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COASTAL REGION OF KENYA

PHYLLIS WAMBUI WACHIRA (B. Env. Sc.)

(Reg. No. N50/CE/26778/2013)

A Thesis submitted in partial fulfillment of the requirements for the award

of the Degree of Master of Environmental Science of Kenyatta University

Nairobi (KENYA)

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DECLARATION

DECLARATION BY CANDIDATE

I declare that this thesis is my original research work and has not been presented or submitted for the award of a diploma or degree in any University.

……….. ……… Phyllis Wambui Wachira DATE

(Reg. No. N50/CE/26778/2013)

DECLARATION BY SUPERVISORS

We confirm that the candidate under our supervision carried out the work reported in this thesis.

……….. ………

DR EZEKIEL NDUNDA DATE

Department of Environmental Sciences Kenyatta University

Nairobi, Kenya

……….. ……… PROF. NOAH SITATI DATE

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DEDICATION

I dedicate this research work to my family, my daughter Kate and my son Alpha. Angels you believed in me and remained my source of inspiration throughout the research work. Your love and understanding made me believe in the one reason why I

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ACKNOWLEDGEMENT

I am grateful to God almighty for his unconditional love, good health, provision, protection and power to accomplish this research work.

I would like to most profoundly appreciate and thank Eng. Simon Mwangi, Manager Kenya Water Security and Climate resilience project, for encouraging my research work and the tremendous support throughout my thesis. Your mentorship, advice on both research as well as on my career have been priceless. I salute you.

I most sincerely express my special gratitude to my supervisor, Dr. Ezekiel Ndunda, Department of Environmental Science. Sir, your aspiring guidance, invaluably constructive criticism and friendly advice were a solid drive to the completion of this work. I am immeasurably thankful to my other supervisor, Professor Noah Sitati, your unwavering support, brilliant comments and advice was a great pillar to this research. I would like to acknowledge the support directly given to me during data collection by the following institutions; Kenya Meteorological Department (KMD) Nairobi, and Kenya Agricultural and Livestock Research Organization (KALRO), Mtwapa, Mombasa.

Special thanks to my family, Words cannot express how grateful I am to my daughter and son, my mother and father, brothers and sister for all the sacrifices that you have made on my behalf. Your love and prayers for me was what sustained me this far.

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TABLE OF CONTENTS

DECLARATION ... ii

DEDICATION ...iii

ACKNOWLEDGEMENT ... iv

TABLE OF CONTENTS ... v

LIST OF FIGURES ... vii

LIST OF TABLES ... viii

LIST OF ABBREVIATIONS ... ix

DEFINITION OF KEY TERMS ... x

ABSTRACT ... xii

CHAPTER ONE: INTRODUCTION ... 1

1.1 Background of the Study ... 1

1.2 Statement of the Problem ... 2

1.3 Objective of the Study ... 3

1.4 Research Questions ... 4

1.5 Research Hypotheses ... 4

1.6 Significance of the Study ... 4

1.7 Conceptual Framework of the Study ... 5

CHAPTER TWO: LITERATURE REVIEW ... 7

2.1 Overview of Climate Change Global Impacts ... 7

2.2 Overview of Climate Change and Agriculture In Kenya ... 9

2.3 The Ricardian Approach Model ... 13

2.4 Application of Ricardian Model in Africa ... 14

2.5 Application of Ricardian Model in Kenya ... 14

2.6 Limitations of Ricardian Approach ... 15

CHAPTER THREE: RESEARCH METHODOLOGY ... 16

3.1 Introduction ... 16

3.2 Study Area ... 16

3.3 Study Design ... 19

3.4Sample Size and Sampling Procedure ... 19

3.5Data Collection Methods ... 20

3.7 Data Analysis Techniques ... 21

CHAPTER FOUR: RESULTS AND DISCUSION ... 27

4.1 Introduction ... 27

4.2 Data Presentation ... 27

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4.4 Ricardian Regression Analysis for Net Livestock Revenue ... 31

4.5 Ricardian Regression Analysis for Net Combined (Crop & Livestock) Revenue ... 33

4.6 Climate Variables and their Marginal Effects ... 35

4.7 Overview of Adaptation to climate change in coastal region of Kenya ... 37

CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS ... 39

5.1 Introduction ... 39

5.2 Conclusions ... 39

5.3 Recommendations ... 40

5.4Summary ... 41

REFERENCES ... 44

APPENDICES ... 48

Appendix 1: Questionnaire ... 48

Appendix 2: Hydrological Variables ... 51

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LIST OF FIGURES

Figure 1.1. Conceptual framework ... 6

Figure 3.1. Map showing the study area ... 17

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LIST OF TABLES

Table 3.1. Coastal region population summary ... 18

Table 3.2. Study Sample size summary ... 19

Table 4.1. Descriptive statistics of variables used in the regression models ... 19

Table 4.2 Ricardian Regression Estimates for Crops Net Revenue ... 29

Table 4.3 Ricardian Regression Estimates for Livestock Net Revenue ... 32

Table 4.4: Ricardian Regression Estimates for Combined Net Revenue ... 33

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LIST OF ABBREVIATIONS

ASDS- Agricultural Sector Development Strategy ASAL- Arid and Semi Arid Land

CGK- County Government of Kwale EEZ- Exclusive Economic Zones GDP- Gross Domestic Product

IPCC- International Panel on Climate Change KMD- Kenya Meteorological Department

NCCAP- National Climate Change Action Plan

NCCRS- National Climate Change Response Strategy

USAID- United States Agency for International Development

UNDP- United Nations Development Programme

UNFCCC- United Nations Framework Convention on Climate Change

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DEFINITION OF KEY TERMS

Climate Change Adaptation: This is the small alteration made in economic, ecological

and social systems, to respond to the expected or actual stimuli in climate as well as their impacts. Changes in practice, methods as well as structures, in order to moderate possible damages or benefiting from chances linked to change in climatic patterns (UNFCCC, 2014).

Climate Change: This are changes in condition of atmospheric state recognizable by

variance in mean or/and changes of properties which are persistent for a long duration of over 30 years, due to natural change of circumstances or as a result of humans activities (IPCC, 2007).

Climate Variability: A change in the average state and distinct atmospheric conditions

on all terrestrial and spatial buildup, exceeding independent occurrence of state of atmosphere. It denotes digression of statistics in climate in specific time period (e.g. year, a month or season) comparable to data lasting for a considerable time on the same duration of time (WMO, 2014).

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The Ricardian model makes an estimation of climate impacts by comparing the net revenues of farmers in different climates across space. Since farmers in various places have adjusted to their unique conditions as an adaptation, it is implicitly captured by the Ricardian approach. The Ricardian model was initially applied in the context of developed countries, and the United States agriculture in particular.

In this study net revenue was used as a function of three regressors namely soil characteristic, climate variables (temperature and precipitation), (erosivity), and social economic variables. Both linear as well as quadratic terms of climatic variables annual mean average were considered.

