Publication 22
REPORT ON THE BIOECONOMIC MODELLING OF KAPENTA FISHERIES ON LAKE KARIBA
Kinadjian Lionel - Mwula Charles Nyikahadzoi Kefasi - Songore Newman
REPORT/RAPPORT: SF-FAO/2014/22
Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
GCP/RAF/466/EC SmartFish Project
Kinadjian Lionel, Mwula Charles, Nyikahadzoi Kefasi & Songore Newman. 2014. Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba. Report/Rapport: SF-FAO/2014/22 March 2014/Mars 2014. SmartFish Programme of the Indian Ocean Commission, FAO Fisheries Management component, Ebene, Mauritius.
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For more information, please contact [email protected]. 3
Acknowledgments
A lot of people contributed in this work.
The authors would like to especially express their thanks to all the Kapenta producers who were extremely courteous in their responses to many probing questions and many of whom provided detailed information about their business as part of the economic survey.
Many other people in Zambia and Zimbabwe, including staff from the FAO-SmartFish Project and FAO Representatives in Zimbabwe and Zambia helped with the preparation of this study and we are most grateful.
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Executive summary
This work on the bioeconomic modelling of the Kapenta fishery on Lake Kariba was conducted as part of a process of joint fisheries management of the fishery between the Governments of Zimbabwe and Zambia, and was supported by the IOC-SmartFish Project under its Fisheries management component (UNFAO).
The first part of the report provides an overview of the Kapenta fishery: information about the bioecology of this resource; harvesting systems in operation on the Lake; management systems in both countries; as well as some elements of processing and marketing. The second part concerns the biological modelling. A summary of the work that has already been done is provided, together with an assessment of available data. Based on the information available, the dynamic population model was chosen and used (surplus production model of Fox 1970). Thereafter, the results of the biological modelling are presented and discussed. The third section of the report concerns the development of the economic part of the model. Previous economic assessments of Kapenta fisheries have been summarized. The results of the Economic Survey, carried out in 2013 to support the bioeconomic modelling exercise, are put forward. Assumptions and analyses to develop the economic part of the model (modelling demand and costs) are also presented in detail. Finally, the fourth section of the document details the bioeconomic model: the way it works and its results. This bioeconomic modelling exercise shows that in 2011, the Kapenta resource was overexploited with an excess of fishing effort of about 40 percent. Fisheries were operating almost at a situation of open-access equilibrium where the rent of the resource is fully dissipated. As a consequence, the fishing industry is achieving very poor economic returns and faced with numerous challenges, is also demonstrating a low level of sustainability.
Other main findings of this report concern the overall performance of the Kapenta fishery: the potential of wealth for economic growth, in terms of rent (estimated at approximately US $24 million per year), is completely lost for the economy of both countries; Kapenta resource productivity and thus fisheries production is negatively affected due to overexploitation, in turn leading to a negative impact on the food security status of those consumers who strongly depend on Kapenta in their diet.
The fisheries generate a substantial amount of ‘on-board’ work (crew), however, remuneration for this type of labour tends to be very low compared to national wage standards. Furthermore, lower levels of production have no doubt had a negative impact on employment in Kapenta processing activities that take place along the lakeshore. This situation requires further study, during the Bioeconomic Working Group, and should be based on information provided by the industry on processing activities.
In addition to the diagnosis of the Kapenta fisheries and the economic situation of the industry in 2011, this report illustrates the potential of the model in terms of simulation for management purposes and its prospects for development.
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The main recommendations of this study concern how to improve the bioeconomic model that has been developed. Thus these recommendations are mainly focused on the need to improve the information necessary feed and to develop the model. Recommended approaches will require strengthening partnerships with the industry.
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Résumé exécutif
Ce travail de modélisation bio économique de la pêcherie de Kapenta sur le Lac Kariba a été réalisé dans le cadre d'un processus de gestion conjoint de cette pêcherie entre le Gouvernement du Zimbabwe et celui de la Zambie. Il a été appuyé par le Projet COI- SmartFish, sous la composante FAO Gestion des Pêches.
Une première partie du rapport décrit succinctement la pêcherie : les connaissances sur la bio écologie de cette ressource, les systèmes d'exploitation mis en œuvre sur le Lac, le système de gestion dans les deux Pays, ainsi que des éléments sur la commercialisation et les marchés du Kapenta. La deuxième partie concerne la modélisation biologique. Un résumé des travaux qui ont déjà été faites est donné, ainsi que l'évaluation des données disponibles. Selon les informations disponibles un modèle dynamique des populations a été choisi et utilisé (modèle global de Fox – 1970). Les résultats de la modélisation biologique sont présentés et discutés. Le troisième chapitre du rapport concerne le développement de la partie économique du modèle. Les évaluations économiques antérieures de la pêcherie de Kapenta ont été résumées. Les résultats de l’enquête économique réalisée en 2013 en appui à cet exercice de modélisation bioéconomiques sont restitués. Les hypothèses et les analyses pour développer la partie économique du modèle (modélisation de la demande et des coûts) sont présentées de façon détaillée.
La quatrième partie détaille enfin le modèle bio économique, son fonctionnement et ses résultats. Cet exercice de modélisation bio économique montre qu'en 2011, la ressource de Kapenta est surexploitée avec un excès d’'effort de pêche de 40% environ. La pêcherie se trouve presque à la situation d'équilibre de libre accès où la rente de la ressource est entièrement dissipée. En conséquence, l'industrie de la pêche atteint des performances économiques très faibles et se trouve dans une situation de difficultés fortes et de faible durabilité. Les autres principales conclusions concernent les performances de la pêcherie de Kapenta dans son ensemble: le potentiel de richesse pour la croissance économique en termes de rente (estimé à environ 24 $ US millions par an) est complètement perdu pour l'économie des deux pays, la productivité de la ressource Kapenta et donc la production de la pêche sont affectée négativement en raison de la surexploitation, cela semble évidemment avoir un impact négatif sur la sécurité alimentaire des consommateurs qui dépendent fortement du Kapenta pour leur alimentation.
La pêche génère bien sûr des emplois à bord (équipage des bateaux), mais la rémunération de ces emplois a tendance à être très faible comparativement par rapport aux normes nationales de salaires dans les deux pays. En outre, la baisse de la production a eu probablement un impact négatif sur l'emploi dans les activités de transformation du Kapenta qui ont lieu sur la rive du lac. Cela reste toutefois à évaluer plus précisément lors du Groupe de travail bioéconomique, selon notamment les informations à fournir par l'industrie sur les activités de transformation.
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En plus du diagnostic de la pêcherie de Kapenta et de la situation économique de l'industrie en 2011, le rapport illustre le potentiel du modèle en termes de simulations pour la gestion et ses perspectives de développement, notamment à l'appui du groupe de travail sur la modélisation bioéconomique sur la pêcherie de Kapenta.
