Do women or men in the Barotse floodplain in Zambia experience higher post harvest fish losses? Why? And what’s the impact – both financially and physically to the fish? The answers to these questions, shown in this presentation, are helping WorldFish design and test appropriate innovations, including ways to overcome harmful norms, behaviours and power relations in the post harvest losses context.

1. GAF6 – Bangkok, Thailand (August 4, 2016)
Alexander Kaminski, Alexander Kefi, Steven M Cole, Kate
Longley, Chifuniro Somanje, Pamela Marinda, Ansen Ward,
Alexander Chilala, and Gethings Chisule
A Gendered
Value Chain
Analysis of Post
Harvest Losses
in the Barotse
Floodplain,
Zambia

2. Research project site
Lake Chilwa

3. Research Question One
What is the gendered
nature of post-harvest
losses (biophysical and
economic) in the value
chain?

4. What are the social and
gender constraints to
post-harvest losses and
does gender inequality
contribute to losses?
The “social”
development
challenge

5. Types of post-harvest losses (PHL)
• Total Loss: consumed by insects
(or other animals), or due to
spoilage, breakage, etc.
Biophysical
Loss
• Biochemical changes and
processing that denatures
nutrients
Nutrient
losses
• Quality Loss: leads to fish sold at
lower cost
• Market force loss: demand and
supply changes
Economic
Loss

6. PHL in sub-Saharan Africa
• The fisheries contribution to GDP in Africa is around 1.26%
• The economic and biodiversity value of fisheries is important
to 200 million people in SSA (NORAD, 2009)
• Women make up 27.3% of the people engaged in fisheries-
related activities in Africa
– 3.6% of fishers and 60% of processors/traders are women (de Graaf &
Garibaldi, 2014)
• It is believed that PHL within small-scale fisheries in Africa are
among the highest for all the food commodities (Morrisey
1988; Hodges et al. 2011)
• Total losses are about 5% in SSA and a further 20-30% are due
to quality losses (FAO, 1996; Akande & Diei-Ouadi, 2010)

7. The Barotse Floodplain fishery
• Poorest region in Zambia – 80.4% of people living in poverty
(CSO, 2012)
• Migratory fishing camps where daily consumption of fish is
five times the national average (Turpie et al., 1999)
• Capture fisheries contribute over 70% to mean household
income in the floodplain
• About 75% of fishers are men and about 25% are women
(Turpie et al., 1999) and more than 80% of traders are women
(Longley et al., 2016)
• Gender inequalities are pervasive in Zambia
– Norms and power relations create gender inequalities in access to
and benefits derived from the fishery (Rajaratnam et al., 2016a,
2016b; Cole et al., 2015)
• Illegal fishing contributes to dwindling fish stocks (Kolding and
van Zwietan, 2014; Turpie et al., 1999; Tweddle et al., 2015)

8. Research methodology
BASELINE INSTRUMENTS
• Exploratory Fish Loss
Assessment Method
(EFLAM)
• Quantitative Fish Loss
Assessment Method
(QLAM)
• Gross Margins Analysis
(GMA)
• Women’s Empowerment
in Fisheries Index (WEFI)
RESEARCH APPROACHES
• Participatory Action Research
(PAR) with women and men
testing the post-harvest fish
processing technologies
• Practical Needs Approach
(PNA) to ensure
women’s/men’s practical
needs are accommodated
• Gender Transformative
Approach (GTA) to challenge
and address the
norms/power relations that
cause gender inequalities in
the fishery value chain

9. EFLAM/QLAM
Women’s
empowerment
Losses Profitability
Designing and testing technologies and social change interventions to reduce PHL
Participatory action
research
Gender
Transformative
approach
GMA WEFI

10. Exploratory Fish Loss Assessment
method (EFLAM)
• Focus group discussions in six fishing camps the
research project is operating in
• Qualitative scoping about losses (for whom, how,
why, where, when, etc.)