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ABSTRACT

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CHAPTER ONE: INTRODUCTION

1.1 Background of the Study

Agriculture is the backbone of the Kenya’s economy contributing 27% and 24% indirectly and directly respectively. In the rural areas, this sector accounts for 65% of all the informal employment. It is clear that Kenya faces notable challenges as far as food security is concerned, mainly resulting from overdependence on agriculture that is rain-fed (Government of Kenya, 2013).

The number of people who required food assistance in Kenya rose to approximately 3.8 million in 2009/2010 from 650,000 in 2007 (Government of Kenya, 2013). Currently, it is approximated that more than 18 million people are starving with no immediate hope for assistance from the state (Ngwiri, 2016). Agricultural areas that are marginal and pastoral are highly vulnerable to impacts arising from change in climate. Livelihood opportunities as well as the resilience ability for communities have been eroded, as a result giving rise to coping strategies that are undesirable.

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As far as the vision 2030 in Kenya is concerned, the agricultural sector has been identified as a sector that will deliver 10% of the entire annual budget under the economic pillar. In order to attain this, there is the urgent need to transform small holders in the sector to be able to innovate, thus being able to operate optimally for improved production (Government of Kenya, 2009).

Kenya’s agriculture is mainly fed through rain, thus depends fully on rainfall in almost the entire country. Only 1.7 per cent of the total area of land in the country under agriculture is under irrigation (Agricultural Sector Development Strategy, 2009). Fluctuating agricultural productivity however is a major concern considering the fast growing population in Kenya. Marginal as well as pastoral agricultural areas are susceptible to the effects of changing climatic patterns. In Kenya, agriculture remains the sole source of food, thus a notable basis of national economy.

1.2 Statement of the Problem

Food policy should serve humanity through the advancement of human goals, thus eradicating hunger and poverty. However, emerging forces including climate change recently challenged these goals.

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climate-dependent in most of its sectors, and the capability to adjust as well as subsist to changing climate are highly limited (Iheoma, 2014). For instance, 75% of all the production in agriculture is accounted for by small-scale farms as well as 70% of entire marketed crop and livestock produce (Agricultural Sector Development Strategy, 2009).

Kenyan coastal region has been hard hit by poverty for many decades. Agricultural Sector Development Support Programme (ASDSP, 2014) sites land tenure, yearly drought and floods and undeveloped agricultural markets as a major challenge alongside increasing poverty levels to non-performing agriculture in the country.

It is clear that, change in climate may interrupt the progress already made as far as eradication of hunger globally is concerned. This is based on the fact that stability of the entire system of food production may be risked by changes in climate owing to the variability in supply that is short- term (Wheeler and Braun, 2013). However, only a few studies have been done in Kenya to explain the results of variations in climate on agriculture and none in coastal region. This study therefore, aims to investigate impacts of change in climate by analyzing trends in temperature variations and rainfall patterns over time in crops and livestock production.

1.3 Objective of the Study

1.3.1 Overall Objective

Assessment of impacts of climate change on economic returns from crop and livestock production in the coastal region, Kenya.

1.3.2 Specific Objectives

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1. To assess the impact of climate change on crop farming income.

2. To evaluate the impact of climate change on livestock farming income.

3. To estimate the impact of climate change on combined income from both crop and livestock production.

1.4 Research Questions

1) How does climate change affect income from crop production in coastal Kenya? 2) How does climate change influence income from livestock in coastal Kenya? 3) How is the combined income from crop and livestock production affected by

climate change?

1.5 Research Hypotheses

1. The income from crop production is significantly influenced by climate change in coastal Kenya.

2. The income from livestock production is significantly affected by climate change in coastal Kenya.

3. The combined income from both crop and livestock production are significantly affected by climate Change in coastal Kenya.

1.6 Significance of the Study

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agriculture. The findings of the study contribute to local farmers understanding on how varying climate variables affect their production and revenues. The farmers also get to learn functional adaptation strategies in the study area. Further, the findings help in making appropriate policy recommendations to guide decision-making on sustainable agricultural production in coastal region.

1.7 Conceptual Framework of the Study

Dependent variables in this study included crop, livestock and combined net revenues measured in Kshs. Independent variables were climatic variables; social economic and geographical variables. They include temperature, precipitation, evaporation, and distance to the market, farming experience, education of the farmer, land under crops, livestock ownership, crop extension services, livestock extension services and credit availability.

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Figure 1.1. Conceptual framework

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CHAPTER TWO: LITERATURE REVIEW

2.1 Overview of Climate Change Global Impacts

In 2007, the Intergovernmental Panel on Climate Change (IPCC) regard climate change as the “changes in condition of atmospheric state recognizable by variance in mean or/and changes of properties which are persistent for a long duration of over 30 years, due to natural change of circumstances or as a result of humans activities” (Nkondze et al. 2014). Climate change effects practically impacts all fields and aspects of the modern day life. It causes changes in level as well as spatial distribution of precipitation, sea level rise, regulate tropical storms and temperatures, alterations in the duration and frequency of extreme atmospheric conditions such as prolonged durations with above average temperature, floods, and timing (Nkondze et al. 2014). The issues of climate and its impacts on crop farming and livestock keeping, and the results on income and livelihood have been widely researched and documented in different parts of the globe. Digambar (2011); Omari (2010); Thornton (2010); Deressa, Hassan & Ringler (2009); Abate (2009) UNFCCC (2007) are some of the recent studies that have extensively looked at the results of change in climate on Agriculture.

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approximated that due to greenhouse gas emission global temperatures could increase by 3% by 2050.

Reports by the IPCC have also shown that a small increase in temperature of 1-2.5% could lead to disastrous effects placing overarching effects on the livelihood of millions of people. Already Africa has been under intense strain from stresses relating to climate and is vastly susceptible to the effects of changes in climate. Studies show that about over 30% of African people are already living in regions that are prone to drought and that more than 200no. Africans are exposed to drought every year (Abate, 2009; Thornton, 2010).

Many countries in Africa, including Kenya are vulnerable to enormous effects of changes in climate to a certain extent due to the lack of adaptive capacity. One of the key factors that make Africa vulnerable to climate changes is the fact that a notable percentage of the populace lives in rural setting and relies on livelihoods sensitive to climate like agriculture. Even so, there are many factors that contribute to the present impacts of alteration of atmospheric condition in Africa and they include lack of essential skills to cope with changing climate, poverty, fragile institutions, low level of primary education, lack of proper technology and technical know–how, constant conflicts, poor management capacities and poor access to resources.

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shortage due to change in climate by 2020. In some African states, the produce from rain-fed agriculture is projected to reduce by up to 50%, a feature that will have notable impacts on the living condition of millions of people. The UNFCCC (2007) indicates that agricultural yields including food availability in many African states, is highly traded off and this might further significantly affect the food security situation in the continent. Alteration in atmospheric condition will lead to a notable reduction in most subsistence crops like maize, groundnuts as well as millet in different parts of Africa (UNFCCC, 2007).

