Influence of climatic elements and non-climatic factors on fishing activities in Lake Victoria, Kisumu County, Kenya


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I would like to give glory to Almighty God for granting me good health, guidance and protection this far.

I express my gratitude to my supervisors, Dr.Ishmail O. Mahiri and Dr Kennedy Obiero for the academic guidance which they accorded me. My heartfelt gratitude to my husband, Mr John Apindi, for the moral and financial support he offered during my study. I thank my children for their patience throughout my studies. I also thank Elizabeth Obura for offering to edit my work. I cannot forget to thank my classmates Norah Moige and Margaret Kasuku who constantly encouraged me.



Declaration ... ii

Dedication ... iii

Acknowledgement... iv

Table of contents ... v

List of tables... ix

List of figures ... x

List of plates... xi

Abbreviations and acronyms ... xii

Abstract ... xiii





1.3.1 General Objective ... 8

1.3.2 Specific Objectives... 8









2.2.1 Temperature ... 14

2.2.2 Rainfall ... 16

2.2.3 Winds ... 21






3.2STUDY AREA ... 34

3.2.1 Climate ... 36

3.2.2 Socio-Economic Activities ... 37





3.7.1 Questionnaire ... 41

3.7.2 Interview Guide ... 42

3.7.3 Document Analysis Guide ... 43






4.2.1 Age of Respondents... 46

4.2.2 Educational Level of Respondents ... 47

4.2.3 Marital Status of Respondents ... 48

4.2.4 Other Sources of Income... 50

4.2.5 Experience of Fishing among Fishermen ... 51

4.2.6 Average Income per Month from Fishing ... 52



4.4.1 Effects of Winds on Fishing Activities in Lake Victoria ... 56

4.4.2 Effects of Rain on Fishing Activities ... 59

4.4.3 Effects of Dry Spell on Fishing Activities in Lake Victoria ... 62






5.3CONCLUSION ... 76













Table 3:1 Beach Management Units in Kisumu County, Kenya ... 36

Table 3.2 Selected beaches and sampling grid ... 40

Table 4.1 Ages of respondents ... 47

Table 4.2 Educational levels of the respondents ... 48

Table 4.3Availability of other sources of income among the fishermen ... 50

Table 4.4 Other sources of income among the fishermen ... 51

Table 4.5 Experience among the fishermen ... 52

Table 4.6 Fishermen‟s average income from fishing per month ... 53

Table 4.7 Average annual weight and value of fish catch ... 55

Table 4.8 Effect of winds on fishing activities ... 57

Table 4.9 Effect of heavy rains on fishing activities ... 59

Table 4.10 Correlation between fish catch and annual rainfall amount ... 60

Table 4.11 SPSS Output on correlation of rain vs fish catch of selected species...64

Table 4.12 Effect of dry spell on fishing activities ... 63



Figure 2.1: Influence of climatic elements and non-climatic factors on fish

production ... 31

Figure 3.1: Some beaches along the shoreline of Lake Victoria, Kisumu County. ... 35

Figure 3.2: Monthly distribution of rainfall over Lake Victoria, Kisumu County ... 37

Figure 4.1: Marital status of the fishermen... 49





BMU Beach Management Unit

DDP District Development Plan

EEZ Ecological Environment Zone

ENSO El Nino Southern Oscillation

GOK Government of Kenya

ICES International Council for the Exploration of the Seas KEMFRI Kenya Marine and Fisheries Research Institute

LVEMP Lake Victoria Environmental Management Programme NEMA National Environmental Management Authority

NPP Net Primary Production

OPI Oregon Production Index

UNIRO Union Research Institute of Marine Fisheries and Oceanography USSR United Soviet Socialist Republic





1.1 Background of the Study

Fishing is known to be one of the oldest occupations of mankind all over the world and is carried out for subsistence and commercial purposes. In developed countries, it is highly commercialized while in developing countries it is mainly carried out for subsistence purposes. Fishing is done along coastlines, in sheltered seas and in inland fresh waters (Kimathi et al, 2013). However, the main fishing grounds of the world are located in the cool waters of Pacific and Atlantic coasts within the temperate latitudes of northern hemisphere. Each accounts for 40% of the world‟s annual total fish hauls while Indian Ocean accounts for about 4% and Aqua culture accounts for about 15% (Aloo-Obudho, 2010).From time immemorial, fish has been an important component of the peoples‟ diet in many parts of the world. Fish catches increased rapidly over the past hundred years due to improved technology, which provided more powerful engines and advanced equipment.


Intergovernmental Panel on Climate Change IPCC(2001), as cited in Muthama et al, (2007) pointed out that fluctuations in fish abundance are increasingly regarded as biological responses to medium term climatic variations in addition to over fishing and other anthropogenic factors.

Studies have been undertaken on various aspects of the effects of anthropogenic and environmental factors on the viability of the specific living marine resources under contemporary climatic conditions. Glantz (2007) explains that subtle changes in key environmental variables such as temperature, salinity, wind speed and direction, ocean currents and strength of upwelling, as well as those affecting predator populations, can sharply alter the abundance, distribution, and availability of fish population. An obvious environmental effect of global warming would see changes in sea surface temperatures (SSTs) which in turn would have an effect on fish populations during all life stages. Stram and Evans (2009) noted that, in North Pacific, warming trends coupled with a declining sea ice, raise concerns about the effects of climate change on fish populations and ecosystem dynamics; which is likely to affect the plankton population to major commercial fish species distribution. They also reported that the North Pacific Fishing Management Council has undertaken risk-averse management actions in light of uncertainty about the effects of warming (and loss of sea ice) and resulting changes to fishing activities in the North Pacific.


conditions, sea ice cover, permafrost and vegetation indicate that the Arctic is experiencing warming trends in the ocean temperatures and major rapid declines in the seasonal sea ice. Greater ice-free seasons, coupled with warming waters and fish range expansion could create conditions that will certainly result in commercial fishery development, in the Alaska Ecological Environment Zone (EEZ) in the Arctic zone (Stram and Evans, 2009).

The occurrence of the pink shrimp mainly offshore in 27m-55m depth was reported by Kusemiju (1991) in his detailed study which was carried out in Nigeria on the monthly variation in abundance as well as size and depth distribution of the shrimp, off the Coast of Lagos. The plan was carried out for the twelve months of the year, and the hauls were limited to one hour. The average catch of the shrimps per one hour haul varied from 3.6kg -19.6 kg. The lowest catch was recorded in March and the highest in December. The latter month experienced low rainfall. The best catches were obtained in the dry months at depths of 40m – 55m.