11. Quantitative Loss Assessment
Method
• Sample of 176 people (33% women, 67% men)
from six fishing camps
– Given some fishers also processed fish [28.3% of
fishers (all men)], total sample = 206, with:
• Fishers = 106 (2% women, 98% men)
• Processors = 60 (40% women, 60% men)
• Traders = 40 (80% women, 20% men)
• Measured physical loss and reasons for loss, by
sex and node
• Measured economic loss and reasons for loss, by
sex and node

12. Women: n=2
Men: n=104
Women: n=32
Men: n=8
Women: n=24
Men: n=36
17.78
28.49
36.42
37.98
16.64
45.95
Fishing Processing Trading
Mean kilogram of fish per node disaggregated
by sex
women men

13. 15.09% of fishers experienced loss
primarily due to spoilage (53%)
44.83% of processors (63% of
women, 32% of men: p-value =
0.0228) experienced loss primarily
due to breakage (82% of men, 47%
of women) and over processing
(33% of women, 9% of men)
30% of traders (35% of women,
13% of men: p = 0.2379)
experienced loss due to breakage
(64% of women, 100% of men) and
spoilage (36% of women)
6.98
1.56
5.9
16.5
5.35
9.97
25
2.94
3.35
0 5 10 15 20 25 30
women
men
total
women
men
total
women
men
total
TradingProcessingFishing
Total Loss (% of catch)

14. Fishers experienced loss primarily due
to spoilage (34%) and market forces*
(55%)
Processors experienced loss primarily
due to breakage (58% of women, 55%
of men) and market forces (42% of
women, 36% of men)
Traders experienced loss due to
breakage (25% of men, 17% of
women), spoilage (42% of women,
25% of men), and market forces (50%
of men, 42% of women)
*Size variation, high supply, price variation
25.26
25.38
25.29
34.38
19.19
25.26
18.75
18.16
18.18
0 5 10 15 20 25 30 35 40
women
men
total
women
men
total
women
men
total
TradingProcessingFishing
Economic Loss (% of total
catch)

15. Gross Margins Analysis
• Sample of 239 people (33% women, 67% men)
from fishing camps and in town
– Fishers = 113 (100% men)
– Processors = 50 (70% women, 30% men)
– Traders = 76 (56% women, 44% men)
• Gross margins analysis measures the
difference between revenue and costs of
goods sold and expressed as a percentage
indicating profitability of an enterprise

16. 12.54
30.75
39.28
27.83 25.79
Men Women Men Women Men
Fisher Processor Trader
Mean Price (ZMW/kg)
79.06
677.59
465.39
532.37
980.8
Men Women Men Women Men
Fisher Processor Trader
Mean Total Variable Costs
(ZMW)
Women processors incur higher
variable costs on average and
receive lower prices for fish
Women traders receive better
prices but incur much lower
variable costs.
Men traders have very high
variable cost

17. 21.49
2.56
5.5
13.79
12.21
Men Women Men Women Men
Fisher Processor Trader
Mean Gross Margin (%)
100% men 70% women 56% women

18. WEFI
• Adapted from the women’s empowerment in agriculture index
(WEAI) (IFPRI, 2012)
• Sample of 151 people (39% women, 61% men)
• Measured access to assets, decision-making powers, individual
leadership capabilities, gender attitudes and allocation of time
• Primary reason women are in the fishing camps:
– 73% to trade fish
– 15% to process fish
– 7% to fish
– 5% to do something else
• Primary reason men are in the fishing camps:
– 90% to fish
– 9% to trade fish
– 1% to do something else

19. 0
10
20
30
40
50
60
70
80
90
100
Participation of women/men in key nodes of the fishery
value chain and their own decision-making powers
Women
Men

20. Access to assets
• A larger percentage of women’s households own
locally-produced fishing equipment (e.g., fishing
baskets) compared to men’s (46% versus 30%)
• A larger percentage of men’s households own
externally-produced fishing equipment (e.g., seine
nets, hooks) compared to women’s (86% versus 34%)
– Importantly, 0% of women own such gears themselves,
while 62% of men do
• No women make their own decisions to sell, rent/give
away or purchase new externally-produced fishing
gears, while majority of men do
• Similar results for women and men regarding
processing and trading

21. Individual leadership in the camps
• 51% of women felt very comfortable speaking
in public to help decide on projects or issues
affecting the fishing camp, compared to 83%
of men.
• 56% of women felt very comfortable speaking
in public to protest the use of illegal fishing
gears or activities, compared to 87% of men.