It is expected that as the 21st century comes to an end, sea level rise will affect several coastal regions that are low lying. Change in climate pose a major danger to the Kenyan agriculture due to the fact that many of the agricultural systems in the country are dependent on climate and because of the complex role agriculture serves in rural Africa and in the economic and social systems (Abate 2008). Literature is showing that change in climate offers notable effects on livestock systems and livestock production. For example, Thornton (2010) concludes that livestock and mixed farming systems will be the most affected by changes in climate in third world nations where the majority of the people are least resilient. The need for adaptation and increase resilience to changing climatic patterns will unquestionably increase the production costs in various regions.

2.2 Overview of Climate Change and Agriculture In Kenya

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the issue, change in climate continue to pose notable challenges to livestock development in Kenya. In light of this, redressing the challenges emanating from change in climate calls for proper formulation of policies in order to take the appropriate adaptation and mitigation alternatives for the agriculture sector. In agro-pastoral systems, a term that aptly describes the majority of the people in the coastal part of Kenya, livestock are key assets providing numerous social, economic as well as risk management purposes. Issues pertaining change in climate as far as the agro-pastoral systems are most likely to worsen the vulnerability of livestock systems, as well as reinforcing the existing factors that are at the same time affecting livestock production such as raised demand for food and rapid population and economic growth.

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lands and farming areas, raised scarcity of water resources, buffering ability of the entire ecosystems, increased desertification and lower grain yields.

According to Abate (2009) climate change has an effect on production of livestock, which invariably reduces the revenue generated by the farmers. The study established that delay in the onset of rains and drought in different parts resulted in poor grass regeneration, water scarcity and heat strain of existing livestock. The study also showed that delay in rainfall and drought resulted in increased mortality of livestock, physical deterioration and susceptibility to diseases as a result of lack of enough foods and water. Digambar (2011) indicated that as a consequence of prolonged drought, a direct effect on the growth of fodder species was indentified which is palatable, and that the regeneration of pasture has decreased substantially as a result of climate change. Digambar (2011) confirmed Abate (2009) and FAO (2008) findings that changes to climate affects livestock production and population, which thereafter have an effect on the revenue that is generated by the farmers from the livestock.

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Climatic conditions in Kenya vary from humid tropical areas along the coastal region to arid inland areas. Rainfall is highly variable, particularly in areas that are arid and semi-arid, hence unreliable for a country that mainly depends on agriculture that is rain fed and livestock production. In Kenya the rainy season can be extremely wet, causing floods in certain regions. Even in areas that are arid and semi-arid, which makes about 80 percent of the total Kenya land area, are susceptible to floods in spite of the low levels of rainfall received per year (estimated to be between 300 to 500millimetres per annum).

The negative impact of these droughts experienced in Kenya in every decade (major drought experienced one per decades and minor ones every three to four year) is likely to increase. Risk assessment studies show that future climate change can give rise to changing rates or severity of such intense weather events. This may potentially worsen the impacts, together with annual as well as rainfall changes that are seasonal. In addition, the extreme weather events will have a possible negative consequence on livestock as well as agriculture sector (Nicholson and Entekhabi, 1986; Ward 1998).

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2.3 The Ricardian Approach Model

The vulnerability of the sector of agriculture to both climate change as well as variability is well indicated in literature. It is agreeable that changing temperature and the resulting precipitation will give rise to changes in land and water regimes thereby affecting agricultural productivity. Early crop production models of the impacts of change in climate on the agriculture employed production function approach (Rosenzweig and Iglesias, 1994). Impacts on economy from climate change are overstated by these models (Mendelsohn et al., 1994). The method depended on complex crop-yield models, and rather failed to put into account all activities pertaining to agriculture in farms (used just major grains) and did not include livestock. Also, farmers changing inputs in substitutions or adjustments to reduce their vulnerability to climate change inherently biased them of adaptation measures.

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2.4 Application of Ricardian Model in Africa

Also, the model has been applied to a wider African context. A part of these recent studies include Iheoma (2014), Kurukulasuriya and Mendelsohn (2008). Van Passel et al. (2012), Massetti and Mendelssohn (2011), Seo and Mendelsohn (2008), Kabubo-Mariara and Karanja (2006) and Kurukulasuriya and Mendelsohn (2008). Majority of these studies indicates that, agricultural activities in majority of the developing countries are extremely susceptible to climate change. They disclose that the magnitude as well as direction of the impacts varies depending on regions.

Massetti and Mendelssohn (2011) states that majority of models that are non-market valuation like the Ricardian model are estimated by the use of cross sectional methods with yearly analysis of data. Even though multiple years of data ought to raise the robustness of methods like that repeated cross sections suggest the results are relatively stable. Iheoma (2014) and Kurukulasuriya and Mendelsohn (2008) argue that repeated cross sections do not properly specify the model. The Ricardian model is described as the best model to weigh the impacts of climate change on crop and livestock production due to the fact that it implicitly takes into account the adaptation measures by the subject under study (Van Passel et al. 2012).

2.5 Application of Ricardian Model in Kenya

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be affected further from raised temperature emanating from global warming as compared to fall of precipitation. The study result tells that climate change has an effect on agricultural produces. Increased levels of precipitation have the impact of increasing net crop revenue.

2.5.1 Literature Gap

The study above concentrated on the effects which climate change possess on crop agriculture for analysis. It fails to consider revenue from livestock production, although majority of farmers combine livestock and production of crop for both commercial as well as subsistence purposes in Kenya. It is this gap in knowledge that this study in coastal region of Kenya sought to fill.

2.6 Limitations of Ricardian Approach

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CHAPTER THREE: RESEARCH METHODOLOGY

3.1Introduction

This chapter describes the study area, design and data obtained in terms of its nature, type and sources. It also shows the description of the techniques, data collection methods, respondent sampling and statistical tools used for data analysis.

3.2 Study Area

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Source: Author, 2016

Figure 3.1. Map showing the study area

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include Ramisi, Kombeni, Umba, Tsalu, Nzovuni, Voi, and Mwachema (FAO Corporate Document Repository, 2015).

Population: The population data in the coastal region can be summarized as indicated

below as per the 2009 census (Ipos Public Affairs, 2013). Table 3.1. Coastal region population summary

Code County Population (Census 2009) % Total

1 Mombasa 939,370 28.24

2 Kwale 649,931 19.54

3 Kilifi 1,109,735 33.37

4 Tana River 240,075 7.22

5 Lamu 101,539 3.06

6 Taita Taveta 284,657 8.56

Totals 3,325,307 100

From the table, it is clear that, Kilifi County leads in size among the six counties with 33.37% of the entire population within the region. It is also important to note that, within the six counties, Giriama, one of the Mijikenda people forms 57% of the total population living in Kilifi County. On the other hand, Digo, another subgroup within Mijikenda constitutes 49% of the total population in Kwale County. Mombasa County is a metropolitan with the entire Mijikenda group being almost equally represented (Ipos Public Affairs, 2013). Other tribes such as Kamba, Luhya, Kisii, Arabs among others are also present within all the six counties.

