College Papers

THE COPPERBELT UNIVERSITY 246697534163000 SCHOOL OF GRADUATE STUDIES SUBSISTENCE FARMERS ADAPTATION STRATEGIES AND DETERMINANTS OF ADAPTION CHOICES TO CLIMATE VARIABILITY IN KABWE DISTRICT ZAMBIA

THE COPPERBELT UNIVERSITY
246697534163000

SCHOOL OF GRADUATE STUDIES
SUBSISTENCE FARMERS ADAPTATION STRATEGIES AND DETERMINANTS OF ADAPTION CHOICES TO CLIMATE VARIABILITY IN KABWE DISTRICT ZAMBIA.

BY
DINAH SIAMUBI.

COMP # : 13049581.

A THESIS SUBMITTED IN PARTIAL FUFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN NATURAL RESOURCES MANAGEMENT.

© 2018 by SIAMUBI DINAH
All rights reserved.

DECLARATIONI Dinah Siamubi declare that this thesis is my own work and that it has not previously been submitted for a master of science in natural resources management degree (MSC.NRM) at the Copperbelt University.

Name of Student Signature Date
Dinah Siamubi ………………. ………………….
Name of Supervisor Signature Date
Dr. G. Kabwe ………………. ……………….
Dr. K. Kalaba ………………. ………………….
DEDICATIONTo my husband, Michael Siandizya, my children, Siswaniso, Munkombwe and Chibi.

ABSTRACTClimate variability is expected to have serious environmental, economic, and social impacts on, among others, subsistence farmers in Zambia. In particular, rural farmers, whose livelihoods depend on the use of natural resources, are likely to bear the brunt of adverse impacts as they are the primary stakeholders with the least means to adapt notwithstanding the fact that the extent to which impacts of climate variability are felt depends in large part on the extent of response in terms of adaptation. This research used a “bottom-up” approach, which seeks to gain insights from the farmers themselves based on a farm household survey. The study examine factors influencing adaptation uptake using the following objectives: i) To find out adaptation strategies to climate variability undertaken by subsistence farmers in Kabwe District; ii) Find out determinants of farmers choice of their adaptation choices in farming to climate variability in Kabwe district and ; iii) To assess implications of subsistence farmers choices of adaptation strategies on household food security.

Both primary and secondary data were used in this study. Primary data was collected from household questionnaire survey and focus group discussions (FGDs) with farmers and secondary data was got from published research papers and relevant reports, rainfall and temperature data from the Meteorological department, internet search and other relevant literature. A Binary logistic model and will be used to examine the determinants of adaptation measures and the SPSS (a statistical software package) was used to analyze the collected data.

Farmers’ actual adaptation measures and practices actually followed, thus, grouped into ten categories. These strategies, however, are mostly followed in combination with other strategies and grouped into the following adaptation options: diversifying into multiple and 8 mixed crop-livestock systems, and switching from crops to livestock.

Out of the tem explanatory variables hypothesized to affect farmers’ adaptation sex, age education level, household size, membership to a cooperative farm size, and farming experience were flagged as being statistically significant at 5% level and above. The more experienced farmers are, the more likely to adapt. Marital status of the farmer did not seem to be of significance in influencing adaptation, as the marginal effect coefficient was statistically insignificant and signs do not suggest any particular pattern. These results suggest that it is the experience rather than marital status that matters for adapting to climate variability.

The researcher made the following recommendations. Policies must aim at promoting farm-level adaptation through emphasis on the early warning systems and disaster risk management and also, effective participation of farmers in adopting better agricultural and land use practices. There is an urgent need for meteorological reports and alerts to be made accessible (when necessary) to farmers in an understandable forms. Massive campaign on the reality of climate variability and its serious consequences on food production is highly recommended so as to persuade against farmers’ believe from spiritual angle. Need of readily availability emerging technologies and land management practices that could greatly reduce agriculture’s negative impacts on the environment and enhancement of its positive impacts.

OPERATIONAL DEFINITION OF TERMSAdaptive capacity – The ability of a system to adjust to climate change (including climate variability and extremes), to moderate potential damages, to take advantage of opportunities, or to cope with the consequences.
Climate change is a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties and that persists for an extended period, typically decades or longer.

Coping Capacity – The means by which people or organizations use available resources and abilities to face adverse consequences that could lead to a disaster. (In general, this involves managing resources, both in normal times as well as during crises or adverse conditions.

Correlation is the assessment of the relationship or degree of association between variables.

Climate Variability – Climate variability refers to variations in the mean state and other statistics (such as standard deviations, the occurrence of extremes, etc.) of the climate on all temporal and spatial scales beyond that of individual weather events. Variability may be due to natural internal processes within the climate system (internal variability), or to variations in natural or anthropogenic external forcing (external variability).
Climate Impact Assessment – The practice of identifying and evaluating, both the detrimental and beneficial consequences of, climate change on natural and human systems.

Drought – the extended period of dry weather of sufficient duration to cause widespread crop failure, death of livestock, water crisis and personal hardships.

Extreme event – An extreme weather event refers to meteorological conditions that are rare for a particular place and/or time, such as an intense storm or heat wave. An extreme climate event is an unusual average over time of a number of weather events, for example heavy rainfall over a season.

Resilience – Resilience is a tendency to maintain integrity when subject to disturbance.

Sensitivity – Is the degree to which a system is affected, either adversely or beneficially, by climate related stimuli. The effect may be direct (e.g., a change in crop yield in response to a change in the mean, range, or variability of temperature) or indirect (e.g., damages caused by an increase in the frequency of coastal flooding due to sea level rise).

Subsistence (small-scale, smallholder) farming consists farming and associated activities which together form a livelihood strategy where the main output is consumed directly, where there are few if any purchased inputs and where only a minor proportion of output is marketed.

Time series is a set of quantitative data arranged in their order of occurrence and allows the analysis of different components of the variation in time.

Vulnerability – The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity.
LIST OF ACRONYMSACGAllen Consulting Group
CDM Clean Development Mechanism
COP Conference of Parties
GDP Gross Domestic Product
GHG Greenhouse Gases
GWP Global Warming Potential
ICRISAT International Crops Research Institute for the Semi-Arid-Tropics
IPCC Intergovernmental Panel on Climate Change
ITCZ Inter-tropical Convergence Zone
LDC Less developed countries
MDG Millennium Development Goals
N2O Nitrous Oxide
NAPA National Adaptation Programs of Action
NASANational Aeronautics and Space Administration
PET Potential Evapo-transpiration
PRAParticipatory Rural Appraisal
SA Sustainable Agriculture
SSA Sub-Saharan Africa
TAR Third Assessment Report
UNFCCC United Nations Framework Convention on Climate Change
USCCSPUnited States Climate Change Science Programme
TABLE OF CONTENTS TOC o “1-3” h z u DECLARATION PAGEREF _Toc515448014 h iDEDICATION PAGEREF _Toc515448015 h iiABSTRACT PAGEREF _Toc515448016 h iiiOPERATIONAL DEFINITION OF TERMS PAGEREF _Toc515448017 h ivLIST OF ACRONYMS PAGEREF _Toc515448018 h viTABLE OF CONTENTS PAGEREF _Toc515448019 h viiLIST OF FIGURES PAGEREF _Toc515448020 h xLIST OF TABLES PAGEREF _Toc515448021 h xiLIST OF APPENDICES PAGEREF _Toc515448022 h xiiCHAPTER ONE PAGEREF _Toc515448023 h 11.0 Introduction PAGEREF _Toc515448024 h 11.1Background of the study PAGEREF _Toc515448025 h 11.2 Statement of the problem PAGEREF _Toc515448026 h 21.3 General objective of the study PAGEREF _Toc515448027 h 31.4 Specific objectives PAGEREF _Toc515448028 h 31.5 Research questions used in the study PAGEREF _Toc515448029 h 31.6Research hypothesis of the study PAGEREF _Toc515448030 h 41.7 Significance of the studyb PAGEREF _Toc515448031 h 41.8 Theoretical framework PAGEREF _Toc515448032 h 51.9 Conceptual framework of the study PAGEREF _Toc515448033 h 5CHAPTER TWO PAGEREF _Toc515448046 h 72.0 LITERATURE REVIEW PAGEREF _Toc515448047 h 72.1 Climate Variability Adaptation according to the UNFCCC PAGEREF _Toc515448048 h 72.2 Adaptation to Climate variability PAGEREF _Toc515448049 h 72.3 Determinants of adoption of adaptation strategies to climate variability by subsistence farmers PAGEREF _Toc515448050 h 82.4 Gaps in Literature Review PAGEREF _Toc515448051 h 12CHAPTER THREE PAGEREF _Toc515448052 h 133.0 METHODOLOGY PAGEREF _Toc515448053 h 133.1 Location of Kabwe district PAGEREF _Toc515448054 h 133.2 Location of agricultural blocks and camps in Kabwe district PAGEREF _Toc515448055 h 143.3 Climate for Kabwe district PAGEREF _Toc515448056 h 143.4 Temperature PAGEREF _Toc515448057 h 153.5 Rainfall in Kabwe district PAGEREF _Toc515448058 h 163.6 Geology and drainage for Kabwe district PAGEREF _Toc515448059 h 173.7 Vegetation for Kabwe district PAGEREF _Toc515448060 h 173.8 Socio-economic conditions of Kabwe district PAGEREF _Toc515448061 h 183.9 Methodological framework of the study PAGEREF _Toc515448062 h 183.10 Data sources PAGEREF _Toc515448063 h 183.11 Sampling PAGEREF _Toc515448064 h 193.11.1 Selection of study samples PAGEREF _Toc515448065 h 203.11.2 Sampling procedure PAGEREF _Toc515448066 h 203.12 Methods PAGEREF _Toc515448067 h 213.12.1 Household structured interviews PAGEREF _Toc515448068 h 213.12.2Focus Group Discussions (FGDs) PAGEREF _Toc515448069 h 223.13 Analytical framework PAGEREF _Toc515448070 h 243.13.1Data analysis PAGEREF _Toc515448071 h 243.13.1.1 Dependent and independent variables PAGEREF _Toc515448072 h 243.13.1.2 Marginal effects and partial elasticity’s PAGEREF _Toc515448073 h 253.13.1.3 Description of explanatory variables PAGEREF _Toc515448074 h 263.13.1.4 Adaptation Strategy Index PAGEREF _Toc515448075 h 263.14 Ethical considerations PAGEREF _Toc515448076 h 27CHAPTER 4-PRESENTATION OF FINDINGD PAGEREF _Toc515448077 h 284.1 Socio-Economic Characteristics of Respondents PAGEREF _Toc515448078 h 284.1.1 Agricultural Blocks under Study PAGEREF _Toc515448079 h 284.1.2 Sex of Respondents PAGEREF _Toc515448080 h 284.1.3 Age of Respondents PAGEREF _Toc515448081 h 294.1.4 Marital Status of Respondents PAGEREF _Toc515448082 h 314.1.5 Respondents Level of Education PAGEREF _Toc515448083 h 314.1.6 Household Size PAGEREF _Toc515448084 h 324.1.7 Average household income PAGEREF _Toc515448085 h 334.1.8 Major Source of Income PAGEREF _Toc515448086 h 344.1.9 Land Tenure PAGEREF _Toc515448087 h 354.1.10 Land Size under Farming PAGEREF _Toc515448088 h 354.1.11 Duration in Farming PAGEREF _Toc515448089 h 374.1.12 Type of agriculture PAGEREF _Toc515448090 h 374.1.13 Membership to Cooperatives PAGEREF _Toc515448091 h 384.2 Farmer’s perception on climate variability PAGEREF _Toc515448092 h 394.3 adaptation strategies to climate variability undertaken by subsistence farmers PAGEREF _Toc515448093 h 424.4 determinants of farmer’s choice of their adaptation strategies PAGEREF _Toc515448094 h 444.5 Implications of adaptation strategies on household food security. PAGEREF _Toc515448095 h 464.5 Suggestions for improvement PAGEREF _Toc515448096 h 49CHAPTER 5- DISCUSSION OF THE RESULT PAGEREF _Toc515448097 h 515.1 adaptation strategies to climate variability undertaken by subsistence farmers PAGEREF _Toc515448098 h 515.2 determinants of farmers choice of their adaptation choices PAGEREF _Toc515448099 h 515.3 Subsistence farmer’s choices of adaptation strategies on household food security PAGEREF _Toc515448100 h 52References PAGEREF _Toc515448101 h 55
LIST OF FIGURESFigure 1.1: Conceptual framework …………………………………………………………..6
Figure 3. 2: Map of Kabwe district……………………………………..……………………12
Figure 3.3: Kabwe district map showing agricultural blocks and camps…………….…..….13
Figure 3.4: Temperature in Kabwe (1981-2011)……………………………………………..14
Figure 3.5: Rainfall in Kabwe (1984-2015)………………………………………………….15
Figure 4.1 Sex of respondents……………………………………………………………….29
Figure 4.2: Age of Respondents …………………………………………………………….30
Figure 4.3: household size …………………………………………………………………..32
Figure: 4.6 size of farmed land ………………………………………………………………33
Figure 4.3: household size …………………………………………………………………..36
Figure 4.6 size of farmed land ………………………………………………………………38
Figure 4.7: Membership to Cooperative ………………………………………………….…39
Figure 4.8 changes in seasonal weather ………………………………………………….….41
Figure 4. 9 weather forcasting information …………………………………………….……43
Figure 4.11 source of weather forecasting information …………………………………….45
Figure 4.10 MAHP …………………………………………………………………………..47
Figure 4.11 food security prevalence indicator ………………………………………………48

