194 smallholder farmer households were 
randomly selected to constitute the sample size. The sample size of communities was 
then estimated as 60, 48, 62, and 24 for Billengangn, Gengenkpe, Naapaal and Ketuo, 
respectively, using the probability proportional to size procedure. 
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3.7 Sampling 
3.7.1 Household Sampling and Data Collection 
Simple random and purposive sampling techniques were employed to select the study 
communities from originally classified flood prone communities. The communities 
were selected in such a way that each had an equal chance of being chosen and each 
choice was independent of the other. The name of each of the flood risk communities 
was written on pieces of paper and four (4) were randomly selected for the study. 
3.7.2 Soil Sampling 
Soil samples were collected from two (2) selected farmlands (1 hectare each) at a depth 
of 0-20 cm from the study communities, on 20th July, 2016 between 7 am and 2 pm. A 
total of eight (8) composite soil samples were collected for analysis with each made up 
of twenty (20) sub-samples (See Figure 3). the use of composite samples is permissible. 
The soil samples were collected with a plastic spade into a plastic bucket and stirred 
thoroughly to obtain a homogenous mixture. In addition, core samples of moist soil at 
a depth of 0-20 cm for the determination of bulk density were randomly collected from 
each farmland. The uniform composite soil samples were packed into transparent 
zipped lock plastic bags and transported to the laboratory in an ice chest (Lente et al., 
2012) for analysis. The soil sampling was done prior to cropping on farmlands that were 
not previously treated with agrochemicals such as fertilizers and pesticides (Estefan et 
al., 2013). Discussions and interviews with farmers, agricultural officers, assemblymen, 
and stakeholders provided valuable information for the identification and selection of 
these sampled farmlands. The purpose of the soil analysis was to ascertain the quality 
regarding fertility status and the ability to support the livelihood strategies of farmers. 
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Soil tests provide a means for determining the fertility status of a soil but do not provide 
direct means of the actual quantities of plant available nutrients in the soil. Hence, the 
choice to use the composite samples, which is also an acceptable soil sampling protocol 
(Estefan et al., 2013). 
 
Figure 3: A sketch of random soil sampling of farmland. 
 
3.7.3 Water Sampling 
Water from the Black Volta River at points bordering Gengenkpe, Naapaal, Ketuo and 
one other River (“Babiara” River) in Billengangn, respectively, were sampled. For each 
of the rivers, samples were taken from upstream, midstream and downstream. In 
addition, water from three (3) boreholes from each of the study communities and a 
hand-dug well from Billengangn were collected for analysis. All water samples were 
put into previously cleaned–prepared 500 mL plastic bottles. Sample bottles were 
rinsed with sampled water three times and then filled to the brim in-situ. Separate plastic 
bottles were used for water samples for the determination of dissolved oxygen (DO), 
biochemical oxygen demand (BOD), physico–chemical parameters and heavy metals. 
In all, twenty-five (25) water samples were taken. Winkler I and II were added to BOD 
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water samples for BOD preservation and 2 mL of 10% HNO3 to acidify waters for metal 
preservation. Oxygen was fixed in the sample at the sampling site by the Azide 
modification method (APHA, AWWA and WEF, 2001). For the 500 mL plastic bottle 
containing the water sample for the dissolved oxygen determination, 2 mL of Winkler 
I was added to each sample followed by 2 mL of Winkler II (alkali iodide azide 
solution) and corked to exclude air bubbles. The plastic bottles were kept on ice and 
transported to the laboratory for analysis. The water samples for the BOD was kept in 
dark plastic bottles, placed on ice and then transported to the laboratory. Water samples 
were collected on July 23, 2016 between 7 am and 2 pm. This was done to be able to 
transport samples from Nandom to Accra within 24 hours. The GPS coordinates for 
water and soil sampling points are shown in Table 3. 
Table 3: GPS coordinates for water and soil sampling location. 
Community/Sample Code Description  Latitude “N” Longitude “W” Altitude (m) 
Billengangn 
BR1 River upstream 10.85565 -2.75301 267 
BR2 Midstream 10.85546 -2.75362 269 
BR3 Downstream 10.85537 -2.75462 268 
BB1 Borehole 10.85655 -2.75051 267 
BB2 Borehole 10.856130 -2.74614 274 
BB3 Borehole 10.86089 -2.75029 273 
BHDW Hand dug-well 10.85833 -2.75145 273 
BS1 Soil at farm 1 10.85674 -2.74985 269 
BS2 Soil at farm 2 10.85730 -2.75183 269 
Gengenkpe 
GR1 River upstream 10.81598 -2.87862 239 
GR2 Midstream 10.81566 -2.87857 237 
GR3 Downstream 10.81533 -2.87859 240  
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Community/Sample Code Description  Latitude “N” Longitude “W” Altitude (m) 
GB1 Borehole 10.82258 -2.85014 271 
GB2 Borehole 10.82335 -2.84860 269 
GB3 Borehole 10.81939 -2.84514 269 
BS1 Soil at farm 1 10.81491 -2.87392 245 
BS2 Soil at farm 2 10.81910 -2.85353 267 
Naapaal 
NR1 River upstream 10.88959 -2.83705 237 
NR2 Midstream 10.88940 -2.83734 235 
NR3 Downstream 10.88912 -2.83745 236 
NB1 Borehole 10.86969 -2.81036 264 
NB2 Borehole 10.86829 -2.80738 267 
NB3 Borehole 10.86707 -2.80634 271 
NS1 Soil at farm 1 10.88022 -2.82267 250 
NS2 Soil at farm 2 10.86588 -2.80062 270 
Ketuo 
KR1 River upstream 10.91113 -2.82379 237 
KR2 Midstream 10.91142 -2.82364 237 
KR3 Downstream 10.91084 -2.82365 238 
KB1 Borehole 10.904390 -2.80060 254 
KB2 Borehole 10.90619 -2.80699 251 
KB3 Borehole 10.90101 -2.79968 254 
KS1 Soil at farm 1 10.90928 -2.81404 245 
KS2 Soil at farm 2 10.90032 -2.79959 251 
Source: Field work, 2016/2017 
3.7.4 Rainfall and Temperature Data Collection 
A thirty-year historical rainfall and temperature data spanning a period of 1984 to 2014 
were obtained from the Ghana Meteorological Agency’s (GMet) gauged station at 
Babile for analysis to examine and understand the extent and nature of variability in 
mean annual and monthly as well as seasonal rainfall and temperature variables 
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(Nyantakyi-Frimpong & Bezner-Kerr, 2015). The climate data were taken from the 
station at Babile (65 % data availability) instead of the one at Lawra because of existing 
high gaps in the Lawra data (only about 13 % data are available). 
In addition, merged satellite dataset (from the combination of gauge and satellite data 
using merging techniques) for village level rainfall and temperature for the period 1984 
to 2014 was obtained from the Ghana Meteorological Agency for analysis and 
comparison. The mean monthly and annual rainfall and temperature at each of the 
communities were analyzed for temporal variability and trends. The gauged and merged 
climate data were compared with the primary data acquired through the community 
survey based on the perception of respondents on changes in the climatic variables and 
associated extreme events in the study area. The data on rainfall and temperature were 
incorporated into the community vulnerability analysis using the Livelihood 
Vulnerability Index (LVI). 
3.8 Method of Analysis 
3.8.1 Soil Fertility and Quality Analysis 
Farm soil samples were prepared using standard methods (APHA-AWWA-WEF, 2001). 
Trace elements were analyzed in the Ghana Atomic Energy Commission whilst the 
physical and chemical analyses were done in the University of Ghana Ecological Lab. 
The soil samples were placed in plastic trays with labels to ensure easy identification 
of the samples. The samples were then air and oven dried remove moisture. The 
samples were ground using ceramic pestle and mortar, all larger aggregates crushed and 
mixed thoroughly to obtain uniform mixtures. The grinding and sieving was done 
repeating by returning coarse particles into the mortar and regrinding till all aggregates 
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particles were fine to pass through a 2 mm sieve. The entire sample of a particular site 
or farmland was sieved to obtain fine particle sizes with larger surface area for the 
actions of reagent during physical and chemical analysis. The sieved soil samples were 
kept in clean plastic bags and stored appropriately. 
Soil physical and chemical analyses were conducted to determine the soil nutrient status 
of the selected farmlands. The parameters analyzed included hydrogen ion 
concentration (pH), electrical conductivity (EC), total dissolved solids (TDS), total 
suspended solids (TSS), salinity, organic carbon (OC), organic matter (OM), -moisture, 
dry matter (DM), nitrogen (N), phosphorus (P) and potassium (K). In addition, the 
levels of the following trace elements were determined in the soil samples: chromium 
(Cr), iron (Fe), manganese (Mn), copper (Cu), zinc (Zn), lead (Pb), nickel (Ni), 
cadmium (Cd), cobalt (Co), and arsenic (As). 
All analyses to determine these parameters in the soil samples followed the standard 
methods of the American Public Health Association, American Water Works 
Association, Water Environment Federation (APHA-AWWA-WEF, 2001), and that 
described by the International Centre for Agricultural Research in Dry Areas, ICARDA 
(Estefan et al., 2013). Descriptive statistics were used to analyze the laboratory results 
to assess relationships between and within samples and communities.  
3.8.2 Water Quality Analysis 
Physical and chemical parameters in water samples. In the laboratory, acidified water 
samples for trace metal analysis were kept in the fridge at a temperature of about 4 ℃. 
Both physical and chemical as well as bacteriological analyses were carried out on the 
water samples to assess the water quality. 
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In the analysis of water samples, the Ghana raw water quality criteria and guidelines by 
the WRC and the WHO guidelines for drinking water were employed for river (surface) 
water and groundwater sources, respectively. 
Analytical Quality Assurance and Control 
 Reagents and standards used in the analysis were obtained from BDH Chemicals 
Ltd, Poole England and Philip Harris Chemicals; 
 Sampling bottles and glassware were washed with acetone, detergents, 0.1 M 
H2SO4 and rinsed with distilled water; 
  All the equipment were cleaned with acetone, deionized water, and placed in an 
oven before each sample treatment  
  All equipment were calibrated using the appropriate standards before use. 
 The soil samples were taken with a plastic spade.  
 Samples were kept in plastic bottles and bags.  
 For validation of the analytical procedure, repeated analysis of the soil samples 
against internationally certified IAEA soil–7 standard reference material were used, 
and the results were found below 10 % of the certified values. 
3.8.3 Observed Rainfall and Temperature Data Validation and Analysis 
Observed rainfall and temperature data were subjected to quality control measures to 
detect and identify possible errors made during data collection, recording, transmitting 
and archiving (WMO, 2002). Data were analyzed to remove outliers, negative rainfall 
and temperature values, checked internal consistency and temporal-spatial coherency. 
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For instance, outliers were removed for cases where the minimum temperature was 
higher than the maximum temperature. Periods without data for all the rainfall and 
temperature variables were set to a missing value.  
Homogeneity testing was conducted on the data to ensure that time fluctuations in the 
data were only due to the vagaries of weather and climate. This was done using the 
standard normal homogeneity test incorporated in the climate data tool (CDT) software. 
The process involves four (4) steps namely, metadata analysis and quality control; 
creation of a reference time series; breakpoint detection and data adjustment. 
The observed climate data were obtained from the Ghana Meteorological Agency’s 
climate station at Babile in the Lawra District. The purpose was to examine the extent 
and nature of variability in the rainfall and temperature and the corresponding effects 
on smallholder livelihoods in the study area (Nkrumah et al., 2014). The GMet station 
at Lawra and data for recent years (2015 and 2016) were not used due to data 
unavailability and gaps. 
Merged satellite rainfall data for each of the study communities were obtained and 
analyzed based on TAMSAT rainfall retrieval algorithm calibrated over Ghana using 
quality control data from weather stations (Grimes et al., 1999). The Raw METEOSAT 
data spanning as far back as 1981 were obtained and processed by the TAMSAT 
(Tropical Applications of Meteorology using SATellite data) at the University of 
Reading in the United Kingdom, UK. The steps followed to merge the rainfall data 
included: extraction of rainfall estimate values at rain gauge values at each station 
location; computation of the difference between satellite and rain gauge values at each 
station location; interpolation of these differences to each grid point or satellite pixel 
centres and addition of the interpolated differences back to the satellite estimate 
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(Source: GMet). Climate data tool software and R statistical package were used through 
regression kriging in the quality control and merging of the data sets (Odeh et al., 1995; 
Hengl et al., 2004; Hengl et al., 2007).  
ANOVA was performed to evaluate the differences in total annual, mean maximum, 
and mean minimum rainfall values among the four communities studied.  
3.8.4 Perception of Climate Change and Weather Events by Smallholder Farmers 
The Likert rating scale was used to ascertain and analyze household-level perceptions 
of climate change and variability as well as extreme weather events. The respondents 
indicated their level of agreement or disagreement for given statements in respect of 
climate-related and extreme events in their communities over time. The Likert scale 
(Likert, 1932) comprises a statement concerning an event and an evaluative part of a 
list of response categories ranging from “strongly agree” to “strongly disagree”. In this 
section, the perception of smallholder farmers towards climate change and related 
events was investigated using a five point Likert scale. The observed climate data were 
compared with the perception of climate change and weather events by smallholder 
farmers and their related drivers and effects on livelihood strategies of farmers and 
policy. 
3.8.5 Availability, Utilization and Changes in Agroecosystem Services in Study Area 
Literature was reviewed extensively to gather in-depth information on agroecosystem 
services and other beneficial natural resources that underpin the livelihoods of 
smallholder farmers in semi-arid regions. In addition, a questionnaire was administered 
to smallholder households to identify agroecosystem services that support their 
livelihoods and well-being. Key informant interviews and focus group discussions were 
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conducted to relevant stakeholders and gendered groups in each of the communities to 
augment the household questionnaire survey. Key informants were interviewed in 
respect of their role in the management of agroecosystem services and climate-related 
issues in the district. The household interviews obtained perceptions regarding the 
availability and access to agroecosystem services by different groups of smallholder 
farmers, the changing patterns, and the management and governance of agroecosystem 
services. The data (i.e. perceptions of farmers) gathered from the survey were analyzed 
using descriptive statistics and presented as frequencies, percentages, and charts or 
graphs. The Microsoft Spreadsheet Application (Excel 2016) and SPSS version 22 were 
employed to capture and process the data of the study. 
The Likert scale (Likert, 1932) was employed to identify important trends in the 
availability and perceived changes in agroecosystem services over time. For instance, 
ranking terms such as “Very high”, “High”, “Medium”, “Low” and “Very low” and 
“Increased sharply”, “Increased slightly”, “Remained constant”, “Decreased slightly” 
and Decreased sharply” were used to describe the respondents’ perceptions of the extent 
of availability or supply and change in identified agroecosystem services over time in 
the study sites. 
3.8.6 Vulnerability to Recent Agroecosystem and Climate Change 
The conceptual framework adopted from Turner II et al. (2003) for this study explains 
the connections between the drivers of climate change and associated hazards within 
the vulnerability context in relation to current and expected responses, how climate 
change and variability and agroecosystem services change affect smallholder farmers 
in the study area. Household vulnerability to climate change and variability is driven 
by alterations in biophysical, socioeconomic, and socio-political factors (Hahn et al., 
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2009; Etwire et al., 2013). The biophysical drivers are mostly the natural and 
anthropogenic climate variability including extreme weather events. Such events 
include floods, droughts, bushfires and environmental degradation, and changes in the 
natural capital assets like agroecosystem services. The socioeconomic drivers comprise 
policy, demography, land tenure, governance, and economic (local markets) factors 
whereas the socio-political factors are institutional arrangements and knowledge 
systems. 
The extent of vulnerability of the smallholder farmers is a function of their exposure 
level, sensitivity, and adaptive capacity. Hence, the impacts of changes in climate and 
agroecosystem services are dependent on the extent of interaction between exposure, 
sensitivity, and adaptive capacity (Turner II et al., 2003; Luers, 2005; Smit & Wandel, 
2006; IPCC, 2007b; Fussel, 2007, 2009). 
According to Ribot (2009) and Turner II et al. (2003), vulnerability analysis comprises 
two key models, the Risk-Hazard (R-H) and the Pressure-and-Release (PAR) models. 
The R-H considers the impacts of a hazard as the function of the exposure to risk and 
the sensitivity (dose-response) of the affected system. However, this model has been 
criticized among other things as its inability to highlight the ways in which the exposed 
system amplifies the impacts of a hazard. In addition, it is not able to identify the 
differentiated consequences of hazards among exposed systems and associated 
components and the inability to explain explicitly how socioeconomic, socio-political, 
and institutions affect the level of the differential exposure and consequences to the 
hazard (IPCC, 2001; Hahn et al., 2009).  
These shortcomings of the R-H led to emergence of the pressure-and-release (PAR) 
model, which clearly explains risk as a function of a hazard and vulnerability of the 
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exposed system. The PAR model identifies the causes and conditions that render the 
exposed units vulnerable and unsafe. The PAR analyzes the severity and differentiated 
impacts of the hazard to the exposed social group, system, component or subcomponent 
(Turner II et al., 2003; Hahn et al., 2009). However, unlike R-H, PAR does not consider 
the complexities that exist in human-nature and nature-human interactions in terms of 
the vulnerability of the biophysical components. 
Alternatively, the integrated approach of vulnerability analysis takes into consideration 
the R-H and PAR models (Turner II et al., 2003) and combines both socioeconomic 
and biophysical drivers into the vulnerability context (Fussel, 2007). This study adopted 
the integrated approach to assess the vulnerability of households in local communities 
through the application of the Livelihood Vulnerability Index (Hahn et al., 2009; Etwire 
et al., 2013). The Livelihood Vulnerability Index (LVI) refers to a quantitative tool that 
enhances understanding of the contributions of socioeconomic, socio-political, and 
biophysical drivers to the vulnerability context (Hahn et al., 2009). This approach 
integrates climatic and non-climatic exposures and adaptation strategies to evaluate 
livelihood risks of smallholder farmers stemming from changes in agroecosystem 
services and climate variability. The LVI employs multiple indicators to assess 
exposure to extreme weather events and climate variability, socioeconomic profile of 
households that influence their adaptive capacity as well as their sensitivity to effects 
of climate change due to health, food, and water resources characteristics (Hahn et al., 
2009; Etwire et al., 2013). Furthermore, the LVI uses household-level primary data 
from the field surveys to form sub-components and indices, which are aggregated into 
major indicators for the estimation of household vulnerability to climate change. 
Indicators are quantifiable constructs that provide information either on matters of 
wider significance than that which is measured, or on a process or trend that otherwise 
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might not be apparent (Hammond et al., 1995; Vincent, 2004; Vincent, 2007). They are 
a means of encapsulating a complex reality in a single construct and are of use to 
decision makers at all levels, particularly in comparing across space and monitoring 
change over time (Etwire et al., 2013). This quantitative vulnerability analysis is either 
evaluated using an econometric or indicator approach (Hahn et al., 2009; Etwire et al., 
2013). The econometric approach employs regression techniques and uses a lot more 
secondary data (Antwi-Agyei et al., 2012). The indicator approach was employed to 
calculate the extent of vulnerability of farming households to changes in agroecosystem 
services and climate change (Hahn et al., 2009; Etwire et al., 2013).  
Vulnerability is a theoretically diverse field and difficult to assess due to the variety of 
determinants acting and interacting on different scales (Chambers & Conway, 1992; 
IPCC, 2014). It is therefore appropriate to rely on indicators that best represent the 
complex underlying local processes that lead to the vulnerability context.  
In incorporating sub-components to form major components, several methods such as 
the gap method and the balance weighted vulnerability index have been used. The gap 
method assesses vulnerability based on the deviation of a households’ empirical 
impacts and responses from theoretical standard living conditions in the absence of 
climate change and variability. On the other hand, the balance weighted method 
correctly values indicators based on the significant contributions each indicator makes 
into the vulnerability context of smallholder households to climate change and 
variability (Hahn et al., 2009; Etwire et al., 2013). The balance weighted approach was 
employed in estimating livelihood vulnerability index (LVI) as described below. 
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3.8.7 Assessing the Level of Vulnerability of Smallholder Farmers 
The Livelihood Vulnerability Index (LVI) used seven major components which include 
socio-demographic profile (SDP), livelihood strategies (LS), social networks (SN), 
health (H), access to food (F), access to water (W) and natural disasters and climate 
variability (NDCV) (Hahn et al., 2009; Etwire et al., 2013) and twenty-one sub-
components. The sub-components were generated from household-level primary data 
based on four key biophysical, socioeconomic, socio-political, and institutional 
variables. 
Each of the sub-components was standardized as an index or major components using 
Equation (3.2): 
S
index c
−Smin
s =         (3.2) c Smax−Smin
Where sc is the observed sub-component or indicator for community c, and smin and smax 
are the minimum and maximum values, respectively. After each was standardized, the 
sub-component or indicators were averaged using Equation (3.3): 
∑n
Mc =  i=1
indexsci        (3.3) 
n
Where Mc is one of the seven major components (SDP, LS, SN, H, F, W or NDCV) for 
community c, indexsci represents the sub-components in each major component. The 
computed values for each of the seven major components for a community were then 
averaged using Equation (3.4) to obtain the community level LVI: 
∑7 W
LVI =  i=1 Mi
Mci
c ∑7
        (3.4) 
i=1 WMi
Equation (3.29) is expanded to form Equation (3.5): 
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WSDP SDPc+ WLSLSc+WHHc+WSNSNc+ WFFc+ WLVI =  W
Wc+ WNDCV NDCVc
C  (3.5) WSDP+ WLS+ WH+ WSN+ WF+ WW+ WNDCV
The weights of each major component, WMi, is a function of the number of sub-
components associated with it. These are included to ensure that all sub-components 
contribute equally to the overall LVI. 
Following from equations (3.2) to (3.4), a new variable, LVI-IPCC is calculated taking 
into consideration IPCC definition of vulnerability (Hahn et al., 2009; Etwire et al., 
2013). The LVI-IPCC comes from the combination of the major components. Rather 
than merge the major components into the LVI in Equation (3.4), the major components 
were first combined according to the three categories or functions of vulnerability 
namely exposure, sensitivity and adaptation capacity using the Equation (3.6): 
∑n
CF =  i=1
WMiMci
c n         (3.6) ∑i=1 WMi
Where CFc is an IPCC-defined contributing factor (exposure, sensitivity, or adaptive 
capacity) for community c, Mci are the major components for community c indexed by 
i, WMi is the weight of each major component and n is the number of major components 
in each contributing factor. Once exposure, sensitivity and adaptation capacity were 
calculated, the three contributing factors were then combined using Equation (3.7): 
LVI − IPCCc =  (ec −  a
∗
c)  Sc      (3.7) 
Where LVI-IPCCc is the LVI for community, c, expressed using the IPCC vulnerability 
framework, e is the calculated exposure score for community, c (equivalent to NDCV 
major component), a is the calculated adaptation capacity score for community, c 
(weighted average of the SDP, LS, and SN major components) and s is the calculated 
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sensitivity score for community, c (weighted average of the H, F and W major 
components) as described in Table 4. 
The LVI-IPCC is scaled from-1 (least vulnerable) to 1 (most vulnerable). The Microsoft 
Office Excel 2016, Stata13 and SPSS statistical packages were employed in estimating 
the livelihood vulnerability index of smallholder households in semi-arid Ghana. 
Table 4: Contributing factors to LVI per IPCC approach, 
Contributing factor Major Component 
I. Exposure Natural disasters and climate variability 
II. Adaptive capacity 1. Socio-demographic profile 
2. Livelihood strategies 
3. Social networks 
III. Sensitivity 1. Health 
2. Food 
3. Water 
 