22. Gender attitudes
• Women have more gender equal attitudes than men (p-value = 0.0019)
• A greater percentage of men compared to women feel that women
should not get involved in fishing and women should primarily be the
ones who clean and process fish
• More men than women feel that they should primarily be the ones
who control the earnings obtained from the sale of fish
• A greater percentage of men compared to women felt men should
primarily be the ones who transport fish to a market for sale
• Women and men almost equally believe that women should primarily
be the ones who prepare meals (including fish)
Gender Attitudes Mean
Women 19.98*
Men 18.11
*Perfect gender equal attitude score = 24, perfect gender unequal attitude score = 8

23. 0
1
2
3
4
5
6
7
8
9
Time allocation (average hours per day)
Women
Men

24. Conclusions
• Women face higher physical and economic losses than
men
• Women incur smaller gross margins in processing node
• A greater percentage of men make individual decisions
on many fishing-, processing-, and trading-related
processes
• Gender attitudes about women’s and men’s
involvement in key activities in (and outside) the
fishery value chain and their allocation of time devoted
to paid and unpaid (e.g., home-based) activities may
influence women’ abilities (in particular) to process
higher-quality fish with minimal losses

25. Drivers of PHL
• Complex no doubt
• Poverty and marginalization,
lack of access to improved
technologies and markets,
climate change, etc.
BUT also…
• Women’s access to resources,
lack of individual decision-
making powers, socially-
assigned roles and gender
attitudes, and time allocation
(especially carrying out
unpaid tasks)

26. How the project is utilizing these
results
Social and
gender analysis
to highlight
harmful norms,
behaviors and
power relations
in a PHL context
Design and test
innovations that
address that cause
gender inequalities
and prohibit
women from
processing higher
quality fish
Reduced gender
gaps
Greater
adoption and
utilization of
technologies
Improved
gender relations
More
sustained
development
impact for all
Research output Outcomes Impact
Figure: Gender transformative impact pathway to change

27. Participatory action research
Community
defines the
problem and
together plan
actions
Undertake
actions
Observe and
analyse the
results
Reflect on
what the
results mean
PAR on Post
Harvest Fish
Losses
Undertake
varietal
trials with
farmers
Observe
and
analyse
results
Reflect on
what the
results
mean
Plan trials
Undertake
action with
group
Observe
and
analyse
results
Reflect on
what the
results
mean
Plan new
actions
PAR on
Solar Drier
PAR on
gender roles

28. PHL-reducing technologies

29. Using PAR to implement technologies

30. Gender transformative
communication tool
Dramas are
performed in
fishing camps
Questions are
presented to PAR
groups
Actions to
address the
harmful social
and gender
norms are
carried out

31. Key insights
• Post-harvest fish losses literature lacks gendered
analyses
• Gender norms and power relations influence how,
when, where fish get processed and as a corollary
contribute to post-harvest losses in the value chain,
and especially for women occupying the processing
node
• Gender transformative approaches and post-harvest
fish processing technologies should be
tested/promoted together to address both the
technical and social issues that lead to PHL
• PAR can be a vehicle for implementation by building
ownership of the research and developing capacities

32. Acknowledgments
• The research project is funded by IDRC and
ACIAR under the Cultivate Africa’s Future
(CultiAF) fund
• The Zambia component of the research
project is being implemented by the
Department of Fisheries, University of Zambia,
WorldFish, and NoNo Enterprises

33. Thank You!