LIST OF TABLESTable 4.1: Agricultural Blocks ……………………………………………………………..28
Table 4.2 marital status of respondent’s ……………….……………………………………31
Table 4.3: Respondents level of Education ………………………………………………..32
Table 4.4: monthly average household income ……….……………………………………33
Table 4.4 major source of income ………………………………………………………….34
Table 4.5: land tenure ………………………………………………………………………35
Table 4.6: years in farming …………………………………………………………………37
Table 4.4: monthly average household income ……………………………………………37
Table 4.4 major source of income ………………………………………………………….39
Table 4.5: land tenure ………………………………………………………………………40
Table 4.6: years in farming ………………………………….……………………………..41
Table 4.7: Type of agriculture …………….……………………………………………….41
Table 4.8: changes in temperature …………………………………………………………42
Table 4.9: changes in rainfall ………………………………………………………………43
Table 4.10 changes in relation to rainy season …………………………………………….44
Table 4.12 farmers adaption strategies to climate variability ………………………………45
Table 4.13 constraints which the farmers face ……………………………………………..46
Table 4.14 determinants of farmer’s choice of their adaptation choices …………………..46
Table 4.15 household food security in 12 months …………………………………………47
Table 4.16: recommendations for improvement …………………………………………..50
LIST OF APPENDICESAppendix A1:Household Questionnaire…………………………………………………….35
Appendix A5: Guidelines for focus group discussion (FDGs)……………………………….53
Appendix A6: Matrix for assessing level of consensus in focus group………………………54
Appendix A7: Ranking Impacts of climate change ad variability in study area……………..55
Appendix A8: Ranking coping strategies/resources in study area……………………………56
Appendix A9: Ranking constraints to coping strategies/resources…………………………..57
Appendix B:Budget for research……………………………………………………….….58
Appendix C:Time line…………………………………………………………………..…59
Appendix D: Agro-ecological zones of Zambia……………………………………………60
Appendix E: Classification of farmers in Zambia………………………………………….61
Appendix F: Information on agricultural blocks and camps in Kabwe…………………….62
CHAPTER ONE1.0 IntroductionSubsistence farming which is the main source of livelihood for most rural communities today evolved from shifting cultivation. Currently, subsistence agriculture is being practiced on permanent plots due to, among other reasons, rapid population growth, land tenure systems, gazettement of remnant wildlands into protected areas, and state policies to sedentary agriculture (Boserup (1965). Even after being sedentarised, subsistence agriculture still involves use of low-level inputs, elementary tools and techniques (Geertz, 1963; Ruthenberg, 1976).
The wide spread existence of subsistence farmers is not temporary and is evidently spread across the entire globe where the consumption of some major agricultural products is provided from self-consumption oriented production (Mishev et al., 1999, Kwasniewski, 1999, Sarris et al.,1999 and Serova et al., 1999). In addition Subsistence farmers account for, in among other countries in Africca, 76 percent of agricultural produce in Botswana, 85 percent in Kenya, 90 percent in Malawi and in Zimbabwe, 70-80 percent (Rockstrom, 1999; Ngigi, 2011). Furthermore, the agriculture sector in Zambia is also characterized by the existence of subsistence farmers who co-exist with emergent, large scale and large corporate operation in the country’s agriculture sector as shown in appendix E and are all spread in various agro-ecological regions of the country (MACO, 2004) according to appendix D.

Background of the studyThere is a consensus that climate variability is a global environmental threat and development concern as it is expected to affect global food security by the middle of the 21st century. Developing countries are the most adversely affected by the negative effects of climate-induced events because they have the least means to adapt hence their low level of adaptation (IFAD, 2010). The largest number of food-insecure and natural resource-dependent poor subsistence farmers will be pushed on a razor’s edge of survival (Adger, 2001, 2003; Burton, Diringer & Smith 2006) without adaptation, but with adaptation vulnerability can largely be reduced (Adams et al., 1998; FAO, 2008).
This paper used the term climate variability in a broad context that includes changes in weather variability because not only are climate baselines shifting over the long term, but models also warn for increasing variability in terms of inter annual and seasonal climate patterns within the long term shift (Cranea, Roncolib & Hoogenboomb, 2011). The paper also looked at subsistence farming according to Barnett et al. (1997) as farming and associated activities which together form a livelihood strategy where the main output is consumed directly, where there are few if any purchased inputs and where only a minor proportion of output is marketed.

1.2 Statement of the problemClimate variability is rapidly emerging as one of the most serious global problems resulting in depressed crop yields and loss of livestock (IPCC, 2007; Huq et al., 2006 and Hulme, 1996). A number of studies conducted in Zambia have recognized that climate variability is happening and is coupled with significant impact on various natural resources thereby affecting agriculture which is the main source of livelihood in rural areas (Banda et.al, 1997). Studies by Sichingabula (1998), Shitumbanuma,( 2008), Jain (2008) and Kurji et.al, (2003) on drought and famine in Zambia indicate that the presence of floods and dry spells due to changes in rain fall intensity and temperatures in critical periods damage crops and lead to livestock deaths resulting in food insecurity and low-income generation.
Smallholder farmers in Zambia are aware of climate variability and some of the adaptation strategies they have adopted include early land preparation, conservation agriculture and mixed cropping (Haggblade, S., & Tembo, G. (2003), Adjer et.al, (2003) and Kalinda, (2011)) though a number of studies including that of Kalinda (2011) have revealed that uptake of adaptation strategies by subsistence farmers to climate variability in some instances is very low. Review of literature shows that the donor community, international development agencies, regional political bodies, Non-Governmental Organisations (NGOs), farmers’ organisations and national government have come up with a series of funded development projects and programmes aimed at promoting adaptation of agriculture to impacts of climate variability in Zambia. A recent study by Kalinda (2011) was funded to look at conservation agriculture as an adaptation strategy to climate variability in Zambia. So many studies have been done on farmers’ adaptation strategies but there is a dearth of information on the determinants of farmers choice of adaptation and it its implications on household welfare hence the need for this research to fill this gap.

1.3 General objective of the studyAssess subsistence farmer’s adaption strategies, and determinants of adaptation choices to climate variability.

1.4 Specific objectivesArising from the general objective, the specific objectives were as follows:
To find out adaptation strategies to climate variability undertaken by subsistence farmers in Kabwe District;
Find out determinants of farmers choice of their adaptation choices in farming to climate variability in Kabwe district and;
To quantify the determinants of farmers adaption strategies to climate variability.

1.5 Research questions used in the studyWhat strategies of adaptation are available to farmers in the study area?
What are the determinants of subsistence farmer’s adoption of adaptation strategies to climate variability?
Do determinants of adaption influence farmer’s adoption strategies to climate variability.

1.6Research hypothesis of the studyH1: There is a significant relationship between determinants of farmers’ adaptation strategies to climate variability and farmers’ adoption of given farm-level adaptation strategies.

H0: There is no significant relationship between determinants of farmer’s adaption strategy to climate variability and farmers’ adoption of given farm-level adaptation strategies.

1.7 Significance of the studyAgriculture is the main source of support for the majority of the rural households and attached urban populations in developing countries as well as in Zambia. Hence, adapting the agricultural sector to the negative effects of climate variability is critical to assure food security for the country and to protect the livelihood of rural households. Adaptation to climate variability is an effective measure at the farm level, which can reduce climate vulnerability by making rural households and communities better able to prepare themselves and their farming to variability in climate, avoiding projected damages and supporting them in dealing with adverse events. Climatic conditions are inherently variable, from year to year and decade to decade globally and in Zambia in particular. Hence most impact and adaptation studies to datehave been based on climate scenarios that include variability and associated extremes. Therefore, this study posits an urgent platform to quantify the determinants of adaptation strategies that are being practiced by farmers and farming communities in Kabwe in this period of climate variability. This study is therefore was conducted in line with vulnerability studies which have shifted the focus of research from the estimation to the actual quantification of determinants of farm level adaptation. This paper thus provides important policy information of ways in which various adaptations to climate variability can be promoted in Zambia.
1.8 Theoretical frameworkThe adaptation theory
The adaptation theory posits that social, economic, ecological and institutional systems as well as individuals can and do adapt to changing environment (Smithers ; Smit, 2009) depending on factors such as knowledge about climate variability, assets, access to appropriate technology, institutions, policies and perceptions inter alia (Adger et al., 2003; IFAD, 2008).