Social Economic: The sources of household income, income level, and employment

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3.3 Study Design

This study used survey research design. Cross sectional data on farmland on actual observations of farm performance in areas where agriculture is practiced within the different Counties. The target group was farmers practicing mixed farming.

3.4Sample Size and Sampling Procedure

Proportional and random sampling was employed in selecting household during this study. Proportional sampling reduces biasness as well as make sure that particular parts of the population are not over represented. Random selection method was done to identify the households. A sample size of 640 households was derived based on the formula (Israel, 2009) indicated below:

 

2 1 N e

N n

 

Where a 95% confidence level and P = 0.05 are assumed for the equation, n is the sample size, N is the population size, and e is the level of precision that is 0.01. Based on the figure above, the response rate was 98.4%, 631 questionnaires.

Table 3.2. Study Sample size summary

County

Population of households practicing mixed

farming Sample Size

Tana River 92 91

Mombasa 67 67

Taita Taveta 141 139

Kilifi 93 92

Kwale 117 116

Lamu 137 135

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List of households was obtained from Nyumba Kumi village elders at the location level through local National Administration (NA) officers (Chiefs). The questionnaire was pretested before the actual data collection exercise. In each of the six counties, ten questionnaires were administered. This helped to perfect the tool for relevance and quality data collection. The questionnaires were then administered with the assistance of trained enumerators. Section C of the questionnaire was an in depth interview with the respondents.

3.5Data Collection Methods

3.5.1 Primary data

Primary data for this study was achieved through the administration of semi-structured questionnaires to household heads. Enumerators were trained on administration of the questionnaires prior to data collection. This was to standardize data collection procedure, minimize mistakes and ensure quality data. The questionnaires contained information on socioeconomics and adaptation to climate change (Appendix 1). As an output, this tool captured 2015 production data incomes. Enumerators interviewed farmers practicing mixed farming in every tenth household within the areas earlier identified as practicing agriculture within the counties.

3.5.2 Secondary sources of data

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Institutional websites and textbooks. Climate data employed for the study covered a period of 40 years (1972 - 2012). Secondary data collected included average precipitation, temperature and evaporation of every month within the 40 year analyzed by this study.

3.7 Data Analysis Techniques

3.7.1 Theory and Model

A Ricardian model is a linear regression model relating to data that is cross sectional in nature. Net revenue was a function of three regressors; climate variables covering temperature as well as precipitation, characteristic of soil covering fertility as well as erosivity, and economic variables that are social which includes education level, age, gender, and distance to the market and land ownership. The quadratic as well as linear terms of climatic variables (temperature, precipitation and evaporation) annual mean average were considered for 40 years. This study therefore analyzed three modified Ricardian models for crop net revenue that is livestock net revenue as well as combined net revenue of entire farm. The modified models were analyzed using STATA 2014 software. The model was used to calculate crop net revenue, livestock net revenue and the combined farm revenue for both crops and livestock as dependent variables. It is worth noting that climate change affects variables across the three modified Ricardian model (Gebreegziabher et al., 2013). The results obtained were presented in tables and graphs format.

The study used the model to measure welfare change. According to Mendelsohn

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climate change on Kenyan coastal region agriculture that generally is mixed farming. This study tried to offer an analysis on the effect of change of climate on crop farming as well as livestock farming, and the combined agriculture of both the crops and livestock. The principle captured in the equation below indicates that net productivity of land is equivalent to its total farmland net revenue as shown below;

Vrt = PtQrt ßrt Zt- Mt Xrt e-∂t dt (1)

Where Pt is the market price of every crop at location t, Qrt is the output of each crop at farm r at location t, ßrtis a vector of inputs for each crop at farm r (other than land) Zt, is a vector exogenous variables at location t, Mt is a vector of input prices at location t and ∂ is the interest rate, t is time period. Note also that the -∂t term is for discounting. The farmer is wanted to select X to raise net revenues for crop net given farm characteristics as well as prices in the market. Land value Vrt can therefore be expressed as a function of exogenous variables only by solving equation (1) above. These site-specific exogenous environmental factors include climate variables (temperature (T) and precipitation (P)), soil variables (S), socio-economic variables (H) and geographical variables (G). To capture returns from livestock production, an assumption was made that the farmer maximizes net revenue through selecting which livestock to buy and the inputs to apply.

Max (ß = P qx Q x (GL, ∞, L, µ, C, Ω ø,) PF ∞ PWL PM µ (2)

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vector of labor inputs, µ is a vector of capital such as milking cans, C is a vector of climate variables, Ω is available water, ø is a vector of soil characteristics of grazing land, PF a vector of prices for each type of feed, PWis a vector of prices for every variety of labor, and PM capital (rental price). If a farmer rears the species (animal) x and the number of animals maximizing profits then, condition maximizing profits to the representative problem affecting the farmer can be specified as:

ß*= ß (Pq, C, W, ø, PF, PW, PM) (3)

Equation (3) is the Ricardian equivalent to livestock production. Seo and Mendelsohn (2006) tells how profits vary across all the exogenous variables a farmer faces.

3.7.2 Empirical Model and Data 3.7.2.1 Empirical Model

This study adopted Log Linear Ricardian analysis, Massetti and Mendelsohn (2011), as land values are log normally distributed. As noted earlier, Ricardian model make use of actual observations of performance from farm, (Mendelsohn et al., 1994) and the analysis comes from a number of explanatory variables like climate, geographical and variables in social economic directly affecting farm revenue. The main aim of the Ricardian technique is assessing changes in agricultural land in terms of net revenue across climate gradients (Wood and Mendelsohn, 2014).

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Where VL is the net revenue per hectare, ∂ the vector for climate variables, ∂2 squire vector of climate variables, ∞ set of soil variable, ß set of social economic variables, µ is the error term, R’s are parameters requiring estimation and R0 is the constant term and the rest are coefficients. ∂ and ∂2 captures linear as well as quadratic terms for temperature and precipitation respectively. When the quadratic term is positive, net revenue function is U shaped and when the quadratic term is negative the function has the shape of a hill (Mendelsohn et al., 1994).

Marginal impact of climate variables on value of land per hectare (direct crop revenue) was put as the equation below;

E {dV⁄ d∂i}= E {R1i +2R2i *∂i} (5)

=R1i+2R2i*E (∂i)

The above equation established the net economic welfare change. The change in welfare brought about by change in the state of environment from x to y, which consequently causes a change in the farm inputs from ßa to ßb . Impacts of non-marginal change in climate on land per hectare value in a certain farm were expressed as the comparison between the approximated value of land under the new climate variables (precipitation (P) and temperature (T)) T1P1 and the approximated land value from the current climate variables T0P0.