Variation in Sea Surface Temperature (SST), storm frequency, freshwater flow and runoff patterns, oxygen levels and strength of wind all have impacts on estuarine, inshore and offshore ecosystems, affecting recruitment, fish behaviour and physiology, influencing fish size, and increasing fish mortalities. These have resulted in significant adverse impacts on subsistence fishing activities (DEA, 2013).

A report on Envisioning 2050: Climate Change, Aquaculture and Fisheries in West Africa as reported in Kathleen (2010) observed that the fishery of Ghana is seasonal in nature and it is closely associated with upwelling. During the period of steady upwelling, spawning and recruitment of fish stocks are enhanced resulting in abundance and availability of fish stocks, especially pelagic ones such as Sardinella (Kathleen, 2010). Fishing effort increases, leading to excessive fishing pressure that causes fisheries to collapse. The livelihoods of dependent communities are severely impacted, as a result, poverty increase and there is always increased in demand for credits to venture into other fields, or even for daily sustenance during this period of upwelling (Kathleen, 2010).

Lake Victoria, which is the world‟s second largest freshwater lake in surface area


nearly ten years in the Lake Victoria aquatic system, however, revealed that a significant portion of the cichlid fauna considered lost from Lake Victoria is still

extant. He also established that there had been a decreasing trend in the Nile perch standing stocks in Lake Victoria, while the small pelagics increased. The causes of the decline in Nile perch include over-exploitation, the use of illegal fishing gear and environmental degradation from the catchment areas. Different areas of the lake exhibit different fish production, due to different nutrient levels. The proliferation of water hyacinth has led to increased catches of Clarias gariepianus, Protopterus aethiopicus, Ovechromis spp and haplochromines spp. The weed provides refuge, breeding and feeding areas (Njiru et al, 2012).

In Kenya, a report issued by the Wildlife Conservation Society (WCS) on 12th August, 2011, indicated that, increased development along the coastline coupled with increased rainfall can generate run-off, impact water flows, and cause sedimentation in coastal waters (WCS, 2011). It further stated that the greatest threats in the country‟s marine ecosystems is the unsustainable levels of fishing and

the impacts of global climate change, both of which have brought havoc on the Indian Ocean coral reefs. The inland fishing grounds in Kenya, for example Lakes Turkana, Naivasha, Victoria, and major rivers such as the Tana and Nzoia are also experiencing decline in fish population due to climate variability among other factors (Ochieng, 2012).


1995, when there were 34,000 fishermen and 238,000 dependents (Okonga, 2010). In 1995, for instance 560,000 were estimated to have been employed in the fishing industry in Kenya, accounting for 25% of the country‟s total employment (Omwenga et al., 2004). A study by LVEMP showed that Nile perch stock dropped from 750,000 tonnes in 2005 to 337,000 tonnes in 2008 and tilapia from 27,061 to 24,811 tonnes. This is majorly attributed to environmental pollution (LVEMP, 2011).

Although presently slightly under control, water hyacinth, absent in the lake as late as 1989, choked important waterways and fish landings in the late 1990s. Over-fishing, introduced fish species and oxygen depletion at lower lake depths threaten the artisanal fisheries and biodiversity (LVEMP, 2003). These extensive changes in this water ecosystem have been attributed both to the introduction of Nile perch that altered the food web structure, and to increased nutrient input to the lake, resulting in eutrophication. Increased pollution from human activities and industrial discharges is visible in some of the rivers feeding the lake, and in urban areas along the shoreline (LVEMP, 2011).


predation and competition on these species leading to their increase (Okonga, 2010).

Although the reviews of the previous studies point out that climatic and non-climatic factors have an effect on the fishing activities in the areas where the studies were conducted (LVEMP, 2011;Okonga, 2010), this has not been established in Kisumu County. Hence, this study sought to determine the relationship between climatic elements and fishing activities, in terms of the different types of species and the quantity of fish caught, specifically during the rainy and dry seasons. It also examined the effects of non-climatic factors on the amount and species of fish caught. The study went further and investigated mitigation measures that have been put in place by the fishermen to cope up with challenges in Lake Victoria, Kisumu County, Kenya.

1.2. Statement of the Problem


1.3 Study Objective

1.3.1 General Objective

The general objective of this study was to determine the influence of climatic elements and non-climatic factors on fishing activities in Lake Victoria, Kisumu County, Kenya.

1.3.2 Specific Objectives

To be able to achieve the above objective, the following specific objectives were addressed:

i. To determine the relationship between climatic elements and fishing activities.

ii. To determine the relationship between non-climatic factors and fishing activities.

iii. To examine mitigation measures by fishermen to cope with the influence of climatic elements and non-climatic factors on fishing activities.

1.4 Research Questions

In order to achieve the above objectives, the study was guided by the following research questions:


ii. What is the relationship between non-climatic factors and fishing activities?

iii. Which mitigation measures have been put in place by the fishermen to cope with the influence of climate elements and non-climatic factors on fishing activities?

1.5 Study Hypotheses

The study was guided by the following null hypotheses:

i. Climatic elements have no significant influence on fishingactivities. ii. Non-climatic factors have no significant influence on fishingactivities. iii. There are no measures put in place by the fishermen to cope with the

influence of climatic elements and non-climatic factors have no significant effect on fishingactivities.

1.6 Justification and Significance of the Study


Kisumu County, as well as managing the impact of non-climatic factors on fish production, and recommend appropriate mitigation measures. The results should also help the National Environmental Management Authority (NEMA) and Ministry of Agriculture Fisheries Department to come up with policies that can control activities that may affect fishing activities in Lake Victoria. The fishing community in Kisumu County will also be able to come up with alternative sources of income, hence reduce poverty.

1.7 Scope and Limitations of the Study

The study mainly focused on fishing activities in Lake Victoria in Kisumu County, Kenya. The target populations were the fishermen and beach managers operating in the shores of the Lake. The study focused only in Kisumu County because of time and resources, and also because it is endowed with 32 beaches (fish landing points), which enabled the researcher to have sufficient sample size of the beaches for investigation. The data used for the period of study was between the years 2001 to 2011.This period of the study was appropriate given that the beach managers and fishermen were to give information according to what they could recall; longer period would not have been ideal given the respondents would not easily remember.


the type and amount of the fish species caught. In the context of this study, non-climatic factors were pollution, water hyacinth, overfishing and predators. The study also sought to identify the mitigation measures put in place by the fishermen to enable them cope with these negative influence.

In general, this study confines itself on investigating the degree of influence of climatic elements on fish catches and activities by studying the distribution and quantity of fishery and climate (temperature, rainfall and wind) over the Lake Victoria within Kisumu County, Kenya, and determining the degree of relationship between fish catches and these climatic variables.


limitations, the researcher verified some of information received at the beaches with records kept at Kenya Marine and Fisheries Department, Kisumu County.