1.9 Conceptual framework of the study
Research on interactions between climate variability and agriculture has evolved from a top-down approach to a bottom-up approach. The top down mode starts with climate variability scenarios, and estimates impacts through scenario analysis, based on which possible adaptation practices are identified. The bottom-up approach, on the other hand, takes on a vulnerability perspective where adaptation strategies are considered more as a process involving the socio-economic and policy environments, producers’ perceptions, and elements of decision making (Bryant et al. 2000; Wall & Smit 2005; Belliveau et al. 2006). Other researches have used a combination of both the top-down and bottom-up approach. However, this research used a bottom-up approach, which seeks to gain insights from the farmers themselves as shown below:
1882140186690Global development of enhanced adaptive capacity for communities to reduce vulnerability
(Climate adaptation policy and planning)
00Global development of enhanced adaptive capacity for communities to reduce vulnerability
(Climate adaptation policy and planning)
338328091440002651760140970National policies and programs promoting adaptation
00National policies and programs promoting adaptation
4160520137160001112520259080001112520259080005554980137160004907280354330No adaptation
00No adaptation
5897880358140004632960152400004632960152400002727960289560Intentions to adapt
00Intentions to adapt
510540271145Adaptation to climate change
00Adaptation to climate change

205740012573000423672012573000
47548802540Constraints to adaptation e.g. lack of information, lack of money (Vulnerability).

.

00Constraints to adaptation e.g. lack of information, lack of money (Vulnerability).

.

287274025400030632402540003437255254000
-205740-571500-205740-571500201930031432500510540123825Internal factors e.g. farming experience, household size, education.

00Internal factors e.g. farming experience, household size, education.

595122018859500201930018859500272796059055Perceptions of climate change
00Perceptions of climate change

3543300184785002346960552450023469605524500
510540307340External factors e.g. access to credit, weather and seasonal information, institutional services such as extension.

.

00External factors e.g. access to credit, weather and seasonal information, institutional services such as extension.

.

2781300324485Climate change including variability (Location specific impacts)
00Climate change including variability (Location specific impacts)
-20574024701500
429006031940500201930017462500234696017462500Figure 1: Conceptual framework involving the bottom-up vulnerability perspective approach (Modified from Abid et.al, 2015)
CHAPTER TWO2.0 LITERATURE REVIEW2.1 Climate Variability Adaptation according to the UNFCCCAdaptation to climate variability has become one of the focal points of current development discourse, particularly agriculture. As a result, it has found expression as a response strategy in the UNFCCC. For example, Article 4.1(e) states that all parties should cooperate in preparing for adaptation to the impacts of climate change and it recognizes the vulnerability of Africa whose agriculture is affected by drought and desertification, as well as floods (UN, 1992). To this effect various COPs have been held, for example, the COP 17 which was held in Durban, South Africa, where there was a breakthrough on the international community’s response with a view to mobilizing USD 100 billion annually from a variety of sources both for adaptation and mitigation by 2020. The actors in adaptation programs include UN agencies, development assistance agencies, international financial institutions, multilaterals like the OECD, the WTO, the ADB, the private sector, local or municipal governments, development NGOs, and CBOs and national governments. Zambia as a country has however not lagged behind in these issues as a NAMA proposal was developed in 2014 with the aim of attracting potential financing from donors for the implementation of sustainable agriculture practices in the country.

2.2 Adaptation to Climate variabilityThe term adaptation is derived from the Latin word adaptare meaning to fit. According to Maddison, (2007), the word has its very core meaning in adjustment to new circumstances, or to make suitable for a purpose. Hence in its broadest descriptive terms, adaptation can be reactionary as in adjusting to new conditions, or precautionary with respect to making something suitable for a purpose (Pittock and Jones, 2009).

Adaptation types have been differentiated according to numerous attributes with commonly used distinctions being purposefulness and timing (Smit ; Olga, 2001). The IPCC (2007) recognises three types of adaptation: Firstly, autonomous, or spontaneous adaptations are considered to be those that take place, invariably in unconscious and reactive response, after initial impacts are manifest to climatic stimuli as a matter of course, without the intervention of public policy. Secondly, anticipatory, or proactive adaptation takes place before the impacts of climate variability are apparent. Thirdly, planned adaptation is based on an awareness that conditions have changed or are about to change and that action is required to return to, maintain, or achieve a desired state. However, due to institutional constraints, planned adaptation has been slow in forthcoming in many developing countries, and populations are most vulnerable to disrupted agricultural production (Maddison, 2006).
Although there is a considerable scientific uncertainty about the future trajectory of climate variability, its impacts are already discernible and will increasingly affect the basic elements of life for people around the world (IPCC, 2007). Adaptation to climate variability has thus been widely acknowledged as a vital component in low input farming systems, such as subsistence agriculture because with adaptation vulnerability can largely be reduced (Adams et al., 1998 and FAO, 2008).

2.3 Determinants of adoption of adaptation strategies to climate variability by subsistence farmersAccording to Maddison (2006) and Gbetibouo (2009), the literature on adoption identifies a range of household and farm characteristics, institutional factors, and local climatic and agro-ecological conditions as the key the determinants of the speed of adoption of adaptation strategies to climate variability by given farmers.
Gbegeh ; Akubuilo (2012), point out the fact that the household characteristics which have significant impact on adoption decisions include age, education level, gender of the head of the household, family size, years of farming experience, and wealth. The age of a farmer may positively or negatively influence the decision to adopt new technologies. Older farmers have more experience in farming and are better able to assess the characteristics of modern technology than younger farmers, and hence a higher probability of adopting the practice. However, Adesina ; Forson, 1995 state that older farmers are more risk-averse and less likely to be flexible than younger farmers and thus have a lesser likelihood of adopting new technologies. Marenya ; Barrett (2007), further argue that younger farmers are likely to adapt due to lower switching costs in implementing new farming practices associated with them since they have limited experience and therefore, adjustment costs involved in adopting new technologies may be lower for them.
In terms of education and human capital endowments, Nkonya et. al, (2008) states that these are often assumed to increase the likelihood of embracing new technologies because they enhance the ability of farmers to perceive climate change. Adesina ; Forson (1995) and Daberkow ; McBride (2003) state that, education enables households to access and conceptualize information relevant to making innovative decisions. However, higher educational attainment can present a constraint to adoption because it offers alternative livelihood strategies, which may compete with agricultural production (Shiferaw, Okello ; Reddy, 2009; Ochieng, Owuor ; Bebe, 2012 and Gbegeh ; Akubuilo, 2012).
In terms of effect of gender of the household head on adoption decisions, Gbetibouo (2009), states that it is location-specific with Gbegeh ; Akubuilo, (2012) with support from other authors further alluding to the fact that in many parts of Africa, women are often deprived of property rights due to social barriers and consequently, they have fewer capabilities and resources than men (Quisumbing et al., 1995; De Groote ; Coulibaly, 1998; Marenya ; Barrett, 2002; OECD, 2009; Gbegeh ; Akubuilo, 2012). This often undermines their capacity to embrace labour-intensive agricultural innovations. However, according to Nhemachena ; Hassan, (2007) and Gbetibouo (2009) female-headed households are more likely to take up climate change adaptation measures because in most rural smallholder farming communities in Africa, more women than men live in rural areas where much of the agricultural work is done with Nhemachena ; Hassan (2007) further stating that women have more farming experience and information on various management practices and how to change them, based on prevailing climatic conditions and other factors such as market and household food needs.

Asset endowments and wealth also have significant influence on the ability of smallholder farmers to adopt certain technological practices (Reardon ; Vosti, 1995; Nkonya et al., 2008; Gbetibouo, 2009). Households with higher income and greater assets are less risk averse than lower income households, and therefore in better position to adopt new farming technologies (Shiferaw ; Holden, 1998).
The influence of household size on the decision to adapt is uncertain. Household size as a proxy to labour availability may influence the adoption of a new technology positively as its availability reduces the labour constraints (Marenya ; Barrett, 2007; Teklewold et al., 2006). Given that the bulk of labour for most farm operations in sub-Saharan Africa is provided by the family rather than hired, lack of adequate family labour accompanied by inability to hire labour can seriously constrain adoption practices (Nkonya et al., 2008). Nonetheless, households with many family members may be forced to divert part of the labour force to off-farm activities in an attempt to earn income to ease the consumption burden imposed by a large family size (Tizale, 2007; Gbetibouo, 2009).
The farm characteristics that could influence the adoption decisions include farm size and soil fertility. Farm size influences both the access to information and the adoption decisions. More crop acreage is likely to enhance the information exposure to site-specific crop management technologies because these technologies would likely be marketed to larger farms (Marenya ; Barrett, 2007; Daberkow ; McBride, 2003). Given the uncertainty and the fixed transaction and information costs associated with innovation, there may be a critical lower limit on farm size that prevents smaller farms from adapting (Daberkow ; McBride, 2003; Gbetibouo, 2009; Gbegeh ; Akubuilo, 2012). Thus, large mechanised farms will probably be the first to adapt to climate variability.
Institutional factors that influence adoption of new technologies includes access to credit, information provision, off-farm employment, and land tenure. Institutional strengthening via access to formal and informal institutions and meteorological capability increases the likelihood of uptake of adaptation techniques. Households with access to formal agricultural extension, farmer – to – farmer extension and information about future climate variability are more likely to adjust their farming practices in response to climate variability (Smit et al., 2001; Mariara ; Karanja 2007; Yesuf et al., 2008; Nkonya et al., 2008). In addition, farmers with access to extension services are likely to perceive changes in the climate because they have information about climate and weather changes (Gbetibouo, 2009). However, certain information sources can be more effective “change agents” than others and various information sources can influence the probability of adoption differently (McBride ; Daberkow, 2003).
Similarly, different sources of information become influential during different stages of adoption process. The mass media for instance, are important in the early awareness stage, while interpersonal information sources such as extension officers and other farmers are critical in transferring more technical and adoption-promoting information (Ibid). Although technical information from extension services is shown to be most important to the potential adopter, the extension-farmer linkages are extremely weak in some parts of Sub-Saharan Africa and most agricultural information is obtained via farmer-farmer contacts (Adesina ; Forson, 1995). This suggests that farmers are also important as sources of technology information and agents of technology transfer. Studies also reveal that adoption technologies flow through social networks, and do not necessarily spread because of geographical proximity (Maddison, 2006). Thus future extension should engage farmer cooperatives in research process and on-farm trials for a variety of evaluation and demonstrations. The trained farmers will then be able diffuse the adoption technologies since heterogeneity of farm situation invariably makes it difficult to provide government extension (Pannell, 1999)
Studies have shown that under conditions of imperfect credit, smallholder farmers and resource users will adopt certain conservation practices (Reardon ; Vosti 1995; Gbetibouo, 2009).This is because the adoption of new technologies requires borrowed or owned capital. Thus lack of borrowing capacity may hamper any efforts to embrace adaptation measures that require heavy investment upfront such as irrigation, terracing, tree planting and fertilizer use.
The other institutional factor conditioning the adoption of adaptation technologies mainly relate to the prevailing system of property rights (Gbetibouo, 2009; Shiferaw, Okello ; Reddy, 2009). Tenure security can contribute to adoption of technologies linked to land such as irrigation equipment or soil conservation practices. Farmers lack economic incentives to invest their time or money if they cannot capture the full benefits of their investments (Ibid). This condition may prevail when they have insecure rights to land or when the natural resource is governed by open access property regime.