Les principales recommandations de cette étude concernent la façon d'améliorer le modèle bioéconomique qui a été développé. Ces recommandations sont donc essentiellement portées sur l'amélioration de l'information pour alimenter et pour développer le modèle. Une telle démarche nécessitera de renforcer le partenariat avec l'industrie.
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Contents Acknowledgments ______3 Executive summary ______4 Résumé exécutif ______6 Contents ______8 List of tables ______9 List of figures ______10 List of Boxes ______11 Acronyms and abbreviations ______12 1. Introduction ______13 2. Fisheries overview ______14 2.1 Physical environment ______14 2.2 Ecobiology of Kapenta (Limnothrissa miodon) ______15 2.2.1 Biological data ______15 2.2.2 Environmental factors ______18 2.3 Harvesting System ______19 2.3.1 Vessels ______19 2.3.2 Fishing equipment ______21 2.3.3 Fishing operations ______22 2.4 Fish processing, trade and markets ______23 2.4.1 Fish processing ______23 2.4.2 Fish trading ______23 2.4.3 Markets ______24 2.5 Management systems ______25 2.5.1 Zimbabwe ______25 2.5.2 Zambia ______27 2.5.3 Joint fishery management process ______29 3. Biological modelling ______29 3.1 Previous work ______29 3.2 Available data ______31 3.3 Choice of model and results ______34 3.3.1 General introduction of the surplus production model ______34 3.3.2 Surplus production models: Schaefer and Fox ______38 3.3.3 Results of the biological modelling ______40 3.4 Economic modelling ______44 3.5 Previous work ______44 3.5.1 Economic Survey of the Kapenta Fishery 1991, IFIP Project ______44 3.5.2 An Economic Assessment of the Lake Kariba Fishery in Zambia and Zimbabwe. SADC Fisheries Project Report N°14. 1992______45 3.6 Available data ______46 3.6.3 Prices and demand ______49 3.6.4 Costs ______52 3.6.5 Summary of the cost analysis ______64 3.6.6 Added value and profit ______67 3.6.7 Estimated total cost per rig per night fished ______69 3.6.8 Estimated rent of the Kapenta fishery per rig per night fished ______71 3.6.9 Estimated rate of value added to costs ______71 4. Bioeconomic model of the Kapenta fishery and its results ______72 9
4.1 Previous work ______72 4.1.1 Sub-regional Workshop in Fishery Bioeconomic Modelling Zimbabwe, Kariba, 17-21 February 1992 ______72 4.1.2 Kapenta Bioeconomic Assessment Working Group, Lake Kariba, Zambia, 11-19 July 1997. ______73 4.1.3 Economic Development of the Kapenta Fishery of Lake Kariba (Zimbabwe and Zambia), May 2002 ______73 4.2 Description of the model ______74 4.3 Results, potential use of the model for the Working Group, and perspectives ______78 4.3.1 Results ______78 4.3.2 Potential use of the model for the Working Group ______80 4.3.3 Perspectives ______81 5. Findings and recommendations ______85 5.1 Costs and prices ______86 5.2 Environmental factors ______87 5.3 Illegal trading and illegal fishing ______87 5.4 Value chain and demand ______87 5.5 Quality of data ______88 6. References ______89 Annex 1. Time series Kapenta fishery: analysis of consistency between various sources of data ______92 Annex 2. Times series of data used to run the Surplus Production Model of Fox ______93 Annex 3. Mathematical formulations of the Schaeffer and the Fox models ______95 Annex 4. Questionnaire - Economic survey ______99 Annex 5. Estimation of investment and depreciation costs in the Kapenta fishery ______105 Annex 6. Cost sensitivity according to the size of the fishing enterprise ______107
List of tables Table 1: Estimates of the MSY and corresponding fishing effort (F) from the Schaeffer and Fox models. ______31 Table 2: Results of the biological modelling using the Fox surplus production model, time series N°1. ______42 Table 3: Results of the biological modelling using the Fox surplus production mode, time series N°2 ______43 Table 4: Distribution of Kapenta enterprises (companies, cooperatives, individuals) in Zambia ______47 Table 5: Distribution of Kapenta enterprises (companies, cooperatives, individuals) in Zimbabwe ______48 Table 6: Price ranges given by stakeholders in the Kapenta fishery (2011/2012) ______49 Table 7: Evolution of Kapenta prices per annum in Zambia and Zimbabwe, 2009 -2011 ______52 Table 8: Average estimates of fuel and lubrication costs per basin, Zimbabwe ______54 Table 9: Average estimates of fuel and lubrication costs per stratum, Zambia ______54 Table 10: Average estimates for wages in the Kapenta fishery, Zimbabwe ______57 Table 11: Average estimates for wages in the Kapenta fishery, Zambia ______58 Table 12: Estimates of average costs per rig per unit of fishing effort (night fished) in the different basins in Zimbabwe (US $) ______65 Table 13: Estimates of average cost per rig per unit of fishing effort (night fished) in the different stratum in Zambia (US $) ______65 10
Table 14: Estimated monthly account of a Kapenta fishing enterprise in Zimbabwe (US $) ____ 68 Table 15: Estimated monthly account of a Kapenta fishing enterprise in Zambia (US $) ______69 Table 16: Estimated average cost per night fished (US $) in different fishing areas in Zambia and Zimbabwe and the number of rigs in these fishing areas ______69 Table 17: Estimated investment costs (US $) in the Kapenta fishery, Zambia and Zimbabwe ___ 70 Table 18: Estimated proxy for rent in the Kapenta fishery ______71 Table 19: Results of the bioeconomic modelling at the fishery level ______78 Table 20: Results of the bioeconomic modelling at the rig level ______80
List of figures Figure 1: Satellite image of Lake Kariba ______14 Figure 2: Picture of a Limnothrissa miodon ______15 Figure 3: Rigs in Zimbabwe ______20 Figure 4: Rigs in Zambia ______20 Figure 5: Dip net and light bulbs ______21 Figure 6: Use of bycatches in the Kapenta fishery ______22 Figure 7: Kapenta on drying racks ______23 Figure 8: Kapenta packaged for wholesalers and retailers ______24 Figure 9: Kapenta on sale at the market ______25 Figure 10: Evolution of fishing effort and catches in the Kapenta fishery (Time series N°2) _____ 32 Figure 11: Evolution of fishing Catches and CPUE in the Kapenta fishery (Time series N°2) _____ 33 Figure 12: Relationship between increase rate (absolute and relative) and biomass in the model from Versulst-Pearl ______35 Figure 13: Schematic representation of the surplus production model ______36 Figure 14: Overall growth rate of the biomass of a stock ______36 Figure 15: CPUE trends of the Zimbabwean and Zambian fleets ______41 Figure 16: Evolution of CPUE observed and at equilibrium according to a multiplier of effort __ 42 Figure 17: Evolution of yields observed and at equilibrium according to a multiplier of effort __ 42 Figure 18: Evolution of CPUE observed and at equilibrium according to a multiplier of effort __ 43 Figure 19: Evolution of the yields observed and at equilibrium according to a multiplier of effort ______43 Figure 20: Evolution of