1.8 Operational Definition of Terms

Climatic factors refer to elements of weather such as rainfall, temperature and wind which vary from time to time and thus influence fish production in Lake Victoria.

Non-climatic factors refer to other factors such as environmental pollution, water hyacinth, overfishing and predators which have influence on fish production in Lake Victoria.

El Nino is the name given to the occasional invasion of warm surface water from the western equatorial part of the Pacific Basin to the eastern equatorial Pacific, where these events produce a rise in sea level, higher sea surface temperature and a depressed thermo cline (Okonga, 2010).

Fishing Activities refers to the action of extracting fish from the lake and the amount caught, as it is weighed and recorded at the beaches.

Overfishing means over extraction of fish from the lake which leads to reduced fish catch.

Pollution is the introduction of foreign unwanted materials such as industrial effluents, agricultural wastes, and sewage wastes into the fishing areas.

Predators are those living organisms that depend on fish as their source of food thus lowering their production.



2.1 Introduction

This section deals with the literature review on the influence of climatic elements and non-climatic factors on fishing activities and measures that have been adopted by fishermen to cope with the challenges. The literature review is structured in a way that it reflects the study objectives. From the literature review, a conceptual framework has also been derived.

2.2 Climatic elements

Although several studies have been done on factors that influence fishing activities, a few have looked at climatic elements. Climate has a significant impact on fisheries, affecting both productivity and distribution of fish. Inland fishery is rendered more vulnerable to episodic drought and habitat destruction as a result of water stress and land degradation. Oceanic studies show that the weather influences fish catches as catch-per-unit-effort is related to water temperature and atmospheric circulation patterns (Mackenzie and Koster, 2004). Changes in the velocity and direction of ocean currents affect the availability of nutrients and the disposition of larval and juvenile organisms thereby influencing recruitment, growth and mortality (Fedoulov et al, 1996, as reported by Ogutu-Ohwayo, 2003).


temperatures in Lake Victoria can result in slackened winds, less intense mixing and changes in the nutrient dynamics which would affect fisheries productivity and completely alter the trophic structures of fish communities (Okonga, 2010).. The effect of climate change such as rising temperature and changes in precipitation are becoming clear with the impact already affecting the ecosystem in East African region. Changes in water level significantly affect, directly or indirectly, subsistence and commercial fishing (Okonga, 2010). Environmentalists have attributed changes in water level to reduced rainfall experienced in East Africa region. Climate history in Lake Victoria basin for the last 100 years have revealed evidence of varied climatic conditions over the century in the Lake basin, with climatic extremes experienced in early and late part of the 20th Century(FAO, 2007). Landing beaches and piers become useless as water continues to recede from the shore (Lejju, 2012).

2.2.1 Temperature


temperature at and above 21˚C is lethal to adult salmon. In northeast Pacific Ocean

with the cool temperatures, salmon productivity is high given that salmon stocks is abundant. This means that cool temperature favour the production of salmon. In some cases, however, warm temperatures favour the production of certain fish species (McCullough, 1999, as reported by Muthama et al, 2007).

In the high latitude regions, increase in temperature may influence fish production positively. Glantz (2007) reported that scientists at the All Union Research Institute of Marine Fisheries and Oceanography (UNIRO) in the USSR carried out a research on changes in Atlanto–Scandian on herring during the warmer decades of the 1970‟s and 1980‟s, and found that a global warming might be favourable for the


variability of Sprat recruitment have consequences for predator prey interactions among the major fish species in the Baltic Sea like Cod.

Mackenzie and Koster (2004) also reported on the findings of a study they carried out in the Baltic Sea and Black sea, on the recruitment of Sprat fish based on the variation of water temperature which revealed that, the catch was significantly above average during warm years than cold years, in the Baltic Sea. Thus, temperature is positively related to catch of Sprat in the North (Baltic Sea), but negatively related to it in the South (Black Sea). In addition, it was noted that Peak spawning time for Sprat in the Baltic Sea is in the spring – summer (Grauman and Yula, 1989; ICES, 2001), but in the fall – winter in the Black Sea (Daskalov, 1999). Salmon in the Fraser River, Canada suffered enhanced mortality when summer temperatures exceeded the levels previously recorded in a 60 year time series over a period of weeks in the summer of 2004 (Brander, 2007).

In their research, on weather influence on fish catches in Lake Victoria, Muthama et al

(2007) noted that of all the weather parameters studied, temperature had a higher negative influence on the fish catches in all the three regions of his study in Lake Victoria.

2.2.2 Rainfall


chain. NPP depends on the availability of light and nutrients which in turn are governed by runoff, atmospheric dust deposition, and ocean mixing processes, cloud cover, and the solar cycle. A report released by Brander (2007) described how recent changes in the distribution and productivity of a number of fish species can be ascribed with high confidence to regional climate variability, such as the El Nino – Southern Oscillation (ENSO). For example, annual catches for Peruvian anchorveta (Engraulisringens), the biggest single species fishery in the world range from 94,000 tons to 13 million tons during the period 1970 – 2004, with much of the variability resulting from changes in the ENSO (Barber, 2010). Inland fisheries are additionally threatened by changes in precipitation and water management. Thus, the frequency and intensity of temperature or rainfall event is likely to have a major impact on future fisheries production in both inland and marine systems (Brander, 2007).


A study carried out by Barber (2010) on the effect of rainfall as a component of climate change on estuarine fish production in Queensland, Australia showed that many commercially important fish species use estuarine habitats such as mangroves, tidal flats and sea grass beds as nurseries or breeding grounds and have lifecycles

correlated to rainfall and temperature patterns. Catch per unit effort (CPUE), rainfall, the Southern Oscillation Index (SOI) and catch time series for specific

combinations of climate seasons and regions were explored and surplus production models applied to Queensland‟s commercial fish catch data with the program

CLIMPROD. Results indicated that up to 30% of Queensland‟s total fish catch and up to 80% of the barramundi catch variation for specific regions could be explained

by rainfall often with a lagged response to rainfall events.


vertical stability of the water column, resulting in reduced availability of nutrients (Brander, 2007).

In Nigeria, a detailed study was carried out on the monthly variation in abundance and distribution of the Shrimps, off the Coast of Lagos; the study revealed that the average catch of the Shrimps per one hour haul varied from 3.6 – 16.9 kg. The lowest catch was recorded in March while the highest in December and January, that is, the best catches were made in the dry months at depth of 40-55m (Kusemiju, 1991).