2.4 Gaps in Literature Review A great number of studies have been done on farm-level adaptation to climate variability across different disciplines in various countries which explored farmers’ adaptive behaviour and its determinants (Bryan et al., 2009; Deressa et al., 2009; Hassan and Nhemachena, 2008; Thomas et al., 2007). Despite internationally extensive research on adaptation in the agriculture sector to climate variability, little work has been done so far in Kabwe. Similarly in Zambia, the scope of research linking climate variability to agriculture is very restricted (TFCC, 2010). To date, studies on climate variability and agriculture in Zambia have been entirely limited to impacts of climate variability on commercial farming while issues of subsistence farming have been relegated to the background (Nomman and Schmitz, 2011; Hussain and Mudasser, 2007; Hanif et al., 2010; Ashfaq et al., 2011). In addition, the aspect of quantifying determinants of adopting adaptation strategies to climate variability by given farmers is largely absent most researches. This research, further wishes to document various important literature which some officers from the ministry of agriculture have but have not had the means of publishing the information. Hence, this study was designed to fill the existing research gap in studies pertaining to Kabwe district with respect to climate variation adaptation strategies in the agriculture sector.

CHAPTER THREE3.0 METHODOLOGY3.1 Location of Kabwe districtKabwe district is located in the central province of Zambia and is surrounded by Kapiri Mposhi in the north and Chibombo in the South. It is located between latitude 140 27′ South and longitude 280 27′ East on the central plateau (Kabwe District State of Environment Outlook Report, 2010). This study was conducted in Zambia’s Kabwe district which is shown below:

Figure 2: Map of Zambia showing location of study area.

Source: Khotari and Kodayama, (2009)
3.2 Location of agricultural blocks and camps in Kabwe districtThe study was conducted in three agricultural camps which included Mpima, Munyama and Munga as shown below:

Figure 3: Map of Kabwe district showing various agricultural blocks and camps.

Source: Ministry of agriculture, 2017.

Kabwe district has four agricultural blocks but only three blocks were considered as they were part of the study and these are Munyama, Munga and Mpima blocks.

3.3 Climate for Kabwe districtKabwe like the rest of central province has a sub- tropical climate that is modified by altitude. The annual pattern of weather is largely determined by the movement across the country of the ITCZ with three distinct seasons; a cool dry season from April to August, a hot season from September to October and a rainy season from November to April.

3.4 TemperatureThe average temperature of Kabwe is similar to the rest of Central Province and is shown below:

Figure 4: Line graph showing temperature trends in Kabwe
Source: Kabwe meteorological station (2016).

As shown above, the average temperatures of Kabwe district range from about 350C in terms of the maximum to about a minimum of 130C according to the Zambia Meteorological Department.

3.5 Rainfall in Kabwe districtThe average total annual rainfall in Kabwe is about 966mm. The rainy season occurs between October and April. December and January are the wettest months. Due to high temperature prevailing in the area, evapo-transpiration is high and exceeds precipitation as shown above. In line with the above information, the distribution of mean monthly sunshine for Kabwe is also shown below:

Figure 5: Rainfall in Kabwe (1984-2015)
Source: Kabwe meteorological station (2016).

The line graph above clearly shows how rainfall has been performing in Kabwe district from 1984 to 2015. As can be seen from the diagram above it is clear that rainfall variations have sometimes resulted in below normal, normal as well as above normal rainfall. As can be clearly observed there have been instance when rainfall received has been below the normal lower limit as in 1986-1987, 1988-1989, 1991-1992, 2005-2005, and 2013-2014. These below lower limit figures are a clear indication that kabwe district has experienced drought in some years meaning that the area is prone to the risk of drought which can have devastating effects on various sectors of the economy including the agriculture sector. This evidence clearly shows that subsistence agriculture in Kabwe is sometimes at the mercy of weather.

3.6 Geology and drainage for Kabwe districtTwo principal soil types occur in Kabwe of which the distribution displays a strong bedrock lithological control. Soils of kabwe are classified by Trupnell in Clayton (1980) as sandveldt. According to the term sandveldt, the soils comprise a wide range of coarse and fine- grained sandy to clay soils and formed through long periods of seasonal leaching on a maturely eroded topography. The topography has a dominant gradient from south-west to north-east at an average slope of approximately 4m/km (Kabwe District State of Environment Outlook Report, 2010).
The soils are usually dark grey to brown somewhat paler when cultivated depending on the content of organic matter. Sub-soil texture characteristics become more clayey with increased depth. The soils range from strongly to slightly acidic (pH 4.5 to 6.0) and medium fertility. Most notably, clayey vertisols are well developed in dambo areas such as reed dambo in the head waters of the Muswishi River. Greyed, leached dambo soils also occur in various localities in which drainage is impeded causing seasonal water logging (Ibid).

In terms of drainage , Kabwe which is situated east of the main watershed has its smaller western part draining towards the Lukanga swamps whereas the remaining surface water flows towards the east into the catchment area of Kafue river system (Kabwe District State of Environment Outlook Report, 2010).

3.7 Vegetation for Kabwe districtVegetation in Kabwe is predominantly Miombo woodland. Chipya, Munga, Termitaria and grasslands types of vegetation are also found.The main species of trees are Brachystegia and Combreturm species. However there are no appreciable commercial trees species except for secondary generations of Pterocarpus angolensis (Mukwa) and Albizia species. Kabwe has also open grasslands and termitaria ecotypes (Kabwe District State of Environment Outlook Report, 2010).

3.8 Socio-economic conditions of Kabwe districtThe main occupation of the people in the study area is agriculture. Farming and grazing lands can be found in a diversity of environments. Farmers practice both crop and livestock raising on a small scale with very few on commercial bases. Agriculture production here is oriented towards local needs and only limited amounts of farmers products are sold on the market.
3.9 Methodological framework of the study
Different researches have got different strengths. It is therefore very important to combine multiple methods in order to produce a more meaningful and comprehensive information than each individual method would in isolation (Morgan, 2006 and Denzin ; Lincoln, 2005). Thus this research used a combination of methods and incorporate results of multiple source of evidence which will add validity to this research, given the inevitable strength and short-comings associated with single methods studies (Frey et.al, 2000).
3.10 Data sources
Various primary and secondary data was collected for this research. Primary data on agronomic practices, the state of knowledge of subsistent famers on climate change and variability, nature of crop production constraints and adaptation strategies were determined using the following two methods. Household interviews (structured interviews) and FGDs were held mainly with heads of households in the study area. As can be seen from the foregoing, mixed research methods (MRMs) were used in this study and according to Balnaves ; Caputi (2001) and Flick (2006) this approach allows for data triangulation and thus checking for validity of data. Johnson ; Onwuegbuzie (2004) reckon that the multi- methods nature of the MRM approach often results in enhanced research. Statistical tests and diagrams were mainly used to present quantitative data, while findings on qualitative data were narrated. Three camps were selected from three farming blocks for the study and these included Mpima, Munga and Munyama. In each camp, one village was be selected.
Furthermore, secondary data on impacts of climate variability on farming and adaptation strategies was obtained from libraries, literature from the Ministry of Agriculture and Livestock in Kabwe, Central Statistics Office, Disaster Management and Mitigation Unit as well as NGOs such as Conservation Farming Unit. Sources included maps, reports, published refereed articles and farm management handbooks. Secondary data (Bless ; Higson-Smith, 2000 and Hox ; Boeije, 2005), on the other hand, district agricultural documents were mainly used to better understand characteristics of smallholder farmers in the study area and their physical environment, agronomic practices as well as farmer perceptions of climate variability and change.
3.11 Sampling
Multi-stage sampling technique which is a further development of the principle of cluster sampling and data collection was used to select the study sites and sample farm households in the study area. In the first stage, Kabwe district of Zambia was selected as the overall study area. In the second stage, three agriculture blocks were selected from four agricultural blocks using a raffle. Multistage sampling occurs when a researcher must cluster together certain groups because a master list is not available. In this case the research was about investigating subsistence farmers’ adaptation strategies to climate variability in Kabwe district and a sample of 170 subsistence farmers were purposively sampled. The first stage was to select large primary sampling unit such as agricultural blocks in the district and then select certain farming camps from the agricultural blocks where some subsistence farmers in the chosen camps were be interviewed. This was to represent a two-stage sampling design with the ultimate sampling units being clusters of farming camps. Instead of interviewing farmers from farming camps which are big clusters, from a census of all camps within the district, villages were selected and certain households were further identified from where one member was interviewed and in this case it was be the household head. This would represent a four-stage sampling design. The household was be the primary sampling unit representing a group of people in a dwelling unit. Since the numbers of household were large, cluster and multistage sampling techniques will work well for the research.

The survey was conducted in April, 2018. For the data collection, about 180 farmers were interviewed irrespective of gender, farm size or tenancy status through a farm household survey. Interviews will be conducted for the crop year 2017 to 2018. A fully structured questionnaire was used to gather information on socioeconomic characteristics, crop and domestic livestock management, land tenure, detail of farm inputs and outputs, access to various institutional services and current adaptation measures undertaken.
3.11.1 Selection of study samplesKabwe district has four agricultural blocks which are Munyama, Munga, Mpima and Waya blocks and the information pertaining to the blocks in terms of the camps, number of villages, number of households, total population and number of farmers is shown in the table in appendix F.