Each of the major components comprised a given number of sub-components or 
indicators. In all, twenty-one (21) key indicators were used for relative vulnerability 
analysis of four (4) semi-arid communities using the LVI as described above and in 
Appendix 7. The specific number of sub-components for each of the major components 
are 4, 2, 2, 4, 2, 2, and 5 for socio-demographic profile, livelihood strategies, social 
networks, food, water, health and natural disasters and climate change variability 
respectively.  
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3.8.8 Analysis of Perceptions of Smallholder Farmers on Adaptation Strategies 
Literature was reviewed to identify the adaptation strategies available and used by 
smallholder farmers to withstand and overcome risks from changes in agroecosystem 
services (such as soil fertility, food, fuelwood, water, and forest cover) and climate 
(such as low rainfall, high rainfall, and high temperature) in semi-arid regions. 
Household survey using questionnaire, focus group discussions, and key informant 
interviews were conducted at both community and district levels, to identify and rank 
the adaptation options of smallholder farmer households.  
In the household survey, respondents were asked to indicate whether they are adapting 
and using adaptation strategies to moderate the impacts of agroecosystem services and 
climate change. 
Descriptive statistics were employed to analyze the responses and presented using 
frequencies, percentages, charts, or graphs. The available adaptation strategies were 
ranked in order of highest number of responses. Respondents were also asked to 
indicate reasons why they did not adopt certain adaptation strategies. Factors 
responsible for each respondent’s inability to adopt the available adaptation strategies 
were captures, ranked, and discussed qualitatively. 
Furthermore, households’ biophysical, socioeconomic, and institutional factors that 
influenced adoption of adaptation strategies by smallholder farmers were captured and 
analyzed. 
Multiple regression through maximum likelihood estimation (Wooldridge, 2002) was 
done using adaptation strategies as the dependent variable and factors marked by 
respondents as influencing adaptation as independent variables. The Durbin-Watson 
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Statistic was used to test for autocorrelation among the independent variables in relation 
to the dependent variable. The independent or explanatory variables identified include 
availability of agroecosystem services, farm size of respondents in hectares, access to 
climate information, access to training on best farming practices, farmland ownership, 
how satisfied a smallholder farmer is with life in relation to major challenges, gender, 
access to information on managing ecosystem services. The explanatory variables were 
derived from household characteristics, biophysical, socioeconomic, and institutional 
factors (Apata et al., 2009; Deressa et al., 2009). 
3.8.10 Analysis of Knowledge and Information Sources on Adaptation Strategies 
Household surveys with semi-structured questionnaire, key informant interviews and 
focus group discussions were used to obtain information on the sources of climate-
related information and knowledge for smallholder farmers for managing climate 
variability and extreme weather stresses as well as the changes in agroecosystems and 
ecosystem services. The data were analyzed qualitatively using descriptive statistics. 
Frequencies, percentages and charts used to rank and display specific sources of climate 
information available to respondents. The extent of usage of climate information, 
knowledge as well as the barriers to accessing climate-related and agroecosystem 
services management knowledge were identified and presented using frequencies, 
percentages, means, and charts. 
3.9 Limitations of the Study 
The research had to address several key terms and themes that are subject to different 
theoretical and conceptual interpretations. These include vulnerability, agroecosystem 
services, adaptation, human well-being, and smallholder farmers among others. 
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Participatory research techniques such as household questionnaire, focus group 
discussions. and key informant interviews employed in this study are subject to both 
researcher and participant biases which may affect reliability and validity of findings. 
More than four communities could have been selected for to widen the scope of 
coverage in the district and to enrich the household data. However, the detailed nature 
of interview questions as well as the integration of the quantitative and qualitative data 
should curtail any weakness. In addition, experts were relied on for their opinions to 
provide valuable insights into the various research themes. 
The gauge data analyzed in this study were limited with regards to gaps in recorded 
rainfall and temperature values which may represent a challenge for the climate 
findings. However, the data were subjected to quality control to remove negative values 
and outliers. 
  
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CHAPTER FOUR: RESULTS 
4.1 Household Profile of Respondents 
This section discusses the results of the socio-demographic, socioeconomic status and 
farm characteristics of respondents as presented in Table 5. 
Table 5: Household characteristics of respondents. 
Household Pooled Billengang Gengenkp Naapaal Ketuo 
Profile Sample n e (n=62) (n=24) 
(n=194) (n=60) (n=48) 
Gender % % % % % 
Male 52.6 55.0 60.4 53.2 29.2 
Female 47.4 45.0 39.6 46.8 70.8 
Gender of Household Head  
Male 68.0 71.4 72.7 64.3 60.0 
Female 32.0 28.6 27.3 35.7 40.0 
Age Group 
< 20 Years 8.2 21.7 0.0 4.8 0.0 
20-29 13.4 11.7 12.5 9.7 29.2 
30-39 15.5 13.3 20.8 11.3 20.8 
40-49 22.2 18.3 25.0 25.8 16.7 
≥ 50 years 40.7 35.0 41.7 48.4 33.3 
Marital Status 
Single 19.1 28.3 10.4 22.6 4.2 
Married 69.6 58.3 72.9 67.7 95.8 
Widowed 11.3 13.3 16.7 9.7 0.0 
Level of Education 
Primary 19.1 6.7 14.6 0.0 0.0 
JSS/Middle Sch. 17.5 30.0 10.5 32.3 25.0 
Sec./Voc./Tech. 5.7 5.0 8.3 12.9 20.8 
Tertiary 2.6 1.7 6.3 6.5 4.2 
None 54.1 56.7 60.4 48.4 50.0 
Household Size 
0-5 29.9 26.7 29.2 35.5 25.0 
6-10 56.2 58.3 58.3 56.5 45.8 
11+ 13.9 12.5 12.5 8.1 29.2 
Occupation 
Farming 96.9 95.0 93.8 100.0 100.0 
Fishing 0.5 1.7 0.0 0.0 0.0 
Others 2.6 3.3 6.2 0.0 0.0 
Farm Size (Hectares) 
0-2 73.2 86.7 66.7 69.4 58.3 
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Household Pooled Billengang Gengenkp Naapaal Ketuo 
Profile Sample n e (n=62) (n=24) 
(n=194) (n=60) (n=48) 
3-4 22.2 8.3 25.0 25.8 41.7 
5-6 4.1 5.0 6.3 3.2 0.0 
7+ 0.5 0.0 2.1 1.6 0.0 
Land Ownership 
Family 82.5 86.7 52.1 96.8 95.8 
Rental 4.1 3.3 12.5 0.0 4.2 
Landlord 9.8 10.0 20.8 3.2 0.0 
Source: Field work, 2016 
The household survey indicated 52.6 % male and 47.4 % female respondents. However, 
Ketuo recorded more females (70.8 %) than males (29.2 %). Majority of the 
respondents (40.7 %) were within the age group of fifty (50) years and above with only 
a small proportion (8.2 %) within the age group below twenty (20) years. For the gender 
of interviewed household heads, about 68.0 % were male whereas 32.0 % were female. 
Ketuo recorded the highest proportion (40.0 %) of female household heads with the 
lowest (27.3 %) in Gengenkpe. The highest proportion of respondents (48.4 %) within 
age group 50 years and above was recorded in Naapaal whereas the lowest was in Ketuo. 
Even though respondents were mainly household heads, observation of household 
composition showed that there were very few young (below 25 years) household 
members during the survey. 
In terms of marital status, the survey showed that 19.1 % of respondents were single, 
69.6 % reported married, and 11.3 % widowed. Majority of married respondents 
(95.8 %) were in Ketuo, whilst the minority (58.3 %) were in Billengangn. However, 
there was no widow or widower among respondents in Ketuo compared to the other 
communities. 
With regards to educational level, more than half of the respondents (54.1 %) received 
no formal education or non-formal education, whilst only 19.1 % obtained primary 
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education. The largest proportion of respondents (60.4 %) with no education was 
recorded in Gengenkpe, whilst the smallest proportion (48.4) was in Naapaal. 
More than half of the survey respondents (56.2 %) had family sizes of between 6 and 
10 persons and the highest of this number of household membership was in both 
Billengangn and Gengenkpe, whilst the lowest was recorded in Ketuo (45.8 %). About 
29.2 % of interviewed households in Ketuo reported household size of eleven (11) and 
above persons whilst Naapaal accounted for (8.1 %). In terms of occupation, majority 
(96.9 %) of household heads were farmers. The results indicated that most of the 
respondents (73.2 %) held maximum land sizes of about two (2) hectares with the 
highest proportion (86.7 %) in Billengangn and the lowest (58.3 %) in Ketuo. 
Meanwhile, a smaller proportion (0.5 %) of farmers had about seven (7) or more 
hectares of land holdings in Gengenkpe and Naapaal, whilst no farmer in both 
Billengangn and Ketuo held up to 7 hectares of land. 
The survey revealed three (3) main land ownership systems in the study area, family, 
rental, and landlord systems. The results indicated that most of the respondents (82.5 
%) owned and cultivated family lands, about 9.8 % were landlords of farmlands, and 
just a few (4.1 %) rented land for farming activities. Naapaal had the highest proportion 
(96.8 %) of respondents owning land by the family system whereas Gengenkpe had the 
lowest (52.1 %). 
The survey further indicated that about 91.8 % respondents cultivated both maize and 
groundnut as major crops (Table 6), whilst 4.6 % grew vegetables as minor crops. 
About 96.7 % of respondents in Billengangn cultivated maize representing the highest 
proportion in relation to other communities. In Gengenkpe, the main crop grown was 
groundnut represented by 89.6 % of the respondents whilst 95.2 % of respondents in 
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Naapaal indicated that maize was their major crop. In Ketuo, about 87.5 % cultivated 
maize, millet, and groundnut. 
4.1.1 Major Crops Cultivated 
Table 6 shows the major crops cultivated by respondents in the study communities.  
Table 6: Major crops cultivated by respondents. 
 Pooled Billengangn Gengenkpe Naapaal Ketuo 
(n=194) (n=60) (n=48) (n=62) (n=24) 
 Freq % Freq % Freq % Freq % Freq % 
Maize 178 91.8 58 96.7 40 83.3 59 95.2 21 87.5 
Millet 146 75.3 34 56.7 41 85.4 50 80.6 21 87.5 
Groundnut 178 91.8 56 93.3 43 89.6 58 93.5 21 87.5 
Rice 115 59.3 43 71.7 26 54.2 30 48.4 16 66.7 
Beans 108 55.7 34 56.7 30 62.5 35 56.5 9 37.5 
Vegetables 9  4.6 2  3.3 3  6.3 2  3.2 2  8.3 
Guinea Corn 53 27.3 19 31.7 6 12.5 22 35.5 6 25.0 
Yam 43 22.2 23 38.3 3 6.3 6 9.7 11 45.8 
Source: Field Survey, 2016/2017 
4.1.2 Crop Utilization by Respondents 
The survey revealed that a high percentage of respondents used all crops produced at 
the end of the farming season at home (Figure 4). They hardly had any surplus of crop 
yields for sale to improve their income with the exception of groundnuts and beans. 
The low yield crops identified were maize, millet, guinea corn, and rice. The study 
identified groundnut as the crop which generated income for respondents in the study 
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communities because they were able to produce enough to be used at home and also for 
sale. 
Use all at home Use more at home than sell
Use at home and sell equally Sell more than use at home
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Cultivated crops  
Figure 4: Crop utilization by respondents. 
 
4.1.3 Access to Credit 
The results of the survey as presented in Figure 5 show that only about 14.4 % of the 
respondents had access to credit whilst a higher proportion (85.6 %) had no access to 
credit for their agricultural activities. 
100.0
80.0
60.0
40.0
20.0
0.0
Billengangn Gengenkpe Naapaal Ketuo Pooled
Study community
 
Figure 5: Access to credit by respondents. 
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Percentage of respondents
Percentage of respondents
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None of the respondents in Gengenkpe had access to credit from either the bank or 
money lenders for their farming activities over the period whilst a higher percentage 
(27.4 %) in Naapaal indicated otherwise. 
Focus group discussions revealed that Naapaal had a “women group” who were able to 
solicit credit for their farming activities. This group had signed an agreement with 
Guinness Ghana Limited, where the company gives credit to the women to cultivate 
sorghum which was then sold to the company. In Ketuo, however, Nandom Deanery 
Integrated Rural Development Programme (NANDIRDEP), an NGO in agriculture 
assisted farmers with both technical training and credits for their farming activities. 
These may be some of the reasons why Naapaal and Ketuo seem to have relatively high 
percentage of respondents indicating they had access to credit. 
As shown in Figure 6, about 42.0 % of respondents received amounts between One 
Hundred Ghana Cedis and Three Hundred Ghana Cedis only (GHs100.00-GHs300.00) 
and Four Hundred Ghana Cedis and Six Hundred Ghana Cedis only (GHs400.00-
GHs600.00). A lower percentage of 16.0 % received between Seven Hundred Ghana 
Cedis and Nine Hundred Ghana Cedis only (GHs700.0-GHs900.00). 
16%
42% GHS100-300
GHS400-600
GHS700-900
42%
 
Figure 6: Amount of credit received by respondents (1USD:4.7GHS). 
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The Figure 7 shows the sources of credit for respondents and indicated that about half 
of them obtained credit from money lenders, whereas a quarter got credit from banks 
and others. Others consisted of family, friends, and the village savings and loans 
association (VSLA). 
Bank
25% 25%
Money Lenders
Others
50%
 
Figure 7: Sources of credit for respondents. 
 
The respondents also indicated reasons why they were unable to access credit as 
presented in Figure 8. Majority of respondents (40.0 %) were not aware of sources of 
credit in their communities and for some others (32.0 %), they would not be able to pay 
back if they took any loan. A few indicated they did not have loan guarantors. 
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8% Not interested
20%
Cannot pay back loan
Not aware of credit
source
32% No loan guarantor
40%
 
Figure 8: Reasons why respondents (%) do not access credit. 
 
4.2 Soil Fertility Status 
Physical and Chemical Properties of Farm Soil Samples from Study Communities 
The results of the analysis of nutrients status, chemical, and physical parameters of soil 
samples from selected farms in the study communities are shown in Table 7. The 
analysis revealed that soil pH ranged from 5.60 to 8.00 with the highest value recorded 
in Billengangn and the lowest in Gengenkpe. The mean value of electrical conductivity 
(EC) of the soil samples ranged between 13.30 μS/cm at the farm in Gengenkpe and 
104.00 μS/cm in Ketuo. The mean value of organic matter (OM) ranged from 0.92 % 
at Naapaal to 2.22 % at Billengangn. The total nitrogen levels ranged from 0.06 % at a 
farm sites in Naapaal to 0.22 % in Billengangn. 
 
 
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Table 7: Physical and chemical properties of farm soil samples. 
Parameter Billengangn Gengenkpe Naapaal Ketuo 
pH 7.8 5.85 6.35 6.1 
Conductivity (µS/cm) 62.25 17.5 39.25 67.6 
Organic matter (%) 2.22 1.47 0.92 1.10 
Organic Carbon (%) 1.29 0.85 0.53 0.64 
Moisture (%) 6.33 5.59 8.3 6.11 
Nitrogen (%) 0.22 0.09 0.06 0.11 
Phosphorus (%) 0.25 0.12 0.15 0.12 
Potassium (%) 0.1 0.04 0.06 0.05 
FAO (1980) Guidelines organic matter: <0.86 %-low, 0.86-1.29 %-marginal and >1.29 %-
adequate for growth of crops. 
 
Table 1 of Appendix 8 shows the levels of total suspended solids and salinity in the 
soil samples as well as the results of particle size analysis. Two main soil texture were 
found, loamy-sand for soils from Billengangn and Naapaal and sandy-loam for soils in 
Gengenkpe and Ketuo The results of the analysis of all trace metals in soil samples are 
shown in Table 3 of Appendix 8. The results indicated that the concentrations were 
below the recommended maximum limits (Ewers, 1991). Meanwhile, the concentration 
levels of Cd and Co were below detection limit except for Co (0.34 mg/kg) at one of 
the farms in Billengangn. 
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4.3 Water Quality 
4.3.1 Physico-Chemical Parameters in Water Samples 
The results of the physico-chemical parameters analyzed in river water samples are 
shown in Table 8. 
Table 8: Physico-chemical parameters of Black Volta River samples. 
Community Measured parameters  
pH TDS Conductivity Turbidity  BOD 
(mg/L) (µS/cm) (NTU) (mg/L) 
Billengangn 7.80 50.00 53.30 8.00 2.57 
Gengenkpe 7.30 18.30 35.60 76.70 3.12 
Naapaal 7.40 28.60 33.80 100.00 2.37 
Ketuo 7.50 72.00 91.90 68.30 4.20 
TWQRs 6.9-8.5 0.450 0-70 0-1 0.0-8.7 
WHO 6.5-8.5 1000 - 5 - 
TWQRs=Target water quality guidelines by the WRC, WHO=World Health Organization 
 
The pH levels in all river water samples ranged from 7.3 to 7.8, whilst for the boreholes, 
the range was between 7.2 to 8.9 (Table 8). The concentration of total dissolved solids 
(TDS) in the river water samples were within the range of 18.30 mg/L at Gengenkpe 
and 72.0 mg/L at Ketuo, whilst in the boreholes the range was 16.8 mg/L at Naapaal to 
20.9.7 mg/L at Ketuo. The measured turbidity levels ranged from 8.0 NTU at 
Billengangn to 100.0 NTU at Naapaal. Turbidity levels in borehole water samples 
ranged from 0 to 49 with the highest value at Billengangn, whilst a hand-dug well also 
recorded a value of 14 at Billengangn (See Appendix 8). The levels of electrical 
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conductivity in water from Ketuo (91.9 mg/L) exceeded the guideline range of 0-70 
(TWQRs, 2003). 
4.3.2 Major Ions in River Water Samples from Study Communities 
The concentration levels of major ions of determines in water samples from the Black 
Volta River in the study communities are shown in Table 9. 
Table 9: Major ion in the Black Volta River water samples. 
Community Measured parameters (mg/L) 
 Na+ K+ Ca2+ Mg2+ NO - HCO - PO 3- SO 2-3 3 4 4  F- Cl- 
Billengangn 15.23 4.73 0.47 4.87 2.09 0.73 0.29 1.00 0.05 0.40 
Gengenkpe 8.37 8.17 0.30 2.20 0.99 0.58 0.15 1.00 0.00 0.27 
Naapaal 7.87 7.70 0.30 2.73 0.84 0.70 0.17 5.00 0.00 0.40 
Ketuo 9.67 6.90 1.00 3.27 4.80 0.64 0.19 6.00 0.00 0.30 
TWQRs 20.00 50.00 32.00 30.00 6.00 Nil Nil 20.00 1.00 100.00 
TWQRs=Target water quality guidelines by the WRC 
 
The concentration levels of Na+ measured in water samples at Billengangn was about 
three times that of K and Mg and thirty-two times that of Ca as presented in Table 9. 
For Gengenkpe, the level of Na was almost equal as K, about twenty-eight times that 
of Ca and four times that of Mg. The levels of Na at Naapaal was about equal that of 
K, twenty-six times that of Ca and three times that of Mg. For water samples from 
Ketuo, the measured value of Na was about equal that of K, ten times of Ca and three 
times that of Mg. 
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For sampled water from boreholes in the study communities as shown in Figure 9, the 
results of the analysis of major ions indicated that for Billengangn, the concentration 
ranges were Ca2+ (3.20-11.20) mg/L, Cl- (2.99-4.99) mg/L, F- (0-0.37) mg/L, K+ (3.70-
7.00) mg/L, Na+ (17.9-73.6) mg/L, NO -3  (1.37-2.17) mg/L, PO 3-4  (0.03-1.63) mg/L, and 
SO 2-4  (8.14-21.93) mg/L. 
For Gengenkpe, the ranges were Ca2+ (4.80-6.40) mg/L, Cl- (2.99-4.99) mg/L, F- (0.23-
0.54) mg/L, K+ (3.00-7.80) mg/L, Na+ (38.70-44.90) mg/L, NO -3  (0.35-1.41) mg/L, 
PO 3-4  (0.09-0.48) mg/L, and SO 2-4  (5.01-14.15) mg/L. 
For Naapaal, the ranges were Ca2+ (20.80-35.20) mg/L, Cl- (0.99-4.99) mg/L, F- (0.14-
0.16) mg/L, K+ (3.60-6.20) mg/L, Na+ (30.20-45.20) mg/L, NO -3  (0.83-3.20) mg/L, 
PO 3-4  (0.89-3.49) mg/L, and SO 2-4  (10.05-21.28) mg/L, whilst the ranges for Ketuo 
were Ca2+ (9.60-22.40) mg/L, Cl- (2.99-4.99) mg/L, F- (0.14-0.38) mg/L, K+ (1.50-
3.50) mg/L, Na+ (9.30-91.50) mg/L, NO -3  (0.23-1.47) mg/L, PO 3-4  (0.02-0.13) mg/L, 
and SO 2-4  (27.92-35.15) mg/L (WHO guideline values; NO - 3 = 50 mg/L). 
 
 
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Figure 9: Major ions in borehole water from the study communities. 
 
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4.3.3 Trace Metals Results 
The concentration levels of Fe, Mn, Pb, Ni, and As in water samples collected from the 
Black Volta River in the study communities are presented in Table 10. 
Table 10: Mean concentrations of trace metals in river water. 
Community Trace metals (mg/L) 
 Fe Mn Pb Ni As 
Billengangn 0.122 0.007 0.002 0.006 0 
Gengenkpe 0.36 0.013 0.007 0.01 0.01 
Naapaal 0.437 0.011 0 0.005 0.004 
Ketuo 0.201 0.016 0.006 0.006 0.002 
TWQRs 0.1 0.05 10 0 10 
NB: TWQRs=Target Water Quality Ranges, Source: WRC, 2003). The results of the analysis 
of trace metals in borehole water is also shown in Table 5 of Appendix 8 and indicated that 
the concentrations of Cr, Cd, Co, and Cu are below detection limits. 
 