Muthama et al (2007) reported that in Lake Victoria, Tanzania side, most fish species do have breeding and spawning seasons in different times but with a peak at the end of the rainy season. Normally during the rainy seasons, nutrients are injected into the lake from the land. This leads to phytoplankton bloom. Heavy rains play an important role in the breeding activities in some fish species of Lake Victoria. A correlation analysis done by Muthama et al (2007) on the annual rainfall and fishery data records from some beaches in Lake Victoria, Tanzania side, showed that only annual rainfall for some beaches indicated significant relationship with fish catches. This suggests that although fish catches were influenced by annual rainfall, the influence was varied for different locations. The results further showed that there exists a threshold value above or below which rainfall influences the fish catches positively or negatively.

Another study by LVEMP (2011) also established that long rains in Kisumu and its environs bring in silt and other dirt through the rivers into Lake Victoria. As a result, more indigenous fish such as lung fish and cat fish increase during this period. However, Nile perch and tilapia became scarce due to the dirt in the water brought in during this period.


fishermen. Further, noise from thunderstorm affects the distribution of fish near the water surface, thus affecting the abundance of fish at that time and in the area concerned. He also noted that some fish species are also influenced by moonlight, therefore it could consequently be expected that during rainy seasons, short periods of moonlight due to cloudiness will negatively influence fish catches. In addition, it was also noticed that some fish species are influenced by sunlight. In rainy seasons when the sky is almost covered by clouds for many hours, such species are less available as they depend on sunlight to catch zooplankton.

2.2.3 Winds


However, Lake Victoria being a vast open water body, could also be having particular winds which may blow over the water and cause variations on the amount of fish caught at different times of the year, as well as the availability of the fish species. Occasionally, winds also bring a lot of water hyacinth on the surface of water; that is why it was worth carrying out a study in this region to assess how fishermen coped with these challenges.

A study by Muthama et al (2007) which investigated the influence of wind as climatic component on the fishing activities in Lake Victoria, Tanzania side revealed that fishery data and mean annual wind speed and direction had significant relationship. Mild wind was found to have positive influence on fish catch; it facilitated fish movement in water thus enabling the fishermen to catch fish, breaks the penetration of sun‟s rays into the water thus creating a suitable environment for


2.3 Non-climatic Factors

Several studies have been done to investigate if there are other factors influencing the fishing activities in marine and inland water bodies all over the world, a part from the influence of climate variability on fish production.

Brander (2007) observed that in the Indian Ocean, fisheries production is likely to be affected by the loss or reduced structural complexity of coral communities, which results in reduced fish species. In South Atlantic Ocean, for example, the resilience of species and systems is being compromised by concurrent pressures, including over fishing, loss of genetic diversity, habitat destruction, pollution, introduced and invasive species, as well as pathogens. Brander (2007) further established that rising levels of carbon dioxide are lowering the pH of the oceans, with consequences that are largely unknown. In North Atlantic Ocean, the subsequent cooling period accelerated the decline in Cod stock due to over fishing, which resulted in another ecosystem switch and an increase in the fishery for shrimps, which largely replaced the revenue generated by the Cod fishery.


fishermen. On the same note, he noted that Nile perch predate on other small fish

thus interfering with the supply of valuable fish types.

A study carried out in the Marine waters of the coast of Nigeria by Kusemiju (1991), discovered a number of problems facing fish in their habitat, especially diseases which include, cancer, rickets, blindness, liver disorders and developmental anomalies such as the birth of Siamese twins. Also, fish suffered the heaviest mortality from man through fishing. Other environmental issues influencing fish production in Nigeria included; water pollution caused by domestic pollutants which reduce oxygen and equally affect the lives of other organisms. Other pollutants are from industries and oil spills, besides water hyacinth menace.


Hecky et al (1994), Bohannon and Stone (2006) reported that as the severity of strong events increase as predicted under future climate change scenarios, it is likely that the influx of phosphorus rich sediments into the lake will also increase. This increased nutrient loading will likely exacerbate ongoing eutrophication and decreasing oxygen availability in the Lake, providing additional stress to social ecological life in the basin. This may promote excessive growth of algae. As the algae die and decompose, high levels of organic matter and the decomposing organisms deplete the water of available oxygen, causing the death of other organisms, including fish.

Other than pollution in Lake Victoria, there are other factors that hinder fishing activities; a report of the study by COUTEAUX (1981), for Lake Basin Development Authority (LBDA), indicated that the fish are landed on beaches which are rarely equipped with wharfs. A few have small sheds under which the fish are sorted. Running water and electricity are usually unavailable. There is no means of storing fish, and access to major roads is often via trails unsuitable for vehicles.


(2003), since the introduction of Nile perch (Lates nilotica) in Lake Victoria in 1950‟s, there has been a steady increase of fish catch in the Lake. They found that

between 1978-1990 fish catch grew up by 60%. However, the Nile perch has had a devastating effect on the species composition of Lake Victoria. The study by Phoon et al (2004) also supported that introduction of Nile perch in the 1950s increased fish production nearly ten-fold, but the Lake„s biodiversity has considerably diminished: 60% of the Lake„s endemic cichlids are now possibly extinct.

Although many studies have been done on the effect of Nile perch in Lake Victoria, none of the studies has provided information on the possible factors that may have influenced increase of fish catch. It is therefore not clear if this increase could be attributed to climatic elements or non-climatic factors which this study addressed. On the other hand, some studies such as Muthama et al (2007) demonstrated that introduction of Nile perch led to a decline in the Haplochromines spp and the mixture of other fish, while some species have virtually vanished from the commercial catch. Possible explanation for this decline has not been addressed by the previous studies. It is not clear whether the decline is due to the predator-prey relationship and therefore this study sought to establish this relationship in Kisumu County.


interfering with breeding and nursery grounds for fish, particularly Tilapia. The presence of expansive mass of water hyacinth had also interfered a great deal with harbours and fish landing operations. Reduced accessibility to the harbours had led to delay in water-borne transport resulting in losses for fishermen, especially when their catches rot due to delay.

Kateregga and Sterner (2007) in their study on water hyacinth in Lake Victoria observed that the water hyacinth moved seasonally with the waves from bay to bay blocking water-ways and affecting aquatic life as it sucked oxygen from the water. The weed also entangles nets, making it difficult to fish. It becomes harder to catch the Nile Perch as the fish moves into the open waters away from the oxygen-deprived waters near the weeds. Tilapia catch is also affected with the decomposing hyacinth blocking breeding grounds.


agents of malaria and bilharzia and harboured snakes. It turned water green and dirty, making the supply unsuitable for drinking and other domestic use. Reduction of oxygen levels in the water creates an environment unsuitable for fish survival, subsequently reducing species diversity (Njiru et al,2012).