∆W=W (ßb) –W (ßa) (6)

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∆W =∑n (PLb Qbi - PLa Qai) (7)

i=1

PLb Q bi =PiQi-Ci (ßi Mi, Zi) (8)

Where PLb Qbi are at ßb and PLa Qai are at ßa.

3.7.2.2 The Data analysis

A cross sectional survey of 640 households farm was carried out in Coastal region of Kenya to obtain dependent variables that includes crop net revenue, livestock net revenue and net revenue from the whole farm as indicators of likely effects of climate change as well as variability in climate on farm production at the household level.

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obtained for both crops and livestock. Livestock and crop production data was obtained from respective district records.

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CHAPTER FOUR: RESULTS AND DISCUSION

4.1 Introduction

This chapter represents results and discussion of the descriptive statistics and analysis of the regression models. The regression models presented in this section are as follows: Ricardian model for crops net income, Livestock net income and entire farm combined net income.

4.2 Data Presentation

4.2.1 Descriptive statistics of variables used in the regression models

Table 4.1. Descriptive statistics of variables used in the regression models

Variable Mean Std. dev. Min Max

Crops Net Revenue (KSHS) 46414.55 59464.04 -36000 560,800 Livestock Net Revenue (KSHS) 52753.69 81769.79 -32000 977,500 Combined Net revenue (KSHS) 99168.24 104117.7 -36000 973,000

Precipitation (mm) 76.59 20.20 38.9 95

Temperature (0C) 30.62 0.43 29.88 30.96

Evaporation (mm) 6.38 0.59 5.82 7.19

Market Distance (Km) 8.33 5.40 2 22

Adaptation (0/1) 0.52 0.49 0 1

Farmers to farmer information (0/1) 0.46 0.49 0 1

Climate change awareness (0/1) 0.32 0.48 0 1

Access media (0/1) 0.81 0.38 0 1

Access to Credit services (0/1) 0.20 0.40 0 1

Hired labor use (0/1) 0.76 0.42 0 1

Land owned (acres) 1.05 3.55 0 16

Climate change awareness (0/1) 0.68 0.46 0 1

Employment status (0/1) 0.19 0.38 0 1

Soil erosion severity (0/1) 0.11 0.31 0 1

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Education level of household head (years) 6.14 0.80 1 11

Age of household head (years) 40.99 17.15 0 77

Gender of household head (0/1) 0.70 0.56 0 1

Table 4.1 presents the summary statistics of variables used for regression analysis in this study for both secondary and primary data from the 631respondents and climate data. According to the results, annual minimum crops net revenue was Kshs. -36,000 while the maximum returns per hectare was Kshs. 560,800. In Livestock production, minimum net revenue was Kshs. -32000, while maximum returns annually were Kshs. 977,500. Combined agriculture minimum annual net revenue was Kshs. -36,000 and a maximum of Kshs. 973,000. The mean total revenue of the studied area was Kshs. 99168, slightly largely contributed by livestock production with 53% (Kshs. 52,754) and crop production at 47% (Kshs. 46,414).

The minimum and maximum precipitation of the area was 38.9 and 95.0 mm respectively. Mean precipitation during the 40year period was found to be 76.59mm. Minimum temperature was 29.88 Celsius, and a maximum 30.9 Celsius. The mean temperature and Evaporation was 30.62 Celsius and 6.38 respectively.

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This study made use of Ricardian model as described in the methodology chapter to assess the effect of climate change on agriculture in the coastal region of Kenya. The dependent variables in the regression models are: net crop income, net livestock income and combined net income. According to the Student t test for the significance of every estimated coefficient, climate variables were found to be significant (p < 0.05) parameters in determining the net incomes from crops, livestock and the net combined income. Using the Fisher-Snedecor tests (Snedecor and Cochran, 1989) the three models used in this study were validated since their regressions were all significant (p < 0.05). The coefficients of determination crop, livestock and combined models were 37%, 48%, and 42%, respectively.

4.3 Crop Net Revenue Analysis on Ricardian Regression

Table 4.2 Ricardian Regression Estimates for Crops Net Revenue

Variable Coeff. Std. Err. t-test

Precipitation 981.53*** 174.40 5.63

Precipitations_ SQ 6.62*** 1.13 5.84

Temperature -51272.92*** 6084.46 -8.43

Temperature_ SQ 840.94*** 99.12 8.48

Evaporation -19809.23*** 4383.86 -4.52

Evaporation_ SQ -79.87 85.67 -0.93

Distance market -8487.11*** 1414.97 -6.00

Erosion severity -16042.57*** 5977.35 -2.68

Employment status 11642.41*** 4074.02 2.86

Access to media 16035.65*** 5321.58 3.01

Credit services access 20708.27*** 4716.24 4.39

Farmer to farmer extension services 11079.11*** 5058.67 2.19

Size of land owned 3428.97*** 651.10 5.27

Climate change awareness 9244.50*** 3868.37 2.39

Adaptation to climate change 22393.21*** 3740.88 5.99

Hired labor use 10001.50*** 4461.34 2.24

Education level of household head 4308.09 2366.97 1.82

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Gender of household head -5194.55 3811.60 1.36

Soil fertility 1024.12 2154.51 0.48

Constant 646372.90*** 255509.00 2.53

F (20, 610) 20.58

Prob > F 0.00

R-SQ 0.37

Root MSE 61078

N 631

*** p < 0.05

The Results from Ricardian regression model based on net crop revenue showed that both the linear and the squared terms of climate change significantly (p <0.05) affect the net crop revenue in coastal region (Table 4.2), thus change in climate has non-linear impact on overall crop revenue in the study area (Mendelsohn et al 1994, 2003; Kurukulasuriya et al., 2006). According to the linear terms, an increase in precipitation raises net crop income. However, the increase of both temperature and evaporation lowered crop income in the study area. This depicted that prolonged high temperatures are detrimental to crop productivity indicating that global warming is probable to poses damaging impact on agricultural activities unless government and farmers enhances measures to adapt in order to counter the expected impacts of changes in climate (Kurukulasuriya et al., 2006)

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inverse relationship between distance to the market and the net revenue from crop production may be attributed to costs incurred in transporting inputs to the farms as well as outputs to market. Awareness to climate change was found to significantly raise net crop income. This is attributable to ability of the farmer to seek varieties of crops that can yield optimally in various climatic conditions.

A positive effect of adaptation to change in climate on net income from crop farming could be attributed to production of crops that are resilient and hence able to yield well under harsh climate conditions. The access to credit facilities can enable farmers to acquire farm inputs and hence increase farm productivity. The coefficient of erosion severity in the farms was as anticipated. An increase in erosion lowers the ability of the farm to support optimal crop yields hence lowering revenue from crops. The increased revenue due to use of hired labor may be explained by timely farm operations such planting, weeding, harvesting and postharvest processes.