The results of trace metal analysis indicated that the mean concentrations of Fe, Mn, 
Pb, Ni, and As were in the ranges as follows: (0.12-0.44) mg/L, (0.007-0.016) mg/L, 
(0.0-0.006) mg/L, (0.005-0.1) mg/L, (0.0-0.01) mg/L, respectively. The concentration 
of Cr, Cd, Co, Cu and Zn were found below detection limit (BDL) in all water samples 
whilst Fe and Ni exceeded the guidelines of the TWQRs for surface water for domestic 
use. The results indicated relatively high concentrations of Fe with the highest in water 
samples from Naapaal and the lowest in Billengangn. 
4.3.4 Bacteriological Water Quality 
The results in Table 11 showed that the levels of faecal and total coliform of river water 
from each of the study communities exceeded the recommended maximum limits of 
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the target water quality ranges (TWQRs). The highest faecal coliform counts were 
recorded in water samples from Gengenkpe and the lowest at Ketuo. In terms of total 
coliform counts, water from Gengenkpe again had the highest whilst Naapaal recorded 
the lowest. Faecal coliform counts ranged from 10 to 120 (cfu/100 mL), whilst total 
coliform were between 230 and 1300 (cfu/100 mL). 
Table 11: Levels of microbial contamination in River water. 
Community/ Faecal Coliforms Total Coliforms 
Water Source (cfu/100 mL) (cfu/100 mL) 
Billengangn 30 230 
Gengenkpe 1202 240 
Naapaal 60 400 
Ketuo 10 1300 
TWQRs (cfu/100 mL) 0 5-100 
TWQRs Target Water Quality Ranges for Domestic Water Use (Source: WRC, 2003) 
  
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4.4 Rainfall and Temperature Trends in the Study Communities 
4.4.1 Gauge and Observed Data 
The total annual rainfall data for Babile and the study communities spanning the period 
1984 to 2014 are shown Table 12 and the plots presented in Figure 10. 
Table 12: Annual rainfall for Babile and the Study Communities (1984-2014). 
Variable Observation Mean (mm) Std. Deviation Min Max 
Billengangn 31 990.4656 151.6828 569.4 1224.0 
Gengenkpe 31 958.3438 158.1446 553.1 1126.7 
Naapaal 31 981.9031 157.4171 571.6 1161.0 
Ketuo 31 976.5469 154.095 560.6 1167.5 
Babile(Gauge) 31 981.1516 164.4 565.5 1274.0 
Data source: Ghana Meteorological Agency (GMet), Accra. 
The long-term mean for the annual rainfall for the study area (Babile) was 981.2 mm 
with a minimum of 565.5 mm, maximum of 1274.0 mm, and standard deviation of 
164.4. The analysis of total annual rainfall for three decades indicated high interannual 
variability of rainfall amounts. 
 
Figure 10: Annual rainfall data-1984-2014 (Source: GMet). 
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Between 2009 and 2014, the first three years (2009-2011) recorded total annual rainfall 
values of 1065.8 mm, 1004.8 mm, and 1022.0 mm which were above the annual 
average with no regular order of trend whilst the last three years per the data (2012-
2014) recorded values (951.5 mm, 980.9 mm, and 937.0 mm) below the total annual 
mean value of 981.2 mm, respectively. The highest total annual rainfall was recorded 
in the year 2000 (1274.1 mm) and the lowest in 2004 (565.5 mm). Rainfall over the 
period showed much variability and did not show any visible trend. The trend line as 
shown by Figure 10 is not statistically significant with a p-value of 0.83. 
The yearly total rainfall anomaly for the study area as shown in Figure 11 indicated 
high temporal variability with no regular trend over the period spanning 1984 to 2014. 
The plot showed clearly that very low total annual rainfall values were recorded in 2004 
(note that in 2004, daily rainfall data was available for only nine months), 1990, and 
2007. Overall, the study area experienced lower amounts of total annual rainfall 
between 2011 and 2014. 
 
Figure 11: Annual rainfall anomaly plot for 1984-2014 (Source: GMet) 
 
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Figure 12 illustrates the seasonal trend by total monthly rainfall for the 31-year period, 
which indicated that the peak of rainfall in the study area was in August, whilst the 
main rainy season started in June and ended in October. The results indicated a 
unimodal rainfall annual cycle over the study area. 
300
250 238.9
200 186.7
200.1
150 126.6
107.8
100 68.8 62.3
50 24.6
2.4 5.3 10.4 4.9
0
Month
 
Figure 12: Seasonal rainfall cycle (1984-2014). 
 
The yearly mean maximum temperature observed at Babile is presented in Table 13, 
whilst Figure 13 illustrates the trend for the annual mean maximum temperature data 
and the anomaly plot shown in Figure 14. 
Table 13: Summary of the maximum temperature for Babile and Study Communities (1984-
2014). 
Variable Observations Mean (℃) Std. Deviation Min Max 
Billengangn 31 34.8031 0.3188 34.2 35.5 
Gengenkpe 31 35.0719 0.3314 34.4 35.7 
Naapaal 31 34.9781 0.3289 34.3 35.7 
Ketuo 31 35.0156 0.3264 34.4 35.7 
Babile (Gauge) 31 34.75323 0.3894 33.9 35.6 
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Figure 13: Annual mean maximum temperature for Babile (1984-2014). 
 
 
Figure 14: Annual maximum temperature anomaly for Babile (1984-2014). 
 
The results of the analyses showed that the highest mean maximum temperature was 
35.6 ℃ recorded in 1987, whilst 2007 had the lowest, 33.9 ℃. The annual mean 
maximum temperature increased by 0.4 ℃ over the period. The data analysis indicated 
an increasing trend of annual mean maximum temperature between 1984-2014. 
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However, there was no significance difference (p-value=0.129) between the mean 
maximum temperature values for the period.  
The highest monthly mean maximum temperature was recorded in March, whilst 
August had the lowest (Figure 15). The results indicated that temperature over the 
study area was high from November to march. The hottest month is March and the 
coldest is August, which is also the peak of the rainy season. 
45.0
38.9
40.0 37.3 38.034.9 36.1 34.2 36.133.5 35.0
35.0 31.5 30.6 31.4
30.0
25.0
20.0
15.0
10.0
5.0
0.0
JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
Month
 
Figure 15: Seasonal maximum temperature cycle (1984-2014). 
 
The yearly mean minimum temperature (nighttime temperature) for Babile and the 
study communities spanning 1984 to 2014 are presented in Table 14 and the trend 
graph in Figure 16, whilst the anomaly plot is shown in Figure 17. 
  
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Table 14: Summary of the minimum temperature for Babile and Study Communities (1984-
2014). 
Variable Observations Mean (℃) Std. Deviation Min Max 
Billengangn 31 21.9988 0.3258 21.4 22.8 
Gengenkpe 31 22.5281 0.3304 21.9 23.4 
Naapaal 31 22.3188 0.3247 21.7 23.2 
Ketuo 31 22.4156 0.3204 21.8 23.3 
Babile (Gauge) 31 22.1207 0.4316 21.4 22.9 
 
Analysis of the gauge data, indicated that the average minimum temperature recorded 
was 22.1 ℃ with minimum value of 21.4 ℃ (2001), and maximum value of 22.9 ℃ 
(2005)., whilst a standard deviation of 0.4316 was recorded. whilst GSS (2014) reported 
23 ℃. 
 
Figure 16: Annual minimum temperature for Babile (1984-2014). 
 
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Figure 17: Annual minimum temperature anomaly for Babile (1984-2014). 
 
The anomaly plot above depicts inter annual variation in the night temperature over the 
study area. However, most of the bars in recent years shows positive anomaly compared 
to the past. 
The seasonal minimum temperature is presented in Figure 18 and the analysis indicated 
that the highest was 26.2 ℃ recorded in April, whilst the lowest value of 16.9 ℃ was 
measured in December. 
30.0 26.2
24.5 25.3 23.8
25.0 22.9 22.5 22.4 22.8
20.2 20.3
20.0 17.0 16.9
15.0
10.0
5.0
0.0
JAN FEB MAR APR MAY JUN JUL AUG SEPT OCT NOV DEC
Month
 
Figure 18: Seasonal minimum temperature cycle (1984-2014).  
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The analysis of the temperature data showed that high maximum (daytime) temperature 
was observed in March whilst minimum (nighttime) temperature occurred in April. The 
analysis depicts that both minimum and maximum temperatures are increasing. This 
means that its becoming warmer during the day and the night. The results confirmed 
that the hottest period of the year was march and the coldest was August. 
4.4.2 Perceptions of Rainfall and Temperature Trends in Study Communities 
The survey data on the perceptions of smallholder farmers on rainfall and temperature 
trends and variability, and related weather events are presented by Figures 19 to 23.  
35.0
29.9
30.0
25.0 24.2
20.6
20.0
16.0
15.0
10.0 9.3
5.0
0.0
Strongly agree Agree Uncertain Disagree Strongly disagree
 
Figure 19: Perception of increased rainfall by respondents (Field work, 2016/2017). 
 
The survey revealed that majority (54.1 %) respondents agreed that annual total rainfall 
had increased over the last ten years or more, whilst a few were uncertain. About 36.6 
% of respondents observed decreased annual total rainfall. Out of the number of 
respondents who perceived an increasing trend in total annual rainfall over the period 
under consideration, 56 % were males, whilst 49 % were females. 
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The perceptions regarding the trends and variations in start of the planting season as 
shown in Figure 20 indicated that majority (82.4 %) of respondents agreed that the start 
of the planting season had changed over the last 10 years or more in the study area. 
50.0 44.8
45.0
40.0 37.6
35.0
30.0
25.0
20.0
15.0 10.8
10.0 4.6
5.0 2.1
0.0
Strongly agree Agree Uncertain Disagree Strongly disagree
 
Figure 20: Perception of change of start of planting season by respondents. 
 
Regarding trends in temperature, the perception presented in Figure 21 indicated that, 
majority of the respondents agreed they had observed increases in temperature during 
the last 10 years or more. A few (9.3 %) respondents disagreed that temperature 
increased over the years. 
50.0 44.3
45.0
40.0 34.5
35.0
30.0
25.0
20.0
15.0 11.9
10.0 6.2
5.0 3.1
0.0
Strongly agree Agree Uncertain Disagree Strongly disagree
 
Figure 21: Perception of increased temperature by respondents. 
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The survey data on respondents who experienced severe droughts over the last 10 years 
or more are presented in Figure 22 below. The results indicated that a large proportion 
of the respondents (69.6 %) experienced severe droughts over the last 10 years with 
only a few (13.4 %) disagreeing. 
40.0 37.6
35.0 32.0
30.0
25.0
20.0 17.0
15.0
10.3
10.0
5.0 3.1
0.0
Strongly agree Agree Uncertain Disagree Strongly disagree
 
Figure 22: Perception of severe drought by respondents. 
 
The perceptions on severe floods presented in Figure 23 indicated that about two thirds 
of respondents (63.4 %) agreed to have experienced severe floods in the last 10 years 
or more. The proportion of respondents who agreed that they had been affected by 
floods formed about 63.4 %. The respondents who strongly disagreed were about 
18.0 % whilst 18.6 % indicated were uncertain. 
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35.0
31.4 32.0
30.0
25.0
20.0 18.6
15.0 13.9
10.0
5.0 4.1
0.0
Strongly agree Agree Uncertain Disagree Strongly disagree
 
Figure 23: Perception of severe floods by respondents. 
 
Some other survey data on the perceptions of respondents regarding climate change and 
variability are presented in Table 15. The perceptions of respondents on all climate 
change and variability and related events except total annual rainfall were consistent 
with the gauge data analyzed. With regards to rainfall, about 75.2 % perceived that the 
rains started late, for 73.2 %, the rains ended early whilst 77.3 % reported that the rainy 
season had become shorter. Furthermore, about 66.0 % observed frequent dry spells 
whilst and 76.3 % observed frequent stronger winds over the past 10 years. 
 
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Table 15: Perception of climate change and variability by respondents. 
Perceived Trends Strongly Agree  Agree Uncertain Disagree Strongly 
(%) (%) (%) (%) Disagree (%) 
Rains start early in the last 10 years/more 9.3 16.5 4.1 45.9 24.2 
Rains starts late in the last 10 years/more 34.5 40.7 5.2 13.4 6.2 
Rains end early in the last 10 years/more 36.6 36.6 4.1 16.5 6.2 
Rains end late in the last 10 years/more  9.3 17.0 7.2 43.3 23.2 
Rainy season has become shorter in the last 10 years/more 33.5 43.8 7.2 12.4 3.1 
Rainy season has become longer in the last 10 years/more 8.8 9.8 8.2 47.4 25.8 
Dry spells are more frequent in the last 10 years/more 27.3 38.7 13.4 17.5 3.1 
Stronger winds have observed in the last 10 years/more 36.1 40.2 10.8 10.3 2.6 
(Source: Field Survey, 2016/2017) 
 
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The perceptions of respondents on the impacts of observed rainfall and temperature 
trends in respect of drought and flooding are shown in Figures 24 and 25. The survey 
data indicated that about 89.7 % of the respondents agreed with the statement that their 
livelihood strategies were adversely affected by drought, whilst on a few (5.7 %) 
disagreed. With regards to flooding, about 78.9 % of the respondents also confirmed 
that they were negatively affected by flooding over the past ten or more years whereas 
8.3 % disagreed with the statement.  
60.0
51.0
50.0
40.0 38.7
30.0
20.0
10.0 4.6 3.1 2.6
0.0
Strongly agree Agree Uncertain Disagree Strongly disagree
 
Figure 24: Perception of drought situation by respondents. 
 
50.0
45.0 43.8
40.0 35.1
35.0
30.0
25.0
20.0
15.0 12.9
10.0
3.1 5.25.0
0.0
Strongly agree Agree Uncertain Disagree Strongly disagree
 
Figure 25: Perception of flood situation by respondents.  
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The results of the survey indicated that majority of the respondents correctly perceived 
the changes in temperature, other indicators of rainfall, and not total annual rainfall. 
Respondents reported having perceived increased total annual rainfall in the last 10 
years contrary to the gauge data. The results also showed that about 35.6 % and 35.1 % 
of the respondents perceived changes in temperature and rainfall, respectively had 
adversely impacted their livelihoods strategies. 
With regards to the impacts of rainfall on livelihoods, the key informant interviews and 
Focus Group Discussions (FGDs) revealed that low, erratic and unpredicted rainfall 
patterns were the major challenges to crop production. According to participants, low 
rainfall had caused drought and in most cases led to crop failure. They also cited heavy 
rainfall as the main cause of flooding, which in most cases washed away premature 
crops and livestock. These were summed up by a participant as: 
“Too little or too much rainfall at wrong times affect crops and livestock and create 
food shortage and poverty”  
Discussants in FGDs agreed that the rainfall figures have not changed over the years 
but the distribution was bad for crop production hence poor crop yield. The information 
gathered was that previously the rains started around April-May and spread all the way 
to October but in the last 5 to 10 years or more, all the required rains came within 2 or 
3 months usually in June-August. Participants in the interviews opined that the rainfall 
period had become very short over the years reducing the cropping season to two or 
three months annually. Therefore, for about seven to eight months of the year, 
smallholder farmers had nothing to do in terms of their livelihood activities. According 
to participants, the delay and early end of the rains adversely affected crop production 
and smallholder farmer livelihoods strategies as a whole. 
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In terms of water resources, participant pointed out that low rainfall had resulted in 
frequent and early drying up of streams, rivers and dugouts which hitherto supported 
their crops and livestock. In addition, the low and irregular rainfall pattern adversely 
affected vegetation growth and the many dugouts available which usually collected 
rainwater during the rainy season. They contended that the situation impacted 
negatively on farming and livestock production in the area. Another key informant 
supported the above assertions by stating that:  
“The cropping system of about eighty (80) percent of smallholder farmers in the district 
is rain-fed, however, rainfall has become erratic and does not support their livelihood 
activities. The rains arrive late in recent years and the untimely cessation of rainfall 
within the planting season is now a common experience here and disrupts our cropping 
systems. This is serious because many of the farmers are incapable of undertaking 
irrigation farming during the prolong dry periods” 
The study gathered that the paramount chief of the Nandom District had placed an 
annual ban on the celebration of funerals in March-April each year. This was to prevent 
overcrowding and avoid exposure to the extreme heat wave during this period. The 
results of the gauge data analysis were consistent with the perceptions of respondents 
on temperature trends and variability as per the highest seasonal mean maximum and 
minimum temperature values recorded in March and April. 
Participants attributed incidences of pests and diseases in the study to the observed high 
temperature. They explained that high temperature was responsible for the invasion of 
croplands by alien plants (weeds) and animals (insects) species in the area. In addition, 
the frequent and prolonged high temperature led to the early drying up of streams and 
rivers and loss of soil moisture. They attributed early drying or wilting of vegetation 
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and the frequent bushfires to high temperature. A key informant noted that most soil 
microorganisms did not thrive in extreme heat and were not able to perform their roles 
in enriching the soil for optimal crop production. Discussants in the FGDs noted that 
the high temperature observed affected labour or work on farms with respect to the size 
of farmland one was able to cultivate per season. However, some participants noted that 
high temperature enabled them to preserve their crops. 
4.5 Availability, Utilization and Changes in Agroecosystem Services 
4.5.1 Agroecosystem Services in the Study Area 
Respondents in the survey identified and ranked agroecosystem services perceived to 
be highly available and as they were able to draw from their environment to support 
livelihood activities. These agroecosystem services were classified and presented in 
Figure 26. 
The provisioning services were roots, fruits, leaves, animal products, barks, seeds, hides, 
bush meat, fish, honey, mushroom, spices, fodder, cola nuts, and snails. The regulatory 
agroecosystem services identified were pollination, water quality, air quality (dust or 
no dust), pest/disease control (disease prevalence), flood control (presence of wetlands 
and valleys), rain attraction (forest cover), local climate (duration of harmattan) and 
wind breaks. For cultural agroecosystem services, respondents identified educational, 
spiritual and religious values, habitats for gods, royal burial grounds, recreation & 
ecotourism, and aesthetics (outlook of village). The supporting agroecosystem services 
identified and ranked included NTFP (wood trays, grinders, mortars, pestles, chewing 
sticks and mats), fuelwood, timber, primary production, soil fertility (crop yield) and 
soil nutrients (amount of fertilizer used). 
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In all about thirteen (13) agroecosystem services were perceived by respondents to 
underpin their livelihoods strategies in the study communities. 
Majority of respondents (48.5 %) identified bushmeat as most important agroecosystem 
service that underpinned their livelihoods because it contributed largely to their food 
and income needs during prolonged dry spells and drought conditions in the study area. 
This was followed by freshwater represented by about 39.2 % of respondents. The main 
freshwater sources for households were boreholes, the Black Volta River, seasonal 
rivers, and streams as well as hand-dug wells. Averagely, each community had three 
boreholes which supplied freshwater to them constantly. These water sources were used 
mainly for domestic purposes with only negligible number of farmers drawing water 
for irrigation. Fruits were ranked by 38.7 % of respondents followed by soil fertility 
(30.9 %). High proportion of respondents (96.9 %) in the survey were farmers and 
depended heavily on the nutrient status of their croplands for crop production. About 
24.7 % of respondents indicated that fuelwood was one of the key agroecosystem 
services that support their livelihoods. Fuelwood was their main source of energy for 
cooking in the study area. Apart from using fuelwood as a source of energy, it also 
served as source of income for many households. Generally, households relied on 
agroecosystem services for food, water, energy, shelter, and income during prolonged 
dry spells, and climate related extreme events, and crop failure. 
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Figure 26: Available agroecosystem services in the study area (Source: Field Survey 2016/2017). 
NB: NTFP=non-timber forest products 
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4.5.2 Perceptions of Utilization of Agroecosystem Services by Smallholder Farmers 
Respondents indicated the extent of their households’ access to agroecosystem services 
during extreme climate events in the study communities (Figure 27). Majority (83.0 
%) of respondents perceived that they sometimes have access to agroecosystem 
services, whilst 5.2 % indicated that they never had access to any of the important 
agroecosystem services in times of climate risks. 
90 83
80
70
60
50
40
30
20 11.95.2
10
0
Sometimes Never Always
Level of access to agroecosystem services
 
Figure 27: Extent of household’s access to agroecosystem services. 
 
Respondents were asked to indicate the state of their household’s well-being in relation 
to other households in terms of the impacts of limited access to agroecosystem services. 
The results in Figure 28 revealed that more than half of the respondents (51.0 %) 
perceived to be in the same state as other households in the study communities. About 
20.1 % of respondents indicated that they were better off, whilst 18.6 % perceived they 
were worse off compared to other households regarding their access to agroecosystem 
services. 
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Percentage of respondents
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60.0
51.0
50.0
40.0
30.0
20.1
20.0 18.6
10.0 8.8
1.5
0.0
much better better off the same worse off much worse
off off
State of well-being
 
Figure 28: Household’s perceptions of relative access to agroecosystem services. 
 
4.5.3 Perceptions of Changes in Agroecosystem Services 
The survey regarding observed changes in agroecosystem services indicated that about 
91.8 % of the respondents observed changes in agroecosystem services whilst 8.2 % 
did not observe any change. The perceptions of households on changes in specific 
agroecosystem services as shown in Figure 29, revealed that majority (48.5 %) of 
respondents observed changes in bushmeat, followed by freshwater (39.2 %), fruits 
(38.7 %), soil fertility (30.9 %), fuelwood (24.7 %), fish (16.5 %), mushroom (16.0 %), 
NTFP (e.g. chewing sticks, mats, pestles etc.) (11.3 %), cropland (7.7 %), vegetation 
(7.2 %), leaves (6.2 %), honey (4.6 %), and animal products (e.g. skin, hides, horns, 
etc.) (1.0 %), respectively. The survey revealed significant changes in bushmeat, 
freshwater, fruits, and soil fertility over the years in the study area. 
 108 
 
Percentage of respondents
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60.0
50.0 48.5
39.2 38.7
40.0
30.9
30.0 24.7
20.0 16.5 16.0
11.3
10.0 7.7 6.2 4.6 7.2
1.0
0.0
Agrocosystem services
 
Figure 29: Perceptions of respondents on agroecosystem services change in the study area. 
 