However, Kateregga and Sterner (2007) also pointed out that despite its adverse effects, the water hyacinth has, however, led to the flourishing of other fish species better adapted to less oxygenated water, including cat fish and lung fish. The weed also provides a "closed season", preventing over-fishing in the bays it clogs up, allowing for the regeneration of the lake's fish stock as some species hide within the hyacinth.

Recent study by Lejju (2012) on the influence of climatic variability and human induced environmental degradation on Lake Victoria reports that to a greater extent human activities have contributed to the problems that hails the fishing industry in Lake Victoria; overfishing especially of the young fish due to use of unsuitable fishing nets and other fishing gears, and contaminating the water hence interfering with the fish habitat. The study pointed out that lack of stringent measures to control activities at the lake has led to blunt flouting of the rules and regulation already put in place to maintain the ecosystem at the lake.

2.4 Measures put by fishermen to cope with the challenges faced


Council (NPFMC) and the National Marine Fisheries Service (NMFS) have jurisdiction over off shore fisheries in Alaska, USA (Stram and Evans, 2009). The Council has undertaken risk adverse management actions in light of uncertainty about the effects of warming trends (and loss of sea ice) and resulting changes to fishing activities in the North Pacific. NPFMC had to assess whether opportunities for unregulated fishing could result from changes in fish distribution, closed all commercial fishing pending further research, and had established extensive area closures where fishing with bottom trawl gear was prohibited to protect vulnerable crab habitat and to control the northern expansion of the trawl fleet into new ice free waters. In cases where linkages between climate variables and fish distributions could be identified, NPFMC developed adaptive management measures to respond to varying distribution of fish and shell fish (Stram & Evans, 2009).


On the invasive weed, hyacinth, control methods that are often used include mechanical, chemical and biological control. However, existing methods have often been insufficient to contain the aggressive propagation of the weed and viability of its seeds despite substantial monetary investments over the years (Gichuki et al, 2012), mainly due to lack of continued policy and management support by governments. Physical methods for control of water hyacinth involve drainage of the water body, manual removal of the weeds or pulling through nets. Yet, while mechanical removal has been effective to a considerable extent, the infestations soon return because shredded bunch of the weeds are carried by waves to other unaffected areas where they establish and start proliferating (LVEMP, 2011). In recent years, focus has shifted to use of chemical methods and natural enemies of water hyacinth including plant pathogens. The aim of any biological control is not to eradicate the weed, but to reduce its abundance to a level where it is no longer problematic (Gichuki et al, 2012).


2.5 Conceptual Framework

The conceptual framework used in this study was developed as a result of review of literature from Johnson (2009), Kusemiju (1991) and Glantz (2007). Figure 2.1 shows a situation in which climatic factors such as temperature, rainfall and wind influence fish production both positively and negatively.

(Independent Variables) (Dependent Variable)

(Intervening variables)

Figure 2.1: Influence of climatic and non-climatic factors on fish production Source: Developed from; Johnson (2009), Kusemiju (1991) and Glantz (2007).

As has been discussed in the literature review, it is clear that in oceans and seas in the temperate region such as the North Atlantic, Arctic and in the Baltic Sea, high temperatures have resulted in unsuitable conditions for fish production. This has enabled places which had never been used before for commercial fishing to become

Climatic elements

Temperature Rainfall Wind

Non Climatic factors

Water Hyacinth Predators

Fishing activities

Amount and species fish caught


favourable. Sprat recruitment was significantly more above average during the warm years than cold years (Mackenzie and Koster, 2004) while on the other hand, in the tropical region increased temperature has led to increased evaporation that has resulted in decline in pelagic fish catches, especially in Lake Tanganyika. Dry season has resulted in high production of shrimps in Lagos-coast while high rainfall has contributed to high production of Clarias spp in Lake Victoria.

Non-climatic factors have also influenced fish production negatively. For instance, pollution in the Lagos coast has choked fish resulting into some species becoming extinct. Water hyacinth has made it difficult for the fish to get oxygen thus lowering production. Overfishing is the biggest threat to fish production of some species, for instance sprat preying on cod fish in the Baltic Sea.

Winds blowing over water have pushed water hyacinth to cover large areas of Lake Victoria depriving fish of oxygen. On the other hand, the water hyacinth has also formed a mat over the water bodies such as the Lekki Lagoon off the coast of Nigeria, thus preventing evaporation from taking place effectively, resulting into low precipitation (Lejju, 2012). This is an indicator that climatic factors are also interacting with non-climatic factors both positively and negatively as shown in Figure 2.1.



3.1 Research design

The research design used in this study was descriptive survey whereby both quantitative and qualitative techniques were used in obtaining information from the sample population on the influence of climate variability on fish production. According to (Muganda, 2010), the goal of descriptive study is to offer the researcher a profile or to describe relevant aspects of the phenomena of interest from an individual, organization, industry, and some other prospective. For this study, descriptive survey design was chosen because it enabled the researcher to describe in details factors that influence fishing activities in Lake Victoria.

3.2 Study Area


Figure 3.1: Some beaches along the shoreline of Lake Victoria, Kisumu County. Source: Google maps (2013)


districts namely, Kisumu West, Kisumu East and Nyando. There are thirty two beaches found in these three districts. The names of the beaches are shown on Table 3.1.

Table3.1 Beach Management Units in Kisumu County, Kenya

Source: GoK (2008a, 2008b, 2008c)

3.2.1 Climate

Kisumu County experiences varied climatic conditions between the North and the South sides of Lake Victoria (GoK, 2008a, 2008b, and 2008c). The northern side, which covers both the Kisumu East and Kisumu West districts, experiences a mean annual temperature range of between 200C and 350C.The annual rainfall pattern is bimodal throughout the County (Figure 3.2) with long rains being received between

Kisumu West Population of fishermen Kisumu East Population of fishermen Nyando District Population of fishermen Total Population

Asat 183 Paga 173 Singida 109

Arongo 70 Usoma 76 Kusa 230

Nanga 155 Nyandiwa 52 Kombewa 230 Kihanja 55 Oseth/Obange 126 Bala 120 Nyamarwaka 149 Kaloleni 61 Sangorota 270

Kobudho 123 Rare 201 Koguta 220

Kagwel 36 Ugwe 55

Othany 98 Ochok 68 Kaloka 91 Ongenya 84

Bao 252 Usare 141

Nduru 86 Ogal 60 Ngege 83 Dunga 393 Kichinjio 233 Mawembe 110


March and May while short rains occur between September and November. Some areas endowed with hills like Maseno Division and Muhoroni receive high rainfall ranging between 1500mm and 1800mm annually (GoK, 2008a) as recorded at the Maseno Agricultural Training Centre (ATC). Regions of low altitude such as Kano plains and Kombewa Division receive lower annual rainfall ranging between 600mm and 1280 mm.