4.4 Ricardian Regression Analysis for Net Livestock Revenue

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Table 4.3 Ricardian Regression Estimates for Livestock Net Revenue

Variable Coeff. Std. Err. t-test

Precipitation 2089.73*** 1090.01 2.11

Precipitations_ SQ 49.56 37.74 1.31

Temperature 229.10*** 110.21 2.08

Temperature_ SQ -531.42*** 130.81 -4.06

Evaporation -1052.91*** 482.00 -2.18

Evaporation_ SQ -75.27*** 33.17 -2.27

Distance market -3244.01*** 914.97 -3.55

Erosion severity -3982.57 3166.21 -1.26

Employment status 2842.41 2918.22 0.97

Access to media 1595.15*** 729.81 2.19

Credit services access 4281.27*** 1716.24 2.49

Farmer to farmer extension services -1066.13 2188.02 -0.49

Size of land owned 2009.51*** 998.01 2.01

Climate change awareness 2818.20*** 1301.09 2.17

Adaptation to climate change 12921.34*** 5192.99 2.49

Hired labor use -10011.50 5193.17 -1.93

Education level of household head 6302.99 4116.92 1.53

Age of household head 909.19*** 409.37 2.22

Gender of household head 4701.37 2704.99 1.74

Soil fertility 978.86 701.21 1.40

Constant 337624.50 270515.50 1.25

F (20, 610) 15.53

Prob > F 0.00

R-SQ 0.38

Root MSE 60542

N 631

*** p < 0.05

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Access to media turned out positive and significant, suggesting that provision of information through the local media outlets to farmers can influence their decisions greatly. Change in climate awareness had a notable and positive outcome on net livestock revenue. This implied that farmers who have knowledge about the existing climatic conditions are likely to choose breeds of livestock that can well suit their areas. Adaptation to climate change had the expected positively significant coefficient. Livestock farmers who have adapted to climate change have realized increased net revenue. The results showed that household head’s age had positive and significant coefficient. This may be partly attributable to experience gained in livestock production that can help in making appropriate decisions and thus increasing the net revenue (Gebreegziabher et al., 2013).

4.5 Ricardian Regression Analysis for Net Combined (Crop & Livestock) Revenue

The regression model results based on net crop and livestock revenue (whole farm) showed that both the linear and the squared terms of change in climate have a significant (p < 0.05) consequence on the combined net revenue in coastal region (Table 4.4). Therefore, there was a non-linear impact change in climate on net combined revenue in the study area. The linear term for precipitation had a significant and positive coefficient whereas the coefficients for temperature and evaporation were negative.

Table 4.4: Ricardian Regression Estimates for Combined Net Revenue

Variable Coeff. Std. Err. t-test

Precipitation -481.53*** 174.40 -2.76

Precipitations_ SQ 31.11*** 4.06 7.66

Temperature -329.26*** 101.96 -3.23

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Evaporation -19809.23*** 4383.86 -4.52

Evaporation_ SQ -7700.24*** 1912.34 -4.03

Distance market -2143.27*** 819.97 -2.61

Erosion severity -921.30 3109.41 -0.30

Employment status 1108.41 7114.21 0.16

Access to media 2172.33*** 818.53 2.65

Credit services access 10019.23*** 3702.45 2.71

Farmer to farmer extension services 21179.71*** 8793.10 2.41

Size of land owned 1008.29*** 379.44 2.66

Climate change awareness 1819.37*** 717.11 2.37

Adaptation to climate change 10401.92*** 4393.41 2.37

Hired labor use 1770.18 3809.53 0.46

Education level of household head 3804.67*** 1873.15 2.03

Age of household head 109.03 86.33 1.26

Gender of household head -7099.01*** 3299.00 -2.15

Soil fertility 602.93 499.07 1.21

Constant 1488413.00*** 541470.10 2.75

F (20, 610) 5.85

Prob > F 0.00

R-SQ 0.42

Root MSE 1.00E+05

N 631

*** p < 0.05

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The level of education of a household head had a significant as well as positive coefficient. Education contributed to a farmer understanding complex aspects of climate change easily compared to illiterate farmers. The gender variable had a negatively significant coefficient. A female-headed household in the study area increased combined net revenue significantly. This is attributable to the role of women in provision of farm labor in the coastal region of Kenya. The farmers’ awareness to climate change was established to be a significant factor that influences the combined net revenue. This compares well with the results from earlier regression models where crop and livestock net revenues were considered in isolation. Similar results were also observed for the farmers who had adapted to change in climate. The coefficient of adaptation to change in climate was found to be positive and significant. This underscores the relevance of appropriate adaptation strategies in order to realize an increase in combined net revenue.

4.6 Climate Variables and their Marginal Effects

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Table 4.5: Climate Variables Marginal Effects

Climate Variables Crop Livestock Agriculture (total)

Precipitation 374.02*** -119.04*** 201.31***

Temperature -4893.37*** 229.65*** -374.88***

Evaporation -10312.92*** -391.11 -1127.56

*** p < 0.05

The marginal impacts of temperature on the overall revenues showed that annual rise of 10C of temperature would have a significantly positive effect on livestock net revenue, but negative impact on farm net income and crop net revenue. According to the results, an annual net gain of Kshs 229.65 is expected from livestock agriculture when the annual temperature increases by 10C. However, net losses of Kshs 4893.35 and 374.88 results from a 10C increase in temperature. These results compare with the ones obtained on similar studies by other researchers (e.g., Gebreegziabher et al., 2013; Seo and Mendelsohn, 2006).

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4.7 Overview of Adaptation to climate change in coastal region of Kenya

Figure 4.1. Adaptation to climate change in coastal region of Kenya

Previous studies on adaptation to climate change shows that it significantly reduces the susceptibility to expected imminent effects of climate change (Kabubo-Mariara & Karanja, 2006; Kurukulasuriya & Rosenthal, 2003). These studies show that the potential of strategies of adaptation in lessening the extreme effects of global warming is huge. The adaptations addressed in this study were on livestock and crop production. In this study, the considered climate change adaptations in the coastal region of Kenya included: improved livestock and crop varieties, optimal water management approaches, increased use of organic fertilizer, conservation tillage farming and adoption of agroforestry techniques.

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CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

This chapter presents conclusions and recommendations based on results from this study.

5.2 Conclusions

The aim of this study was to evaluate the economic impact of climate change on agriculture in the coastal region of Kenya with the aid of a Ricardian model. To achieve this, all the six counties (Lamu, Tana River, Kilifi, Mombasa, Kwale and Taita Taveta) in the coastal region were included in the study. The dependent variables were annual net revenue from crops, livestock and total farm. These were regressed against climate and socioeconomic characteristics.

The results showed varied effects of different parameters across the Ricardian models. According to these, a nonlinear relationship exists between climate variables and net revenues from crop, livestock and agriculture as a whole.

5.2.1 Climate Change Affects Crop Production Revenue

Climate change affects crop production. The marginal impact analysis showed that a unit increase in precipitation increases crop revenues while a unit increase in mean annual temperature significantly reduced crop and total farm income. Lastly, the marginal effect of a unit increase in evaporation was found to be negative for crop net revenue.