4.5.4 How Long Smallholder Farmers Noticed Changes in Agroecosystem Services 
Figure 30 shows the perceptions of smallholder farmers on the period of observed 
changes in agroecosystem services in the study area. 
45 41.8
40
35
30.4
30
25
20 18.6
15
10
5
0
5 years 10 years 10+ years
 
Figure 30: Perceived period of observed agroecosystem services change. 
 109 
 
Percentage of respondents Percentage of respondents
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Most (41.8 %) of the respondents reported that they had observed changes in 
agroecosystem services within the last five (5) years of the study, whilst a relatively 
smaller proportion (18.6 %) reported indicated the last ten (10) years. Majority of 
respondents within the age group of 50 years and above perceived changes in 
agroecosystem services over the past five (5) years. With regards to gender, more males 
perceived these changes also within the last five (5) years than their female respondents. 
4.5.5 Perceived Drivers of Agroecosystem Services Change 
The perception of respondents on drivers of agroecosystems and agroecosystem service 
change is shown in Figure 31. The perceived drivers were low rainfall, bush burning, 
tree cutting, flood, agricultural expansion, population increase, agrochemical use, 
livestock grazing, extreme temperature, bad farming practices, water pollution, strong 
winds, overexploitation, and government policy (e.g. extension services, irrigation, 
bushfires, river buffer zone, ecosystem management). 
90
80 77.3
70
60
50
38.7 38.1
40
30 27.822.7
20 14.9 17.511.9 16.5
10 6.2 5.20.5 4.6
7.7
0
Drivers of agroecosystem services change
 
Figure 31: Drivers of agroecosystem services change identified by respondents in study area. 
 110 
 
Percentage of respondents
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NB: Bad farming practices include burning of crop residue that expose the soil during the long 
dry spell, Low rainfall=drought & prolong dry spells 
 
In addition, respondents indicated the specific factors that caused change in each of the 
key agroecosystem services (i.e. those that underpinned their livelihoods) identified in 
the study area as presented in Table 16. For soil fertility, one of the key agroecosystem 
services supporting the cropping systems of smallholder farmers, respondents observed 
that change was mainly due to continuous cropping, bad farming practices, and low 
rainfall (drought). The results showed that among the four perceived drivers of change 
in soil fertility by respondents, only rainfall was climate-related whilst the other three 
emanated from anthropogenic activities. 
Table 16: Perceived drivers of change in specific agroecosystem services. 
NO. Agroecosystem services Drivers of change 
1. Soil fertility Continuous cropping (11.9 %), low rainfall (4.6 %), 
Erosion (3.6 %), bad agronomic practices (10.3 %),  
2. Fuelwood Tree cutting (18.6 %), Bushfires (5.2 %) 
3. Freshwater Low rainfall (18.0 %) , Tree cutting (6.2 %), 
Agrochemical use (5.2 %), Increased population (4.1 %) 
4. Cropland Overexploitation (4.1 %), Low rainfall (1.0 %), Increased 
population (1.0 %) 
5. Bushmeat Bushfires (32.5 %), Excessive hunting (10.3 %) 
6. Fruits Low rainfall (15.5 %), Strong winds (10.3 %), tree 
cutting (7.2 %) 
7. Fish Water pollution 7.2 %), Low rainfall (6.2 %) 
8. Vegetation cover Bushfires (3.6 %), Tree cutting (2.1 %) 
Source: Field work, 2016 
 
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For fuelwood, respondents perceived tree cutting as the main driver of change, followed 
by bush burning (bushfire), and charcoal production. With regard to drivers of change 
in freshwater, respondents perceived low rainfall, tree cutting, use of chemicals, 
increased population, bush burning (bushfires), and extreme temperature. For the 
cropland, respondents perceived overexploitation as a main driver, followed by 1ow 
rainfall and increased population. Respondents noted that bushmeat was one of the key 
agroecosystem services that support their livelihoods and subsequently mentioned that 
they had observed major changes in this service. Respondents also perceived bushfires 
as a major driver, followed by excessive hunting (overexploitation) and tree cutting as 
drivers of change in bushmeat. The results of the perceived drivers of change in fruits 
included low rainfall as a major driver, followed by strong winds, tree cutting, harsh 
weather and prey birds. Respondents indicated that fruits constitute one of the key 
ecosystem services that they depend upon especially during the dry season but have 
observed changes in this services which had affected supply. The results on drivers of 
change in fish showed that water pollution, low rainfall and overfishing were the main 
causes. With regard to water pollution, respondents indicated that farming along and 
close to river banks as well as the use of “dangerous” chemicals were responsible for 
the decline in fish stocks in their river bodies. Low rainfall was identified as a driver of 
change in the availability of fish in the area as a result of the early drying up of most 
rivers and streams. 
Respondents perceived drivers of change in vegetation in the area to include bushfires, 
tree cutting, low rainfall, extreme temperature and increased population (larger 
household sizes). Most of the respondents indicated that the perennial bushfires do not 
promote growth of vegetation on the land and therefore had exposed the surface of the 
land to harsh weather conditions. For mushroom, a key ecosystem services in the study 
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area, about 15.5% of respondents perceived low rainfall (drought) as the only driver of 
change whereas for honey, they named tree cutting as the only and main cause of change 
and loss. 
4.5.6 Perceptions on Trends in Changes in Agroecosystem Services 
Figure 32 shows the perception of respondents on the observed pattern of change in 
agroecosystem services. The results indicated that for four key agroecosystem services 
including soil fertility, fuelwood, freshwater and cropland, respondents observed varied 
patterns of changes. For soil fertility, more than half of the respondents observed a 
slight decrease, whilst 26.8 % perceived a sharp decrease. 
60
53.1
50
42.8
40 35.1 36.6
30 26.8
23.2 25.3 25.821.1 21.1
20 18
11.9 13.9
7.2 9.810 4.1 6.7
9.3
5.7
2.6
0
Increased Increased Remained Decreased Decreased
Sharply Slightly Constant Slightly Sharply
Soil Fertility Fuelwood Freshwater Cropland
 
Figure 32: Perceived changing trends in specific agroecosystem services. 
 
With regards to fuelwood, more than a third of respondents perceived slight decrease, 
whilst 42.8 % observed a slightly decreased pattern in freshwater. About 36.6 % of 
respondents perceived indicated that they observed a slight decrease in cropland over 
the years. 
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Percentage of Respondents
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The analysis of the results indicated that a high percentage of respondents perceived 
slight decreases in all the agroecosystem services identified. For instance, 53.1 % of the 
respondents observed slight decrease in soil fertility, 35.1 % fuelwood, 42.8 % 
freshwater and 36.6 % cropland, respectively. The respondents observed sharp 
decreases in soil fertility, cropland, freshwater, and fuelwood. 
4.5.7 Perceptions on Impacts of Changes in Agroecosystem Services on Livelihood 
Strategies of Smallholder Farmers 
The results in Figure 33 indicates the extent to which respondents perceived the 
impacts of changes in specific agroecosystem services on their livelihood strategies. 
40.0 35.6 35.6
35.0 33.0
30.0 28.4
25.0 23.7 20.6
20.0
13.9 16.015.0
10.0
5.0
0.0
Agroecosystem services 
 
Figure 33: Extent of agroecosystem service impact on smallholder livelihood strategies. 
 
The respondents reported that for a change in any of the identified agroecosystem 
services there was differentiated impact on the livelihood strategies. The results of the 
survey showed that severity of the impact of change in agroecosystem services in order 
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Percentage of Respondents
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of importance was soil water, soil fertility, cropland, fuelwood, forest cover, pollination, 
livestock pasture, and charcoal production. 
The focus group discussions (FGDs) and key informant interviews revealed that 
smallholder farmers relied heavily on the fertility of the soil as an agroecosystem 
service for food production as well as fuelwood for energy and income, freshwater from 
rivers, streams and dugouts for dry season farming and livestock watering, cropland for 
crop production and livestock grazing pasture, non-timber forest products (wooden 
trays, pestles, chewing sticks, mats), vegetation and forest cover, fruits (shea and 
“dawadawa”) and fish. 
In response to the question: “How has the supply and utilization of agroecosystem 
services changed over the years?” a key informant from the EPA answered: 
“We don’t have any data to show but then with our experiences from the farmers, 
they will always tell you the yield they use to get back 10 years’ time for an acre 
and then the yield they have now, is the major determinant that we use to tell 
that they are really suffering when it comes to the changes that have affected 
their farming. Like 10 years back, someone will tell you that he gets 10 bags for 
an acre and today when he farms one acre how to even get 1 bag is a problem” 
Also. a male discussant in a FGDs stated emotionally as follows: 
“In the “hiin” river, fish harvesting used to be every three years but now it is 
done annually with very little catch. There are no bigger trees around the 
river. Our rivers have become shallow caused by siltation and sedimentation 
as a result of farming close to the river banks and also due to bushfires” 
 
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4.6 Vulnerability to Changes in Agroecosystems and Climate 
4.6.1 Exposures and Sensitivities of Respondents (Smallholder Farmers) 
The major challenges, risks, and shocks reported by respondents were low income 
levels, food shortage, low crop yield, hunger, low soil fertility, cholera, low rainfall, 
cropland (farmland) shortage, fuelwood shortage, flood, water pollution, environmental 
degradation, and bushfires (Figure 34). 
80
69.1
70 64.4
60
50
40
30 24.2
20.1 21.6
20 13.9 15.5
9.8
10 5.2 5.2 5.2 8.22.1
0
Challenge/risk/shock
 
Figure 34: Major exposures of respondents. 
 
The survey found that respondents faced a myriad of challenges that made them 
vulnerable to the impacts of climate change. The findings showed that a large 
proportion of respondents (69.1 %) cited low income whilst 64.4 % mentioned food 
shortage as their major challenges amidst the others. Other challenges included low 
crop yield, low soil fertility, and then hunger. In all the survey identified about thirteen 
(13) major challenges of smallholder farmers.  
 116 
 
Percentage of "YES" responses
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The main challenges participants and discussants in key informant interviews and 
FGDs identified were bushfires, deforestation, low income, extreme heat, floods, erratic 
rainfall, drought, and windstorms. Others included lack of agricultural extension 
services, lack of improved seeds, soil infertility, pest and diseases, lack of chemical 
fertilizers, inadequate cropland, hunger, crop failure, lack of tractor services, lack of 
irrigation services, poverty, loss of economic trees, depletion of fish stocks, soil erosion, 
lack of access to farm inputs, lack of access to labour, and lack of access to markets. 
These data are consistent with the survey on the major challenges of smallholder 
farmers in the study. 
In terms of social differentiation with respect to gender, responses from the FGDs 
indicated that the major challenges above affected both male and female groups. 
However, the female groups pointed out that lack of access to cropland, labour, and 
tractor services were among their major worries. 
4.6.2 Causes of Major Challenges of Smallholder Farmers 
In Figure 35, respondents associated the identified livelihood challenges, risks or 
shocks with several drivers. The results of the survey indicated that the major drivers 
of challenges, risks or shocks of smallholder farmers were low rainfall, bushfires, tree 
cutting, soil infertility, low crop production, and increased population. 
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70 66.5
60
50
41.8 41.2
40
30
19.6 22.2
20
10 8.8
0
Low Bushfires Low crop Soil Tree Increased
rainfall production infertility cutting population
Cause of challenges of respondents
 
Figure 35: Causes of major challenges/risks/shocks (sensitivities) of respondents. 
 
Even though, respondents did not rate high low rainfall as a major challenge in relation 
to food shortage, low income, low crop yield, hunger and low soil fertility, it was 
perceived by majority of the respondents (66.5 %) as a major cause of their challenges, 
risks and shocks.  
The survey identified low rainfall and bushfires as the main drivers of challenges of 
smallholder farmers. In FGDs and key informant interviews, participants recognized 
drivers of challenges such as deforestation, extreme heat, floods, windstorms, lack of 
agricultural extension officers, lack of improved seeds, pest and diseases, lack of 
chemical fertilizers, continuous cropping on same land and lack of financial support 
and markets. 
4.6.3 Perceptions of Social Groups’ Vulnerability to Agroecosystems Change 
The results of the survey on the perceptions of vulnerability of different social groups 
to changes particular agroecosystem service in the study area are presented in Figure 
 118 
 
Percentage of respondents
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36. In terms of changes in cropland, majority of respondents perceived both men and 
women as most vulnerable. For food supply, most of the respondents stated men, 
women, and children were more vulnerable. For changes in soil fertility, half of the 
respondents perceived both men and women to be most vulnerable. In terms of changes 
in fuelwood, over two-thirds of the respondents claimed women were most vulnerable 
while with regard to changes in charcoal production, majority of the respondents 
identified men as most vulnerable. 
Freshwater
Forest cover
Soil water
Grazing land
Charcoal
Cropland
Fuelwood
Soil fertility
Food
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Men Women Men & Women Elderly Youth Children PWDs All
 
Figure 36: Perceived most vulnerable group to changes in agroecosystem services and 
climate. 
 
The results also indicated that over half of the respondents perceived women as most 
vulnerable to changes in grazing land or pasture, whilst for 44.0 %, men were most 
vulnerable. For changes in soil water, most of the respondents reported that both men 
and women were equally most vulnerable. The survey also showed that about sixty-
five percent of respondents perceived both men and women as most vulnerable to forest 
cover changes and non-timber forest products. Majority of respondents perceived that 
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both men and women were most vulnerable to changes in freshwater in the study 
communities. 
The survey results further revealed that gendered groups (male and female) were the 
main social groups in the study area and in terms of which social group was most 
vulnerable to changes in agroecosystem services, a high proportion of respondents cited 
both men and women. The exception was observed with regards to changes in 
fuelwood, where a high percentage (76.0 %) of respondents perceived women as most 
vulnerable whereas 42.0 % and 40.0 %, respectively indicated that men were most 
vulnerable to changes in soil fertility and cropland. 
However, in the FGDs, discussants in most cases agreed that children were most 
vulnerable followed by women in all situations of changes in agroecosystem services. 
They argued that for persons with disabilities (PWDs) and the elderly, other able-bodied 
family members always cared for their welfare. 
4.6.4 Dealing with Vulnerability to Agroecosystem Service Change 
Most of the respondents reported that the responsibility of dealing with challenges 
associated with the observed changes in freshwater was mainly that of the household 
(Figure 37). For changes in food supply, more than half of the respondents said it was 
the household that dealt with any observed change. In terms of changes in soil fertility, 
majority claimed that it was the individual that dealt with the changes while a large 
portion indicated that the community was responsible for dealing with any changes 
associated with soil water. In terms of changes in fuelwood, large percentage of 
respondents indicated that the individual was responsible for dealing with the changes 
observed. For changes in forest cover, a higher proportion of the respondents said it 
was the community that dealt with the changes.  
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Freshwater
Soil water
Forest cover
Grazing Land
Charcoal
Cropland
Fuelwood
Soil fertility
Food
0% 20% 40% 60% 80% 100%
Individual Household Social group Community
District Regional National NGOs
 
Figure 37: Responsibility for dealing with changes in the agroecosystem services and climate. 
 
In terms of changes in grazing land, most respondents claimed that it was the individual 
that dealt with the changes. With regards to changes in cropland, respondents reported 
that the household was responsible for dealing with any change observed. Finally, for 
more than half of the respondents, the individual was responsible for dealing with 
changes that occurred in charcoal production. 
4.6.5 Perceptions of the Level of Satisfaction with Life by Respondents 
Figure 38 shows the survey data on the perceptions of smallholder farmers on their 
subjective well-being. 
 
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50.0 44.3
45.0
40.0
35.0
30.0
25.0 20.1
20.0 16.5 14.4
15.0
10.0 4.6
5.0
0.0
very moderately neither moderately very satisfied
unsatisfied unsatisfied satisfied nor satisfied
unsatisfied
State of Subjective Well-being
 
Figure 38: Subjective well-being of respondents in the study area. 
 
The survey data revealed that majority (44.3 %) of the respondents reported to be 
moderately satisfied with life as a whole (acceptance of the current impacts of climatic 
and agroecosystem services change) with only a few (4.6 %) who were neither satisfied 
nor unsatisfied. However, more than fifteen percent of the respondents felt very 
unsatisfied with life as a whole. About two-thirds of the respondents felt satisfied with 
life as a whole with respect to the ability to cope with current challenges. 
In terms of comparing the study communities (Figure 39), a high proportion (59.7 %) 
of respondents in Naapaal reported to be moderately satisfied followed by households 
in Ketuo (45.8 %), Billengangn (38.3 %), and Gengenkpe (31.3 %). No respondent in 
Gengenkpe reported very satisfied with a few in Billengangn, Ketuo and Naapaal. The 
highest proportion of respondents who reported very unsatisfied with life as a whole 
were in Gengenkpe. The survey data also showed that a high percentage of respondents 
(74.2 %) in Naapaal were satisfied with life as a whole with a few of them in 
Gengenkpe. 
 122 
 
Percentage of respondents
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Figure 39: Subjective well-being of the study communities. 
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4.6.6 Estimated Vulnerability of the Study Communities 
The results of the estimation of the level of vulnerability of households in the study 
communities are presented in Figures 40 to 43 as well as in Tables 1 and 2 of 
Appendix 7. 
SDP
1 0=Least Vulnerable
1=Most Vulnerable
0.8
NDCV 0.6 LS
0.4
0.2
0
Health SN
Water Food
Billengangn Gengenkpe Naapaal Ketuo
 
Figure 40: Vulnerability spider diagram of major components of LVI. 
 
The livelihood vulnerability index for the socio-demographic profile (SDP) for 
households in Billengangn, Gengenkpe, Naapaal, and Ketu were 0.477, 0.416, 0.446 
and 0.528, respectively. These results indicated that Ketuo (0.528) was most vulnerable 
whereas Gengenkpe (0.416) was least vulnerable in terms of socio-demographic 
profile. The LVI in respect of livelihood strategies (LS) for Billengangn, Gengenkpe, 
Naapaal, and Ketuo were 0.495, 0.501, 0.895, and 0.895, respectively. The result 
indicated that Ketuo (0.895) and Naapaal (0.895) were the most vulnerable and the least 
vulnerable community was Billengangn (0.495). 
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The results also indicated that the composite LVI for social networks (SN) was 0.067, 
0.0, 0.164, and 0.459 in Billengangn, Gengenkpe, Naapaal and Ketuo, respectively. 
Hence, the most vulnerable community in terms of social networks was Ketuo whilst 
the least vulnerable was Gengenkpe. 
For the vulnerability index for the food, the results showed that Ketuo (0.872) was most 
vulnerable while the least vulnerable was Naapaal (0.407). The LVI for Billengangn 
and Gengenkpe in terms of food were 0.820 and 0.631, respectively. 
The LVI for water in Billengangn, Gengenkpe, Naapaal, and Ketu were 0.937, 0.865, 
0.887, and 0.972, respectively. This indicated that Ketuo (0.972) was most vulnerable 
whereas Gengenkpe (0.865) was least vulnerable in terms of water. 
The result of the LVI for health showed 0.917, 0.146, 0.895, and 0.750 for Billengangn, 
Gengenkpe, Naapaal and Ketuo, respectively. Hence, the most vulnerable community 
concerning health was Billengangn (0.917) whilst Gengenkpe (0.146) represented the 
least vulnerable community. 
With regards to natural disaster and climate variability (NDCV), Billengangn (0.616) 
was the most vulnerable whereas the least vulnerable was Gengenkpe (0.424) in terms 
of this major component. The composite LVI for Billengangn, Gengenkpe, Naapaal 
and Ketuo were 0.616, 0.424, 0.582 and 0.602, respectively. 
The overall livelihood vulnerability indices as shown in Figure 41 for Billengangn, 
Gengenkpe, Naapaal, and Ketuo were 0.624, 0.426, 0.611 and 0.703, respectively. The 
overall livelihood vulnerability index estimation incorporates the socio-demographic 
profile, livelihood strategies, social networks, food, water, health, and natural disaster 
and climate variability major components to analyze the vulnerability of households. 
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The results of the overall LVI indicated that Ketuo was most vulnerable (0.703) whilst 
Gengenkpe (0.426) was the least vulnerable community. 
0.8
0.703
0.7 0.624 0.611
0.6
0.5 0.426
0.4
0.3
0.2
0.1
0
Billengangn Gengenkpe Naapaal Ketuo
Study community
 
Figure 41: Estimated overall livelihood vulnerability indices of respondents. 
 
Furthermore, the results of the LVI in Figure 42 and in accordance with the IPCC’s 
definition of vulnerability showed that Ketuo (0.602) was the community with most 
vulnerable households (i.e. comprising socio-demographic, livelihood strategies, and 
social networks). The community that was most sensitive to climate change and 
variability and agroecosystem services change was Billengangn (0.891) in respect of 
food, water, and health. The most vulnerable community with respect to exposure to 
natural disaster and climate variability and agroecosystems change was Billengangn 
(0.612). In terms of adaptation capacity for the four study communities Ketuo (0.620) 
was more vulnerable followed by Naapaal (0.502), Billengangn (0.346), and 
Gengenkpe (0.306). For sensitivity, Billengangn (0.891) was more vulnerable followed 
by Ketuo (0.865), Naapaal (0.730), and Gengenkpe (0.547), whilst in terms of exposure, 
Billengangn (0.612) was more vulnerable followed by Ketuo (0.602); Naapaal (0.582,) 
and Gengenkpe (0.424), respectively. 
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Index
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Adaptive
Capacity 0=low contributing factor
1 1=high contributing factor 
0.8
0.6
0.4
0.2
0
Exposure Sensitivity
Billengangn Gengenkpe Naapaal Ketuo
 
Figure 42: Vulnerability triangle diagram for IPCC contributing factor. 
 
The indices for IPCC-LVI as shown in Figure 43 for the study communities indicated 
that even though there was no much differences between the values as per the range of 
zero (0) to one (1), Ketuo was most vulnerable whilst Billengangn, and Naapaal were 
the least vulnerable. 
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1
0.9 0.867
0.8
0.7
0.6
0.5
0.4
0.3 0.204
0.2
0.1 0.065 0.061
0
Billengangn Gengenkpe Naapaal Ketuo
 
Figure 43: LVI-IPCC value for the study communities. 
 
The LVI-IPCC computations based on the integration of adaptation capacity, 
sensitivity, and exposure variables of the vulnerability analysis were 0.204, 0.065, 
0.031 and 0.867, for Billengangn, Gengenkpe, Naapaal, and Ketuo, respectively. The 
values used for the LVI-IPCC estimation were derived from the seven major 
components of the LVI. The results indicated that Ketuo (0.867) was the most 
vulnerable whereas Naapaal (0.061) was the least vulnerable community. 
4.7 Adaptation Options of Smallholder Farmers 
Figure 44 shows the results of the perceptions of smallholder farmers on adaptation 
strategies employed on their farms to improve crop production and t overcome impacts 
of changes in agroecosystem services and climate. The survey showed that a large 
proportion of respondents (91.8 %) reported the use of organic manure and inorganic 
(chemical) fertilizers (80.4 %). Other adaptation strategies used were tree planting, no 
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Index
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bush burning or burning of crop residues on their farms, mulching, crop diversification, 
use improved seeds, and change of farming practices. 
100 91.8
90 80.4
80
70
60 56.250.5
50 38.7 40.2
40 27.8 32.030
20 13.4
10 2.6 3.1 1.0 5.7 3.1
0
A B C D E F G H I J K L M N
Adaptation option
 
Figure 44: Adaptation options and strategies of respondents. 
Note: A=Avoidance of bush burning, B=Tree planting, C=Changing farming practices, D=Increasing 
farm size, E=Growing drought resistant crops, F=Livestock production, G=Crop diversification, 
H=Mulching, I=Improved seeds, J=Attracting pollinators, K=Application of Organic manure, 
L=application of Fertilizer, M=Terracing, N=Trenching 
 
The results indicated that the adaptation options used less frequently by smallholder 
farmers were increasing farm sizes, growing drought resistant crops, livestock 
production, attracting pollinators, terracing, and trenching. 
FGDs revealed that effective adaptation strategies of smallholder farmers were soil 
enrichment and soil water conservation strategies such as stone bonding and compost 
usage. Other feasible adaptation strategies included “no burning and no tree cutting”, 
application of chemical fertilizers, dry season farming, livestock rearing, relocation of 
farms from flood prone areas to upland areas, ponding of small water bodies to conserve 
water in their farms, planting trees on depleted lands, creation of fire belts to protect 
crops on the farms, ridging to hold water on farms to conserve soil moisture. 
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The other category of adaptation options employed by discussants was livelihood 
diversification strategies. These included raising seedlings for sale, sale of forest 
products (fruits and leaves), sale of fuelwood, hunting, engaging in construction of 
home buildings for a fee. In most cases, the women were engaged in weaving, migration, 
formation of village savings and loans association (VSLA), soap and pomade making, 
“pito brewing”, and shea butter processing. Few respondents had also resorted to 
forming farmer groups to support members as the case of Naapaal where there was a 
vibrant “sorghum growers” association of women. 
4.7.1 Perceptions of Farmers on Determinants of Adaptation Strategies 
The survey identified factors that influenced farmers’ decision whether to use or not 
use a particular adaptation option as in Table 17. The study found that a large 
proportion of respondents cited cost and not known (lack of information) as reasons 
why they were not using certain adaptation strategies available in the study area. For 
instance, majority of farmers reported that the use of drought resistant crops was not 
known, whilst those who even knew mentioned cost as the reasons for not adopting that 
option. In terms of diversification of crop, a high percentage of respondents reported 
the option was not known or had no information about it, and for some it was expensive. 
Most respondents also indicated that mulching was not known. Majority of farmers 
were not adopting the use of improved seeds because they did not know about it, whilst 
a few mentioned cost as a barrier. More than half of the respondents did not know about 
attracting pollinators as an adaptation strategy. 
  