the price of dried Kapenta (ZMK/kg) per stratum in Zambia, 2009 - 2011 ______50 Figure 21: Spatial variation of the average price of dried Kapenta, Zambia, 2009 - 2011 ______50 Figure 22: Evolution of the price of dried Kapenta (US $/kg) per basin in Zimbabwe, 2009 - 2011 ______51 Figure 23: Spatial variation of the average price of dried Kapenta, Zimbabwe, 2009 - 2011 ___ 51 Figure 24: Cost of fuel and lubrication (US $/rig/night fished) per fishing area, Zambia and Zimbabwe ______55 Figure 25: Cost of repairs and maintenance (US $/rig/night fished) per fishing area, Zambia and Zimbabwe ______56 Figure 26: Cost of supplies (US $/rig/night fished) per fishing area, Zambia and Zimbabwe ____ 56 Figure 27: Cost of wages (US $/rig/night fished) per fishing area, Zambia and Zimbabwe _____ 58 Figure 28: Crew experience, Zambia and Zimbabwe ______59 Figure 29: Depreciation costs (US$/rig/night fished) per fishing area, Zambia and Zimbabwe ______61 Figure 30: Source of capital for investment in the Kapenta fishery, Zambia and Zimbabwe ___ 62 Figure 31: Licence fees payments in Zambia and Zimbabwe ______63 11
Figure 32: Management costs (US $/rig/night fished) per fishing area, Zambia and Zimbabwe ______63 Figure 33: Average cost per rig per night fished in according to the size of the fishing enterprise, Zambia and Zimbabwe ______66 Figure 34: Bioeconomic model of the Kapenta fishery and presentation of various management situations, including the situation in 2011 ____ 79 Figure 35: Bioeconomic modelling of the Kapenta fishery with a simulation of illegal trading effects ______82 Figure 36: Production function of the Kapenta fishery with environmental effects ______83 Figure 37: Bioeconomic modelling of the Kapenta fishery with environmental effects ______83
List of Boxes
Box 1: Main features and morphology of Lake Kariba, 485 m above sea level ______14
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Acronyms and abbreviations
CASS Centre for Applied Social Sciences, University of Zimbabwe
CPUE Catch Per Unit of Effort DoF Department of Fisheries (Zambia) EU European Union FAO Food and Agriculture Organization of the United Nations F MSY Fishing Effort at Maximum Sustainable Yield I KPA Indigenous Kapenta Producers Association GDP Gross Domestic Product IPS Institute for Policy Studies I UU Illegal, Unreported and Unregulated fishing KFA Kapenta Fishers Association KPA Kapenta Producers Association LKFRI Lake Kariba Fisheries Research Institute MAL Ministry of Agriculture and Livestock MSY Maximum Sustainable Yield NORAD Norwegian Agency for Development Cooperation NGO Non-Government Organization R Rent SADC Southern African Development Community TC Total Cost TO Turnover Ue Catch per Unit of Effort at equilibrium Uo Catch per Unit of Effort Observed VPA Virtual Population Analysis WG Working Group Y Yield Ye Yield at equilibrium ZNFU Zambia National Farmers Union ZPW MA Zimbabwe Park and Wildlife Management Authority ZRA Zambezi River Authority Introduction 13
1. Introduction
Pelagic fisheries rapidly developed on Lake Kariba in the wake of the introduction of Kapenta (Limnothrissa miodon) in the late 1960s. The Kapenta resource has grown significantly throughout the Lake and stocks are known to move, they are not limited by distance or depth. The Kapenta fishery is shared between Zambia and Zimbabwe and nowadays stocks are mainly harvested with rigs and dip nets in both riparian countries. Harvesting systems and the technical efficiency of the different fishing units are also relatively homogeneous (Frame Survey, 2011). This situation differs greatly from the beginning of the fishery where many different types of boats were operating (mono hull, rig, etc.) and there was a substantial difference between the catch per unit of effort (CPUE) of the Zimbabwean and Zambian fleets.
This fishery now contributes to the economy and livelihoods of fishing communities on the shores of Lake Kariba and fish traders in most urban and rural parts of Zambia and Zimbabwe. A joint consultation process, which has been supported by the FAO since 2000, has been put in place. Under this framework, technical consultation meetings have been held in both countries every two years to discuss fisheries management issues, in particular with regard to the Kapenta fishery. Since mid-2000 fishing capacities (number of rigs) and fishing effort (nights fished per rig) have increased dramatically. In addition, there are a large number of other rigs on the Lake that are fishing without licenses.
At the same time, production levels have been experiencing a downward trend since the early 1990's. The causes are not very well known or documented (IUU fishing and non- declared catches, overexploitation, influence of climate conditions on resource productivity, etc.). However, there are strong indications that confirm an economic overexploitation of the fishery and wide dissipation of its wealth potential related to the rent of the resource. Such conditions put the fishing industry in a risky environment and cause those with fishing licenses to be more exploitive with resources.
The Fourth Technical Consultation, held on 18-20 May 2010 in Kariba, Zimbabwe, on the development and management of Lake Kariba fisheries recommended that a bioeconomic study of the Kapenta fishery on Lake Kariba be conducted to assess the optimum economic rent with the model, and discuss the possibility of using the Maximum Economic Yield (MEY) as a management target. During the Fifth Technical Consultation, held on 11- 13 September 2012 in Siavonga, Zambia, it was agreed that a one-week Working Group should be organized to allow the riparian Fisheries Management Authorities and relevant stakeholders to jointly participate in a bioeconomic modelling and analysis of the Kapenta fishery.
The main objective of bioeconomic modelling is to examine distinct management scenarios with regard to fishing effort allocation and to offer advice on the optimum number of rigs that should be licensed in Zambia and Zimbabwe. 14 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
2. Fisheries overview 2.1 Physical environment
Kariba is a very large man-made lake built up on the Zambezi River. It is situated on the border between Zambia and Zimbabwe and was completed in the early sixties. Its main purpose is to generate hydroelectricity for both Zambia and Zimbabwe. The main characteristics of the Lake are given in Box 1 below.
Box 1: Main features and morphology of Lake Kariba, 485 m above sea level
Length: 277 km
Width (mean) 19.4 km Depth (mean) 29.2 m Depth (maximum) 120 m Area 5,364 square kilometers of which 55% lies in Zimbabwe 2 2 (2,952 km ) and some 45 % in Zambia (2,412 km ). Volume 156 x 10 m
The Lake is oligotrophic1 with low productivity and is naturally divided into 5 basins. The Lake is almost equally shared by the two riparian countries, Zambia and Zimbabwe.