3.11.2 Sampling procedureThe total population of Kabwe district is 202,360 with 39,862 households located in two constituencies and twenty-seven wards (CSO, 2010). In terms of agriculture production, Kabwe district has four agricultural blocks as shown in figure and fourteen agricultural camps as shown in appendix F. The total target sample for the study will be 180 respondents and it will be drawn for Mpima, Munyama and Munga camps which will be the study areas. The proportions between sample wards will be given by F= n/N Where F is the proportion between sample wards, n is sample size and N is the number of households in the study wards (Bless & Achola, 1990). The total number of households in the study wards were 2799; With Munga (870), Munyama (350) and Mpima (1579), (MOA, 2016). Therefore F= 180/2799 which yield 0.064 was be multiplied by the number of households in each ward in order to get the number of households to be interviewed in each ward, hence the number of respondents in each ward will be 56, 23 and 101 respectively.
To the research was initially supposed to look at 30 percent of the population to show the true reflection of the data collected from the field, and this denoted 840. Due to financial constraints the research will have a sample size of 21 percent of the initial respondent
21100*840=180
Thus, 180 respondents will be considered for the study
3.12 Methods
The study area comprises Mpima, Munga and Munyama. Both quantitative and qualitative methods will be used to collect data and information on impacts of climate change and variability, current agronomic practices, constraints to crop production and on coping and adaptation strategies employed by farmers in the study area. Data will be collected using structured interviews (Fowler, 1998, Bless & Higson-Smith, 2000; Mason, 2004; Punch, 2005; Gill et al., 2008; and Newton, 2010) and Focus Group Discussions (FGDs) (Steward & Shamdasan, 1998; Flick 2006; Gill et al., 2008 and Harrell & Bradley, 2009).
3.12.1 Household structured interviewsStructured interviews are a quantitative research technique which involves use of pre-determined and standardized questions (Bayer et al., undated; Bless & Higson-Smith, 2000; Balnaves & Caputi, 2001; Mason, 2004; Punch, 2005; Flick 2006; Gill et al., 2008; Newton, 2010) with questions and responses largely closed, although open- ended questions may be used (Punch, 2005). These structured interviews can be used in many types of research, ranging from case studies to interviews (Bayer et al., undated). The major advantage of using this technique includes the fact that comparisons between sample sub-groups and/or between survey periods are possible (Bryant, undated), since exactly the same questions are asked and in the same manner, each time the interviews are conducted. Thus conclusions about the subject being investigated can then easily be made with confidence. In addition, the method is also relatively quick and questionnaires are easy to administer, since there is no probing (Gill et al., 2008). In terms of comparison to other methods, the method is not only less flexible (Mason, 2004; Punch, 2005; Gill et al., 2008) but is also not as useful where in-depth knowledge and understanding are required (Gill et al., 2008).

3.12.2Focus Group Discussions (FGDs)
Furthermore, focus group discussions (FGDs) are appropriate for probing people’s views and behaviour (Barbour ; Schostak, 2005 and Punch, 2005) where insight into these issues cannot be easily obtained through individual interviews (Barbour ; Schostak, 2005). Traditionally, focus group research is a way of collecting qualitative data, which essentially involves engaging a small number of people in an informal group discussion (or discussions), focused around a particular topic or set of issues (Wilkinson, 2004). Well-designed focus group discussions usually last between 1 and 2 hours (Morgan, 1997 and Vaughn et al., 1996) and consist of between 6 and 12 participants (Baumgartner, Strong, ; Hensley, 2002; Bernard, 1995; Johnson ; Christensen, 2004; Krueger, 2000; Langford, Schoenfeld, ; Izzo, 2002; Morgan, 1997; Onwuegbuzie, Jiao, ; Bostick, 2004). When prescribing the size of the focus group, it should be pondered that the group be small enough that everybody has an opportunity to share his perceptions, and big enough to provide diversity of perceptions (Oppenhein, 1993; Krueger, 1994; Morgan, 1988; and Mattar, 1994). In focus group discussions the researcher acts more as moderator unlike in interviews, where the researcher’s main role is to ask questions, (Punch, 2005). To facilitate the information-gathering process in FGDs, a matrix template sheet in appendix A6 as well as rank sheets (appendix A7, A8, and A9 will be used).

Focus group discussions are relatively inexpensive (Punch, 2005; Flick, 2006) and they also provide a lot of data and are flexible, elaborative and stimulating (Punch 2005). However, the method has its own limitations which include the fact that findings of the research work cannot be applied beyond that group (Harrel and Bradley, 2009). The other disadvantage includes the fact that during FGDs it may also be difficult to have balanced group interactions (Punch, 2005) as some participants can dominate the discussions, while other participants may fail to express their views (Flick, 2006). In addition, FGDs may also be inappropriate for investigating sensitive topics (Gill et al., 2008).
An interview guide was also further be used for focus group discussions which was hinge on impacts of climate variability on the subsistence farmers as well as constraints faced. The sole purpose of the focus group discussion were be to conduct a PRA as well as to validate questionnaire information as well as to bring out other information that could have been left out during the interviews. Focus group discussions (FGDs) are appropriate for probing people’s views and behaviour (Barbour ; Schostak, 2005; Punch, 2005) and insight into these issues cannot be easily obtained through individual interviews (Barbour ; Schostak, 2005). Unlike with interviews, where the researcher’s main role is to ask questions, in group discussions the researcher acts more as moderator (Punch, 2005). FGDs are relatively inexpensive (Punch, 2005; Flick, 2006) and they also provide a lot of data and are flexible, elaborative and stimulating (Punch 2005). Focus group discussions was thus used to validate information collected form questionnaires in terms of disparities or similarities between questionnaire responses and the responses from focus group discussions. Limitations of the method include the fact that findings of the research work cannot be applied beyond that group (Harrel & Bradley, 2009). During FGDs it may also be difficult to have balanced group interactions (Punch, 2005) as some participants can dominate the discussions, while other participants may fail to express their views (Flick, 2006). FGDs may also be inappropriate for investigating sensitive topics (Gill et al., 2008).
The FGDs (one per village) was focus on farming systems, crop production constraints, adaptation and coping strategies to climate variability. For each FGD approximately 56 (Munga), 23 (Munyama) and 101 (Mpima) farmers comprised the total sample from which farmers were be randomly selected from three villages in each camp bringing the total number to 180 and the maximum number of participating farmers for each period of investigations will be 12. Where possible, farmers were further grouped by gender within a focus group to allow men and women to express their views freely. Methods of data collection used in the FGDs included brainstorming, time charts, matrix scoring and ranking (Chambers, 1994; Sutherland, 1998).
3.13 Analytical framework3.13.1Data analysisSPSS (a statistical software package) was used to analyse the collected data including descriptive statistics such as mean, number, standard deviation, range, percentage and econometric analysis.

3.13.1.1 Dependent and independent variablesIn this study, a binary logistic model was used to examine the factors influencing the choice of different adaptation measures applied by the farm households in the study area as used by Abid et.al, 2014. According to Abid et.al, (2014) based on the tests of logistic regression models for significance and accuracy of predictions, results showed that all the models selected for that study were fit and could accurately estimate the factors affecting the adoption of different adaptation methods hence the need to use the same models in this study. Since agricultural adaptation measures can reduce losses (Hassan and Nhemachena, 2008), the decision to adapt requires that farm households recognize local changes in the long term climate such as temperature and rainfall patterns (Bryan et al., 2013). According to Kato et al. (2011) and Bryan et al. (2013), farm households will adapt only if they perceive a reduction in the risk to crop production or an increase in expected net farm benefits. Consider a latent variable (Y*i j) which is equal to expected benefits from the adoption of certain adaptation measures:
Y*i j = ? + ? ?k Xk + ? Y*ij (1)
In this equation, Y*I j is a latent binary variable with subscript i depicting the household which adapted to climate variability and j depicting eight different adaptation measures. Xk represents the vector of exogenous explanatory variables that influence the farmers’ choice of adopting particular adaptation measures and k in the subscript shows the specific explanatory variable (varies from zero to 14). The symbol ? denotes the model intercept, ?k the vector of binary regression coefficients and ? Y*i j = N (0, ?2) is the error term which is normally distributed and homoscedastic (zero mean and constant variance; Schmidheiny, 2013).

The latent variable (Y*ij ) is not observed directly but what is observed is:
Yij=10 if Y*ij;0 if Y*ij?0, (2)
where Yij is an observed variable which indicates that household i will opt for certain measures to adapt to perceived changes in climate (Yij =1) if their anticipated benefits are greater than zero (Yi?j ; 0), and otherwise household I will not choose adaptation measure j if the expected benefits are equal to or less than zero (Yij? 0) .

Hence, we can interpret Eq. (2) in terms of the observed binary variable (Yij ) as
Pr (Yij = 1) = Yij = G (Xk?k) , (3)
where G (:) takes the specific binomial distribution (Fernihough, 2011).

3.13.1.2 Marginal effects and partial elasticity’sThe estimated parameters (?k) of the binary logistic model only give the direction of the effect of the independent variables on the binary dependent variable and statistical significance associated with the effect of increasing an independent variable just like ordinary least square (OLS) coefficients (Peng et al., 2002). Thus, a positive coefficient ?k shows that an independent variable Xk increases the likelihood that Yij=1 (which is the adoption of a particular adaptation measure in our case). But this coefficient cannot ex- plain how much the probability of household i adopting a particular adaptation measure (Yij=1) will change when we change Xk, i.e., the coefficient (?k) does not show the magnitude of the effect f a change in explanatory variable Xk on Pr (Yij 1). Thus, to interpret and quantify the results, we need to calculate either marginal effects or partial elasticity.
Marginal effects (Yi’j ) describe the effect of a unit change in the explanatory variable on the probability of a dependent 1641475-2540=
00=
variable, i.e., Pr(Yij= 1). Derivation of marginal effects is discussed in detail in Appendix A. The final equation of the marginal effect (yijj ) after derivation becomes,
yi’j = Pr (Yij = 1) · (1 – (Pr Yij = 1)) ?k.(4)
Another alternative to interpret the results of a logistic regression is to use partial elasticities which measure the percent- age change in probability of the dependent variable (adoption of certain adaptation measure to climate variability) due to a 1 % increase in the explanatory variable Xk. We may interpret the partial elasticity of the logit model calculated at mean as,
?Y (Xk)= ?kXKPr (Yij = 1) ·(1 – (Pr .Yij = 1)).

3.13.1.3 Description of explanatory variablesThe choice of explanatory (independent) variables to be used in this study will be based on data availability and review of the literature. The independent variables will include household characteristics (e.g., farming experience of household head, household head’s education, size of household, landholding and tenancy status of the farm household), institutional factors (e.g., access to credit, market information, weather forecasting information, information on water delivery, agricultural extension services), and agro-ecological factors (e.g., temperature and rainfall). Prior to the survey, a multinomial logit (MNL) modelling approach was proposed based on literature where most of the previous studies of farmers’ adaptation to climate change employed the MNL approach (Deressa et al., 2009; Hassan and Nhemachena, 2008; Hisali et al., 2011), where respondents are restricted to select only one from a given set of adaptation measure. However, in the course of this study, it was frequently found that farm households adopted more than one adaptation measure simultaneously. This behaviour will make the use of the MNL approach inappropriate. Therefore, the logistic regression technique was employed to examine the factors that affect the choice of adaptation measures.
3.13.1.4 Adaptation Strategy IndexTo identify those adaptive strategies which held relative importance over others an adaptation index procedure was implemented, as measured by the formula presented below (6). Farmers will be asked to assess different adaptation strategies by using the same four-point rating scale in order to rate the importance of each strategy to their agricultural enterprises. The relative importance of adaptation strategies to climate change will be calculated based on the following index formula (Uddin, M.N., 2012):
ASI = ASn × 0+ ASl × 1 + ASm × 2+ASh × 3 (6)
where,
ASI = Adaptation Strategy Index
ASn = Frequency of farmers rating adaptation strategy as having no importance
ASl = Frequency of farmers rating adaptation strategy as having low importance
ASm = Frequency of farmers rating adaptation strategy as having moderate importance
ASh = Frequency of farmers rating adaptation strategy as having high importance
3.14 Ethical considerationsThe researcher will ensure that respondents are not forced to release information during interviews but should instead be willing to take part. The researcher will further ensure that interviews are conducted in conducive places where respondents should feel comfortable to provide information. In addition, the researcher will ensure that maximum confidentiality of the information given by the responded is observed both during and after the interview. Finally, the researcher will also ensure that the time allocated for visitation to a particular institution for the purpose of interviewing respondents is adhered to.