Figure 3.2: Mean monthly distribution of rainfall over Lake Victoria, Kisumu County (2009 – 2011).

Source: Kenya Meteorological Department- Kisumu Airport (2014)

3.2.2 Socio-Economic Activities


2008c). The main source of livelihood for those who live along the shores of Lake Victoria is fishing while those who live away from the lake practice agriculture. According to Omoro (2011), commercially exploited fish species in Lake Victoria, Kisumu County are: Restrineobola argentea, Orechromis spp, Lates niloticus, Protoptrerus spp, Clarias gamepinus, Haplochromine spp and Synodontis spp. He further noted that sugarcane is the main cash crop grown in Kisumu County particularly in Muhoroni, Miwani, Kibos and parts of Nyando District. Rice is another cash crop grown on swamps along riversNyando, Awach, in Miwani and lower Nyakach respectively. Both Ahero and West Kano irrigation schemes which are used for the cultivation of rice are located in the County.

High altitude regions such as Maseno, Muhoroni and Upper Nyakach that receive reliable rainfall are very potential in the cultivation of food crops like maize, beans, sorghum, cowpeas and fruits. Livestock keeping is practiced in these high altitude regions especially dairy cattle in Koru, Muhoroni and Maseno. Other economic activities such as trade and industry are predominant in urban centers within the County. These include sugar processing factories in Kibos, Chemelil, Muhoroni and rice milling in Ahero and Kibos (Okonga, 2010).

3.4 Target Population


total target population of 4,393 registered fishermen (Table 3.1). This is according to the District Development Plans GOK (2008).

3.5 Sampling Technique and Sample Size


Table 3.2: Selected beaches and sampling grid

Source: GOK (2008)

3.6 Research Instruments

The research instruments that were used in this study to collect data were questionnaires, interview guide and document analysis guide (Appendices 1-3). Questionnaires were very instrumental for collecting primary data from the fishermen while interview guide was used to collect data from the beach managers, at the beaches. The reason for using questionnaires as a method of data collections is because it was handy, practical and a large amount of information was collected from a large number of people in a short period of time in a relatively cost effective way.

An interview schedule was the most appropriate because of its flexibility as an interactive and generative tool that explores meanings and language in depth (Orodho, 2008). It also enabled the researcher to understand and interpret social reality through meanings that the respondents attached to their involvement with

Name of Beach (n = 8)

Beach Manager

Registered fishermen

No. of fishermen in the sample

Sample size (%)

Kihanja 1 55 14 25.45

Othany 1 98 25 25.51

Usoma 1 76 19 25.00

Rare 1 201 50 24.87

Usare 1 141 35 24.82

Dunga 1 393 98 24.94

Kusa 1 230 58 25.22

Koguta 1 220 55 25.00


fishing activities. It further allowed the researcher to explore in depth relevant issues to the study. Document analysis guide was used by the researcher to collect secondary data from the Fishery and Meteorological departments.

3.7 Data Collection

To obtain primary data, questionnaires were administered to the sampled respondents in the 8 selected beaches within Kisumu County.

3.7.1 Questionnaire


This return rate was considered satisfactory for the study, and therefore the researcher proceeded with the data analysis.

3.7.2 Interview Guide


according what they could remember. The study also used interviews to obtain information from the beach managers to establish the influence of climatic and non-climatic factors on fishing activities (Appendix 2). Beach managers who had been carrying out fishing activities for the past ten years consistently for the period 2001-2011 were considered as key informants for the study. The researcher interviewed beach managers who said they had been involved in the fishing activities in that region consistently for at least 10 years presiding 2011.

3.7.3 Document Analysis Guide

According to Kombo (2006) document analysis is a social research method and is an important research tool in its own right and is an invaluable part of most schemes of triangulation. Documentary research is the use of outside sources, documents, to support the viewpoint or argument of an academic work. The process of documentary research often involves some or all of conceptualizing, using and assessing documents. The analysis of the documents in documentary research would be either quantitative or qualitative analysis (or both). The key issues surrounding types of documents and our ability to use them as reliable sources of evidence on the social world must be considered by all who use documents in their research.


beaches visited, as well as from Kenya Marine and Fisheries Department in Kisumu. Data on the amount of fish caught in the beaches was established by analyzing records obtained from Kenya Marine Fisheries Department. The fishery data used were weight of fish or fish catches (in kilograms) in the beaches within Kisumu County. Monthly fishery data, which showed amount and type of fish, were available from 2005 to 2011 and the annual records were from 2001 to 2011 for regions. This information was got from document analysis guide (Appendix 3).On the other hand, secondary data on weather elements such as rainfall and temperature were collected from Meteorological Department in Kisumu to determine the trend of these elements for the same period (2001 to 2011). The data obtained were in daily form. Rainfall and Temperature data were consistent; however wind speed and direction were quite scanty from the Kenya Meteorological Department. The data was received in a soft copy; the extract is shown in Appendix7.

3.8 Data Processing and Analysis


inferential statistics was used to make inferences and draw conclusions. The inferential statistics was mainly Pearson's product moment of correlation, which was used to assess the relationship between annual amount of rainfall and amount of fish. All tests of significance was computed at α = 0.05. The Statistical Package for

Social Sciences (SPSS) version 20 was used to analyse the data, given its flexibility and ability to manipulate large amount of data.

3.9. Data Management and Ethical Considerations



4.1 Introduction

The purpose of the study was to investigate the influence of climatic and non-climatic factors on fishing activities in Lake Victoria, Kisumu County, Kenya. Among the factors considered were: wind strength, amounts and distribution of rain, dry spell and fishing methods. The study also sought to establish other factors which affected fishing activities in Lake Victoria as well as measures put in place to cope with these issues. This chapter presents the results, discussion and interpretation of the findings from the study. The results in this chapter have been organized according to the research objectives, with the first section dealing with demographic characteristics of the respondents.

4.2 Demographic Characteristics of the Respondents

The demographic characteristics of the respondents discussed were age, level of education, marital status, other sources of income, experience in the fishing activities and average income from fishing. These characteristics were important for the study because they had a bearing on the fishing activities.

4.2.1 Age of Respondents


Table 4.1Age of respondents

Age Category (Years)

Frequency (n=298)

Percentage (%)

15-19 28 9.40

20-25 69 23.15

26-30 63 21.14

31+ 138 46.31

Total 298 100.00

From Table 4.1, 90.6% of the fishermen were aged between 20 years and above, as compared to the remaining 9.4% who were aged between 15 – 19 years. These findings show that majority of the fishermen were adults and therefore of the right age to be engaged in fishing activities. Those who were aged between 15-19 years were established to be class 8 and Form 4 leavers who had just joined fishing activities as a source of livelihood.

