Materials and

Methods

Conclusions

CLIMATE SMART ADAPTATION ON LAKE KARIBA:

A CASE STUDY OF SIAVONGA DISTRICT

POSTER BY MULAKO KABISA¹

¹ UNIVERSITY OF ZAMBIA, DEPARTMENT OF GEOGRAPHY AND ENVIRONMENTAL STUDIESEMAIL: mulakokay@yahoo.co.uk

References

AbstractResults and

Discussion

Acknowledge

mentsI would like to thank my supervisor Dr. D.

Chibamba for the encouragement and

support during the course of this work, the

Food and Agriculture Organization of the

United Nations (UNFAO) for the technical

support and IRD-NORD for enabling my

travel for the OCFUCC International

Scientific Conference.

Introduction

A total of 90 Kapenta fishers

were randomly sampled on

Lake Kariba. Their

perceptions and adaptation

strategies to climate

variability were determined

using a Likert scale, multiple

regression analysis and

content analysis. It was

found that 79 (87.7%)

respondents perceived

climate variability in terms of

temperature and rainfall and

about 81 (90.5%)

respondents were adapting

to climate variability. Of the

strategies that were used,

64.3% have the potential to

be climate-smart.

Siavonga district is found

in Agro-ecological region

(AER) I in Zambia and has

for two decades

experienced declining,

unpredictable and poorly

distributed rainfall. It is

currently the driest zone

and is experiencing the

impacts of climate change

(USAID, 2012). Climate

impact work on Lake

Kariba Kapenta fish stocks

shows that increased

temperature and reduced

rainfall are the main

climatic factors affecting

fish catch (Ndebele-Murisa

et al, 2011).The

temperature, rainfall and

catch trends in Siavonga

and Lake Kariba are

summarised by the

following figures:

SPECIFIC

OBJECTIVE

DATA

COLLECTION

DATA

ANALYSIS

To assess

fishers’

perceptions of

climate

variability

Questionnaire Coding of

Likert scale

from

perceptual

statements

To determine

the relationship

between

perception and

other

independent

variables

Questionnaire Pearson’s

Correlation

Coefficient and

Multiple

Regression

Analysis using

SPSS 16.0

To assess

fishers’

adaptation to

climate

variability

Questionnaire Content

analysis using:

FAO Code of

Conduct for

responsible

fisheries,

Climate Smart

Agriculture

Sourcebook

and Threefold

Typology of

Responses.

Using Bless and Achola’s

(1988) ‘Rule of Thumb’

technique;

90 fishers from 90 rigs

were sampled randomly

from a population of 1098

rigs (Kinadjian, 2012).

5 1098 54.9100

0

200

400

600

800

1000

1200

1400

1990 1995 2000 2005 2010 2015

Me

an R

ain

fall

in S

iavo

nga

(mm

)

Year

Mean annual rainfall

Линейная (Mean annual rainfall)

28.5

29

29.5

30

30.5

31

31.5

32

32.5

33

1960 1980 2000 2020

Max

. Te

mp

era

ture

L. K

arib

a (˚

C)

Year

Max T

Линейная (Max T)

19.2

19.4

19.6

19.8

20

20.2

20.4

20.6

20.8

21

21.2

21.4

1990 1995 2000 2005 2010 2015

Siav

on

ga D

istr

ict

Me

an t

em

pe

ratu

re ˚

C

Year

Mean Temperature

Линейная (Mean Temperature)

0

200

400

600

800

1000

1200

1400

1960 1980 2000 2020

Lake

Kar

iba

Rai

nfa

ll tr

en

ds

(mm

)

Year

Rainfall

Линейная (Rainfall)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1980 1990 2000 2010

Kap

en

ta C

atch

tre

nd

s o

n L

ake

Kar

iba

(me

tric

to

nn

es)

Year

catches

Линейная (catches)

The study revealed that

79(87.7%) fishers perceived

climate variability, 81(90%) fishers

were adapting using a variety of

strategies, 63.7% of the strategies

used have the potential to be

climate smart and 66(82%)fishers

reported constraints to adapting

appropriately. These results are

summarised in the following

figures:

Lack of Information

33%

Lack of Money67%

Barriers to Adaptation

Shifting fishing

times, 26

Fishing further

away, 63

Change of fishing gear, 8

Alternative livelihood, 5 Other, 6

Potentially Climate Smart Options

1. Bless C. and Achola P. (1988). Fundamentals

of Social Research Methods. An African

Perspective Lusaka: Government Printers

2. Kinadjian Lionel (2012) Bio-economic Analysis

of the Kapenta Fisheries Lake Kariba –

Zimbabwe & Zambia, Mission Report No.

1, SF-FAO/2012/09, Smart Fish Programme

of the Indian Ocean

Commission, Ebene, Mauritius )

Strengthening Collective Action to Address

Resource Conflict in Lake

Kariba, Zambia, Program

Report, Collaborating for Resilience

3. Ndebele-Murisa R. M, Mashonjowa E and Hill

T (2011) The Implications of a Changing

Climate on the Kapenta Fish Stocks of Lake

Kariba, Zimbabwe, The Royal Society of

South Africa, Vol. 66 (2)

4. USAID (2012) Climate Change Impact on

Agricultural Production and Adaptation

Strategies: Farmers’ Perception and

Experiences, Summary Results of Focus

Group Interviews, Improved Modeling of

Household Food Security, Decision Making

and Investments Given Climate Uncertainty

Food Security III Project

Fishers are perceiving climate variability

and are actively adapting to its impacts.

• Improving extension services for

dissemination of correct climatic trends

and adaptation support on already

existing potentially climate smart options

can be done

•Coordinating adaptation activities with

fishers and various stakeholders is

required

•Improving enforcement against

unsustainable practices

•Providing financing and structural

support for alternative livelihoods