1. FADs Fishery along the Kenyan Coast: Socio-Economic Problems and ProspectsBy
Horace Owiti
5th Engineering Week and 3rd Africa Engineering Conference, Pride Inn, Msa, Kenya, 17th -21st September, 2018

2. Contents Overview Sampling FADs Fishery
Status of Marine Fisheries, Kenya
Methodology
Results and Conclusion
Effort , Catch & Value
Problems & Prospects
Objectives
Overview
Sampling
Data collection
Analysis
Fishers & Fishing
Perception on FADs
Conclusions & Recommendation
FADs Fishery

3. The Fisherman’s blue economy

4. What do Engineers eat? 1. Fish 6 cookingforengineers.com
Survey of robotics engineers in St Louis, US at a building jam packed with engineers (Mark Colzani)
FOOD
FREQUENCY
1. Fish 6
9. Pie 1
2. Pork 4
10. Pirozhki
3. BBQ 2
11. Italian
4. Chicken
12. Lamb
6. Thai
13. Coffee
7. Asian
14. Biscuits
8. Dairy
15. Whatever my wife sets before me

5. Introduction
“With one out of every seven Somali children dying before their fifth birth day, Somalia is the only country where people are dying of malnutrition whereas fish are dying of old age.”
Least Developed Countries (LDCs) are largely characterized by low levels of innovative capacity in spite of their enormous endowment with natural resources

6. Kenya Marine Fisheries – Catch & Value

7. Kenya Marine Fisheries - Geography

8. Kenya Marine Fisheries - Geography

9. Kenya Marine Fisheries - Prospects
640 Km coast line;
2/3 rds of Kenya’s Land Mass in the Indian Ocean
Exclusive Economic Zone (EEZ) of 230,000 Km2;
Extended continental shelf of 103,320 Km2 (UNCLOS);
EEZ Lies within the richest tuna belt of the South West Indian Ocean (SWIO)
Potential thousand metric tonnes of high valued fish off-shore under-exploited
Potential for additional $532 M = 1.4% of GDP (Current: $185 M= 0.5% of GDP)

10. Kenya Marine Fisheries – The case of Tuna
Contributes $ 42 billion to global economy -the world’s most valuable fish.
Locally: Ksh 350/Kg (2014)
Globally: Ksh 22,500/Kg (2014);
Lucrative Tuna fish could earn the country 12 billion annually;
Four species are of major commercial importance in the Indian Ocean: skipjack, yellowfin, bigeye, and albacore.
The most important tuna-like species in the Indian Ocean is swordfish.

11. Kenya Marine Fisheries – Low off-shore catches, why?
About 1,000 tonnes of off-shore pelagic species with a proportion 322 tonnes being high valued tunas

12. Tuna Fishing Methods – Indian Ocean

13. Do they look like these?
Vessels?
Gear?
Dugout
Ngalawa
Mashua

14. Why??? – Fishing vs technology

15. Vessel & Gear Limitations
80% small-scale artisanal operating in the coastal near-shores;
Traditional vessels, gears and methods

16. Fish Aggregating Devices (FADs)
Tuna species show a tendency to gather around floating objects;
FADs are manmade objects placed in the ocean to specifically draw fish to an area.
Oceanic purse seiners deploy modern FADs to target tunas. .

17. Objective of Study
Assesses viability FADs as an alternative fishing technology for improved livelihoods through exploitation of high valued tuna species.
Provide a socio-economics lens with which to view the current status of FADs fishery; with respect to adoption and management challenges.

18. Methodology – Study Area
Choice: SWIOFP & KCDP had introduced trial artificial FADs to fishers in these areas

19. Data Collection
Primary data: Questionnaires, Key informants & participant observations;
Secondary data:
FS,2016, KMFRI, CAS &
IOTC-2018-DATASETS-NCDB.xls

20. Results and Discussion – Socio-demographic
INDICATOR
STATISTIC N 98
AGE
41 (19-81)
MARITAL STATUS
84% Married
HOUSEHOLD SIZE
7 (1-22)
EDUCATION
None (24%); Primary (51%); Madrass (9%); Sec (9%)

21. High household size indicates high dependency ratio.
Socio-demographics
Strong family and marital unity, implying better prospects for household resource mobilization;
High household size indicates high dependency ratio.

22. Fishing dynamics INDICATOR STATISTIC EXPERIENCE (YRS)
18.7 (2Mnths-60 Yrs)
Fishing Days
6 (NEM); 4 (SEM) ~7Hrs/days
Outer Reef fishing
15% ( At one point)
Major Vessels
Dug out (59%); Ngalawa (20%); Motorized (7%)
Dist. Off-shore
7 Km
Gears
Basket Trap (41%); Longline (24%); Gillnet (17%); Speargun (5%)

23. Fishing dynamics
Need to modernize fishing vessels at the Kenya coast in order to enable fishers to reach off-shore;
Fishers prefer gears that reduce fish search time – basket traps

24. Fishing Income – Fisher’s daily income
Statistic
Fish Landing Site
TOTAL Average
Mkunguni
Munje
Mwaembe
Shimoni
1Ksh =~$0.01
Mean
± SE
1, ± ±
1, ±
2, ±
1, ±
Median
1,000.00
700.00
1,200.00
Minimum
200.00
260.00
300.00
Maximum
7,500.00
4,000.00
6,000.00
8,330.00 N 31 35 15 16 97

25. Fishing income
Fishing Quantities, Prices & Income are erratic by seasons -- credit is vital in smoothening it;
Poor Value addition techniques.

26. Awareness of artificial FADs

27. Adoption of FADs
Only 13% of fishers indicated that they had occasionally fished around experimental FADs moored off-shore

28. Effectiveness of FADs - perception

29. Conclusion and Recommendations
Technological development and innovation are central to the socio-economic progress and trade competitiveness of nations (UNCTAD, 2011).

30. Conclusion and Recommendations
Use of inexpensive FADs – use locally available

31. Conclusion and Recommendations
High valued pelagic species need a well structured market and value-addition mechanisms for desired economic outcomes.

32. The Engineer’s Role

33. QUESTIONS

34. CONCLUSION