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Table 17: Factors that affect utilization of available adaptation strategies. 
Adaptation strategies Cost Not Not Sufficient Uncertain Not 
(%) Known Information Outcome Enough 
(%) (%) (%) (%) 
Drought Resistant Crops 23.2 28.9 3.2 4.6 1.0 
Diversification of Crops 19.6 26.8 7.2 4.6 2.6 
Mulching 14.9 20.6 13.9 4.6 4.6 
Improved Seeds 18.0 33.0 13.4 2.6 2.6 
Pollination 11.3 55.2 16.0 6.2 7.2 
Organic Manure 6.2 2.1 1.5 1.5 4.6 
Fertilizer 18.6 0.5 1.0 0.5 3.1 
Source: Field Survey, 2016/2017 
4.7.2 Determinants of Adaptation Strategies for Smallholder Farmers 
Factors that influenced adoption or otherwise of indigenous and introduced adaptation 
strategies of smallholder farmers were availability of agroecosystem services, farm size 
of respondents in hectares, access to climate information, access to training, farmland 
ownership, how satisfied a respondent is with life in relation to major challenges, 
gender and access to information on managing ecosystem services. However, 
regression analysis indicated access to climate information, how satisfied a respondent 
is with life with regards to major challenges, and availability of agroecosystem services 
were the significant determinants of adaptation strategies (see Table 18 and the output 
in Appendix 9). Durbin-Watson statistic also showed that some of the predictors were 
autocorrelated. 
 
 
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Table 18: Significant determinants of adaptation strategies. 
Model Unstandardized Standardized t Sig 
Coefficients Coefficient 
 B Std. Error Beta   
(Constant) 1.806 .095  19.013 .000 
Access to training -.008 .024 -.018 -.321 .748 
Access to climate information -.397 .030 -.688 -13.200 .000 
Gender .015 .023 .034 .644 .521 
Farm size of participant in 
-.018 .019 -.047 -.912 .363 
hectares 
Farmland ownership .012 .013 .046 .911 .363 
Access to information on 
managing Agroecosystem .003 .025 .006 .110 .913 
services 
How satisfied a farmer is with 
life in relation to major -.023 .008 -.144 -2.780 .006 
challenges 
Available Agroecosystem 
.090 .037 .119 2.435 .016 
services 
a. Dependent Variable: adaptation strategies. 
  
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4.8 Knowledge and Information Sources on Adaptation Options 
The perceptions of household respondents on the knowledge and information sources 
on available adaptation strategies are shown in (Figure 45). The survey found that, 
majority of respondents received information on agroecosystem services and climate 
change through radio services, followed by family and friends, village leaders such as 
traditional authorities and assembly members, government authorities like the District 
Assembly, extension officers, and school teachers. 
100 91.2
90 86.6 79.9
80
70 67.5 66.0 61.3
60
50
40 32.5
30 27.819.6
20 10.8 15.0
10 4.6 2.1 5.2
0
A B C D E F G H I J K L M N
Knowledge & climate information sources
 
Figure 45: Source of knowledge and climate information for respondents. 
NOTE: A=Meteorological services , B=Indigenous forecast information, C=Newspaper D=Radio, 
E=TV, F=NGOs, G=Government Authorities (District Assembly), H=Extension Officers, I=Internet, 
J=School/Teachers, K=Visiting Climate Experts, L=Family & Friends, M=Village Leaders (Traditional 
Authorities), N=Government publications 
 
The survey data showed that the main source of climate and agroecosystem services 
information in the study area was the radio (VON FM 97.1 MHz). Respondents also 
relied on family and friends and community leaders for information regarding extreme 
weather event forecast or warning. 
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Percentage of respondents
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Other sources of knowledge and information identified by discussants during focus 
group discussions included the Department of Agriculture, NANDIRDEP, NANDMO, 
and NGOs (e.g. PRUDA, ADVANCE Care International). A key informant from the 
Department of Agriculture/Ministry of Food and Agriculture (MOFA) intimated that 
their outfit collaborates with the District Assembly to provide extension services to 9 
operational areas in the district through training programmes. During these programmes 
farmers were provided with skills regarding soil enrichment practices, non-burning of 
crop residue, bushfire prevention, and tree planting.  
The survey further revealed that MOFA through extension services provided training 
on improved farming practices to farmers, whilst NADMO and GNFS sensitized 
farmers on bushfire prevention and flood management. The EPA in collaboration with 
partners created awareness and trained farmers on good environmental management 
practices to safeguard existing agroecosystem services. 
4.8.1 Trustworthiness of Knowledge and Information Sources 
Figure 46 shows the results of the perceptions of smallholder farmers on whether they 
trusted the knowledge and information sources on adaptation. Most respondents 
indicated they strongly agreed that they trusted radio, followed by extension officers, 
village leaders such as traditional authorities, family and friends and school teachers. 
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35
29.4
30
26.3
25 23.7
21.6
20 18
14.9
15 12.9
11.3
10 7.2
5.7 5.2
5 2.6 1.5
0
0
A B C D E F G H I J K L M N
Knowledege and climate information sources
 
Figure 46: Trustworthiness of sources of knowledge and climate information. 
NOTE: A=Meteorological services , B=Indigenous forecast information, C=Newspaper D=Radio, 
E=TV, F=NGOs, G=Government Authorities (District Assembly), H=Extension Officers, I=Internet, 
J=School/Teachers, K=Visiting Climate Experts, L=Family & Friends, M=Village Leaders (Traditional 
Authorities), N=Government publications. 
 
4.8.2 Sufficiency of Knowledge and Information Sources 
Respondents were asked to indicate the whether the climate information ever received 
were sufficient and the result of the survey is shown in Figure 47. The results indicated 
that majority reported that knowledge and information from the radio were sufficient, 
next was family and friends and followed by government authorities (District 
Assembly), and village leaders (traditional authorities), extension officers, and school 
teachers. 
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Percentage of "Strongly agree" 
responses
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60
53.6 53.1
50 44.8 44.8
42.3 40.7
40
30
19.1
20 15.5
9.8
10 7.7
2.6 2.1 2.1 3.1
0
A B C D E F G H I J K L M N
Knowledge and Information Source
 
Figure 47: Percentage of respondents on whether source of knowledge and climate 
information is sufficient. 
A=Meteorological services, B=Indigenous forecast information, C=Newspaper, D=Radio, E=TV, 
F=NGOs, G=Government Authorities (District Assembly), H=Extension Officers, I=Internet, 
J=School/Teachers, K=Visiting Climate Experts, L=Family & Friends, M=Village Leaders (Traditional 
Authorities), N=Government publications. 
 
4.9 Managing Changes in Agroecosystem Service and Climate 
4.9.1 Institutional Support in Managing Changes in Agroecosystem and Climate 
For smallholder farmers to effectively manage the potential impacts of changes in 
agroecosystem services and climate, both government and non-government institutions 
were expected to support them. Figure 48 shows the survey data on which institutions 
provided support to respondents. All the respondents reported receiving support from 
their chiefs and elders (traditional authorities) while almost all of them mentioned that 
they received support from co-farmers, followed by the District Assembly, NGOs and 
Department of Agriculture (MOFA). 
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Percentage of "YES" responses
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YES NO
100.0 99.5
100.0 94.8 92.3
88.1
90.0
80.0
70.0
60.0 56.2
50.0 43.8
40.0
30.0
20.0 11.9
10.0 5.2 7.7 0.0 0.5
0.0
District NADMO Chiefs/Elders MOFA/Agric Co-Farmers NGOs
Assembly
Support institution/organization
 
Figure 48: Support institutions/organizations identified by respondents. 
 
The survey revealed that a large portion of respondents relied on village leaders such 
as chiefs and assembly members for support with regards to the management of changes 
associated with agroecosystem services such as water, food, fuelwood, forests cover 
and croplands and climate change and variability. Smallholder farmers in the study area 
depended on colleague farmers (Co-farmers) for information on climate change and 
agroecosystem services management practices. The District Assembly was reported by 
a most respondents to be supportive of their livelihood activities in respect of 
agroecosystems and climate change. Other institutions cited by respondents as sources 
of information for the management of these changes were NADMO, NGOs and 
Department of Agriculture or MOFA. 
4.9.2 Types of Training on Agroecosystems Change Management 
The survey data showed that respondents received training with regards to management 
of agroecosystem change. The results indicated that respondents were divided over 
whether they received training or not. About 50.5 % of the respondents reported not 
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Percentage of respondents
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receiving any training while 49.5 % claimed that they received some training. However, 
three main areas of training were identified by respondents and include bushfires 
prevention, soil enrichment practices and tillage practices as shown in Figure 49. 
30 27.8
25
20
15 12.4
10 9.3
5
0
Bushfires Prevention Soil Enrichment Tillage Practices
Type of training  
Figure 49: Type of training received by respondents. 
 
Results of the study showed that majority of respondents received training on soil 
enrichment practices followed by bushfire prevention and then best tillage practices. 
According to participants, the aim of all training activities was geared at improving soil 
fertility for high crop production and maintenance of vegetation, water and forest 
resources. 
  
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Percentage of respondents
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CHAPTER FIVE: DISCUSSION 
5.1 Household Profile and Farm Characteristics 
The findings revealed that the interviewed households consisted of 52.6 % males and 
47.4 % females. However, the 2010 population and housing Census report for the study 
district comprised 48.4 % males and 51.6 % and females (GSS, 2014). The results of 
gender distribution in the study communities were in contrast with the established 
census data except Ketuo that recorded 70.8 % female participants. Gender plays 
crucial roles in the distribution and right to the use of some natural resources such as 
land and its associated agroecosystem services (Boon & Ahenkan, 2011). The level of 
access to certain assets in the study area was governed by the gender of the household 
head. Majority of female respondents revealed that they did not have access to fertile 
land for crop production. This is because traditionally, males own land and even when 
a male household head passes away his properties would be inherited by the brothers. 
The relatives of the man hold the right to the use of the deceased’s assets at the expense 
of the widow who eventually assumes the responsibilities of the household head. This 
situation has implications for vulnerability and adaptation to agroecosystem services 
and climate change in these communities (Bohle et al., 1994; Rurinda et al., 2014; 
Hameso, 2015). 
Majority of the respondents were within the age group of fifty (50) years and above. 
This age group has implications for vulnerability of smallholder farmers with regards 
to food production.  
The proportion of respondents who did not have any education in the study area was 
54.1 % with the highest in Gengenkpe (60.4 %). According to the 2010 population and 
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housing census (GSS, 2014), 50.2 % of the population 11 years and above in the district 
had basic levels of education, whereas 49.2 % had no education. The level of education 
of smallholder farmers has implications for understanding changes in their environment 
and adoption of adaptation strategies as well as the extent of acceptance and use of 
climate information and best agronomic practices (Hameso, 2015). 
Meanwhile, the 2010 population and housing census (GSS, 2014) reported that the 
average household size for rural areas in the district was 6.2. In addition, Nyantakyi-
Frimpong & Bezner-Kerr, (2015) reported 7.8 as mean household size of respondents 
in a related study. The findings in this study revealed that about 56.2 % of interviewed 
households had household sizes between 6 and 10. Household size can be described as 
a “mixed bag” for families because of the associated negative and positive implications. 
The size of a household is significant with regard to access and use of agroecosystem 
services and the ability to cope with changes. For example, a larger household size has 
negative impacts on the amount of farmland holdings of each member of the family and 
food production, which may amplify vulnerability to climate change (Antwi-Agyei et 
al., 2012; Etwire et al., 2013). On the other hand, a higher proportion of household 
members gives the household an advantage in terms of labour for economic activities 
and support for the aged and persons with disabilities. 
In terms of occupation, majority of household heads (96.9 %) were smallholder farmers. 
This result is similar to a study conducted among households in northern Ghana by 
Etwire et al. (2013) and in Ethiopia by Hameso (2015), where over 96.0 % of 
participants were farmers. In addition, the 2010 population and housing census reported 
85.3 % of households engaged in agriculture in the Nandom district.  
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A larger proportion (73.2 %) of respondents in this study held about two (2) hectares 
of land. The 2010 population and housing census report was not clear on the average 
farm size of households, however, according to the medium-term development plan 
(MTDP) of the Nandom district, the projected average farm size per farmer for 2015 
was 4.3 hectares. In a related study in the Upper West Region, Nyantakyi-Frimpong & 
Bezner-Kerr (2015) reported a mean land size of farmers of 1.5 hectares. The amount 
of land holdings and the percentage of respondents involved in agriculture has 
significant implications for the level of vulnerability and adaptability (World Bank, 
2007; Morten 2004) of the smallholder farmer to climate change, since land constitute 
one of the most important natural capital assets in semi-arid areas. Land holdings affects 
diversification of smallholder livelihood activities with regards to access to options of 
soil and land management practices (Deressa et al., 2009; Antwi-Agyei et al., 2012). 
Land ownership in the study area is largely the family land system where the farmlands 
cultivated by households belonged to their own families. The land tenure system of 
famers has implications for their vulnerability and adaptation strategies in terms of 
changes in agroecosystem services and climate. For example, landlords were able to 
lease part of their land holdings to others from which they received a share in the 
proceeds. Women discussants complained that they did not have rights to own land and 
had to depend on their husbands and friends, who leased smaller and mostly infertile 
parcels of farmland to them. In a related study, Antwi-Agyei et al. (2012) reported that 
complex land tenure systems were serious constraints to adaptation to climate change 
and variability in vulnerable districts in northern Ghana.  
The major crops cultivated by farmers in the study area were maize, groundnut, millet, 
rice, beans, and guinea corn.  Cereals such as maize, millet, and guinea corn serve as 
vital staple food crops for smallholder farmers and are equally vulnerable to low and 
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erratic rainfall regimes because they require adequate water for growth (Schlenker & 
Lobell, 2010). Yam and vegetables were cultivated on relatively smaller scale. 
Similarly, the district reported production of sorghum, groundnut, millet, cowpea, 
maize, rice, soya beans, and yam in their crop yield projection for 2016 and claimed 
that there is an advantage in the production of groundnuts and cowpea (DMTDP, 2015). 
The district report is consistent with the findings of this study in terms of crops 
cultivated. The implication is that there is the opportunity to improve the livelihoods of 
farmers through vegetable production during the dry season by irrigation using water 
from the Black Volta River. There is the need for government policy on institutional 
support to build capacity for irrigation farming all-year round.  
Crop utilization here was a measure to determine whether households were able to store 
enough food, sell, and reserve some as seeds for future use. It was also an indication of 
adequate crop production over the years. The findings in this study suggest that more 
households consumed all crops produced within the season and are unable to even save 
some as seeds for the new farming season.  
A large percentage of household (85.6 %) never received credit to support their farming 
activities over the years. Meanwhile, studies have shown that access to credit positively 
enhance adaptation strategies (Gbetibouo, 2009; Etwire et al., 2013) of farmers. For 
example, farmers who have access to credit are able to purchase improved crop varieties, 
engage in irrigation, and acquire new technologies to adapt to the changing climate 
conditions. It was observed from FGDs and key informant interviews that respondents 
who were members of organized groups such as women groups and Village Savings 
and Loans Association (VSLA) had access to credit for their farming activities 
compared to those without any group or association. The findings revealed that the 
formation of groups may be a transformative strategy to adapt to climate change and 
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extreme events in the study area. The main sources of credit were the bank and money 
lenders. 
The results of the survey data on household profile and farm characteristics were 
incorporated into assessment of vulnerability of households in the study communities. 
The information gathered was used in the development of indicators for the livelihood 
vulnerability index (LVI) model. 
Generally, households relied on the VSLA for financial during support whilst many 
who were not members seemed helpless in times of difficulties during prolonged dry 
periods. 
5.2 Soil Fertility Status 
Analyses of soil samples from selected farms in the study area indicated mean pH range 
between 5.60 and 8.0. In a related study, the Savannah Agricultural Research Institute 
(SARI) of the Council for Scientific and Industrial Research (CSIR) (2013) reported 
pH ranges from 5.35 to 6.25 in soil samples from five districts of the Upper West region. 
The pH is one of the important chemical properties of agricultural soil that influences 
the solubility and availability of soil nutrients, minerals and other elements for plant 
use. The pH range in soil that is acceptable for growth of most crops is from 3 to 9 
(Estefan et al., 2013). The results of the pH values in this study implies that the soil 
were suitable for growth of crops. Usually soil pH between 5.5 and 7.0 stimulates the 
availability of nitrogen, phosphorus, and decomposition of crop remains by soil 
microorganisms. 
Electrical conductivity (EC) is an indication of soil salinity, which influences the degree 
to which the soil is suitable for crop growth (Estefan et al., 2013). EC values of range 
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0-2000 μS/cm are good for all crops but between 2000 to 4000 μS/cm may not be 
favourable for crops. However, most crops will not grow well with values between 
4000 and 8000 μS/cm. 
With regards to organic matter, the analysis indicated a minimum of 0.92 % at a site in 
Naapaal and a maximum of 2.22 % at a farm in Billengangn. Similarly, CSIR-SARI 
(2013) reported lower trends of organic matter content (0.64-1.45 %) in topsoils in the 
study region. The findings from this study implies that farm soils had marginal to 
adequate levels of organic matter (Estefan et al., 2013). Soil organic matter influences 
soil aggregate formation, nutrient reserves and its availability, moisture retention, and 
biological activity. Soils of semi-arid rain-fed areas have normally less than 1.5 % of 
soil organic matter (FAO, 1980; Estefan et al., 2013). According to Estefan et al. (2013), 
levels of organic matter in soil less than 0.86 % can be described as low, 0.86-1.29 %, 
marginal and more than 1.29 % as adequate (FAO, 1980; Martins & Lindsay, 1990). 
The FAO (1980) guidelines further note that total nitrogen (N) of 0.10 % or greater is 
adequate for growth of most crops in dry land areas (Estefan et al., 2013). The levels 
of total nitrogen measured at selected farms ranged between 0.06 % and 0.22 %. In the 
same region, CSIR-SARI (2013) reported total N range of 0.0-0.10 % lower than what 
was recorded in this study. The type of soil in the study area is typical of dryland areas 
and can be considered as suitable for growth of most crops as per the measured values. 
Generally, the levels of chemical and physical properties of soil samples analyzed were 
marginal to adequate and may support growth of plants and contribute positively to the 
agricultural livelihoods of farmers. 
The trend analysis of the soil properties over the years could not be done because 
literature search and consultations with the Department of Agriculture at the Nandom 
District Assembly did not find any historical data on soil levels in the district.  
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Soil is an important natural resources that provide supporting agroecosystem service in 
terms of soil fertility (Boon & Ahenkan, 2011; Smith & Sullivan, 2014). Soil forms the 
basis for other agroecosystem services providing smallholders farmers with, fibre, food, 
and fuel for their livelihoods and well-being. 
5.3 Water Quality 
According to the Joint Monitoring Programme for Water Supply and Sanitation (JMP, 
2008), about 29 % of rural population in worldwide rely on unimproved water sources. 
The Ghana Water Policy notes that access to enough safe water is a fundamental human 
need and efforts must be made to manage and sustain the quality of available water 
resources (Government of Ghana, 2007). In addition, a study by Rossiter et al. (2010) 
that analyzed the quality of water resources in Ghana sampled only two boreholes from 
the Upper West Region and no samples were taken from the Black Volta River. Hence, 
the water quality assessment objective of this study was to ascertain the quality with 
respect to domestic water use. The focus was to determine the extent to which water 
resources in the study area contribute to the agroecosystem services provisioning role 
of water in terms of quality and the effects on livelihood strategies of households. The 
findings revealed that the mean pH range of the river water was 7.3 to 7.8 whereas that 
of the borehole water was 7.2 to 8.5 indicating neutral to slightly alkaline. However, 
concentration of all other water quality parameters including physico-chemicals, ions 
and nutrients and heavy metals were found below the TWQRs in term of the River 
water and WHO drinking guidelines for the borehole water. 
 