CHAPTER 4-PRESENTATION OF FINDINGD4.1 Socio-Economic Characteristics of Respondents4.1.1 Agricultural Blocks under StudyKabwe district has 4 agricultural blocks according to the ministry of Agriculture and cooperatives, these are Munga, Waya, Mpima, and Munyama and 16 camps. The researcher considered only three farming block for the study this was due to resource constraints and time. The results indicate that 57 percent of the respondents were from Munyama block this was because it had the highest number of farmers of about 62, 000, Mpima had 29 percent of the total respondents population and the least Munga with 14 percent respectively.
Count Table N % Table Valid N %
Agricultural Blocks under study Mpima 50 29.4% 29%
Munga 24 14.1% 14%
munyama 96 56.5% 57%
Total 170 100.0% 100%
Table 4.1: Agricultural Blocks source: field data, 2018
4.1.2 Sex of Respondents
Figure 4.1 Sex of respondents Source: field data, 2018
The sample was dominated by males at 55 percent while females where at 45 percent of the total sample population.
4.1.3 Age of RespondentsThe researcher sought to find out the characteristics of the respondents in terms of age and the responses provided are detailed in the figure 4.2 below. This question was meant to determine the respondents age in relation to response to information and change regarding adaptation strategies..

Figure 4.2: Age of Respondents source: field data, 2018
Out of the total percentage, 41 percent were aged between 40-59 years, 30 percent were aged above 60 whereas 23 percent were between 20-39 years and only 6 percent were below 20 years old respectively. This is true reflection of Zambia’s young population and where the majority of the farmers are in their productive age. This shows that this is an area of high agricultural potential as a whole 70 percent of the total respondent are young people able to learn new ideas quickly and strong enough to work in their farms and implement new ideas.

4.1.4 Marital Status of RespondentsThe four categories of marital status in the study area were single, married, widowed and divorced as shown in Table 4.2. By far, most of the respondents were married (70 percent).14 percent were divorced, and 12 percent were widowed. Only 5 percent of the respondents were single.

Count Table N % Table Valid N %
Marital Status of Respondents single 8 4.7% 5%
married 119 70.0% 70%
widowed 20 11.8% 12%
divorced 23 13.5% 13%
Total 170 100.0% 100%
Table 4.2 marital status of respondent’s source: field data, 2018
4.1.5 Respondents Level of EducationIn terms of education, the distribution of respondents was as shown in Table 4.3. The results indicate that 38 percent of the respondent had attained secondary school education, 34 percent primary education and only 12 percent had tertiary education. 16 percent were illiterates. From the analysis, most of the respondents involved in the industry were educated. The reason for this might the fact that poultry farming requires high literacy standard and precision for correct administration and taking of prompt management decisions. Also, good educational background of the respondents can improve and wide their farming horizon and enhance creativity and expansion;
Count Table N % Table Valid N %
Respondents Level of Education non formal 27 15.9% 16%
primary education 58 34.1% 34%
secondary education 64 37.6% 38%
tertiary education 21 12.4% 12%
Total 170 100.0% 100%
Table 4.3: Respondents level of Education source: field data, 2018
4.1.6 Household SizeData in figure 4.3 also indicated that majority (41%) of the respondents had household size between 5-8 people. Also a sizeable proportion (17%) had a household size of above 1-4 people. The results further showed that 31 percent had household size of 9-12 people while 12 percent had more than 12 people in their household. This analysis indicated that most of the respondents have dependent household members benefiting from the farming and may also be contributing their quota in terms of labour.

Figure 4.3: household size source: field data, 2018
4.1.7 Average household incomeThe majority of the households of about 51 percent earned on average below K1000 per month. This shows a=how vulnerable the people are as most of them f=depend on rain f==fed farming hence to producing in the no rain season part of the year. 41 percent earned between K1000-K3000 and 5 percent between 3001-500. The least at 4 percent earned above 50001.

Table 4.4: monthly average household income source: field data, 2018
The results showed that the households depend mostly on agriculture for their livelihoods. On-farm income comes from the sale of crops (cereals and legumes), as well as livestock products. Off-farm income includes cash income from both agricultural work and non-agricultural activities like charcoal burning and sales, piece work, petty trading, remittances and others.

4.1.8 Major Source of IncomeThe researcher wanted to find out the major source of income of the respondents. The majority at 83 percent are full time farmers and this is the true reflection of Zambia’s major economic activates by which is the agricultural sector where most of the citizens are into subsistence and small holder farming. 4 percent were pensioners, part-time job, and business respectively. 2 percent were in full time employment and other dependent on social grants from the Social Cash Transfer and NGOs. The least at 1 percent depended on remittances. The characteristics of the population distribution interms of household income can be attributed to Kabwe being an urban area where people gain formal employment; in public of private companies and doing businesses.
Count Table N % Table Valid N %
Major Source of Income Pension 7 4.1% 4%
farming 141 82.9% 83%
part-time job 7 4.1% 4%
full time job 4 2.4% 2%
remittances 2 1.2% 1%
social grant 3 1.8% 2%
business 6 3.5% 4%
Total 170 100.0% 100%
Table 4.4 major source of income source: field data, 2018
4.1.9 Land Tenure
Table 4.5 shows the three categories of land ownership status in the study area. These are customary land (land held without title deeds) and borrowed or rented land and titled land. Most of the respondents (84 percent) were on customary land, and 7 percent were on borrowed or rented land and none had legal title to the land (own with title deeds) and 9 percent had title to the land. Land is one of the major assets on which small scale farmers depend to generate food and cash incomes. Most of the respondents interviewed possessed land under customary land tenure system. Land was acquired mostly through traditional leadership of the community. One of the critical factors governing agricultural performance is the land tenure system (Serageldin, 2004). Lack of legal title to land is a disincentive to capital investment and thereby contributes to failure to increase agricultural productivity.

Count Table N % Table Valid N %
Land Ownership Traditional/Inherited 142 83.5% 84%
Borrowed/ rented 13 7.6% 7%
titled 15 8.8% 9%
Total 170 100.0% 100.0%
Table 4.5 : land tenure source: field data, 2018
4.1.10 Land Size under FarmingSince this study aimed at finding out determinants of adaption strategies in Kabwe district, the respondents were asked to approximate the size of their pieces of land .it was established that the size of land held by the respondent ranged between less than one acre and 4 acres. This was important as the researcher was interested in comparing the capacity of production and the efficiency undertaken. The result is explained in figure 4.6.

Figure: 4.6 size of farmed land source: field data
Respondents were asked to approximate the size of their land. Majority of the respondents, 49 % said that their land was between 1 to 4 hectare s, 37% said they had 4.1-8 hectares, 10 % said they had above 8 hectares and 2% had less than 1 acres of land. The significance of this distribution of land is that a larger percent of the total sample (47%) have in their possession a large piece of land (more than four acres and above) yet the majority totaling 87 out of 170 respondents have land less than 4 hectares. This causes serious congestion and lack of land for production of food both for own consumption and surplus to be sold for income. This group of people is the one which may need intense intervention from agricultural officers and other concerned stake holders.
4.1.11 Duration in FarmingMajority (70%) of the respondents had between 6 to103 years of experience, 26 percent had between 11-20 years of experience while 19 percent had less than 5 years participation in farming. The least were at 18 percent with over 20 years of farming experience. This analysis showed the respondent that participated in farming were individuals with adequate experience in farming as very few had less than 5 years’ experience.

How Long have been Farming
Frequency Percent Valid Percent Cumulative Percent
Valid less than 5 yrs32 18.8 19 19
6 to 10 yrs63 37.1 37 55
11 to 20 yrs45 26.5 26 82
over 20 years 30 17.6 18 100
Total 170 100.0 100 Table 4.6: years in farming source: field data, 2018
4.1.12 Type of agriculture91 percent of the respondents depend on rain fed agriculture and 9 percent irrigation. Despite Zambia being endowed with plenty of fresh water on both surface and undergrounds, this potential has not been realized in the agricultural sector. Only a minimal of commercial farmers utilize Zambia’s water. Most subsistence farmers rely on rain fed agriculture and those involved in irrigation are mostly in vegetable gardening in dambos, and marshlands (Kodayama, 2009)
Count Table N % Table Valid N %
type of Agriculture rain-fed 154 90.6% 91%
irrigation 16 9.4% 9%
Total 170 100.0% 100%
Table 4.7: Type of agriculture source; field data, 2018
4.1.13 Membership to CooperativesZambia’s agriculture activities are mostly tailored towards cooperatives which are mainly a mandate by government for the FISP program, agricultural extension services and agricultural training and workshops. When farmer are belongs to a cooperative, they are likely to learn new ideas in crop and field management plus free inputs. The respondents were asked if they below to any cooperative. The results in figure 4.7 indicate that 93 percent of the respondents belong to cooperatives and 7 percent do not.

Figure 4.7: Membership to Cooperative. Source: field data, 2018
4.2 Farmer’s perception on climate variability
Figure 4.8 changes in seasonal weather source: field data, 2018
Count Table N % Table Valid N %
What changes in terms of temperature have you noticed? increase 74 43.5% 44%
decrease 27 15.9% 16%
both 57 33.5% 33%
no change 12 7.1% 7%
Total 170 100.0% 100%
Table 4.8: changes in temperature source: field data, 2018
Count Table N % Table Valid N %
what changes in terms of rainfall have you noticed increase 45 26.5% 27%
decrease 72 42.4% 42%
both 53 31.2% 31%
no change 0 0.0% 0.0%
Total 170 100.0% 100%
Table 4.9: changes in rainfall source field data, 2018
A majority of the farmers accounting for 80% indicated that they have noted great variability in seasonal weather pattern. Only a few (20.0%) indicated that they have not noted any changes. Respondents were asked to explain the instability they have noted in seasonal weather pattern using specific weather variables. The results indicated that the majority (44%) indicated that temperatures have become extreme while a reasonable number (7%) indicated that they cannot tell specifically but can note the change. 16 percent said temperatures have decreases while 33 percent said temperature very, they can increase or decrease. (42%) of the famers indicated that there is a reduction in rainfall amount while 31 percent noted that it is now hard to tell when there is more or less rain, that rains have become unreliable and inconsistent . Only a minority of 27 percent of the respondents noted, and decreased.

Results from Focus Group Discussion (FGD) also confirm farmers’ perception on climate change. The community elders concurred that rainfall patterns had changed and become unpredictable. There is also more variability in the rainfall pattern and variability has also affected and messed up their farming calendar and made farming risky.