4.2.2 Educational Level of Respondents


Table4.2 Educational levels of the respondents

Level of Education Frequency (n=298)

Percentage (%)

College Level 0 0.0

Secondary 69 23.15

Primary 207 69.46

Nursery 22 7.39

Total 298 100.00

The findings in Table 4.2 indicate that majority (69.46%) of the sampled fishermen were of primary school level of education, 23.15% had attained secondary education, while the remaining 7.39% possessed only nursery education. This shows that most of the sampled fishermen had primary education as the highest level of education. From the table it was evident that there was no respondent among the sampled fishermen who had acquired any college education. The study established that this was due to the fact that fishing activities do not require any specialized skill; most fishermen were graduates of apprenticeship.

4.2.3 Marital Status of Respondents


Figure 4.1: Marital status of the fishermen


4.2.4 Other Sources of Income

When the researcher sought to find out if the fishermen were engaged in any other income generating activities other than fishing, their responses were as given in Table 4.3.

Table 4.3: Availability of other sources of income among the fishermen

Availability of other Sources of Income Frequency (n=298)

Percentage (%)

Yes 96 32.21

No 202 67.79

Total 298 100.00


Table 4.4. Other sources of income among the fishermen

Other source of income Frequency (n=96)

Percentage (%)

Business 59 61.46

Farming 29 30.21

Pension 8 8.33

Total 96 100.00

Table 4.4shows that the fishermen were engaged in various activities as a source of income other than fishing. About 61.5% of the fishermen were involved in business activities as their other source of income, 30.2% were farmers; while 8.3% were retirees who depended on pension as their alternative source of income. This could be a pointer to the fact that the income realized from fishing activities by the sampled fishermen was inadequate hence the need to supplement it with other sources of income. These results were further confirmed by the findings revealed during the interviews that showed that on average weighted fish caught at the 8 beaches only ranged from 1000 kg to 6,000 kg per month. It was only in one of the beaches where a weighted fish catch of 45,000 kg per month was reported by the beach manager. These, put in monetary value, turned out to be quite inadequate to decently support the families whose bread winners were purely involved in fishing activities as shown in Table 4.6.

4.2.5 Experience of Fishing among Fishermen


expertise among the fishermen, bearing in mind that the fishing skills are acquired informally through apprenticeship. Table 4.5 provides the responses of the fishermen.

Table 4.5 Experience among the fishermen

Fishing Experience Frequency (n=298)

Percentage (%)

10 years& Below 128 42.95

11-15years 58 19.46

16+ years 112 37.59

Total 298 100.00

From Table 4.5, it was evident that a majority (57.1%) of the sampled fishermen had fishing experiences of more than 10 years. Therefore, most of the sampled fishermen had adequate experience in the fishing activities. This information was found to be useful as it indicated that the responses given by the sampled fishermen were reliable basing on their prolonged experience in fishing in Lake Victoria. This information agrees with that obtained from the interviews with the beach managers, which showed that out of the 8 managers, 7 had served for periods ranging from 1-7 years in the same position at the same beach.

4.2.6 Average Income per Month from Fishing


the researcher explored the fishermen income from fishing activities. Table 4.6 shows the summary of average monthly income per fisherman.

Table 4.6 Fishermen’s average income from fishing per month

The findings in Table 4.6indicate that a total 38.6% of the sampled fishermen had monthly average income of Kshs.3000and less, and another38.6%had an average income of Kshs.3001-Kshs.7000. It is therefore clear that a majority of the sampled fishermen had very low average income, but only about 22% of the fishermen had average income of above Kshs.7000. These findings explain why majority of the fishermen may require other income generating activities such as farming and business to supplement their income from fishing. Lack of capital to diversify in other income generating activities was one of the reasons put forth by the fishermen who did not have other sources of income.

Average Income (Ksh.)

Frequency (n=298)

Percentage (%)

1-1000 8 2.68

1001-2000 70 23.49

2001-3000 37 12.42

3001-4000 0 0.00

4001-5000 57 19.13

5001-6000 0 0.00

6001-7000 58 19.46

Above 7,000 68 22.82


4.3 Type and amount of fish caught in Lake Victoria

In order to establish the type and amount of fish caught, the researcher conducted a documentary analysis of the records of fish caught by species and the monetary value annually. From the results obtained from this analysis, some of the major fish species in Lake Victoria included: Rastrineobola argentea, Lates niloticus, Clarias gamepinus, Orechromis spp., Protopterus spp., Haplochromine spp., Schilbe spp., Labeo spp. and Synodontis spp. among others. Out of these species, Orechromis spp., Rastrineobola argentea, Clarias gamepinus, Protopterus spp., and Lates niloticus were the most common fish caught. A closer look at these five most common types of fish caught in Lake Victoria, revealed variant in weight and value as reflected in Table 4.7. The records of fish catch were in kilograms and the value, which was determined by market price paid at the landing beach, was in Kenya shillings.


Table 4.7 Average annual weight and value of fish catch from Lake Victoria,

Kisumu County.

Type of Fish Weight


Value (Ksh.)

Value per Kg (Ksh)

Orechromis spp. 268,361 21,222,638 79.10

Lates niloticus 359,223 29,732,933 82.80

Rastrinoebola argentea 408,427 14,984,296 36.70

Clarias gamepinus 135,140 7,957,835 58.90

Protopterus spp. 57,809 3,205,514 55.50

Others 68,981 2,465,803 35.80

Total 1,297,941 79,569,019

Source: Compiled from Kisumu County Fisheries Department (2014)

On average, 1,297,941 Kg of fish, valued at Ksh.79,569,019 were caught annually in Lake Victoria in Kisumu County. Lates niloticus fetched the highest amount of income (Kshs. 29,732,933) on average annually, Orechromis spp. followed closely at Ksh.21,222,638 and Rastrinoebola argentea, which recorded the highest weight (over 4 tonnes annually) generatingKsh.14,984,296 on average annually. From this analysis, it is evident that Rastrinoebola argentea, though has the highest weight by catch, but it is of low value compared to Orechromis spp. and Lates niloticus.

4.4 Relationship between Climatic elements and Fishing Activities


County. Some of the climatic factors that were considered in this context included winds (strong and mild), rains and dry spell. In response, the respondents gave their views on how each climatic factor had influenced their fishing activities in Lake Victoria.