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This study found that the concentration levels of turbidity in all river water samples 
exceeded the recommended guideline values of 1.0 NTU and 5.0 NTU of the WHO. 
The high levels of turbidity usually affect acceptability of water (WHO, 2011). The 
high levels of turbidity may be as a result of farming activities along the river. The pH 
values of river water samples were found within the TWQRs (6-9) with the highest 
value recorded in Billengangn (7.8) and lowest in Gengenkpe 7.3). The measured pH 
levels in the river water samples indicated neutral to less basic water sources in the 
study communities (Table 8). It has been suggested that at pH less than 6, elements 
such as Fe and Al become highly soluble, whilst atmospheric oxides may dissolve at a 
pH more than 8.5 (Rossiter et al., 2010) and low pH levels adversely affect aquatic life. 
Rossiter et al. (2010) reported pH range of between 3.7 to 8.9 from various water 
sources in Ghana. In terms of electrical conductivity (EC), the highest concentration 
was in the river water at Ketuo, whilst the least was at Billengangn. EC is indication of 
the total dissolved solids (TDS) and salinity in the water sample, which may affect 
acceptability by users in terms of taste. High TDS in water may cause corrosion of 
home appliances and equipment such as hand pumps (Rossiter et al., 2010). 
In addition, the concentrations of all the ions measured in river water samples were 
within the TWQRs guidelines. This is an indication that there may not be any associated 
health, aesthetic, scaling, and corrosion issues for households regarding the domestic 
water use (WHO, 2011). However, the concentration of NO -3  measured in river water 
samples at Ketuo (4.80 mg/L) was near the upper limits of the threshold of the guideline 
value of 6.00 mg/L (TWQRs) and 50 mg/L (WHO). Pelig-Ba et al. (2004) reported a 
mean of 93.3 mg/L of NO -3  and a maximum of 511 mg/L in groundwater in the northern 
region of Ghana, whilst Rossiter et al. (2010) found 508 mg/L of NO -3  in water samples 
in the Western, Southern Volta, Northern and Upper East regions of Ghana. Nitrate, 
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NO -3  is naturally occurring in water and in relatively small amounts as revealed in this 
study. High levels of NO -3  stimulates plant growth such as algae and may cause 
objectionable eutrophication that produces secondary pollution from subsequent death 
and decay of plants (Baird & Cann, 2012). A situation that may affect the use of the 
river water or which may pose health risk to households and cause disease burden for 
households. For instance, nitrates are converted to nitrites in the intestines and once 
absorbed into the bloodstream, nitrites prevent haemoglobin from transporting oxygen 
(Baird & Cann, 2012). High NO -3 N levels in drinking water (>10 mg/L) are linked with 
health problems (i.e. methemoglobinemia). High levels of NO -3 N in water for human 
consumption should be avoided, such water is however, suitable for irrigation as it is 
an additional source of nutrients. 
The levels of two ions Sodium, Na+ and Sulphate, SO 2-4  were relatively high in borehole 
water samples from all study communities with very high Na+ concentration recorded 
in Billengangn. However, Naapaal recorded remarkably high concentrations of 
Calcium, Ca2+ compared to other water from other communities. Na+ usually combines 
with chloride, Cl- to produce salty taste in water that is detectable at about 250 mg/L. 
Calcium causes hardness of water which affects acceptability of drinking water. 
According to the WHO (2011), calcium has a taste threshold of between 100-300 mg/L, 
which is largely determined by the associated anion whereas that of magnesium is less. 
However, water hardness of less than 100 mg/L may cause rusting in water pipes. 
Sulphate, SO 2-4  is not a notable toxic anion, however, in water containing Ca2+ forms 
hard scale in steam boilers. In large amounts, SO 2-4  in combination with other 
constituents gives a bitter taste to water and may affect acceptability in terms of 
aesthetic (Rossiter et al., 2010). Concentrations above 250 mg/L may have a laxative 
effect, but 500 mg/L is considered safe (WHO, 2011). Hardness of water is usually 
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indicated by the precipitation of soap scum and the excess soap required for cleaning 
purposes, which may pose economic burden to households. Hardness may also affect 
acceptability of drinking water since the degree of hardness of water vary from one 
community to another (WHO, 2011). 
The results of trace metal analysis indicated minimal contamination of water samples 
in the study area The concentration of Cr, Cd, Co, Cu and Zn were found below 
detection limit (BDL) in all water samples, whilst Fe and Ni exceeded the guidelines of 
the TWQRs for surface water for domestic use. 
The bacteriological analyses revealed that all river water samples from the study area 
exceeded the guideline values. Consequently, respondents reported cases of diarrhoea 
and cholera as a major challenge. 
The implications of the concentration levels of the ions measured for the households in 
the study communities are in respect of health and aesthetic concerns. For health, nitrate 
NO -3 levels were considered which indicated there may not be health risk problems, 
whilst for aesthetic, SO 2-4  levels do not indicate any major risk for households. High 
levels of fluoride cause skeletal and dental fluorosis and may create health concerns for 
households. Freshwater supply as an agroecosystem service provisioning may not be 
impaired as the water quality regarding ions levels were generally acceptable per the 
guideline values. Water quality has implications for households with regards to high 
levels of calcium and magnesium, which may cause scaling of home appliances, 
corrosion, and creates economic burden which will eventually enhance vulnerability 
and hinder adaptation of smallholder farmers to climate. 
In summary, based on the mean values of pH measured (mean range, 5.6 to 8.0) soils 
in the study area were generally suitable for growth of most crops and may support the 
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agricultural activities of households. This implies that the sampled soils may play the 
agroecosystem services provisioning role in the study area.  
On the other hand, the physico-chemical parameters, ions, and heavy metals analyzed 
were below the TWQRs for surface water and WHO drinking water guidelines except 
for turbidity in all water sample. Generally, the physical and chemical water quality 
may not pose adverse impacts on human health and aesthetics due to the low levels 
detected. However, the results of the bacteriological analysis of water samples from the 
Black Volta River indicated high counts of total coliform and faecal coliform. This may 
result in the outbreak of water-borne diseases like cholera in the area since most of the 
farmers drunk from the river during working hours on their farms. The soil pH values 
were transformed and integrated into the livelihood strategies component, whilst the 
mean levels pH, NO -, and SO 2-3 4  of river water samples were aggregated into the water 
component of the LVI for the vulnerability analysis. 
5.4 Rainfall and Temperature Trends and Variability 
The findings confirmed the unimodal nature of the rainfall annual cycle in the sudan 
savannah zone of Ghana (Nkrumah et al., 2014). The observed high variability nature 
of annual rainfall cycle over the study area affected the selection of livelihood strategies 
of smallholder farmers significantly in study communities. 
On the other hand, majority of respondents’ perceptions regarding total annual rainfall 
contradicted the observed gauge rainfall trend, which was decreasing. The perceptions 
of respondents in this study may be attributed to the erratic and unpredictable nature of 
rainfall and the many indicators of precipitation which prevents accurate perceptions. 
For example, many of the respondents reported having experienced severe floods and 
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therefore, rainfall had increased over the period. Besides the uneven rainfall distribution 
might have contributed to these perceptions. The IPCC (2007a) reported that about 
90 % models were believed to have either overestimated or underestimated rainfall in 
given regions. However, in a related study in the Upper West Region, Nyantakyi-
Frimpong & Bezner-Kerr (2015) reported that the perceptions of smallholder farmers 
reflected the gauge data analyzed. These authors found that most farmers perceived that 
total rainfall decreased over a two-decade period. Similarly, in southern Nigeria, Apata 
(2011) reported that about 58 % of respondents perceived decreased rainfall trend over 
a 10-year period. Other studies reported that majority of farmers perceived a decrease 
in total rainfall events in the Upper West region and noted that a great number of 
farmers agreed that the onset and cessation of the rains was difficult to predict (Antwi-
Agyei et al., 2012; Etwire et al., 2013). 
The implication is that we are experiencing warmer days in recent years compared to 
the past though the trend is not significant. The findings regarding the monthly mean 
maximum temperature showed highest in March and lowest in August, which is similar 
to values (21 ℃ and 32 ℃) reported by Ghana Statistical Services (2014) for the district. 
Similarly, this analysis indicated and average minimum temperature of 22.1 ℃ whereas 
the GSS (2014) reported 23 ℃. The anomaly plots revealed interannual variation in 
night temperature, which means that its becoming warmer during the night in the study 
area. This has implication for human and crop health as was found from the household 
survey, where respondents reported recent incidences of cerebrospinal meningitis. The 
study further confirmed that the paramount chief and the elders of the Nandom 
Traditional area had placed a ban on the celebration of funerals between March and 
April. This was to avoid overcrowding during the heat periods and prevent related 
diseases.  
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Furthermore, the observed high temperature had adversely affected the selection of 
livelihood strategies by smallholder farmers. For example, migration of farmers to the 
southern part of Ghana had been attributed to high temperature (GSS, 2014), whereas 
the prevalence of other diseases like skin diseases were linked to high temperature and 
extreme heat by participants in this study. 
However, participants of the FGDs also contended that high temperature enhanced 
decomposition of organic matter on their fields and decay of compost materials as well 
as aided in the preservation of harvested crops. Nyantakyi-Frimpong & Bezner-Kerr 
(2015) found that most farmers perceived temperature and number of extreme events 
such as droughts, floods, and strong winds increased. Apata (2011) also reported that 
about 53 % of respondents perceived an increased trend in temperature. 
Only a small proportion (8.7 %) of respondents disagreed with the perception that there 
was a change in the planting season. In terms of gender perception, 81 % of males and 
79 % females observed a change in the start of the planting season whilst majority of 
these respondents were within the ages of 50 years and above. Nyantakyi-Frimpong & 
Bezner-Kerr (2015) also reported that farmers perceived the planting rains to be erratic 
which adversely impacted on their livelihood strategies. 
Several authors reported that perceptions on the trends and variations in climate events 
greatly influenced the decisions of smallholder farmers with respect to their livelihood 
activities in the Upper West Region (Antwi-Agyei et al., 2012; Etwire et al., 2013; 
Antwi et al., 2015; Nyantakyi-Frimpong & Bezner-Kerr, 2015). 
The analysis of both observed and perceived climate data confirmed that smallholder 
farmers were exposed to high interannual, seasonal, and inter-monthly variability of 
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rainfall and increasing trends of temperature. These trends had exacerbated challenges 
associated with rain-fed agriculture in the study area. 
In conclusion, the study discovered that majority of the respondents demonstrated 
adequate knowledge and awareness of trends and variability in rainfall and temperature 
except total annual rainfall. However, there was general lack of capability on the part 
of households to effectively adopt available adaptation strategies. 
With regard to gender, a high percentage of males showed greater understanding of 
climate trends and variability than their female counterparts. In terms of age, greater 
proportion of those who demonstrated adequate knowledge on climate trends were 
within 50 years and above. 
The findings on the analysis of rainfall data for three decades spanning 1984 to 2014 
revealed evidence of high rainfall variability, seasonal variations, and decreasing trend 
of rainy days over time. The findings on temperature variables, indicated that 
participants and respondents observed high temperature and excessive heat waves in 
recent years and had adversely affected the selection of their livelihood activities in 
local communities. The general observation was that the farming period had become 
shorter with late arrival and early end of the rains. 
5.5 Perceived Trends in Availability, Utilization, and Agroecosystem Services 
Change 
Availability of agroecosystem services. 
Agroecosystem services play important role as sources of food, fibre, energy, and 
income for many smallholder farming households (Boon & Ahenkan, 2011; Smith & 
Sullivan, 2014; Boafo et al., 2016). According to Millennium Ecosystem Assessment 
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(2005), ecosystem services are natural benefits that flow from the environment to 
society and agricultural production. For Boon & Ahenkan (2011), ecosystem services 
constitute the foundation of livelihoods and human well-being, particularly, for natural 
resource depended households. Besides, these services provide alternative sources of 
livelihoods for households (Boafo et al., 2016). For instance, in this study about 40.7 % 
of respondents indicated high availability of non-timber forest products (NTFPs) such 
as chewing sticks, mats, pestles and mortars, and other provisioning services such as 
bushmeat, fruits, honey, fish and mushroom, which contributed to food and income 
needs of households in times of extreme weather events (e.g. floods and droughts). In 
a related study, Boafo et al. (2016) observed that products such as leaves, tree barks, 
roots, spices, and animal products contributed greatly to the medicinal needs 
(provisioning services) of farmers in the Upper West Region. Findings in this study 
revealed that a small proportion of respondents indicated high availability of fodder 
(1.0 %), cola nuts (0.5 %), and snails (0.5 %), which they attributed to low rainfall and 
harsh weather conditions as well as bush burning by smallholder farmers. 
In addition, Boafo et al. (2016) opined that there were missing linkages between 
agroecosystem services and human well-being in the Upper West Regions because 
provisioning ecosystem services that formed significant part of livelihood assets were 
either lost or had become scarce. Even though, provisioning agroecosystem services 
provide a safety net function for majority of poor rural households and communities, 
the availability of these services was now a matter of concern to local authorities and 
farmers. 
This survey observed that majority of the regulatory agroecosystem services were not 
available in the study environment for easy withdrawal by households to support their 
livelihood strategies. The findings showed that only a small proportion (7.2 %) of 
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respondents indicated high availability of pollination, whilst about 1.5 % indicated 
availability of wind break (i.e. presence of trees and forest cover). In Australia, Smith 
& Sullivan (2014) identified pollination as an important ecosystem service that supports 
crop production and yield. The high level of unavailability of regulatory services as 
found in this study had implications for the welfare of smallholders in terms of fruit and 
cereal production, housing security. and health. 
Generally, the survey revealed relatively high availability of cultural services in the 
study area compared to provisioning, regulatory, and supporting services. Majority of 
the respondents indicated high availability of educational services, and spiritual and 
religious values, and aesthetics. 
FGDs and key informant interviews revealed that during harsh weather conditions, 
households depended on domestic and wild fruits for food and income. Some of these 
fruits included mango (Mangnifera indica), “dawadawa” (Parkia biglobosa) and Shea 
(Vitellaria paradoxa) nut. The Shea nuts provide alternative livelihoods for households 
where most women engaged in processing of Sheabutter to generate income to support 
their households.  
Discussants further indicated that farmers relied heavily on the fertility of the soil for 
their livelihood activities. They also depended on fuelwood for energy and income, 
freshwater from rivers, streams, and dugouts for dry season farming and livestock 
watering, cropland for crop production, and livestock grazing pasture, non-timber forest 
products (wooden trays, pestles, chewing sticks, mats), vegetation, and forest cover, 
fruits (shea and “dawadawa”) and fish. 
However, farmers were worried about soil infertility, which they considered as one of 
the major challenges confronting them and had led to low crop yield and food shortage. 
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Boon & Ahenkan (2011) suggested that adequate policy measures ought to be instituted 
to effectively enhance ecosystem services management and climate change adaptation 
in Ghana due to the important roles these services play in the livelihoods of natural 
resource dependent communities. The significance of these findings is that majority of 
smallholder farmers recognized and depended on specific agroecosystem services at 
specific times of the year. 
Utilization of agroecosystem services. 
Material well-being refers to what people have and use to achieve their goals whereas 
subjective well-being dwells on people’s own perceptions and feelings about a situation 
and quality of life based on values and beliefs that shape these perceptions (McGregor, 
2006, 2008; White, 2010). On the other hand, relational well-being refers to what 
people do and how they interact with others to achieve their needs and quality of life. 
Material well-being in this perspective was perceived as access to agroecosystem 
services to support an individual’s livelihood activities in the study area. Respondents 
were requested to indicate their state of well-being with regards to access to available 
agroecosystem services. The findings showed that majority (51.0 %) of the respondents 
perceived to be in the same state of well-being as other households. 
The periods and seasons of the year when respondents depended on agroecosystem 
services were planting, rainy, dry seasons, and whole year. The survey data showed that 
during the dry season, respondents depended on bushmeat, honey, fruits, and freshwater 
and relied on freshwater, fish, bushmeat, fruits, fuelwood, soil fertility, leaves, and 
NTFP throughout the year. Analysis of the perceptions indicated that respondents relied 
on the provision of agroecosystem services in the selection of livelihood strategies 
throughout the year. 
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Observed changes in agroecosystem services. 
According to Gbetibouo (2009), for smallholder farmers to effectively adapt to changes 
in the environment, they must first of all observe and identify these changes. Also, 
Sinare et al. (2016) assessed ecosystem services and benefits in Burkina Faso and 
concluded that the population in rural areas relied heavily on local ecosystem services 
for sustaining their well-being. These authors noted that farmers recognized the changes 
as well as the role of agroecosystem services in their livelihoods which presented a 
good foundation for mitigation measures to restore and preserve these services in the 
communities. The implication is that the perceptions of smallholder farmers on the 
changes in specific agroecosystem services gives the opportunity to ameliorate the 
adverse impacts and take advantage of the current situation and develop effective 
adaptation and coping strategies in semi-arid areas. 
How long changes in agroecosystem services were noticed. 
The findings from the survey revealed that majority of the respondents aged fifty (5) 
years and above observed changes in agroecosystem services within the last five years 
of the study period. Smallholder farmers have noticed these changes and are worried 
and so community leaders have initiated some actions to restore and preserve the 
agroecosystem services. Such action included tree planting, enforcement of bye-laws 
on bush burning, and avoidance of farming along river banks. With regards to seasons 
when particular agroecosystem services were relied on, majority of the respondents 
reported the whole year, because the farming season had reduced to about three months 
(from August to October). 
  
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Drivers of agroecosystem services change. 
The analysis of the perception of smallholder farmers with respect to the specific 
agroecosystem services such as soil fertility, freshwater, cropland, fuelwood, fruits, fish, 
vegetation, mushroom and honey, showed that in order of frequency, low rainfall, tree 
cutting, bush burning, increased population, and extreme temperature were the major 
drivers of agroecosystem services change. 
A few of the respondents claimed that government policy regarding bush burning, 
irrigation, agricultural extension services, river buffer zones, and tree cutting played a 
role in observed changes in agroecosystem services in the study area. In a study of 
perceptions of farmers in Australia, Smith & Sullivan (2014) reported that the drivers 
of ecosystem service change were agrochemical (fertilizer and pesticide) application, 
soil degradation, invasive weeds, urbanization and poor government action. These 
authors also observed that farmers perceived themselves to be vulnerable to the loss of 
ecosystem services and specifically most vulnerable to loss of soil health. Smith & 
Sullivan (2014) concluded that most of the threats to ecosystem services originated 
from agricultural practices. In northern Ghana, Boafo et al. (2014) also identified 
drought, climate variation, land conversion, overharvesting, and loss of traditional 
ecological knowledge as the main drivers of change in ecosystem services. They 
suggested that an examination of various ecosystem services and well-being at the local 
scale was needed for the development of effective adaptation and coping strategies for 
smallholder farmers. 
Changing trends in agroecosystem services. 
It was also observed that a relatively high percentage of respondents perceived slight 
increases in freshwater whilst about a quarter of respondents observed that cropland 
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had remained constant as compared with the other agroecosystem services in that 
category. Farmers further indicated that because the cropland has remained constant 
over the years, with corresponding increases in household sizes, farmlands were scarce. 
Smallholder farmers also demonstrated knowledge of the patterns of changes in key 
agroecosystem services, which forms an indication for effective adaptation (Gbetibouo, 
2009). 
Perceived impacts of changes in agroecosystem services. 
According to Boon & Ahenkan, (2011), ecosystem services form the foundation of the 
livelihoods and well-being of smallholder farmers, especially those living in resources 
constraint areas such as semi-arid areas. 
Participants and discussants in this study alluded to the fact that agroecosystem services 
underpinned their livelihoods but had undergone changes in terms of losses over the 
years.  
Participants also mentioned fruits as one of the agroecosystem services that support 
them economically and as a source of food in times of low rainfall and droughts. For 
example, the Sheanuts and “dawadawa” are two economic fruits in the study area that 
underpin the livelihoods of households. However, they lamented losses to these 
agroecosystem service and cited factors such as frequent bushfires and tree cutting as 
the main causes. 
In conclusion, this study has established empirically that the perceived agroecosystem 
services that underpinned the agricultural livelihoods of smallholder farmers were 
associated with sectors such as food production tied to soil fertility, water, energy tied 
to fuelwood, and shelter from non-timber forest products. 
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Farmers recognized that agroecosystem services had undergone changes over the years, 
especially, their farm soils had become infertile leading to low crop yield. The 
perceived soil infertility was attributed to bushfires and burning of crop residues that 
expose the soil to harsh weather conditions, indiscriminate felling of trees, continuous 
cropping and limited cropland for the increased population. 
In the midst of these changes in agroecosystem services, participants and discussants 
noted that there were agroecosystem services that provide them with opportunities to 
improve their livelihoods with little assistance from government and non-government 
institutions. These agroecosystem services included water supply from the Black Volta 
River for irrigation farming during the prolong dry spells, fruits such as Sheanuts and 
“dawadawa” from the shea and “dawadawa” trees, different varieties of traditional 
seeds and crops, non-timber forest products from community forests, fish from the 
Black Volta River, and cropland for irrigation and dry season farming. 
In terms of agroecosystem service change, majority of respondents (91.8 %) indicated 
they observed changes in these services in their communities. The study further found 
that a large proportion of respondents (41.8 %) indicated they perceived these changes 
in the last five (5) years of the study period whilst 30.4 % said more than ten (10) years.  
The survey data showed that the causes of agroecosystem and agroecosystem change 
were low rainfall (drought), bush burning (bushfires), tree cutting (deforestation), 
floods and agricultural expansion (extension of farming to flood prone, river banks and 
forest areas). It is clear from the survey data that both climatic and non-climatic drivers 
are responsible to agroecosystem and agroecosystem services change in the study area. 
Furthermore, the drivers of change for specific agroecosystem services were identified. 
Respondents cited continuous cropping on the same parcel of land and bad agronomic 
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practices for change in soil fertility, tree cutting and bushfires for fuelwood, low rainfall 
and tree cutting for freshwater change, low rainfall and increased population for 
cropland change, bushfires and excessive hunting for change in bushmeat, low rainfall 
and strong winds for observed change in fruits, water pollution and low rainfall for 
change in fish, bushfires and tree cutting for vegetation change, low rainfall for change 
in mushroom and tree cutting as the sole driver for observed change in honey.  
In terms of agroecosystem service change and the resultant effects on livelihood 
strategies, the study revealed that changes specific services such as soil water, soil 
fertility, cropland, fuelwood, forest cover, pollination, pasture and charcoal had 
adversely affected smallholder farmers. The findings on the trends in availability, 
utilization, and changes in agroecosystem services revealed that (i) farmers recognized 
about thirteen (13) agroecosystem services that underpinned their livelihood strategies 
and well-being, (ii) there were observed changes in specific agroecosystem services 
over the years, which have adversely affected livelihood strategies, (iii) there were 
important drivers of change (both climatic and non-climatic) in specific agroecosystem 
services in the study area. 
For policy implications, these findings provide household-level data which can be 
relied on to formulate adaptation strategies that may be robust and effective at the local 
scale. 
5.6 Level of Vulnerability of Smallholder Farmers 
Exposures and Sensitivities of Respondents (Smallholder Farmers) 
The results of the survey indicated that food shortage and low income were major 
challenges of respondents. The other challenges mentioned included low crop yield, 
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low soil fertility, and hunger. These findings were similar to what Nyantakyi-Frimpong 
& Bezner-Kerr (2015) found in a related study of the relative importance of climate 
change in the context of multiple stresses in the Upper West Region of Ghana. These 
authors concluded that low income and lack of access to credit were the major 
challenges of smallholder farmers. In this study, household respondents identified 
thirteen (13) major challenges or worries of smallholder farmers. These were both 
climatic and non-climatic stressors that contributed to the level of vulnerability and 
reduced adaptive capacity of smallholder farmers. Nyantakyi-Frimpong & Bezner-Kerr 
(2015) made similar observations in their study, where farmers claimed that they were 
used to the impacts of climate change and not worried about climate change. It is worth 
noting that about 15.5 % of respondents considered cholera as a major challenge, ahead 
of low rainfall (13.9 %), which is a climatic stress. 
Causes of Major Challenges of Smallholder Farmers 
According to the survey data, the major causes of the challenges of smallholder farmers 
were low rainfall, bushfires, tree cutting, soil infertility, low crop production, and 
increased population. The findings again revealed that vulnerability of smallholder 
farmers to climate change and variability was driven by both climatic (rainfall) and 
non-climatic (bushfires) stresses. It was also evident that most of the causes of the major 
challenges of respondents were cited as drivers of agroecosystem services change. In a 
study that analyzed vulnerability of smallholder farmers in the three regions of northern 
Ghana, Etwire et al. (2013) concluded that the Upper West Region was most vulnerable 
to climate change as a result of lack of food and water resources and recommended the 
need for measures to boost food production in the region. This study also found that the 
second major challenge of smallholder farmers was food shortage. Other water related 
challenges included cholera and water pollution. 
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Perceptions of Social Groups’ Vulnerability to Agroecosystems Change 
Perceptions of vulnerability of different social groups to changes in agroecosystem 
services was strongly dependent on the agroecosystem service concerned. For cropland, 
soil fertility, soil water, freshwater, forest cover, and food, both men and women were 
equally affected. For firewood and grazing, women tend to be more vulnerable. For 
charcoal, men were perceived to be most vulnerable. 
Dealing with Vulnerability to Agroecosystem Service Change 
This study identified groups that were responsible for tackling risks associated with 
changes in each of the agroecosystem services of respondents. The findings were that 
the individual smallholder farmer was responsible for dealing with changes that 
occurred in four (soil fertility, fuelwood, grazing land and charcoal production) out of 
the nine agroecosystem services investigated. The household dealt with changes in 
three agroecosystem services (freshwater, food supply and cropland), whilst the 
community was responsible for dealing with two, that is, changes in soil water and 
forest cover. The findings imply that for the changes in agroecosystem services, it was 
the individual and the household concerned that dealt with any risk associated with 
these changes with little or no support from the district or national levels. 
Perceptions of the Level of Satisfaction with Life by Respondents 
Vulnerability of smallholder farmers can be determined using the well-being concept, 
where vulnerability to changes in agroecosystem services and climate depends on the 
state of the three components of well-being as stated earlier. According to McGregor 
(2006), White (2010), and Gough & McGregor (2007), subjective well-being concerns 
people’s own perceptions about how they feel about their conditions and quality of life 
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as a whole in the context of their values and beliefs. Majority of the respondents 
reported to be moderately satisfied with life as a whole (acceptance of the current 
impacts of climatic and agroecosystem services change) with only a few who were 
neither satisfied nor unsatisfied. The results further indicated that Gengenkpe had the 
lowest proportion of respondents who were very satisfied, satisfied or moderately 
satisfied with life as a whole. These findings imply that Gengenkpe was perceived to 
be most vulnerable among the study communities with regards to subjective well-being. 
The LVI and LVI-IPCC for Households in the Study Communities 
Findings from the overall livelihood vulnerability index (LVI) estimation suggested 
that Ketuo households were most vulnerable and sensitive to adaptive capacity due to 
challenges with food, health, and water, indicators, whilst Naapaal households were 
least vulnerable to impacts of agroecosystem services and climate. It was observed that 
Billengangn (0.616, 0.874) may be more exposed to climate change impacts and more 
sensitive to food, health, and water, whilst Gengenkpe (0.426, 0.547) may be less 
exposed and less sensitive. Based on socio-demographic, livelihood strategy, and social 
network, Ketuo (0.602) may be more vulnerable in terms of adaptive capacity, whilst 
Gengenkpe (0.306) may be less vulnerable. Furthermore, the overall LVI-IPCC scores 
indicated that Ketuo households (0.867) may be more vulnerable whereas households 
in Naapaal (0.061) may be less vulnerable to observed changes to agroecosystem 
services and climate in the study communities. Analyses of the sub-components and 
major components of the LVI suggested that Ketuo showed greater vulnerability on the 
socio-demographic profile index than the other study communities. The level of 
vulnerability of households in Ketuo may be attributed to the reported higher proportion 
(40.0%) of female-headed households and a smaller proportion (50.0 %) of household 
heads that attended school. A higher percentage (100.0 %) of household respondents in 
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Ketuo and Naapaal reported relying solely on agriculture for income and therefore 
showed greater vulnerability on the livelihood strategy major component. Based on 
social networks, Ketuo recorded the highest score (0.459), whilst Naapaal scored an 
index of (0.164). Households in Naapaal also reported a higher percentage (27.4 %) of 
respondents who received credit for their farming activities, which suggested why 
Naapaal was found to be least vulnerable in the LVI-IPCC analysis. 
In a related study of vulnerability of smallholder farmers to climate change in the three 
region of northern Ghana using the livelihood vulnerability index, Etwire et al. (2013) 
found the Upper West Region as the most sensitive to climate events and attributed it 
to the region’s lack of food and water resources. 
5.7 Adaptation Strategies of Smallholder Farmers 
Adaptation Strategies used by respondents included (i) Crop diversification and others, 
mainly soil related use of manure, application of fertilizers, crop residues; and trenching, 
ridging, or terracing; and (ii) Livelihood diversification strategies “pito” (local drink 
prepared from millet) brewing; shea butter processing; and soap making (iii) Migration. 
Those they did not include (i) growing drought-resistant varieties; (ii) improved seeds; 
and the attraction of pollinators. 
The findings showed that a large proportion of respondents were unable to adapt most 
of the introduced adaptation strategies. These included growing drought resistant crops, 
improved seeds, crop diversification, and attraction of pollinators. The inability of 
farmers to adopt such strategies were attributed to lack of information and cost. For 
example, respondents reported use of traditional seeds inherited from ancestors and 
claimed that those seeds were better than new varieties or improved seeds introduced. 
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In a related study in northern Ghana, Etwire et al. (2013) identified inorganic fertilizer 
application, compost application, planting in rows, planting trees, establishing fire belt, 
and reducing farm size as actual adaptation strategies of farmers. 
The findings in this study was that farmers rather increased farm sizes to overcome low 
crop yield and other impacts of agroecosystem services and climate change instead of 
reducing the farm size as reported by Etwire et al. (2013). Apata (2011) identified 
mixed farming, mulching, zero tillage, early or late planting, making ridges as actual 
adaptation strategies of smallholder farmers in Southern Nigeria. In Tanzania, Lyimo 
& Kangalawe (2010) identified smallholder adaptation strategies to include growing 
drought tolerant crops, early planting, rainwater harvesting, cultivation in wetlands and 
seasonal migration. 
In a related study in northern Ghana, Etwire et al. (2013) identified key determinants 
of adaptation as access to agricultural extension services, noticing unpredictable 
temperature, and total farm size. What these imply is that factors that determine 
adaptation strategies differ from one location to another even among different groups 
of farmers. 
5.8 Knowledge and Information Sources of Adaptation Strategies 
The findings found that the main knowledge sources on adaptation for farmers were: 
radio, family members, traditional leaders, District Assembly, extension officers, and 
school teachers. The institutions that provided households with information on climate 
and management of agroecosystem services were chiefs and elders (traditional 
authorities), District Assembly, NADMO, NGOs, EPA, and Department of Agriculture 
(MOFA). Meanwhile the Department of Agriculture were in dying need of extension 
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officers. At the time of the survey, there were only three (3) extension staff manning 
nine (9) operational areas. 
The general observation was that, these institutions lacked funding and the staff strength 
to provide adequate support to smallholder farmers regarding climate adaptation. In 
view of these issues, even though, key informants from agencies such as the EPA, the 
Department of Agriculture, and NADMO claimed they provide households with 
training and skills on agroecosystem services management and disaster prevention, 
many household respondents seemed unaware of these programmes. However, a few 
them reported having received training in the areas of bushfires prevention, soil 
enrichment practices, and best tillage practices. For smallholders to adapt effectively 
and boost crop production, both government and non-government institutions must 
increase efforts to provide them with the required information on appropriate adaptation 
strategies. 
  