Analysis was also done to find out what are the changes the farmers have notices as a result of rainfall and temperature variation, the results indicated that 69 percent have notices dry spell in recent years, 26 percent said rains come late. From the focus group discussion, rains usually started in November but have now moved to late December.

Count Table N % Table Valid N %
what changes are these dry spells 118 69.4% 69%
late rains 44 25.9% 26%
plants destroyed due to heavy/little/no rains 8 4.7% 5%
Total 170 100.0% 100%
Table 4.10 changes in relation to rainy season source: field data, 2018
Figure 4.9 indicates that 67 percent of the farmers received weather forecasting information, and 33 percent do not receive the information.

Figure 4. 9 weather forecasting information source: field data, 2018
Count Table N % Table Valid N %
what is the source of information you receive for weather forecasting No source 56 32.9% 32.9%
extension officers 3 1.8% 1.8%
farmer organization 9 5.3% 5.3%
social groups 2 1.2% 1.2%
NGOs 3 1.8% 1.8%
family neighbors 0 0.0% 0.0%
media 97 57.1% 57.1%
Total 170 100.0% 100.0%
4.11 source of weather forecasting information source of information, 2018
4.3 adaptation strategies to climate variability undertaken by subsistence farmersTable 4.12 presents farmers’ actual adaptation measures and practices actually followed, thus, grouped into ten categories (Table 3). These strategies, however, are mostly followed in combination with other strategies and grouped into the following adaptation options: diversifying into multiple and 8 mixed crop-livestock systems, and switching from crops to livestock. the main adaptation strategic measures followed Food and Agriculture Organization (FAO) classification (Dixon et al., 2001) and were used to classify the strategic measures into thirteen.

Count Table N % Table Valid N %
what adaption measures have you adopted to deal with the changes in climate changes of planting date 10 5.9% 6%
change crop varieties 17 10.0% 10%
crop rotation 6 3.5% 4%
no adaptation strategy 5 2.9% 3%
switching from crops to livestock 6 3.5% 4%
switching from livestock to crops 2 1.2% 1%
reduction in number of livestock 0 0.0% 0%
reduction in cultivated land 6 3.5% 4%
increase cultivated land 6 3.5% 4%
use of soil conservation techniques 97 57.1% 57%
search for off-farming jobs 4 2.4% 2%
crop diversification 11 6.5% 7%
Total 170 100.0% 100%
Table 4.12 farmers adaption strategies to climate variability source: field data, 2018
57 percent of the respondents use conservation farming technique of low or zero tillage. 10 percent have change crop varieties. From the FGD, The varieties include early maturing, drought resistant and late maturing crops. Other 7 percent have ventured into the growing of sorghum and sunflower which do not require much water. 6 percent have change planting dates, depending on the time the 3 rains fall. 4 percent each have increased cultivated land, switching from cops to livestock, crop rotation and have reduced cultivated land respectively. 3 percent have no adaptation strategy, 2 percent have moved from farming to off farm activities and the least at 1 percent have switched from livestock to crop farming respectively.

The researcher also wanted to find out the constraints which the farmers face in agriculture in relation to climate variability adaption
Count Table N % Table Valid N %
constraints farmers face when adapting to climate variability limited awareness of information about climate variability 28 16.5% 17%
lack of money 36 21.2% 21%
low level of technology 18 10.6% 11%
shortages of labour 12 7.1% 7%
lack of inputs 70 41.2% 41%
lack of water 6 3.5% 3%
Total 170 100.0% 100.0%
Table 4.13 constraints which the farmers face source: field, data, 2018
4.4 determinants of farmer’s adaptation strategies and their influence on adoption
Binary logistic regression was significant at 0.05. The results therefore, indicate that all the 7 parameter which are gender, age, education, household size, household income, membership to the cooperative are statistically significant and are the determinants of farmer’s choice of adaptation. Marital status with signicance of 0.002 was not significant and does not influence farmers.

Likelihood Ratio Tests
Effect Model Fitting Criteria Likelihood Ratio Tests
AIC of Reduced Model BIC of Reduced Model -2 Log Likelihood of Reduced Model Chi-Square dfSig.

Intercept 116.063 181.915 74.063a .000 0 .

Gender 116.267 178.983 76.267 2.204 1 .138
Age 114.959 171.404 78.959 4.896 3 .180
Marital 125.242 181.687 89.242 15.179 3 .002
education 110.685 167.129 74.685 .622 3 .891
Household 112.570 169.015 76.570 2.507 3 .474
Income 112.328 168.772 76.328 2.265 3 .519
Cooperative 114.108 176.824 74.108 .045 1 .833
Farming 114.503 170.947 78.503 4.440 3 .218
Model Fitting Information
Model Model Fitting Criteria Likelihood Ratio Tests
-2 Log Likelihood Chi-Square dfSig.

Intercept Only 109.725 Final 79.626 30.099 26 .264
Table 4.14 determinants of farmer’s choice of their adaptation choices
Source; data analysis, 2018
The critical value was at 0.264 and the significance level was at 0.05, thus the significance level is greater than 0.05. Based on these results, the null hypothesis (H0) had to be rejected in favor of the alternative (H1) since the calculated value of chi square was greater than the critical value at significant level 0.05. This means that there is a relationship between determinants of farmers’ adaptation strategies to adoption of given farm-level adaptation strategies.

The farmers where asked who influences them in their decisions to adapt to a specific adaptation strategy as a result of climate variability. The results indicated that 27 percent of the respondent 29 percent of the respondents were influenced by NGO advice, 27 percent on their on and 12 percent through the conservation farming unit. 11 percent each were influenced by extension officer’s advice, and farmer to farmer. 9 percent are influenced by agricultural shows through agro-expo. 1 percent it’s through indigenous knowledge which has been passes on from one generation to the other.

Count Table N % Table Valid N %
who influences your farming type extension officer advice 19 11.2% 11%
farmer to farmer advice 18 10.6% 11%
NGO advice 50 29.4% 29%
myself 45 26.5% 27.%
indigineous knowledge 2 1.2% 1%
Conservation Farming Unit 20 11.8% 12%
agricultural shows 16 9.4% 9%
Total 170 100.0% 100%
4.5 Suggestions for improvementRespondents were asked to suggest recommendations for improvement of farming as a result of climate variability. The results in table 4.14 indicates that, as usual, 56 percent said inputs should be delivered on time and activation of e-voucher cars. 10 percent each said they should have access to loans to secure farming equipment and Conservation Farming Unit (CFU) should continue educating farmers through workshops on farming adaption strategies. 9 percent said there is need for the diversification of e-voucher for poultry inputs. 5 percent provision of market for farm products. 4 percent each said relief food is required, information of weather should be given to farmers and buying prices of crops should be revised by government.

Count Table N % Table Valid N %
recommendations for improvement early delivery of inputs and activation of the cards 95 55.9% 56%
acquisition of loans to secure farming equipment 17 10.0% 10%
Conservation Farming Unit should continue educating farmers through workshops 17 10.0% 10%
diversification of E-voucher for poultry farming inputs 15 8.8% 9%
relief food is required 6 3.5% 4%
information of weather should be given to farmers 6 3.5% 4%
buying prices of crops should be revised by government 6 3.5% 4%
provision of market for farm products 8 4.7% 5%
Total 170 100.0% 100.0%
Table 4.16: recommendations for improvement source: field data, 2018
CHAPTER 5- DISCUSSION OF THE RESULTFarmers’ actual adaptation measures and practices actually followed, thus, grouped into ten categories. These strategies, however, are mostly followed in combination with other strategies and grouped into the following adaptation options: diversifying into multiple and 8 mixed crop-livestock systems, and switching from crops to livestock. The main adaptation strategic measures followed Food and Agriculture Organization (FAO) classification (Dixon et al., 2001) and were used to classify the strategic measures into thirteen.

Most of the respondents use conservation farming technique of low or zero tillage and have change crop varieties. From the FGD, The varieties include early maturing, drought resistant and late maturing crops. Others have ventured into the growing of sorghum and sunflower which do not require much water. They have change planting dates, depending on the time the rains fall. Each have increased cultivated land, switching from cops to livestock, crop rotation and have reduced cultivated land respectively. Studies conducted by Haggblade and Tembo (2003) confirmed that conservation farming increases yields by 60% for both maize and cotton growers. With climate change and climate variability impacting food security, conservation farming has been singled out as the best strategy. The practise of early preparation of basins in the dry season makes them able to with the first rains before conventional farmers start preparing the land. Conservation Farmers like Conventional Farmers use HYV seeds and fertilisers which may cause challenges in attributing the yields to conservation farming practises only. However it has been noted that despite this phenomenon Conservation Farming still accounts for 700kgs per hectare compared to 300 – 400 kg due to the use of fertilisers.
The adaptation section of this paper explicitly indicated that the farming community had triedto counteract the impact of climate variability by employing various adaption strategies. However, farmers’ perceived adaptation measures were not the same with the adaptation measures they actually employed, for limited awareness of information about climate variability, lack of money, low level of technology, shortages of labour, and lack of inputs. That education and sensitisation is not a one off event but a continuous process. Studies in Ethiopia showed that adoption is not a one off event but a process that begins with learning – adoption- continuous or discontinuous use of technology. Leadership and empowerment of subsistence farmers is also critical so that the adoption can be effective. Studies referenced in the literature state that farmers are more likely to adopt new farming technologies as they play a huge role in farming in Africa, through intensive extension services and education, Neil: 1998 quoted by (Sanders et al 1996)..
Farmers’ adaptation behavior, especially in low-income countries, is influenced by a complexset of socio-economic, demographic, technical, institutional and biophysical factors (Federet.al., 1985). Hence, modeling farmers’ response to agricultural adaptations has becomeimportant in identifying major determinants of adoption of the various adaptation measures.Statistically influential determinants are factors on which efforts should be exerted to enhancefarm-level agricultural adaptations to climate change and variability in the study area.Out of the ten explanatory variables hypothesized to affect farmers’ adaptation sex, age education level, household size, membership to a cooperative farm size, and farming experience were flagged as being statistically significant at 5% level and above (Table 4.14). The more experienced farmers are, the more likely to adapt. Marital status of the farmer did not seem to be of significance in influencing adaptation, as the marginal effect coefficient was statistically insignificant and signs do not suggest any particular pattern. These results suggest that it is the experience rather than marital status that matters for adapting to climate variability. Findings from Bryan et al., (2010), Mahmud et al., (2008) and Temesgen et al., (2008) identified gender, age, education level, household size and farming experience, access to extension, and credit as factors influencing farmers’ adaptation in Ethiopia, which are in agreement with this study.

CHAPTER 6- RECOMMENDATIONS AND CONCLUSIONS
6.1 conclusions
Farmers in Kabwe district have adopted adaption strategies to climate variability so as to enhance their farm productivity and achieve food security. Some of the adaption strategies they have are those form indigenous knowledge or customs and traditions, from the government through agricultural extension, agricultural shows and the NGOs like IDE, World Food Program, and one acre. Farmers are practicing conservation farming with minimum or zero tillage as the major strategy. Other include changing seed varieties, crop rotation and off farm employment. It is important that the farmers be well acquainted with modern technology and practices of farming in light of climate variability and climate change.