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Climate change affects livestock production. Analysis showed that, marginal effect of a unit increase in precipitation is a reduction of livestock net revenue. Marginal increase in livestock production revenue resulted in a unit increase of linear temperature. Further results showed that an increase in Quadratic temperature would reduce livestock net revenue.

5.2.3 Impacts of Climate Change on Combined Agriculture

Climate change affects combined Agriculture. Analysis of marginal impact showed that, 1mm increase in annual precipitation would result in an increase from total net farm revenue a similar scenario in the case of crop production. It also showed that a unit increase in mean annual temperature significantly reduced total farm income.

5.3 Recommendations

Several recommendations have been made based the net earnings from crops, livestock as well as agriculture in the coastal region of Kenya;

5.3.1 Crop Agriculture

In crop agriculture, awareness about climate change should be enhanced among farmers. This can be achieved through increased access to media outlets that are accessible to most farmers. Farmers should be facilitated to access appropriate strategies of adaptation to climate change (e.g., seeds appropriate for the hot and dry climatic conditions). Access of credit facilities needs to be promoted. This can greatly help farmers to acquire the necessary inputs in time for crop production.

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In livestock agriculture, access to media should be enhanced whereby information on appropriate livestock production technologies may be communicated to farmers. There is need for training of farmer groups since the trained farmers are likely to transfer the learned technologies to others through farmer-to-farmer extension services. Awareness creation about climate change together with providing information about adaptation strategies should be encouraged. This can help farmers in raising breeds that can excel in harsh hot and dry weather conditions.

5.3.3 Combined Agriculture

In total agriculture, credit facilities should be made accessible for timely acquisition of inputs. Farmer to farmer extension services should be encouraged as a means of increasing awareness about adaptations to climate change and resilience. There is need for the County governments to improve local infrastructures such as upgrading the local feeder roads and construct more markets near the production areas to reduce farmers cost in obtaining inputs and venturing the far and old markets.

5.4 Summary

The purpose of my thesis was to assess the economic impacts of climate change on crop and livestock returns in Kenya’s coastal region. The thesis is composed of five chapters each of them dealing with different aspects of the study.

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agriculture and help the local farmers understand how varying climate variables affect their production revenues. Dependent variables in this study included crop, livestock and combined net revenues measured in Kshs. Independent variables in this study were climatic variables; social economic and geographical variables.

Chapter two explored and highlighted the impacts of climate change globally, in Africa and in Kenya in relation to agricultural production. It also described Ricardian model approach and its applications in past studies in Africa and Kenya. This chapter also documented literature gaps that the study sought to fill and the limitations of Ricardian model in research of this nature. The reviewed studies failed to take into account revenue from production in livestock, yet majority of farmers in Kenya combine livestock as well as crop production for both commercial and subsistence purposes. It is this gap in knowledge that this study in coastal region of Kenya sought to fill.

Chapter three looked at the study area, explained sample size and the sampling procedure including the empirical model, data collection methods and data analysis techniques. The study was conducted in all the six counties in the coastal Kenya: Kwale, Mombasa, Kilifi, Lamu, Tana River and Taita Taveta. A total of 631 respondents were interviewed to obtain the cross-sectional survey data. The secondary data on temperature, precipitation and evaporation for 40 years was obtained from Kenya Meteorological Department.

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livestock and combined agriculture) and climate variables and their marginal effects. The chapter also provided an overview of adaptation to climate change in coastal region of Kenya. Results from the study showed that climate change significantly (p<0.05) affected net revenues from crops, livestock and a combination of both livestock and crops.

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REFERENCES

Adams, R., McCarl, B., Segerson, K., Rosenzweig, C., Bryant, K., Dixon, B., Conner, R. Evenson, R., and Ojima, D. (1998). “The Economic Effects of Climate Change on US Agriculture.” In R. Mendelsohn and J. Neumann (Eds.) 1998.The Economics of Climate Change. Cambridge University Press, Cambridge.

Agricultural Sector Development Strategy (ASDS)(2009). Agricultural Sector

Development Strategy 2009-2020. Available at Agricultural Sector Development Strategy (ASDS):

http://www.fao.org/fileadmin/user_upload/drought/docs/Agricultural%20Sector %20Development%20Strategy.pdf

Cline, W. R. (1996). The Impact of Global Warming of Agriculture: Comment. The American Economic Review 86, 1309-1311.

Deschênes, O., Greenstone, M.,2007. The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather.

American Economic Review 97, 354-385.

FAO Corporate Document Repository (2015). Overview of Kenya's Coastal Area. Retrieved from FAO Corporate Document Repository website: http://www.fao.org/docrep/field/003/ac574e/AC574E03.htm

Fisher, C., Hanemann, M., Schlenker, W. (2012). The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Comment. American Economic Review forthcoming.

Fox, J. and Weisberg, S. (2011). An R Companion to Applied Regression. Sage, Thousand Oaks, CA, second edition.

Gebreegziabher, Z., Mekonnen, A., Deribe, R., Abera, S. and Kassahun, M.M. (2013). Crop-Livestock Inter-linkages and Climate Change Implications for Ethiopia’s Agriculture: A Ricardian Approach. Environment for Development, Discussion Paper Series. EfD DP 13-14.

Government of Kenya (2013). Kenya National Climate change Action Plan (2013-2017). Available at Climate Change Action Plan: http://cdkn.org/wp-content/uploads/2013/03/Kenya-National-Climate-Change-Action-Plan.pdf

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Haveman, R. (2009).“What does it mean to be poor in a rich society?” Focus 27(2).

Iheoma, C. (2014). Impact of Climate Change on Agricultural Production and Sustainability in Nigeria. Asian Journal of Agricultural Extension, Economics & Sociology 4(1): 29-41, 2015.

INFOTRAK Research (2015). Coast Region. Retrieved from INFOTRAK Research and Consulting website: http://countytrak.infotrakresearch.com/county-regions/ Ipos Public Affairs (2013). Kenya Coastal Survey.

Israel, D. (2009). Determining Sample Size. Program Evaluation and Organizational Development, Institute of Food and Agricultural Sciences (IFAS), University of Florida, Gainesville 32611.

Kabubo-Mariara, J., and Karanja, F. (2006).“The Economic Impact of Climate Change

on Kenyan Crop Agriculture: A Ricardian Approach. CEEPA Discussion Paper No. 12. Centre for Environmental Economics and Policy in Africa, University of Pretoria.

Kabubo-Mariara, J., Karanja, K. (2007).The economic impact of climate change on Kenyan crop agriculture: a Ricardian approach. World Bank Policy Res. Series Work. P. 4334.