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CHAPTER SIX: CONCLUSION AND RECOMMENDATIONS 
2.4 Conclusion 
Contribution of Soil and Water Quality to Agroecosystem Services and Livelihood 
Strategies of Smallholder Farmers 
Soil and water samples were analyzed to ascertain the status of soil fertility and the 
quality of water resources in the study area. Soil and water are two key resources that 
contribute to agroecosystem services in semi-arid areas and their quality and health 
have implications for smallholder livelihoods strategies. The conclusion drawn for this 
objective was that the soil quality was generally suitable for crop production and in 
comparing it to other studies, the parameters analyzed in this study were in most 
samples marginal to adequate for plant growth. The study has also provided baseline 
data on physical and chemical properties of soil in the district for further soil quality 
studies that would provide guidance for fertilizer application management.  
In terms of water quality, with the exception of total coliform (TC) and faecal coliform 
(FC), all other parameters measured were within the TWQRs guidelines of the Water 
Resources Commission. The counts of TC and FC in water from the Black Volta River 
was of much concern because most of the famers drunk water from the river while they 
work on their farms. Farmers reported incidences of cholera which could be attributed 
to the water quality. The quality of water from boreholes in study communities was 
generally acceptable per the WHO drinking water Guidelines. 
Generally, the chemical and physical properties of soil samples analyzed indicated 
marginal to adequate with regards to the guidelines ranges of the ICARDA (Estefan et 
al., 2013) and may support growth of plants and contribute positively to the agricultural 
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livelihoods of farmers. Microbial levels in water samples from the Black Volta River 
exceeded the guidelines for domestic water use and may pose human health problems, 
and thus cannot be said to support the health needs of the local communities. 
Perceptions of Smallholder Farmers on Rainfall and Temperature Trends and 
Variability in Study Communities 
The gauge or observed data from the study area were analyzed and compared with the 
perceptions of respondents in the household survey and participants in FGDs and key 
informant interviews. The results revealed that perceptions of smallholder farmers 
regarding climate change were high and in most cases consistent with the gauge data 
except their perceptions on annual rainfall. The wrong perceptions with regards to the 
annual rainfall may be attributed to the different indicators of precipitation. Smallholder 
farmers recognized the key drivers of the changing climate trends as well as their 
adverse impacts on livelihoods strategies and human well-being. Most farmers cited 
human activities as major causes of climate change and related events in study 
communities even though some of them claimed climate change was punishment from 
God. 
Perceptions of Smallholder Farmers on the Availability, Utilization and Changes in 
Agroecosystem Services 
To effectively analyze vulnerability of farmers to changes in agroecosystem services 
and climate change, the study first identified key agroecosystem services that sustain 
and support agricultural livelihoods of smallholder farmers through an assessment of 
the perceptions of the farmers. It further analyzed the nature and extent of changes in 
these services as well as the drivers of observed change through household surveys and 
participants in FGDs and key informant interviews. The study found that there were 
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agroecosystem services within study communities that underpinned the agricultural 
livelihoods of smallholder farmers. Smallholder farmers identified key agroecosystem 
services such as bushmeat, freshwater, fruits, soil fertility and fuelwood. The study 
found that farmers perceived changes in agroecosystem services and identified the 
driver of these changes. They also perceived themselves to be vulnerable and unable to 
manage to these changes.  
Vulnerability of Smallholder Farmers to Changes in Recent Agroecosystems and 
Climate Variability 
The study examined the challenges, risks and shocks of respondents to understand how 
these exposures and sensitivities have contributed to vulnerabilities of smallholder 
farming households and communities. The challenges identified relate to sectors such 
as food, water, shelter, energy and finance. These included low income levels, food 
shortage, low crop yield, hunger, low soil fertility, cholera, low rainfall, cropland 
(farmland) shortage, fuelwood shortage, flood, water pollution, environmental 
degradation, and bushfires. 
The data from these evaluations and analysis provided variables that were used to 
generate indicators for the livelihood vulnerability index (LVI) analysis. The 
vulnerability analysis showed Ketuo was most vulnerable while Naapaal was least 
vulnerable. The most vulnerable community (Ketuo) was also sensitive to adaptive 
capacity. 
Perceptions of Smallholder Farmers on Adaptation Options and Knowledge Sources 
on Adaptation Available for Managing Changes in Agroecosystem and Climate 
The study identified a number of adaptation strategies employed by smallholder farmers 
to cope and adapt to agroecosystem and climate change. Generally, most of these 
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adaptation strategies were soil fertility-related and include use of manure, application 
of fertilizers, non-burning of crop residue, trenching or ridging, terracing and crop 
diversification. Many farmers also used livelihood diversification strategies such as 
“pito” (local drink prepared from millet) brewing, sheabutter processing, soap making 
and migration. The study recognized that prominent adaptation options such as growing 
drought resistant varieties, improved seeds and attraction of pollinators were not used 
by most farmers. Respondents identified barriers to using such introduced adaptation 
options that include cost and lack of information. 
Finally, the study identified three factors that determine adaptation strategies of 
smallholder farmers in semi-arid Ghana as access to climate information, how satisfied 
a farmer is with life in relation to current climatic and ecosystem change and availability 
of ecosystem services in the study area. 
2.5 Recommendations 
Following the presentation and discussion of results and conclusions drawn from the 
study, a number of recommendations can be made: 
First, detailed research should be conducted to determine the quality of soil nutrients 
within the district using the data and findings of this study as a baseline. This is 
important so as to provide guidelines for fertilizer applications and soil management 
practices of smallholder farmers. The study observed that farmers perceived loss of soil 
fertility and therefore resorted to indiscriminate and inappropriate use and application 
of fertilizers and other agrochemicals. However, there is limited data on soil and water 
quality from the Nandom district.  
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Second, since livelihoods and well-being of smallholder farmers were strongly tied to 
critical agroecosystem services in semi-arid areas, academia should conduct economic 
evaluation of the key agroecosystem services to identify the actual contribution of 
agroecosystems to ecosystem services. This will provide data for policy and decision 
making with respect to management of these services and improvement of livelihoods 
of smallholder famers. 
Third, the existing ecosystem-based management framework policy guidelines as 
outlined in the Ghana climate change policy should be incorporated into the District 
Assembly’s environmental management plans and actions for implementation by the 
EPA. 
Fourth, government as an employer, should engage more agricultural extension officers 
to provide agricultural extension services to farmers. This is key because according to 
the officials of Department of Agriculture, the office had only three (3) agricultural 
extension officers, who manage nine (9) operational areas. 
Fifth, there were currently many non-governmental organizations (NGOs) in the district 
rendering several farm and community based adaptation services to smallholder 
farmers, whose activities were fragmented. The district assembly should liaise with 
them to identify their plans and activities in the various communities. The study 
recognized that there were duplications with regard to training and services the NGOs 
provide to communities and in some cases the same communities were supported by 
more than one NGOs whilst other communities never received any assistance. 
Also, the Department of Agriculture and the District Assembly should assist farmers 
obtain improved seeds and early maturing varieties. Farmers should be encouraged to 
form groups to enable them access credit for their farming activities.  
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The District Assembly should take steps to provide irrigation services (small dams and 
water pumping machines) for smallholder farmers to carry out dry season farming with 
the existing potential from the Black Volta River. This will generate income, reduce 
poverty, and hunger all year round. 
Further research should be conducted on the economic valuation of agroecosystem 
services to determine their actual contribution to the livelihood strategies of local 
communities in semi-arid Ghana. 
  
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APPENDICES 
 
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Appendix 1: Graphical Representation of Trends & Projections of Temperature & Rainfall for Ghana Until 2080  
 
Figure 1: Maps of projected mean temperatures 1980-2010 (left) 2011-2040 (right)  
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Figure 2: Maps of projected mean temperatures 2041-2060 (left) and 2061-2080 (right). 
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Figure 3: Maps of projected mean rainfall 1980-2010 (left) and 2011-2040 (right)  
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Figure 4: Maps of projected mean rainfall 2041-2060 (left) and 2061-2080 (right). (Source: Ghana’s 3rd National Communication to UNFCCC, 2015) 
 
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Appendix 2: Ethical Consent Letter 
 
UNIVERSITY OF GHANA 
Official Use only 
  Protocol number 
COLLEGE OF BASIC AND APPLIED SCIENCES ECBAS 028/15-16 
Ethics Committee for Basic and Applied Sciences (ECBAS) 
PROTOCOL CONSENT FORM  
 
Section A- BACKGROUND INFORMATION 
 
Title of Study: Vulnerability and Adaptation of Smallholder Farmers to Changes in Agroecosystems 
and Climate in Semi-Arid Regions: The Case of Nandom District of Ghana 
Student ISHMAEL LENTE 
Investigator: 
Certified ECBAS 028/15-16 
Protocol 
Number 
 
Section B– CONSENT TO PARTICIPATE IN RESEARCH 
The purpose of this study is to examine and understand the scope and nature of vulnerabilities and adaptation 
options of smallholder farmers to agroecosystem services and climate change in semi-arid areas of the Nandom 
District of the Upper West Region, Ghana. 
Please, we would really appreciate it if you could spare about one hour (1hr) of your time for this interview. You 
are randomly and purposefully selected to kindly participate in this survey. Participation is completely voluntary 
and one has the right NOT to participate in this survey at all or stop participation at any point in time during the 
survey. 
The answers you give will add to a larger study working toward ensuring better policies and programs for helping 
farmers as they face a variety of problems. No risk associated with this survey is anticipated and there will be 
no monetary or any material benefits accompanying this survey.  
All reports made out of this survey would not mention any names and all analysis would be in general terms. No 
names would accompany photos unless you give specific permission and you can refuse having your photo taken 
at any time. Reports from this survey would be made available to the research community and policy makers.  
If you have any questions about the research and whom to contact in case of research-related injury and any 
issues on your rights as a participant you can contact the address below: 
 
 
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Ishmael Lente 
Institute for Environment and Sanitation Studies (IESS) 
College of Basic and Applied Sciences 
University of Ghana. Legon 
ishmaelente@yahoo.com or inlente@st.ug.edu.gh 
0244105770 
 
Section C- VOLUNTEER AGREEMENT 
"I have read or have had someone read all of the above, asked questions, received answers regarding participation 
in this study, and am willing to give consent for me, my child/ward to participate in this study. I will not have 
waived any of my rights by signing this consent form. Upon signing this consent form, I will receive a copy for 
my personal records." 
…………………………………….    …………………………………. 
Name of Volunteer       Signature or mark of volunteer  
Date……………………………….. 
 
If volunteers cannot read the form themselves, a witness must sign here:  
I was present while the benefits, risks and procedures were read to the volunteer. All questions were answered 
and the volunteer has agreed to take part in the research.  
………………………………………..   ……………………………………… 
Name of witness      Signature of witness  
Date………………………………………. 
 
I certify that the nature and purpose, the potential benefits, and possible risks associated with participating in 
this research have been explained to the above individual.  
………………………………………….  …………………………………………….. 
Name of Person who obtained Consent  Signature of Person who obtained Consent  
Date…………………………………….. 
 
  
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Appendix 3: Focus Group Discussion Guide 
UNIVERSITY OF GHANA, LEGON 
COLLEGE OF BASIC AND APPLIED SCIENCES 
ENVIRONMENTAL SCIENCE PROGRAMME 
 
Community identification 
Questionnaire ID: Date: 
District   Community: 
GPS coordinates X Y Altitude 
Facilitator 1  
Facilitator 2  
 
Demographic characteristics 
Categories Total  Males  Females occupation 
Focus group composition     
Other social groups:  
     
     
     
     
 
SECTION A 
Farm Characteristics of Respondents 
 
Q1 What is the mode of farmland acquisition in this community? 
Q2 What crops do you plant, in order of importance? Utilization of production: [1] use all at home [2] use 
more at home than sell [3] Use at home and sell equally [4] sell more than use at home [5] sell all 
Crops in order of importance What do you use this crop for (using the typology above) 
  
  
  
  
  
Q3 How and where do you access credit (money) and farm inputs for your farming activities and what is the 
average amount received? If not, why? 
Q4 What is your average total income for the farming season? 
 
 
 
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SECTION B 
Trends and Variability of Rainfall and Temperature in Study Communities 
 
Q5 What changes have you observed in the rainfall pattern in your community in the past 10 years or more? 
Q6 What changes have you observed in Temperature in your community in the past 10 years or more? 
Q7 How was your farming activities (agricultural livelihoods) affected, and which extreme weather events 
caused these? 
SECTION C 
Perceived Trends in Availability, Utilization and Changes in Agroecosystem Services in Study 
Communities 
 
Q8 What agroecosystems and ecosystem services (Natural benefits that flow from your environment) 
support your farming activities (agricultural livelihoods) and well-being?  
Q9 How have these services been changing in this area? 
Q10 What are the drivers of these changes in this area?  
Q11 Which of these services are you loosing or have lost? 
SECTION D 
Challenges and Perceived Vulnerability of Smallholder Farmers 
 
Q12 What major challenges, shocks or risks do you face in your community recently? 
Q13 What is the cause (s) of each challenge/ risk/shock facing your household/community? 
Q14 How do you deal with this challenge/shock/risk? 
Q15 How have these challenges affected livelihoods of your household/community and what aspects of life 
is/are most affected? 
Q16 Who is most vulnerable due to the observed changes in agroecosystem services (natural benefits) and 
climate/weather extreme events in your household/community?  
Q17 Who is responsible for dealing with these challenges of the household/community? 
 
SECTION E 
Adaptation Strategies and Knowledge Sources of Smallholder Farming Households 
 
Q18 What strategies/measures do you use/adopt to overcome the changes that occur in your environment that 
affect your farming activities and well-being? 
Q19 What adaptive measures are available to your households for improving upon farm practices in the 
community and which ones do you use? 
Q20 What adaptive measures are you NOT able to use/adopt and why? 
Q21 Where does your household get information from to make decisions about preparing for and responding 
to drought (low rainfall), flood (intense rainfall) and high temperature?  
Q22 What institutions/organizations help you in your agricultural livelihood activities? 
THANK YOU  
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Appendix 4: Key Informant Interview Guide 
Section A 
Background characteristics 
 
1. Name of Officer/respondent………………………………………………………………............... 
2. Unit/Department……………………………………3. Designation/Rank………………………… 
4. Educational background …………….…………………….5. Gender…………………………… 
6 No. of years at post………………………………………………………………………………… 
Section B 
Agroecosystems and Ecosystem Services Supply and Change 
 
Q1 What natural services (agroecosystem services) are you able to draw from your environment to support your 
farming activities? 
Q2 How is supply and utilization of these services changing over time? 
Q3 What are the key drivers (causes) of change in the environment and agroecosystems and ecosystem services 
in this district/area? 
Q4 What key natural services (Agroecosystem services) have been degraded and are at risk in your district? 
Q5 What management measures, plans and policies are in place to deal with degradation of agroecosystem 
services (e.g. soil and water resources) in this district? 
Section D 
Rainfall and Temperature Patterns and Smallholder Livelihood Strategies 
 
Q6 What changes have you observed in the rainfall pattern in your district in the past 10 years or more years? 
Q7 What changes have you observed in temperature in your district in the past 10 years or more years? 
Q8 What specific livelihood activities and strategies have been adversely affected and which extreme weather 
events caused these? 
Section E 
Exposures and Sensitivities of Smallholder Households due to Agroecosystems and Climate Change 
 
Q9 What major challenges, shocks and risks confront smallholder-farming households as a result of 
climate/weather extreme events and changes or loss of the agroecosystem services (natural benefits) in 
your environment in this district? 
Q10 What is the cause (s) of each challenge/ risk/shock? 
Q11 How have these challenges affected livelihood/well-being of households and what aspects of life is/are 
most affected? 
Q12 Who suffer the most to climate/weather extreme events and loss of the ecosystem services (natural 
benefits)?  
Section F 
Adaptation Options and Strategies for Smallholder Farmers 
 
Q13 What measures are used to manage changes in the natural services (agroecosystem services) and climate 
in this district? 
Q14 What are the knowledge and information sources of smallholder households for managing these changes? 
Q15 What adaptive measures/strategies have your outfit put in place to manage challenges of crop production 
by smallholder farmers in this district? 
Q16 What are the challenges your outfit faces in the management of changes or loss of natural services 
(agroecosystem services) and climatic risks? 
 
Thank You  
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Appendix 5: Household Questionnaire 
UNIVERSITY OF GHANA, LEGON 
COLLEGE OF BASIC AND APPLIED SCIENCES 
ENVIRONMENTAL SCIENCE PROGRAMME 
 
The Household Questionnaire 
Questionnaire ID:  Date: 
District: NANDOM Community:  
GPS coordinates: X:  Y:  Altitude: 
Name of enumerator:  
Time:  
 
Please, tick (√) in the brackets [  ] and write at the appropriate space provided. Thank you. 
 
SECTION A 
Background Information of Respondent 
Q1 Gender: a. Male [  ]  b. Female [  ] 
Q2 Age group: a). Less than 20 yrs [  ]  b). 20-29 yrs [  ] c). 30-39 yrs [ ] d). 40-49 yrs [  ] 
 e). 50 yrs and above [  ] 
Q3 Level of Education: a). Primary [  ]  b). JSS/Middle School [  ]  c). Non Formal 
Education [  ]   d).Secondary/Vocational/Technical [ ] e). Tertiary [  ] 
   f). None [  ]  Others (Specify)……………… 
Q4 Marital Status: a). Single [  ] b).Married [  ] c).Divorced/Separated [  ] d).Widowed [  ] 
Q5 Are you the head of the household? a). Yes [  ]   b). No [  ] 
Q6  How many persons are currently living in your household? a). Total [  ] b). Male [  ]  c). 
Female [  ] 
Q7 What is the major occupation of your household? a). Farming [  ] b). Fishing [  ]  c). Trading [  
]  Others (specify)................................................................................................. 
Q8 How long have you been staying in this area/locality?  a). less than 2 yrs [  ]   b). 
2-5 yrs [  ]  c). 6-10 yrs [  ]  d). 11-15 yrs [  ] e). 16 yrs and above [  ] 
Q9 Where did you come from and why? …………………………………….............................................. 
Q10 Which ethnic group do you belong to? ……………………………………………………............. 
 