Figure 1: Satellite image of Lake Kariba
1 Lacking in plant nutrients and having a large amount of dissolved oxygen throughout. Fisheries overview 15
2.2 Ecobiology of Kapenta (Limnothrissa miodon)
Kapenta (Limnothrissa miodon) (see Figure 2), were introduced to Kariba from Lake Tanganyika in 1967-68 2 , to exploit the pelagic water which comprised an open niche (Balon, 1971, Bell-Cross, 1971). Kapenta is a short-lived pelagic species with an extremely high reproductive capacity. Thus, the species spread rapidly and was able to colonize the whole lake in a very short time and from a very limited parental stock. The Kapenta stock is shared between Zambia and Zimbabwe and its movements are not limited by distance or depth.
Figure 2: Picture of a Limnothrissa miodon
2.2.1 Biological data
Several projects have supported fisheries management activities on Lake Kariba over the years. As part of these projects, research was carried out to estimate key biological parameters for stock assessment and modeling (SADCC/NORAD/DANIDA Project - Working Group Assessment of Kapenta in Lake Kariba - Zambia and Zimbabwe - in 1992 and 1996).
These key biological parameters included: estimates of growth parameters (linear and weight), reproduction rates, fishing mortality and natural mortality.
This section provides an overview of key eco biology data on Kapenta that is considered useful for its management and for the Kapenta fishery bioeconomic modelling exercise. Additional information can be found online:
www.fishbase.org/Summary/SpeciesSummary.php?ID=1550&AT=Kapenta
Growth
Length frequency data collections were supported by various development projects. It was found that mean fish length varies according to the season due to the seasonal difference in nutrient availability. Thus, the mean size of fish in catches is variable. This could explain some of the difficulties of using length frequencies for growth rate estimation.
2 Kapenta was introduced in the Lake by the Zambian Department of Fisheries in two exercises in 1967 and 1968. 16 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Thus, it was recognized (Working Group Assessment of Kapenta, 1996) that growth analysis on basis of modal progression in length frequencies was not appropriate for Lake Kariba Kapenta. It was concluded that growth parameter estimates should be based on otolith readings.
Since the last Working Group Assessment of Kapenta Stock in 1996, these growth parameters have not been updated. Apparently no data has been collected from otolith readings, and there is no regular monitoring of length frequencies in the fishery. Therefore, there is no accurate data on age and the size structure of catches.
The growth of Kapenta (Limnothrissa miodon) can be best described by the von Bertalanffy growth equation:
L t = L ∞ [1 – e –K(t-t0)]
Where L t = Length at time t; L ∞ = asymptotic length, K = growth constant; t0 = the ‘age’ at which L t = 0
Estimates for these parameters have been taken from the last Working Group Assessment carried out in 1996.
L ∞ = 13,5 cm
K = 0,95 year-1
The length-weight relationship is: Weight (g) = 0,012 Length (cm)2,86 (Marschall, 1985)
In Lake Kariba, the Kapenta reaches a maximum size of around 5 cm. This is smaller than the sizes observed for this species in the other lakes of the region (Tanganyika and Kivu).
Spawning
The Kapenta spawns twice a year in the Lake and develops from egg to adult in 5 to 6 months. The production potential for this fish is therefore high and this is why the fishery has been known to support high level yields (up to 60 kg/ha). Such high levels are not expected to represent a threat to the viability of the stock due to the reproductive potential of the stock (Working Group Assessment, 1996).
The occurrence of larvae along most of the shoreline sampled implies that Limnothrissa miodon in Lake Kariba utilizes most of the shoreline as nursery grounds (Campana and Jones, 1992).
Natural mortality
The natural mortality of Kapenta is high and includes predation mortality and other mortality.
The main predator of Kapenta (apart from man) seems to be the Tiger Fish (Hydrocynus). However, the 1996 Working Group Assessment states that there is no additional information available on Hydrocynus predation to Limnothrissa, it is therefore unclear how much they affect natural mortality estimates. It is also known that cannibalism is not uncommon within the Kapenta species. Fisheries overview 17
The other major parameter affecting natural mortality is food constraints. As Kapenta is located at the top of the trophic ladder, its natural mortality rate is mainly related to food constraints rather than predation.
Fishing mortality
Fishing mortality was estimated from the last biomass assessment that was conducted in 1994/1995. However, there were some uncertainties regarding the estimation of total mortality (and thus natural mortality).
Fishing mortalities (F) were calculated by relating yield (Y) to the mean biomass (B):
F= Y/B, calculated F values were 2,06 year-1 for 1993 and 2,18 year-1 for 1994
However, the inshore area of the Lake (with depths ranging between 0 and 10 meters) was not covered in the last biomass survey.
The inshore area represents more than 10 percent of the total surface area of the Lake. If the areas that were not covered contain a large number of adult fish, then the fishing mortalities calculated from Y/B were overestimates.
It is recognized that the risk of recruitment overfishing in the fishery is very low due to the bio-ecological characteristics of Kapenta (species with very high fecundity, rapid growth and a short lifespan, combined with the capacity to colonize the whole Lake from a very limited parental stock). The stock is resilient and catch rates recover rapidly when fishing effort is decreased.
Total mortality
An ecological pathway analysis undertaken during the 1996 Working Group Assessment estimated the total mortality for Kapenta at 5,6 year-1 corresponding to a fishing mortality of 2,1 year-1 and a natural mortality of 3,5 year-1.
Recruitment
The Kapenta stock has a huge recruitment potential and the population was able to grow from a very small parental population by developing a dense and lake wide population within four years of its introduction. Recruitment seems to be continuous, however there are peak periods of recruitment that are related to environmental factors (Lake turnover: June, July, August) and also after the rainy season from December to February).
Ongoing studies on the impact of global warming on the productivity of Kapenta are analyzing the impact of warmer temperatures on phytoplankton blooms and zooplankton, which is what Kapenta feed on. Recruitment in the Kapenta fishery therefore appears to be highly dependent on these natural factors.
18 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
2.2.2 Environmental factors
The Kapenta stock size has shown to be influenced by various environmental factors. The inflow of nutrients after rains and the hydrology of the Lake appear to be the main environmental factors affecting productivity and the abundance of Kapenta.
Marshall (1982) related Kapenta catches to the inflow regimes of selected tributaries in the lower catchment area. He demonstrated a relationship between catches and the flow of the major rivers feeding the Lake. Such a relationship indicates a dependence of the Kapenta population on weather conditions. According to Marshall, climatic conditions are a factor that must be considered when planning management policies.
Karenge and Kolding (1995) published an analysis of the relationship between Kapenta and environmental parameters in which they also relate the fishery to the general hydrology of the Lake. They showed environmental factors, in terms of the varying hydrological regime, seemed to explain the variability in catch rates (CPUE). They concluded that Lake Kariba was an allotropic riverine lake where productivity was largely driven by nutrient pulses carried by annual floods (river inflow and changes in water levels).
During the Working Group Assessment it was demonstrated that there are important links between environmental factors and CPUE. Results of the Working Group showed a significant correlation between the CPUE and inflow. The flow was separated into two components: the upper catchment inflow (Zambezi River only above Victoria Falls) and the lower catchment inflow plus rainfall (below Victoria Falls). The relationship between inflow from the lower catchment and fish production is strong, implying that despite a lower contribution to the total inflow, its influence on the fishery is still strong. This is probably due to the higher nutrient load of the inflow drawn from its catchment area. Droughts affect Kapenta production since the main nutrients for Kapenta are a result of river flows (Bioeconomic Modelling, 2002).