4.4.1 Effects of Winds on Fishing Activities in Lake Victoria


Table 4.8 Effect of winds on fishing activities

Effect of strong winds Frequency(n =


Percentage (%)

Hinder the fishermen from sailing in their boats 92 30.87 Sticking water weeds which blocks the way of fish 11 3.69

Accidents to the fishermen 132 44.30

Loss of fish already caught 29 9.73

Loss of baits used by the fishermen 34 11.41

Total 298 100.00

Effect of mild winds Frequency(n=29


Percentage (%)

Slow down movement of fishermen 129 43.29

Increased fish production 169 56.71

Total 298 100.00


On mild winds, 43.3% of the fishermen said that they slowed down their movement during the fishing activities in water, while on the other hand 56.7% of the fishermen confirmed that they witnessed increased fish production during mild winds. It was also discovered from the beach managers that small fish comes to the surface of the water when the wind strength decreases. These results therefore show that mild winds had more positive effects on fishing activities as compared to strong winds, which had quite a number of negative influences on fishing activities. This implies that wind as element of climate influence fishing activities.

These findings confirmed the assertion by Ogutu-Ohwayo(2003) that wind speed and direction, among other factors can sharply alter the abundance, distribution, and availability of fish population.

The oral interviews with the beach managers also confirmed the relationship between wind and fishing activities, which were in agreement with the fishermen‟s responses. For example, one of the beach managers at one of the beaches in the study area said:

“Mild winds like Kus and Matarae usually bring a lot of rains and


the fishing activities as they make fish to hide in the mud, break boats, and can kill fishermen due to accidents”.

4.4.2 Effects of Rain on Fishing Activities

The other climatic factor that was also considered in this study was rain. The respondents were asked to explain specifically the effect of heavy rains on fishing activities. The results were as presented in Table 4.9.

Table 4.9 Effect of heavy rains on fishing activities

Effects of heavy rains Frequency

( n = 298)

Percentage (%)

Fish is carried by floods 174 58.38

Death of fish 15 5.03

Higher fish catch 105 35.24

Illness to fishermen 4 1.35

Total 298 100.00


intensity of rainfall affected fish production, this study established that fish catch for some types of fish were high when there were heavy rains. This fact was established from the interview of the beach managers who attributed heavy rains to increase in Lates niloticus and Protopterus spp.

To establish the relationship between climatic elements and fishing activities in Lake Victoria, Kisumu County, the hypothesis: climate factors have no significant influence on fishing activities in Lake Victoria, Kisumu County, was tested using bivariate Pearson moment correlation.

Table 4.10 Correlation between fish catch and annual rainfall amount

Type of fish

Amount of rainfall

Pearson Correlation Sig. Level(2-tailed)

Orechromis spp. .260 .033

Lates niloticus .130 .022

Rastrinoebola argentea -.786 .051

Claras gariepinus -.012 .014

Protopterus spp. .184 .002

Overall .079 .025

*. Correlation is significant at the 0.05 level (2-tailed).


direct relationship between the rainfall intensity and amount of fish caught (Table 4.10).

The results from correlation analysis on the amount of rainfall and fish catch data records showed that amount of rainfall generally had a positive significant relationship with fish catches for some type of fish (Table 4.10). This suggested that fish catches in Lake Victoria, Kisumu County were influenced by amount of rainfall, though the effect varied by fish types. Whereas Tilapia (r = .260; sig. level = .033), Lates niloticus (r =.130; sig. level = .022) and Protopterus spp. (r=.184) exhibited positive correlation with amount of rainfall, Rastrinoebolaargentea(-.786; sig. level= .051) and Claras gariepinus(r=-.012; sig. level = .014) revealed a negative correlation with the amount of rainfall. In general, the results revealed a positive, though, weak correlation(r = .079, sig. level .025between the amount of rainfall and the total amount of fish catch. From this findings a conclusion was reached that although rainfall intensity had both negative and positive influence on fish caught in the lake, it was evidently clear that in general the higher the amount of rain the higher the amount of fish caught.


Figure 2.1: Influence of climatic and non-climatic factors on fish production Source: Developed from; Johnson (2009), Kusemiju (1991) and Glantz (2007)
Figure 2 1 Influence of climatic and non climatic factors on fish production Source Developed from Johnson 2009 Kusemiju 1991 and Glantz 2007 . View in document p.44
Figure 3.1: Some beaches along the shoreline of Lake Victoria, Kisumu County. Source: Google maps (2013)
Figure 3 1 Some beaches along the shoreline of Lake Victoria Kisumu County Source Google maps 2013 . View in document p.48
Figure 3.2: Mean monthly distribution of rainfall over Lake Victoria, Kisumu County (2009 – 2011)
Figure 3 2 Mean monthly distribution of rainfall over Lake Victoria Kisumu County 2009 2011 . View in document p.50
Table 3.2: Selected beaches and sampling grid
Table 3 2 Selected beaches and sampling grid . View in document p.53
Table 4.1Age of respondents
Table 4 1Age of respondents . View in document p.60
Figure 4.1: Marital status of the fishermen
Figure 4 1 Marital status of the fishermen . View in document p.62
Table 4.3: Availability of other sources of income among the fishermen
Table 4 3 Availability of other sources of income among the fishermen . View in document p.63
Table 4.4. Other sources of income among the fishermen
Table 4 4 Other sources of income among the fishermen . View in document p.64
Table 4.5 Experience among the fishermen
Table 4 5 Experience among the fishermen . View in document p.65
Table 4.6 Fishermen’s average income from fishing per month
Table 4 6 Fishermen s average income from fishing per month . View in document p.66
Table 4.7 Average annual weight and value of fish catch from Lake Victoria,
Table 4 7 Average annual weight and value of fish catch from Lake Victoria . View in document p.68
Table 4.8 Effect of winds on fishing activities
Table 4 8 Effect of winds on fishing activities . View in document p.70
Table 4.9 Effect of heavy rains on fishing activities
Table 4 9 Effect of heavy rains on fishing activities . View in document p.72
Table 4.10 Correlation between fish catch and annual rainfall amount
Table 4 10 Correlation between fish catch and annual rainfall amount . View in document p.73
Table 4.11. Effect of dry spell on fishing activities
Table 4 11 Effect of dry spell on fishing activities . View in document p.76
Table 4.12 Other factors affecting fishing activities in Lake Victoria
Table 4 12 Other factors affecting fishing activities in Lake Victoria . View in document p.78
Table 4.13 shows that 24.2% of the sampled fishermen indicated that improved
Table 4 13 shows that 24 2 of the sampled fishermen indicated that improved . View in document p.82
Figure 4.2 Involvement in other income activities The study sought to find out whether the fishermen were engaged in other income
Figure 4 2 Involvement in other income activities The study sought to find out whether the fishermen were engaged in other income . View in document p.83
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