SECTION B 
Farm Characteristics of Respondents 
Q11 What is the total size of your farm (hectares)? …………………………………………… 
Q12 What is the mode of farmland acquisition?  1). Family [  ]  2). Rental [  ] 
 3). Landlord [  ] 4). Sharecropping [  ]  5. Others (specify) ………… 
Q13 What crops do you plant, in order of importance? 
Crops in order of importance Q14 What do you use this crop for (using the typology below) 
  
  
  
  
  
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Utilization of production: [1] use all at home [2] use more at home than sell  [3] Use at home and 
sell equally [4] sell more than use at home [5] sell all 
Q15 Are you able to access credit for your farming activities? 1). Yes [  ] 2). No [  ] 
Q16 If yes, what is the source and how much? Source: 1). Bank [  ] 2). Moneylender [  ] 3). Others 
(specify)….................... Amount in GHs 1). Less than 100 2). 100-300  3).400-600
  4). 700-900  5). 1000 and above 
Q17 If no, why? ……………………………………………………………………………....... 
 
SECTION C 
Characteristics of Rainfall and Temperature Variables 
Q18  Please, indicate how you observe the following events in your community over time  
(Use 1 to 5) 
 
Climate Change and Variability (climate trend) 1. Strongly agree,  
2. Agree, 3. Uncertain  
4. Disagree, 
5. Strongly disagree 
1 2 3 4 5 
Observed increase in total rainfall events in the past 10 or more years      
Observed decreased in total rainfall events in the past 10 or more years      
Observed a change in start of planting season      
Rains start early recently      
Rains starts late recently      
Rains end early recently      
Rains end late recently      
Rainy season has become shorter recently      
Rainy season has become longer recently      
Dry spells are more frequent in the past 10 or more years      
Observed severe droughts in the past 10  or more years      
Temperature has increased in the past 10 or more years      
Temperature has decreased in the past 10 or more years      
Observed severe floods over the past 10 or more years      
Observed stronger winds over the past 10 or more years      
Drought situations have affected the livelihoods of your household      
Flood situations have affected the livelihoods of your household      
 
  
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SECTION D 
The Availability and Changes in key Agroecosystem Services that Support Agricultural Livelihoods of 
Smallholders 
Q19 Please, indicate by ranking the items in the following list, as you are able to draw these natural benefits 
or ecosystem services from your local environment. 
1= very high, 2= high, 3=Medium, 4= Low and 5=very low 
 
Ecosystem Rank Ecosystem Rank Ecosystem Services Rank Ecosystem Services Rank 
Services Services 
Bush meat  Barks  Pest/disease control  Educational (schools)  
(weeds/insects) 
Fresh water  Leaves  Pollination  Sense of place (  
(insects/bees) 
Fish  Animal products  Flood control (wetlands)  Cultural heritage (belief  
(goats/sheep/fowl systems) 
s/horns) 
Fruits  Hides (animal  Wind breaks (forest  Soil fertility  
skins) cover/trees)) 
Honey  Seed  Rain attraction (forest)  Soil Nutrient  
Mushrooms  Roots  Spiritual & religious  Primary production  
values (pacification of 
gods/churches/mosques) 
Snails  Air quality (dust)  Recreation & ecotourism   Non timber forest  
products (Wooden 
trays, Grinders, 
Mortars, Pestles, 
Chewing sticks, Mat) 
Spices  Local climate  Royal burial grounds  Fuel Wood  
(heat/cold) (cemetery) 
Cola nuts  Erosion control  Habitats for gods  Timber  
(vegetation cover) 
Fodder  Water purification  Aesthetic     
(water quality) 
 
Q20  Which agroecosystem services support your livelihood in your community? 
 
Agroecosystem Services (Natural Benefits) (in order of Q21 What time or period do you depend upon this 
importance) agroecosystem service? 
  
  
  
  
  
  
  
  
 
Q22  Have you observed any changes in these agroecosystem services (natural benefits) indicated above in the 
past recent years?  1). Yes [  ]   2). No [  ] 
Q23  If yes, how long have you noticed these changes?       1). 
5 years [  ]   2). 10 years [  ] 3). More than 10 years [  ] 
 
 
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Q24  What do you think is most responsible for the changes in each of the agroecosystem services that support 
your livelihood in this community? 
 
Agroecosystem Services (Natural Benefits or services) Drivers of change in this agroecosystem service 
(in order of importance) 
  
  
  
  
  
  
  
  
  
 
Q25  Please, indicate how each of the agroecosystem service (natural benefit) you mentioned above has 
changed in your community in the past 10 years (Use 1 to 5) 
 
Agroecosystem services (Natural benefits) 1. Increased sharply 2. Increased slightly, 
3. Remained constant 4. Decreased slightly,  
5. Decreased sharply 
1 2 3 4 5 
      
      
      
      
      
      
      
 
Q26 How would you say your social group’s access to agroecosystem services compares to that of other 
groups?  a) Less access [  ] b) The same [  ] c) More access [  ] 
 
SECTION E 
Vulnerability of Smallholder Farmers due to Agroecosystem Services Change and Climate 
Q27  What major challenges, shocks and risks do you face because of the changes in agroecosystem services 
(natural benefits) and climate in your household or community? 
 
Major challenges/risks/shocks in Q28  What is the cause (s) of this Q29  How do you deal with this 
order of importance challenge/ risk/shock? challenge/shock/risk 
   
   
   
   
   
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Q30 Please, indicate the extent to which change in the following agroecosystem services (natural benefits) and 
climate variables affect your farming activities and well-being. 
Rank using 1= very high, 2= high, 3= medium, 4= low and 5=very low 
 
Agroecosystem Services/Climate Variables Rank 
Soil water  
Soil fertility  
Cropland  
Fuel wood collection  
Charcoal production  
Livestock grazing/pasture  
Forest cover  
Pollination  
Rainfall  
Temperature  
Credit  
 
Q31  Who suffer the most to change in agroecosystem services (natural benefits) and climate indicated in 
sections C and D above in your household?  
For Q31, use {[1]. Men [2]. Women [3]. Men & Women [4]. Elderly  [5]. Youth/Young 
 [6]. Children [7]. Persons with Disability (PWD)  [8]. Others (specify)} 
 
Agroecosystem Services/Climate Who suffer most from change in this Q32  Who is responsible for 
agroecosystem service? (use the dealing with this change 
typology above) or loss? (use the typology 
below) 
    
   
   
   
   
For Q32, use [1]. Individual [2]. Household [3]. Social group [4]. Community [5]. District [6]. Regional 
  [7]. National  [8] NGOs (specify) [9]. Others (specify) 
 
Q33  Does your household have access to key agroecosystem services (natural benefits) you need to make 
changes to your agricultural livelihood when there are difficulties–specifically during drought (low 
rainfall), flood (high rainfall) and extreme high temperatures?   1) Sometimes [  ]  2) Never [  ]
  3) Always [  ] 
Q34  Comparing your household to other households in this area/community, would you say that your 
household's access to key agroecosystem services (natural benefits) is: a). Much better off [  ] b). 
Better off [  ] c) The same [  ] d) Worse off [  ]  e) much worse off [  ] 
Q35  How satisfied are you with your life as a whole these days in relation to the challenges discussed so far?
 a) very unsatisfied [  ]  b) moderately unsatisfied [  ]   c) neither 
satisfied nor unsatisfied [  ] d) moderately satisfied [  ] e) very satisfied [  ] 
  
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SECTION F 
Adaptation Options and Knowledge Sources of Smallholder Farmers for Managing Agroecosystem 
Services Change and Climate 
 
Q36  Please indicate whether your household has adopted any of the strategies/farm practices listed below 
(Use 1 to 3 for Q36 and Q37 and 1 to 5 for Q38) 
 
Adaptive measures/coping Have adopted Q37  Compared Q38  If no, why not? 
strategies this strategy? to other 
households  
1.Yes 1. Less access 1. Cost 2. Not known 
2. No 2. The same 3. Not sufficient information 
3. Uncertain 3. More access 4.Uncertain outcome 
5. Not enough  
1. Growing drought resistant    
crops 
2. Diversifying crops and    
livestock-growing new varieties 
of crops and breeds of livestock 
3. Mulching (covering the soil    
to keep in moisture) 
4. Using seeds that grow better    
in dry weather conditions/using 
improved seeds 
5. Attracting pollinators using    
baits  
6. Using organic manure    
7. Using fertilizer    
 
Q39  What strategies do you use to overcome the changes that occur in your environment that affect your 
livelihood and well-being? 
..................................................................................................................................................................................
..................................................................................................................................................................................
.............................................................................................................................................. 
 
Q40  Where does your household get information from to make decisions about preparing for and responding 
to drought (low rainfall), flood (intense rainfall) and high temperature? 
 
Sources of climate Do you use this Q41  This source of Q42  Do you feel that this 
related knowledge source of information is source of information is 
information? (1. Yes trustworthy (use sufficient? (1. Yes 2. 
2. No) scale 1-5 below No) 
table) 
1. Meteorological    
services 
2. Indigenous forecast    
information 
3. Newspaper    
4. Radio    
5. TV    
6. NGOs    
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Sources of climate Do you use this Q41  This source of Q42  Do you feel that this 
related knowledge source of information is source of information is 
information? (1. Yes trustworthy (use sufficient? (1. Yes 2. 
2. No) scale 1-5 below No) 
table) 
7. Government    
Authorities (district 
office) 
8. Extension officers    
9. Internet    
10. School/teachers    
11.Visiting climate    
experts (universities) 
12. From family and    
friends 
13. Village leaders    
(traditional 
authorities) 
14. Government    
publications 
15. Others (specify)    
For Q41, (Scale of 1 to 5):1. Strongly agree; 2. Agree; 3. Uncertain; 4. Disagree; 5. Strongly disagree) 
 
Q43  Which institutions/organizations help you in your livelihood activities? 
 
Formal institutions in Specific Help/Role of Informal institutions Specific Help/Role of 
order of importance institution in order of institution  
importance 
    
    
    
    
    
    
    
 
Q44  Who is responsible for managing the key agroecosystem services (natural benefits) in your community? 
1)............................................2) .......................................................3) ......................................... .. 
Q45  Do you have access to information on the use and management of the agroecosystem services (natural 
benefits) in your community? 1). Yes [  ]  2). No [  ] 
Q46  If yes, who provides this information? .............................................................. .................. 
Q47  Do you receive training from any institution or organization on the use and management of the 
agroecosystem services (natural benefits) in your community?  1). Yes [  ] 2). No [  ]  
Q48  If so, what kind of training? …………………………………………………………….... 
Q49  Who provides the training? ............................................................................................... .. 
 
THANK YOU 
  
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Appendix 6: Pictures on Field Work-2016/2017 
 
 
  
   
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Courtesy: Ishmael Lente (2016/2017) 
 
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Appendix 7: LVI Indices & Indicators 
Table 1: Indexed sub-components, major component for natural disasters and Climate change and overall LVI for the Four Communities 
Sub-component Billengangn Gengenkpe Naapaal Ketuo Major Components Billengangn Gengenkpe Naapaal Ketuo 
Percentage of female headed household 0.450 0.396 0.468 0.708 Socio-demographic profile 0.477 0.4157 0.446 0.528 
Percentage of households where the head has not 0.567 0.604 0.484 0.5  
attended school 
Percentage of average person living in a household 0.440 0.277 0.365 0.52  
Average age of female headed household 0.450 0.386 0.467 0.382  
Percentage of  Major occupation of household 0.950 0.938 0.9 0.9 Livelihood strategies 0.495 0.501 0.895 0.895 
being agriculture 
Average agricultural livelihood diversification 0.040 0.063 0.89 0.89  
index 
Percentage of household that have access to credit  0.100 0 0.247 0.792 Social networks 0.067 0 0.164 0.459 
Percentage of household that got assistance from 0.033 0 0.081 0.125  
banks 
Percentage of household that depends on farming 0.833 0.649 0.855 0.75 Food 0.820 0.631 0.407 0.872 
as main source of food 
Average crop diversity index 0.550 0.275 0.353 0.87  
Percentage of household that do not save crops 0.967 0.667 0.371 0.917  
Percentage of household that do not save seeds 0.933 0.932 0.048 0.95  
Percentage of household that have notice water 0.950 0.750 0.887 0.958 Water 0.937 0.865 0.887 0.972 
variability 
Percentage of household that depends solely on 0.923 0.980 0.887 0.985  
natural water source 
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Percentage of household that use bark of tress for 0.850 0.125 0.806 0.875 Health 0.917 0.146 0.895 0.75 
medicinal purpose 
Percentage of household that uses leaves for 0.983 0.167 0.984 0.625      
medicinal purpose 
Percentage of household that do not receive flood 0.900 0.271 0.71 0.708 natural disaster and climate 0.616 0.424 0.582 0.602 
control  service variability 
Percentage of household that do not receive wind 0.750 0.375 0.774 0.875  
breaks warning  
Mean standard deviation of  monthly average 0.21998 0.2253 0.22319 0.22415  
minimum temperature (1983-2014) 
Mean standard deviation of  monthly average 0.34809 0.35069 0.34977 0.35024  
maximum temperature (1983-2014) 
Mean standard deviation of  monthly average  0.864 0..899 0.856 0.851  
precipitation(1983-2014) 
Overall livelihood vulnerability index           0.624 0.426 0.611 0.703 
 
  
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Table 2: LVI-IPCC Contribution Calculation for the Four Communities 
Contributin Major Major Component Values Number of Sub Contributing Factors 
g Factors Components Component  
Per Major Component 
    Billengang Gengenkpe Naapaa Ketuo   Billengangn Gengenkpe Naapaal Ketuo 
n l 
Adaptive socio-demographic 0.477 0.4157 0.446 0.528 4 0.379 0.306 0.488 0.602 
Capacity profile 
  livelihood 0.495 0.501 0.895 0.895 2  
strategies 
  social networks 0.067 0 0.164 0.459 2  
             
Sensitivity health 0.917 0.146 0.895 0.75 2 0.874 0.547 0.649 0.867 
  food 0.820 0.631 0.407 0.872 4   
  water 0.937 0.865 0.887 0.972 2  
             
Exposure natural disasters 0.616 0.424 0.582 0.602 5 0.616 0.424 0.582 0.602 
and climate 
variability 
LVI-IPCC        0.204 0.065 0.061 0.867 
Value 
 
 
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Appendix 8: Soil and Water Quality Results 
Table 1: Mean Concentrations of pH and Physical Parameters in Soil Sample 
Community/ pH Conductivity TDS TSS Salinity 
Soil Sample  (µS/cm) (mg/L) (mg/L) (o/oo) 
Billengangn 
Farm 1 7.6 103.1 55 218 0.0 
Farm 2 8.0 21.4 12 350 0.0 
Gengenkpe 
Farm 1 5.6 12.3 7 304 0.0 
Farm 2 6.1 22.7 13 509 0.0 
Napaal 
Farm 1 5.9 51.4 29 360 0.0 
Farm 2 6.8 27.1 15 244 0.0 
Ketuo 
Farm 1 5.8 31.2 17 361 0.0 
Farm 2 6.4 104.0 6 307 0.0 
Source: Field work, 2016 
  
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Table 2: Textural Classes of Soil Samples 
Community/ % Sand % Silt % Clay Textural Bulk % Porosity 
Soil Sample Class Density (g/cm3) 
Billengangn 
Farm 1 81 14 5 Loamy-Sand 1.16 56.3 
Farm 2 78 15 7 Loamy-Sand 1.11 53.5 
Gengenkpe 
Farm 1 75 16 9 Sandy-Loam 1.14 57 
Farm 2 73 14 8 Sandy-Loam 1.12 54 
Napaal 
Farm 1 84 3 13 Loamy-Sand 1.28 51.7 
Farm 2 87 5 11 Loamy-Sand 1.23 49.5 
Ketuo 
Farm 1 73 10 17 Sandy-Loam 1.00 51 
Farm 2 69 13 15 Sandy-Loam 1.11 53 
Source: Field work, 2016 
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Table 3: Mean Concentrations of Trace Metals in Soil Sample 
Community/ Cr Fe Mn Cu Zn Pb Ni Cd Co As 
Soil (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) (mg/kg) 
Billengangn 
Farm 1 0.32 219.0 56.16 2.88 5.96 0.56 2.86 BDL 0.34 2.48 
Farm 2 0.20 217.5 42.14 2.66 2.26 0.60 1.98 BDL BDL 2.52 
Gengenkpe 
Farm 1 BDL 211.3 31.96 1.50 0.58 BDL 2.60 BDL BDL 0.30 
Farm 2 BDL 207.0 14.96 1.44 1.02 BDL 3.28 BDL BDL 0.36 
Napaal 
Farm 1 0.36 213.7 18.00 1.96 1.68 0.48 3.30 BDL BDL 2.6 
Farm 2 0.44 219.6 66.96 2.38 1.06 BDL 4.76 BDL BDL 0.32 
Ketuo 
Farm 1 0.96 216.9 58.64 2.42 0.86 0.64 2.88 BDL BDL 2.72 
Farm 2 BDL 213.4 22.34 1.34 0.60 0.64 0.84 BDL BDL 2.72 
RML (mg/kg) 100* 50000** 2000** 60* 200* 100* 50* 1.5* 50* - 
RML=Recommended Maximum Limited for Agricultural Soils (Source: *Ewers, 1991), **FAO/WHO, 2001 
 
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Table 4: Mean Concentrations of Physico–Chemical Parameters of Water Resources in Study Area 
Community/ pH TDS TSS Conductivity Salinity Turbidity. Colour. Alkalinity. BOD 
Water Source (mg/L) (mg/L) (µS/cm) (o/oo) (NTU) (TCU) (mg/L) (mg/L) 
Billengangn 
River 7.8 50.0 6.7 53.3 0.0 8.0 8 53.3 2.57 
Borehole 1 8.2 65.2 0.0 123.1 0.1 0.0 9 116.0 1.63 
Borehole 2 8.2 56.8 35.0 116.7 0.0 49.0 9 100.0 1.14 
Borehole 3 8.3 76.9 49.0 157.0 0.1 0.0 10 144.0 2.64 
Dug-out well 7.2 22.4 9.0 43.0 0.0 14 3 14.0 2.44 
Gengenkpe 
River 7.3 18.3 59.3 35.6 0.0 76.7 3 42.7 3.12 
Borehole 1 7.5 48.6 0.0 99.1 0.0 0.0 5 52.0 2.44 
Borehole 2 7.9 47.9 0.0 94.1 0.0 0.0 5 80.0 2.03 
Borehole 3 7.9 52.6 0.0 107.5 0.0 1.0 7 100.0 2.64 
Napaal 
River 7.4 28.6 73.3 33.8 0.0 100.2 2 37.3 2.37 
Borehole 1 8.5 16.8 0.0 186.1 0.1 2.0 8 184.0 1.83 
Borehole 2 8.6 89.9 0.0 212.0 0.1 0.0 8 222.0 1.22 
Borehole 3 8.4 103.7 0.0 203.0 0.1 0.0 9 188.0 2.24 
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Community/ pH TDS TSS Conductivity Salinity Turbidity. Colour. Alkalinity. BOD 
Water Source (mg/L) (mg/L) (µS/cm) (o/oo) (NTU) (TCU) (mg/L) (mg/L) 
Ketuo 
River 7.5 72.0 48.0 91.9 0.1 68.3 7 40.7 4.20 
Borehole 1 8.9 209.7 0.0 427.0 0.2 0.0 12 430.0 2.64 
Borehole 2 8.3 54.7 0.0 110.0 0.0 0.0 10 78.0 2.24 
Borehole 3 8.3 97.2 7.0 200.0 0.1 4.0 11 202.0 1.22 
WHO (2011) Guidelines for Drinking water: pH=6.5-8.5, TDS=1000, Turbidity=5, Colour=15, TWQRs: pH=6.9, EC=0-70, TDS=0-450, Turbidity=0-1 
 
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Table 5: Mean Concentrations of Trace Metals in Borehole Water 
Community/ Fe Mn Pb Ni As 
Water Source (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) 
Billengangn 
Borehole 1 BDL BDL BDL 0.0011 BDL 
Borehole 2 0.315 0.005 BDL 0.0095 BDL 
Borehole 3 BDL 0.003 BDL BDL BDL 
Dug-out well 0.0235 0.004 BDL BDL BDL 
Gengenkpe 
Borehole 1 BDL 0.008 BDL 0.025 0.002 
Borehole 2 BDL 0.0035 BDL BDL 0.002 
Borehole 3 BDL BDL 0.016 0.006 0.0015 
Napaal 
Borehole 1 BDL 0.003 0.008 BDL 0.015 
Borehole 2 BDL BDL 0.008 BDL 0.015 
Borehole 3 BDL BDL 0.0135 0.003 0.003 
Ketuo 
Borehole 1 BDL BDL BDL 0.0185 BDL 
Borehole 2 BDL 0.003 BDL BDL BDL 
Borehole 3 0.0125 BDL BDL 0.008 BDL 
TWQRs (mg/L) 0-0.1 0-0.05 0-10 - 0-10 
WHO (2011) Guidelines for Drinking water: Cu=2.0, Zn=4.0, Fe= 3.0, Mn=0.4, Pb=0.010, 
Cr=0.05, Ni=0.070, AS=0.010, Cd=0.003, Meanwhile, Cr, Cd, Co, Cu and zinc were below 
detection limit (BDL) in all water sample. TWQRs=Target Water Quality Ranges (Source: WRC, 
2003) 
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Appendix 9: Durbin-Watson Statistic Output 
 
Model Summary 
Model R R Square Adjusted R Square Std. Error of the Durbin-Watson 
Estimate 
1 .759a .576 .558 .146 1.680 
a. Predictors: (Constant), availability_ecosystems, Farm Size of participant in hectares, 
acess_climate_info, acess_training, Farmland Ownership, How satisfied a smallholder farmer is with 
life in relation to major challenges, Gender, Access to informtion on managing Ecosystem Services 
b. Dependent Variable: adaptation_strategies 
 
ANOVA 
Model Sum of Squares df Mean Square F Sig. 
Regression 5.340 8 .668 31.441 .000 
1 Residual 3.928 185 .021   
Total 9.268 193    
a. Dependent Variable: adaptation_strategies 
b. Predictors: (Constant), availability_ecosystems, Farm Size of participant in hectares, 
acess_climate_info, acess_training, Farmland Ownership, How satisfied a smallholder farmer is with life 
in relation to major challenges, Gender, Access to informtion on managing Ecosystem Services 
 
Residuals Statistics 
 Minimum Maximum Mean Std. Deviation N 
Predicted Value 1.09 1.91 1.56 .166 194 
Residual -.565 .555 .000 .143 194 
Std. Predicted Value -2.830 2.117 .000 1.000 194 
Std. Residual -3.880 3.807 .000 .979 194 
a. Dependent Variable: adaptation_strategies 
 
 
 
 
 
 
 
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Coefficients 
Model Unstandardized Coefficients Standardized t Sig. 
Coefficients 
B Std. Error Beta 
(Constant) 1.806 .095  19.013 .000 
acess_training -.008 .024 -.018 -.321 .748 
acess_climate_info -.397 .030 -.688 -13.200 .000 
Gender .015 .023 .034 .644 .521 
Farm Size of participant in 
hectares -.018 .019 -.047 -.912 .363 
 
Farmland Ownership .012 .013 .046 .911 .363 
Access to informtion on 
managing Ecosystem Services .003 .025 .006 .110 .913 
How satisfied a smallholder 
farmer is with life in relation to -.023 .008 -.144 -2.780 .006 
major challenges 
availability_ecosystems .090 .037 .119 2.435 .016 
a. Dependent Variable: adaptation_strategies 
 
 
 
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