More recent research (Hill, T.; Mashonjowa, E.; Ndebele-Murisa, M.R., 2011) confirms that both climate (maximum temperature in particular) and nutrients, which are influenced by water levels, are the primary determinants of Lake Kariba's Kapenta production.
The influence of environmental factors on the fishery is also recognized by most of the private producers. During the Economic Survey they mentioned that climatic factors had a significant impact on the Kapenta fishery production.
The results from such analyses are important for bioeconomic evaluations and for the management of the fishery. Most of the influences of abiotic factors on the productivity of the Kapenta stock are exogenous and cannot be controlled by external (human) management measures. Thus, the environmental factors that influence Kapenta production should be considered as a constraint to management systems and be taken into account in risk evaluations when planning management policies. Fisheries overview 19
If weather forecasts can be made they will be of great use when planning the allocation of fishing effort in the fishery.
2.3 Harvesting System
In Zimbabwe, the Kapenta fishery started in 1973 with a first licence issued for a purse seiner. The dip net fishery, from pontoons (rigs) at night with lights, began only in 1976. The development of the fishery in Zambia did not lag far behind Zimbabwe and most of the companies operating in Zambia were established at the beginning of the 1980s.
Originally, monohull vessels were used in the fishery in combination with the purse-seine catching techniques. Over time, with new experimental activities and changes of fishing gear (dip nets), vessel design changed to provide greater stability for more mechanized operations3.
A description of the trajectory of the Kapenta fishery is given in the various frame survey reports and reports of biannual consultation meetings. This report specifically highlights information that could be useful for bioeconomic modelling exercises. For more detailed information about the Kapenta fishery (e.g. structure of the industry, distribution of fishing companies, etc.), it is advisable to consult the documents mentioned in section 2.2. 2.3.1 Vessels
Nowadays, rig design is more or less standard due to the fact that the rigs are produced by a limited number of shipyard companies around the Lake. There is, however, a difference in the quality of the rig construction: the cheapest rigs have a short lifespan (approximately 5 years) and thereafter, must be replaced.
The rig size is generally about 8 to 12 meters long and approximately 7 meters wide (see photos below). Most of the rigs are powered with an engine4, but a proportion of the fleet is not mobile and must be towed to the fishing areas5. Engine horsepower varies from one company to another: the least powerful engines are usually the cheapest and have the shortest lifespan. The choice of engine power with the rig quality construction will affect investment cost, depreciation and fuel consumption (more information about this can be found in section 3.6 below).
3 Pontoons represent 97 percent of the boats used in the fishing industry in both Zimbabwe and Zambia. 4 In Zimbabwe, 67 percent of the fishing vessels (243) are fitted with inboard engines (Frame Survey, 2011). 5 In Zimbabwe at least 92 percent of the fishing vessels (246) are motorized; in Zambia 81.5 percent of the rigs are motorized (Frame Survey, 2011). 20 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Figure 3: Rigs in Zimbabwe
Figure 4: Rigs in Zambia
The Frame Survey, carried out in 2011, reported that there were 281 functional rigs in Zimbabwe6 and 632 in Zambia7.
6 Of the total number of rigs identified, 249 rigs were owned and 32 were rented. 7 Of the 632 rigs, 94.9 percent were owned and used by owners, whilst 5.1 percent were rented. Fisheries overview 21
2.3.2 Fishing equipment
Fishing gear on the Lake consists of large circular dip nets8 with lighting systems to attract the fish (see photos below).
The dip net is raised and lowered by manual, mechanical, or hydraulic winches9. The type of winch used will affect the number of hauls during a fishing shift as well the minimum crew size. The Zambian rigs normally operate with a crew of 4 fishermen compared to crews of 2 to 3 fishermen in Zimbabwe (Section 3.6 provides more information on costs and wages). The mesh size of the nets ranges from 4 to 12 mm (regulations allow for 8 mm)10.
Mercury light bulbs are used both above and under the water surface. The number of lights also varies from one rig to another11. On-board generators are used to power the lights.
Figure 5: Dip net and light bulbs
Fishing equipment also includes ‘fish finder’ devices 12 to locate schools of Kapenta, to define the profile of the lake bottom and to select fishing areas.
8 In Zimbabwe, the most common net diameters are 6, 7 and 8 metres. Over 90 percent of the fishing vessels (226) are fitted with such nets; in Zambia, net diameters range from 5 to 12 m. The proportion of rigs using nets up to 7 m in diameter is 76.98 percent (Frame Survey, 2011). 9 In Zimbabwe, over 57 percent of all the fishing rigs (240) are fitted with hydraulic winches, most of these can be found in Basin 5. In Zambia, 91.3 percent of the nets use a manual winch. The greatest number of motorised winches was recorded in stratum 1 (Frame Survey, 2011) 10 In Zimbabwe, the most common net mesh size is 8 mm and over 83 percent of the fishing rigs (227) are fitted with nets of this mesh size. In Zambia, 79.5 percent of the rigs use nets of up to 8 mm with the remaining 20.5 percent of rigs using nets of 8.1 to 12mm (Frame Survey, 2011). 11 In Zimbabwe, the mean number of surface lights is three and underwater lights is one. However, there are some fishing entities that mount as many as 8 surface lights and 3 underwater lights. In Zambia, the average number of surface lights used is 2 to 4; there are exceptional cases where fishers use only 1 or up to 6 surface lights, whilst underwater lights range from 1 to 3 (Frame Survey, 2011). 12 In Zimbabwe, over 52 percent of the fishing vessels identified (244) are fitted with fish finding devices. The majority (68 percent) of the fishing rigs with the fish finding devices are located in Basin 5. In Zambia, 91.5 percent of the rigs use fish finding devices when fishing (Frame Survey, 2011). 22 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
2.3.3 Fishing operations
The Kapenta fishery is the main fishery on Lake Kariba accounting for an average of 90 percent of the total catches from the Lake. As it is a single-species fishery there are very few interactions with gillnet artisanal fisheries that usually operate in the inshore areas of the Lake.
Bycatch species caught by the Kapenta fishery in offshore areas include: Tiger Fish (Hydrocynus vittatus); Squeaker (Synodontis zambezensis); Bottle Fish; Cornish Fish; Mudsuckers; Burble Fish; and Bream (Economic Survey, 2013). Reported quantities of bycatch per rig and per night fished are very low (1 to 2 Kg), except for some companies that report a few dozen kilos of bycatch per night fished. Results of the 2013 Economic Survey show that, in both Zimbabwe and Zambia, any bycatch is kept by the crew and is not sold (see Figure 6 below). Consequently, assumptions will be made in the bioeconomic modelling that bycatches are of no value in the economic activities of the fishing companies, and that the turnover of the fishing companies is equal to the turnover of the Kapenta production.