Farmer’s determinates of adaption strategies are their socio-economic characteristics which are age, sex level of education, household size, farm experience. Marital status does no influence their adoption strategy. Most of the famers depend on agriculture as the sole source of income and thus, they do not mostly have other alternative to survive. Most of the famers are not in irrigation farming as it would have a very imperative strategy to suffice the challenges they face to weather issues. The research found out that personal characteristics influences farmers choice of adaption and it remains to the government and relevant stakeholders come up with policy strategy to overcome challenges which may affect food security and impact on livelihoods
6.2 Recommendations
Policies must aim at promoting farm-level adaptation through emphasis on the early warning systems and disaster risk management and also, effective participation of farmers in adopting better agricultural and land use practices
There is an urgent need for meteorological reports and alerts to be made accessible (when necessary) to farmers in an understandable forms. Massive campaign on the reality of climate variability and its serious consequences on food production is highly recommended so as to persuade against farmers’ believe from spiritual angle.
Need of readily availability emerging technologies and land management practices that could greatly reduce agriculture’s negative impacts on the environment and enhancement of its positive impacts.

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APPENDICESAPPENDIX (A) 1227647519431000
HOUSEHOLDS QUESTIONNAIREDear Participant,
I am a student currently studying for the Master of Science in natural resources management at the Copperbelt University of Zambia. I am currently undertaking a research on the impacts of climate variability on subsistence agriculture in Kabwe.

Issues of climate change and are real in most Sub-Saharan African countries as a whole and Zambia in particular. The information provided by you in this survey about your family activities will contribute to the better understanding of some of the adaptation strategies undertaken by subsistence farmers as a result of climatic variability in Kabwe.

Lastly I would like to appreciate your being part of my respondents and I would like to assure you that there will be maximum confidentiality of the responses where names of respondents will be withheld and that no response will be attributed to a single responded but will instead be treated as group response.

Thank you very much for your time.

Instructions: Tick or fill in the spaces provided.

1. General information
(a) Questionnaire code
(b) Province………………………(c) District…………………………………………….(d)Camp: Munga Munyama Mpima
(e)Village………………………….

2. Household Information
(a)Gender of head of household: M F
(b)Marital Status: S M
…………………………………………………………………………………………………
(c)Household Number: <1 1-4 5-8 9-12 13+
(f)No of years in farming 0-5yrs 6-11yrs 12-17yrs 18-23yrs 23 yrs and above
3. Agriculture type
(a)Agricultural type practiced. Rain fed Irrigation 4. Farming type
(a) Crop Farming
(b) Pastoral Farming
(c) Mixed Farming (Agro pastoral farming)
(d) Other, please specify:
…………………………………………………………………………………………………
…………………………………………………………………………………………………
…………………………………………………………………………………………………..

Socio-economic factors
(d) What is your annual income from agriculture? ………………………
(e) Any other source of income? ………………………………………………………………

(g) Literacy/education level None Primary Secondary Tertiary
(h)Tenure of land or home Tenant Owner Communal
(i) Farm size1 ha 2ha 3ha 4ha 5ha
Institutional factors
(j) Sources of extension: None
Government
Private
NGOs
Frequency of visit………………………
Credit facilities available…………………………………………….4. Perception of farmers on the state of the climate in the study area:
4.1 Temperature
Have you noticed any changes in temperature over the past 40 years?
Yes No
What changes have you noticed in terms of temperature?
Increased Decreased No changes
4.2 Rainfall
4.2.1 Have you noticed any changes in rainfall over the past 40 years?
Yes No
4.2.2 What changes have you noticed?
Increased Decreased No changes
5.0 Current adaptation practices to climate change and variability
5.1 Do you have any specific ways in which you adapt to climate change? (a) Yes (b) No 5.4 Which of the following adaptation strategies would you consider as appropriate measures to take so as to reduce damage of crops or livestock from climate variability and change?
Perceived adaptation strategy Use of integrated farming system Use of drought tolerant crop varieties Use of early maturing crop varieties Change planting dates Increase number of livestock Move to different site Increase in cultivated land Switch from crops to livestock Switch from livestock to crops Reduce number of livestock Search for off-farming jobs Reduce cultivated land Use of irrigation Use of chemicals, fertilizers and pesticides Use of water conservation techniques Soil conservation Practicing inter-cropping Use of insurance Use of crop rotation Agro-forestry Crop diversification Constraints on coping mechanisms to climate variability
Perceived constraints Low-level of technology Lack of available water (irrigation and drinking) Shortage of land Lack of credit/money Unpredicted weather Lack of information on adaptation Lack of market access (poor transport links) Shortage of farm inputs Lack of information on climate change Poor soil fertility Insecure land rights Thank you very much for answering this questionnaire!
If you wish to be informed of the results of this survey please indicate either your email address, telephone number or postal address below.APPENDIX (A) 2229552527622500
APPENDIX (A) 5GUIDELINES FOR FOCUS GROUP DISCUSSIONS (FDGs)1. What are the farmers’ perceptions of temperature trends in the study area?
2. What are the farmers’ perceptions of rainfall trends in the study area?
3. What adaptions options are available to farmers?
4. What actual adaptations do farmers undertake?
5. What are the major constraints to undertaking adaptation measures in the study area?
APPENDIX (A) 6
MATRIX FOR ASSESSING LEVEL OF CONSENSUS IN FOCUS GROUP
Focus Group Question Member
1 Member
2 Member
3 Member
4 Member
5
1
2
3
4
5
6
The following notations can be entered in the cells:
A = Indicated agreement (i.e., verbal or nonverbal)
D = Indicated dissent (i.e., verbal or nonverbal)
SE = Provided significant statement or example suggesting agreement
SD = Provided significant statement or example suggesting dissent
NR = Did not indicate agreement or dissent (i.e., nonresponse)
APPENDIX (A) 7
RANKING: IMPACTS OF CLIMATE CHANGE AND VARIABILITY
Free list:            Rank Order:
1. ______________________  _____________________     
2. ______________________  _____________________     
3. ______________________  _____________________     
4. ______________________  _____________________     
5. ______________________  _____________________     
6. ______________________  _____________________     
7. ______________________  _____________________     
8. ______________________  _____________________     
9. ______________________  _____________________     
10. _____________________   _____________________     
COMMENTS:
  (Write down what the respondents say exactly like they say them). 
…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………..

APPENDIX (A) 8
RANKING: COPING STRATEGIES/RESOURCES  
Free list:            Rank Order:
1. ______________________  _____________________     
2. ______________________  _____________________     
3. ______________________  _____________________     
4. ______________________  _____________________     
5. ______________________  _____________________     
6. ______________________  _____________________     
7. ______________________  _____________________     
8. ______________________  _____________________     
9. ______________________  _____________________     
10. _____________________   _____________________     
COMMENTS:
  (Write down what the respondents say exactly like they say them). 
…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………..

APPENDIX (A) 9
RANKING: CONSTRAINTS TO COPING STRATEGIES/RESOURCES:  
Free list:            Rank Order:
1. ______________________  _____________________     
2. ______________________  _____________________     
3. ______________________  _____________________     
4. ______________________  _____________________     
5. ______________________  _____________________     
6. ______________________  _____________________     
7. ______________________  _____________________     
8. ______________________  _____________________     
9. ______________________  _____________________     
10. _____________________   _____________________     
COMMENTS:
  (Write down what the respondents say exactly like they say them). 
…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………..

APPENDIX B
BUDGET
QUANTITY ITEM UNIT COST COSTING
06 Reams of Paper. K50 K300
05 Note Books. K20 K100
05 Pens and Pencils. K4 K20
02 Packets of tipex. K25 K50
  Printing of Dissertation. K500 K500
  Photocopying of Data collection Tools. K300 K300
   Binding of Dissertation. K300 K300
  Transport cost during Research. K7000 K7000
  Accommodation during Research. K5000 K5000
  Photocopying and Scanning of Maps. K300 K300
TOTAL: K 13, 870.

APPENDIX C
TIME LINE

APPENDIX D
AGRO-ECOLOGICAL ZONES OF ZAMBIA
463555588000
Source: Department of Meteorology, 2016.

APPENDIX E
CLASSIFICATION OF FARMERS IN ZAMBIA
Approx.
# of Producers
Approx. Farm
Size
Technology,
Cultivation
practice Market
Orientation
Location Major
Constraints
Small-Scale
Producers
800 000 hhs< 5 ha
(with majority
cultivating 2 or less
ha of rain-fed land
Hand hoe,
minimal inputs,
household labour
Staple foods,
primarily home
consumption
Entire country Remoteness, climate change, seasonal labour
constraints, lack of input and
output markets
Emergent
Farmers
50 000 hhs5- 20 ha Oxen, hybrid
seed and fertiliser, few
with irrigation, mostly
household labour Staple foods and
cash crops,
primarily market
orientated
Mostly line-of-rail
(Central, Lusaka,
Southern provinces),
some Eastern, Western
provinces Seasonal labour constraints,
lack of credit, weak market
information ,climate change
Large-Scale
Commercial
Farmers
700 farms 50 – 150 ha Tractors, hybrid
seed, fertiliser,
some irrigation,
modern mech., hired
labour Maize and cash
crops
Mostly Central,
Lusaka, Southern
provinces
High cost of
credit, indebtedness, climate change
Large
Corporate
Operations
10 farms 1000 + ha High
mechanisation,
irrigation, modern
mech.,
hired labour Maize, cash
crops, vertical
integration
Mostly Central,
Lusaka,
Southern
provinces Uncertain
policy
environment, climate change
SOURCE: Siegel and Alwang, 2005.

APPENDIX F
INFORMATION ON AGRICULTURAL BLOCKS AND CAMPS IN KABWE
Information about Munyama agricultural blockCamp nameNumber of villagesNumber of house holdsTotal populationNumber of farmersTotal number of farmersMaleFemaleMusebo6343965532433902Kalwelwe4315867466274740Munyama4350939515394909Kawaya4297839455383838Source: Ministry of agriculture and livestock, 2016.Information about Munga agricultural blockCamp nameNumber of villagesNumber of house holdsTotal populationNumber of farmersTotal number of farmersMaleFemaleMunga1187043509209901910Chililalila9458229010609802040Mukobeko1145022509366341569Kawama446423208447501594Source: Ministry of agriculture and livestock, 2016.Information about Waya agricultural blockCamp nameNumber of villagesNumber of house holdsTotal populationNumber of farmersTotal number of farmersMaleFemaleKafulamase1068035387808901670Kang’omba9449188557610191595Natuseko11305015250142715162943Waya411006400130011502450Source: Ministry of agriculture and livestock, 2016.Information about Mpima blockCamp nameNumber of villagesNumber of house holdsTotal populationNumber of farmersTotal number of farmersMaleFemaleMpima915799697068631569Mpima serminary16159027218107801590Source: Ministry of agriculture and livestock, 2016.