Kurukulasuriya, P., and Mendelsohn, R. (2008). “A Ricardian Analysis of the Impact of Climate Change on African Cropland.” CEEPA Discussion Paper No. 8. Centre

for Environmental Economics and Policy in Africa, University of Pretoria. Coastal Region Government (2014). COASTAL REGION- Investment profile. Available

at Coastal Region Government:

http://www.kwalecountygov.com/apps/index.php?

option=com_wordbridge&view=entry&Itemid=219&p=127&slug=kwale-county-investment-profile

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Mendelsohn, R., Nordhaus, D. (1996). The Impact of Global Warming on Agriculture: Reply. The American Economic Review 86, 1312-1315.

Mendelsohn, R., Nordhaus, W. and Shaw, D. (1994). The impact of global warming on agriculture: A Ricardian analysis. Am. Econ. Rev. 84:753-771.

Mendelsohn, R., and Dinar, A. (2003). Climate, water, and agriculture. Land Econ. 79:328-341

Michael, P. (1997). “Understanding Regression Analysis” Plenum press. New York, N. Y.10013

Msughter, A. and Ujoh, F. (2013). "Effect of Variability in Rainfall Characteristics on Maize Yield in Gboko, Nigeria." Journal of Environmental Protection, Vol. 4 No. 9, 2013, pp. 881-887

Polsky, C., and Esterling, W. (2001). “Adaptation to Climate Variability and Change in the US Great Plains: A Multi-scale Analysis of Ricardian Climate Sensitivities.”Agriculture, Ecosystem and Environment 85(3): 133-144.

Polsky, C. (2004). “Putting Space and Time in Ricardian Climate Change Impact Studies: Agriculture in the US Great Plains, 1969–1992.”Annals of the Association of American Geographers 94(3): 549-564.

Rosenzweig. C. and Iglesias A. (1994). Implications of Climate Change for International Agriculture: Crop Modeling Study. (EPA 230-B-94-003).U.S. environmental Protection Agency. Washington. DC.

Seo,S.N., and R. Mendelsohn. 2006. “The Impact of Climate Change on Livestock Management in Africa: A Structural Ricardian Analysis.” CEEPA Discussion Paper No. 9.Center for Environmental Economics and Policy in Africa, University of Pretoria.

Seo, N., Mendelsohn, R. (2008). Measuring impacts and adaptations to climate change: a structural Ricardian model of African livestock management1. Agricultural Economics 38, 151-165.

Snedecor, G.W. and Cochran, W. G. (1989). Statistical Methods, Eighth Edition, Iowa State University Press.

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Van Passel S, Massetti, E., Mendelsohn, R. (2012). A Ricardian analysis of the impact of climate change on European agriculture. FEEM nota di lavoro, 83/2012,

available at

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APPENDICES

Appendix 1: Questionnaire

ASSESSMENT OF ECONOMIC IMPACTS OF CLIMATE CHANGE ON LIVESTOCK AND CROP RETURNS IN THE COASTAL AREA OF KENYA

Questionnaire Number hhid

Interview Sub-County subcounty

Interview Village village

Agro-ecological Zone agrozone

Date of Interview intdate

PREAMBLE Dear Respondent,

You are invited to participate in climate change survey conducted by Phyllis Wambui Wachira, a student in the Department of Environmental Sciences, Kenyatta University. The survey covers all the areas in Coastal Region. The purpose of the study is to undertake a comprehensive assessment of the economic impact of climate change on agriculture.

Please note the following:

1) This study involves a survey. Your name will not appear on the questionnaire and the answers you give will be treated as strictly confidential. You cannot be identified in person based on the answers you give.

2) Your participation in this study is very important to us. You may, however, choose not to participate and you may stop participating at any time without any negative consequences.

3) Please answer the questions in the attached questionnaire as completely and honestly as possible. This should not take more than 30 minutes of your time. 4) The results of this study will be used for academic purpose in attempting to

understand how climate change has affected agricultural productivity in Coastal region. We will provide you with a summary of our findings on request.

5) Please sign the form to show that:

i) You have read and understood the information provided above. ii) You give your consent to participate in the study on a voluntary basis.

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SECTION A: SOCIO-ECONOMIC VARIABLES • Please provide the following information about the household members.

Nam e Age (Year s) Gender 1= Male 0=Fem ale Marital Status 1=Marri ed 0=Singl e Highes t educati on level (Years) Employ ed 1=Yes 0=no Land owned (Acres ) Land under crops (Acres) Land for livesto ck grazing (Acres) S/ n Na me

age gender mstatus Educle

v Employ ed lando wn landcr op landtr ees 1. 2. 3. 4. 5.

SECTION B: HOUSEHOLD FARM VARIABLES

1) How would you describe soil fertility in your farm?

Very Infertile Infertile Moderate Fertile Very Fertile 2) How would you describe soil erosion in your farm?

Very Severe High Moderate Low Very Low 3) Do you have a severe soil erosion problem in your farm? Yes No 4) Do you use farm machinery in your farm? Yes No 5) Do you use animal draft power in your farm? Yes No 6) Do you use certified seeds in your farm? Yes No 7) What is your average labour use per acre (adult days)? ……….. 8) How much seeds do you use per acre (kg) annually? ……… 9) How much fertilizer do you use per acre (kg) annually? ……….. 10) How much manure do you use per acre (kg) annually? ……… 11) Do you practice irrigation farming in your farm? Yes No

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SECTION C: INSTITUTIONAL VARIABLES

• Select the appropriate remark to describe whether your farm has gained from the following functions:

FUNCTION REMARK (TICK AS APPROPRIATE)

YES NO

1. Access to credit services

2. Government extension services 3. Farmer-to-farmer extension

services

4. Radio information 5. Television information 6. Neighbourhood information 7. Climate information

8. Farmers’ field day information

SECTION D: ADAPTATION TO CLIMATE CHANGE

1) Have you adapted to temperature and precipitation change in your farm? Yes No

2) Fill the following table to indicate how you perceive the change in precipitation in your region.

CHANGES IN PRECIPITATION

REMARK (TICK AS APPROPRIATE)

YES NO

1. Heavy rains

2. Late beginning of rains 3. Early stopping of rains 4. Shrinking of rainy season

duration

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3) Fill the following table to indicate how you have adapted to the changes in temperature in your region.

ADAPTATION REMARK (TICK AS

APPROPRIATE)

YES NO

9. Improved variety 10. Diversification 11. Water management 12. Soils management 13. Afforestation 14. Reforestation

15. Organic fertilizer use 16. Agro-forestry techniques 17. Farm mechanization

4) Fill the following table to indicate how you have adapted to the changes in precipitation in your region.

ADAPTATION REMARK (TICK AS APPROPRIATE)

YES NO

1. Improved variety 2. Diversification 3. Water management 4. Soils management 5. Reforestation

6. Organic fertilizer use 7. Agro-forestry techniques 8. Farm mechanization

Appendix 2: Hydrological Variables

VARIABLE UNIT OF

MEASUREMENT

VALUE

1. Precipitation 2. Temperature 3. Evaporation 4.

Figure

Figure 1.1. Conceptual framework
Figure 3.1. Map showing the study area
Table 3.1. Coastal region population summary
Table 3.2. Study Sample size summary
+7

References

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