Figure 6: Use of bycatches in the Kapenta fishery
The vessels operate at night: fishing activities start at sunset. The dip net is lowered to a depth of about 25-30 meters. Underwater, the lights are lowered to a depth of 10 to 15 meters (along the dip net wire) to attract the Kapenta.
In 1992, it was reported (Andrew Palfreman and Jarle Lovland), that the duration of fish attraction to the light was about one hour (depending on the phase of the catch and catch rates). The fishing operation (haul) is repeated 6 to 12 times a night.
Analysis of the CPUE trends in Zimbabwe and Zambia shows significant differences in productivity per vessel between the two countries over the period 1985-1995. However, since 2000, productivity between the Zambian fleet and the Zimbabwean fleet tends to be more homogeneous. Results of the last Economic Survey (2013) show that the CPUE of the Zimbabwean and Zambian fleets are more or less the same. Fisheries overview 23
These results are in line with the fact that Kapenta vessels are nowadays more or less standardized (more information related to this can be found in section 3.3 below).
2.4 Fish processing, trade and markets 2.4.1 Fish processing
Most of the fish are marketed when dry 13, but fish processing is different in the two countries. In Zimbabwe fish processing starts on board the rigs. The catch is salted dry or is put in a brining solution (freshly caught Kapenta is dipped in bring 10 to 15 minutes). On the Zambian side of the Lake, no salting takes place.
At sunrise, when the rigs head back to the landing site, the Kapenta is spread out on drying racks (see Figure 7 below). Usually the sun drying process takes from 1 to 3 days, depending on the fish size and weather conditions (sun intensity and humidity levels).
Figure 7: Kapenta on drying racks
In Zambia the fish is sold before it is dried, meaning that fish processing becomes the responsibility of the fish merchant. In Zambia, salt is hardly used mainly because it is scarce and consumer preference. In general, Kapenta is dried to a third of its original weight in Zimbabwe and to a quarter of its weight in Zambia.
2.4.2 Fish trading Once dry, the fish is packed. Bags of 20 Kg are usually used for wholesalers. At the retail level, Kapenta is usually packaged in 50, 100, 250, 500 and 1000 gram plastic bags (see Figure 8 below).
The Economic Assessment of the Kapenta Fishery, 1992 (Andrew Palfreman and Jarle Lovland) reported that the distribution system in both countries was competitive. However, fish distribution systems are not very well established and further marketing studies need to be undertaken in both countries.
13 In the 1990’s the company Irvin and Johnson was marketing frozen sardine. The company had onshore refrigeration facilities and provided ice for fishing vessels. 24 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Such studies would provide more information on the structure and organization of distribution systems (e.g. are there any monopolistic practices?) and at the same time, enable a better understanding of the value chain (prices and margins along the value chain and in different markets). This will help to better assess the contribution of the fishery to the GDP of both countries.
Figure 8: Kapenta packaged for wholesalers and retailers
2.4.3 Markets
Most of the Kapenta from Lake Kariba is sold on the domestic markets of Zambia and Zimbabwe and there is almost no exportation. This is not the case for Kapenta from the Cahora Bassa in Mozambique, which is largely exported to the sub-region, in particular to Zimbabwe.
As catches are seasonal, it follows that sales are also seasonal. During summer (September to March), Kapenta sardines move inshore to protected waters to breed and the open water population is depleted. Commercial catches rise again after March as the adults return to the open water (Loveness Madamonbe, 2002). Sales of Kapenta appear to be particularly high during holidays and the month of December (Christmas), when a lot of people return to rural areas. The supply, which is also seasonal, reduces the demand for the Kariba fish (Bio-economic Assessment, 1997).
Even though there are not any recent studies on consumption patterns, it appears from all evidence that dried Kapenta is particularly popular in rural areas as refrigeration is not required for conservation (see Figure 9 below). According to a study carried out by Sen in 1995, dried Kapenta was the second most consumed fish in rural areas of Zimbabwe, and the third most consumed fish in urban areas. Dried Kapenta is a recognized by consumers for its taste and affordability. There is, however, a need to update these markets studies to better assess the role of Kapenta in food security and poverty alleviation.
Fisheries overview 25
Figure 9: Kapenta on sale at the market
Some Kapenta is not for human consumption: some crocodile farms use fresh Kapenta as crocodile feed.
2.5 Management systems 2.5.1 Zimbabwe
Fisheries policies, legal frameworks
In Zimbabwe the development of fisheries policies is an on going process. A draft already exists which includes seven main objectives. Among these objectives three are of interest for the Kapenta fishery: knowledge based management approach; economic growth pro poor; and food security. Management of Lake Kariba is aimed at maintaining optimum sustainable yields of fish populations by promulgating and enforcing conservation principles.
The Parks and Wildlife Act (Chapter 20:14 of 1996, as amended) is the principal legislation governing the development, control and management of fisheries in Zimbabwe. Legal foundations of the licensing system in Zimbabwe are set out in the Parks and Wildlife Act (Chapter 20:14) of 1996 and subsequent Statutory Instruments (SI). The Act recognizes the public character of the fisheries resources and the responsibility of the State to provide for their proper management.
Zimbabwe has other more specific legislations and national bylaws for Lake Kariba. These include:
Twenty conditions outlining permits for Kapenta fishing; The Inland Water Shipment Act; and The Water Act and the Zambezi River Authority Act.
26 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Institutional framework
Zimbabwe’s fishery and aquatic resources are managed by the Zimbabwe Parks and Wildlife Management Authority (ZPWMA) based within the Ministry of Environment and Natural Resources Management. The ZPWMA is mandated to control and manage access to the Kapenta fishery on the Zimbabwean waters of Lake Kariba.
The ZPWMA has several research establishments. Lake Kariba is managed on a day-to-day basis by the Lake Kariba Fisheries Research Institute (LKFRI), which is based in Kariba Town. Research is mainly carried out to continually assess the level of stocks and to understand environmental factors that influence the biomass and the distribution of fish in the lake. The LKRFI is also in charge of regulating access to resources through the existing licensing system. Furthermore, LKFRI is involved in the monitoring, control and surveillance of fishing activities in close collaboration with local bodies of the ZPWMA (four stations around the lake). The LKFRI has 20 staff but only two scientists. The LKFRI have had to endure a high staff turnover over the years and have not received adequate funds for the fisheries sector or research from the central government. The University of Zimbabwe Research Station is also involved in limnological (environmental) research.
With regard to private sector involvement, two associations represent Kapenta operators namely:
The Indigenous Kapenta Producers Association (IKPA), which has 17 affiliated members; and
The Kapenta Producers Association (KPA), which has 21 affiliated members.
These associations represent their members to the regulatory Authority or any other body. For example, these associations are currently involved in negotiations with the ZPWMA and the Zimbabwean Ministry of Finance for the revision of fishing licence fees.
Management systems of the Kapenta fishery
In Zimbabwe, the management systems include:
A fishing licensing system to regulate access to the fishery. The Lake Kariba Fisheries Research Institute recommends the number of rigs and fishing permits to be allocated each year (January to December). Kapenta fishing licences are renewable every year. A licence can only be used in a designated basin. Each basin has a Parks Station for law enforcement. Licences are allocated to the fishing companies to operate a specific number of rigs. Licence fees are paid annually by the fishing company upon deliverance of the licence. Licence fees are US $1,500 per year and per rig. A proportion of the LKRFI’s budget is covered by a percentage of the fishing licence fees. Licences/permits are not transferable, however, many rigs are leased/rented (Frame Survey, 2011);
Fisheries overview 27
Technical conservation measures prohibiting fishing in certain areas (shallow water to protect breeding14 or close to holiday resorts, etc.);
Minimum mesh size of the nets (mesh size should not be less than 8 mm when stretched);
Monitoring of catch and effort: data collection is implemented with the collaboration of Kapenta operators. In fact, licence holders are required to submit a monthly catch and effort report to management authorities.
2.5.2 Zambia
Fisheries policies, legal frameworks
In Zambia, the Department of Fisheries has prepared a draft for a National Fisheries Policy, however, it has not yet been approved by the government. One of the main objectives of this policy concerns the issue of food security: to increase fish production in order to reduce the gap between fish supply and demand in a manner that observes environmental protection and conserves biological diversity. This draft policy is consistent with national development priorities in terms of: poverty eradication; decentralization; gender and equity; community participation; and international commitments. The policy is guided by three main principles: (i) Sustainable development; (ii) Precautionary principle (a key element of the FAO Code of Conduct for Responsible Fisheries); and (iii) The user pays principle.
Current fishery legislation in Zambia (Fisheries Act of 1974) needs to be updated to conform to the current needs of the sector (decentralisation of fisheries management and increased involvement of communities in the management of natural resources).
The Fisheries Amendment Act of 2007 introduced regulations specifically related to stakeholder involvement in the management and development programmes of the fisheries sector. Indeed, this Act provides an adapted institutional framework and tools to strengthen fishery management systems through: (i) Fisheries Management Committees, (ii) The development of a fishery management plan approach, (iii) Specific fiscal arrangements within the licensing system to cover management costs.
Institutional framework
In Zambia, the Department of Fisheries (DoF) is the key institution responsible for the management and development of fisheries. More specifically, the Department of the Ministry of Livestock and Fisheries Development is in charge of Capture Fisheries Management & Development and Aquaculture Development. The Department of Fisheries has three stations along the shores of Lake Kariba: Chipepo; Siavonga and Sinazongwe. There is also a provincial office in Choma.
14 On the Zimbabwean side of the Lake, Kapenta fishers are not supposed to fish in areas less than 20 metres deep. 28 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
The station in Sinazongwe is the largest and accommodates three units of the different divisions of the Department of Fisheries: Fisheries Management; Fisheries Research; and Fisheries Training. The other stations at Chipepo and Siavonga are field stations that are mainly involved with fish licensing, the collection of fisheries statistical data and the enforcement of fishing regulations.
It should be noted here that the Department of Fisheries is no longer conducting any meaningful research programmes to support the management of the Kapenta fishery. There is also a need for more effective methods of enforcing fishing regulations (Mudenda H. G., 2010).
In Zambia, there are two Kapenta associations:
The Kapenta Fishermen’s Association (KFA); and
The Kariba Midlands Development Association (KAMIDA).
Management systems of the Kapenta fishery
As in Zimbabwe, the Kapenta fishery in Zambia is characterised several fishing regulations. According to the Fisheries Act, the regulations for the management of the Kapenta fishery include:
The licensing of fishing vessels. Application for permits is mandate to the Director of Fisheries annually. A Fishing Licensing Committee exists and is based in the Headquarters of the Department of Fisheries 15 . This Committee includes 3 representatives from the industry (KFA) and plays a consultative role; licences can be granted against the wishes of the Fishing Licensing Committee without violating the law. Applications must be advertised in the press, and this should be included with the application. The current fee (2013) is ZMK 2 million per rig per year (equivalent to US $411.5 in 2011). The renewal of fishing licences is subject to the submission of Kapenta catch returns forms from the Kapenta operators;
The prohibition of nets with a mesh size less than 8mm;
No fishing at the time of the full moon; and
Requirements for fishing companies to submit relevant catch data.
On the Zambian side of the lake there are no legal restrictions in terms of the areas where fishing for Kapenta should take place. For instance fishermen can fish in shallow areas and breeding grounds without breaking any laws. However, ‘traditional regulations’ are applied by Kapenta fishers and include: (i) no fishing in shallow waters or breeding grounds; (ii) catching and landing fully grown Kapenta for the market; (iii) and no fishing near residential areas (Mudenda H. G., 2010).
15 The Fishing Licensing Committee meets once a year, usually in December or January to approve licences. Fisheries overview 29
2.5.3 Joint fishery management process
A protocol for the economic and technical co-operation between the Government of the Republic of Zimbabwe and the Government of the Republic of Zambia concerning the management and development of fisheries on Lake Kariba and trans-boundary waters of the Zambezi River was signed in November 1999.
Within this framework a Technical Committee was established under the terms of Article 2 of the protocol with the aim to: (i) manage, conserve and regulate the exploitation of the fisheries resources; (ii) control the introduction of exotic species; (iii) undertake research and exchange biological data and statistical information; and (iv) monitor the aquatic environment and support technical cooperation on fisheries matters in general. The Technical Committee is mandated to make decisions on the control of fishing effort.
For Lake Kariba, fishing effort entitlement is based on the proportion of the surface area of the Lake allocated to each of the contracting parties (i.e. 55 percent for Zimbabwe and 45 percent for Zambia).
The protocol calls for the establishment of a fisheries management structure: committees at both technical and policy levels.
Joint patrols between the two countries exist.
3. Biological modelling
Biological modelling usually allows for the identification of specific indicators and key parameters of the stock (biomass, yield, etc.), as well as management targets commonly used in fisheries management (e.g. maximum sustainable yield - MSY - and the corresponding effort).
More specifically, biological modelling enables the production function of the stock to be determined (estimates of various yields according to various levels of fishing effort). This production function is a key input in a bioeconomic modelling exercise.
3.1 Previous work
1992 Working Group Assessment
A Working Group Assessment on Kapenta Stock was carried out in 1992 in Kariba city with the support of the SADCC Fisheries Project. During this assessment, key biological parameters on the Kapenta resources were updated. The Working Group attempted to estimate the Maximum Sustainable Yield (MSY) and the corresponding fishing effort at the MSY (F MSY) using surplus production models. The Group concluded that the correlation found for both the Schaeffer and Fox models were not better than the correlation found from random numbers. 30 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
The time series of data were not long enough (probably not enough information available beyond F MSY) to achieve relevant results concerning the diagnosis of the Kapenta fishery by using these surplus production models. The results from this Working Group indicated that, within the ranges of the biological parameters found, the yield was expected to increase with increased fishing effort. It was also mentioned that recruitment overfishing was very unlikely.
The main conclusion, concerning management, was that fisheries would be limited by decreasing profitability with decreasing catch per unit effort rather than by overfishing. The main limiting factor would therefore be economic rather than biological.
1996 Working Group Assessment
During the 1996 Working Group Assessment, a Thompson and Bell prediction model was used to simulate the expected mean yield and biomass as a function of fishing mortality. The results showed that the effort in 1994 (107,000 boat nights) was, according to the simulation, still below the maximum of the yield curve (approximately 30,000 tonnes). It was highlighted that marginal gains in yield resulting from an increase of fishing effort were relatively small.
2009 Assessment by M. Itai Hilary Tendaupenyu, Senior Ecologist, LKFRI16
A comparative analysis of the Maximum Entropy (ME) model and analytical models for assessing Kapenta stock was undertaken. The ME model estimated a MSY of 25,372 tonnes and a corresponding effort of 109,731 fishing nights at MSY, suggesting over fishing at the effort level F = 150,332 nights fished. Biomass at MSY (B MSY) was estimated at 120,250 tonnes. The model estimated that stocks would decline from 1988 to 2009. Results of this assessment suggested that the fishery was being overfished with an excess of fishing effort of 27 percent.
2010 Sustainability and management of the Lake Kariba Kapenta fishery, by Mudenda H. G., Institute for Policy Studies
A study on the sustainability and management of the Lake Kariba Kapenta fishery was done in March 2010 by Mudenda H. G, from the Institute for Policy Studies (IPS), and was submitted to the Zambia National Farmers Union (ZNFU). This report, produced at the request of the Kapenta Fishermen’s Association (KFA), describes the Lake Kariba Kapenta fishery and analyses development trends. The report also provides information on the legal and institutional frameworks of the Kapenta fishery management systems. The study uses surplus production models (Schaeffer and Fox models) to estimate the number of fishing units (rigs) required to exploit the Maximum Sustainable Yield. The main results of these assessments are given in Table 1 below.
16 Not yet published but the results were presented at the Fifth Technical Consultation Meeting held in Siavonga, Zambia, 2012) Biological modelling 31
Table 1: Estimates of the MSY and corresponding fishing effort (F) from the Schaeffer and Fox models.
Estimated Estimated MSY Excess of fishing Model F MSY (nights F cur (tonnes) effort (%) fished)
Schaeffer 23,525 108,109 177,224 39
Fox 24,271 141,243 177,224 21
Both models indicated that the maximum sustainable yield for the Kapenta fishery should be in the region of 24,000 tonnes of fish a year. A significant difference was obtained with estimates of the corresponding effort at MSY. The Schaeffer model estimated an effort 30.5 percent lower than that estimated with the Fox model. The study concluded that if the past performance of the fishery is taken into account the Schaeffer model provides estimates that are very low compared to actual fishery performance. The Fox model therefore, seems to provide more realistic and consistent results than the Schaeffer model.
The two Kapenta stock assessments carried out in 2009 (Itai Hilary Tendaupenyu and al.) and 2010 (Mudenda H. G) suggested a MSY ranging between 23,500 and 25,400 tonnes with a corresponding fishing effort of approximately 110,000 nights fished per year. The diagnoses confirm a situation of overfishing (F cur > F MSY), with an excess of fishing effort ranging from 21 to 39 percent.
3.2 Available data
The main biological parameters of the Kapenta resource have not been updated since the end of the SADC Project in the late 90's. Moreover there is no recent information available on the structure of catches by size (length frequency). Direct assessments of the resource (biomass estimates) are not undertaken on a regular basis. The last biomass survey was carried out 1997.
Consequently, the main data that can be used for the bioeconomic modelling exercise are fishing statistics namely: catches and fishing effort. The effort is measured in number of nights fished. The Kapenta fishery is subject to regular monitoring of fishing effort and catches through voluntary declarations of authorized fishing companies.
32 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
From the catch and effort data, the catch per unit of effort (CPUE) can easily be calculated. The CPUE can be considered as an index of abundance of Kapenta in the Lake. The fishery started in 1974 in Zimbabwe and in 1982 in Zambia. The time series of statistics of catches and fishing effort therefore start from these dates.
Statistical data on catches and effort from the Lake Kariba Fishery Research Institute (LKFRI) in Zimbabwe and from the Department of Fisheries (DoF) in Zambia were recovered. The time series of these data were compared with other statistical data on catch and effort that were available in various documents: scientific and technical reports were reviewed to identify the most consistent historical series to run the model.
Tables in Annex 1 provide a summary of the data quality analysis. Two series appeared homogeneous and consistent enough to feed into a model of population dynamics, these were:
Time series N°1 (TS 1): Data provided by LKFRI and the DoF during the Economic Survey;
Time series N°2 (TS 2): Data used by Itai Hilary Tendaupenyu, and al. to assess Kapenta Stocks in Lake Kariba, presented at the Fifth Technical Consultation Meeting, 2012.
More information about these time series is given in Annex 2.
The evolution of catches, fishing effort and CPUE for time series N°2 are given in Figures 10 and 11 below.
Figure 10: Evolution of fishing effort and catches in the Kapenta fishery (Time series N°2)
Biological modelling 33
Figure 11: Evolution of fishing Catches and CPUE in the Kapenta fishery (Time series N°2)
It was assumed that the landings declared by the Kapenta fishermen were reflective of fish production in the lake, although there was some evidence that some fishermen declare smaller catches as they often trade illegally, selling off part of the catch before landing (Madamombe, 2002) and the presence of poachers on the Lake was also noted (Tendaupenyu).
The phenomenon of illegal trading is not recent. The 1992 Economic Assessment Report (Andrew Palfreman and Jarle Lovland, 1992) already mentioned that sales of fish by crews to buyers is widespread and this was of particular concern in Zambia. Such illegal trading on the Lake could reach up to 50 percent of the quantities caught by the rigs at night. Thus there is probably a significant under-recording of fish catches. This illegal trading is obviously a source of great inaccuracy and could influence biological assessments and ultimately management decisions.
A Bioeconomic Working Group would allow for further discussions on the quality of data recorded by the industry as well as other assumptions that should be considered with regards to poaching (IUU fishing) and illegal trading on the Lake.
Time Series N°1 (Annex 1) was used to run the biological model for this report.
34 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
3.3 Choice of model and results
Based on information available on the Kapenta resource and the main characteristics of this fishery, a specific type of biological modelling was selected. The information available suggested that surplus production models would be possible. 3.3.1 General introduction of the surplus production model
There are two types of models that provide an indirect assessment of fishery resources: surplus production models and analytical models.
The principle of surplus production models, used in this study, is based on the main assumption that: for the state of a stock characterized by a biomass B, corresponds a stable state of equilibrium with the environment. There are, of course, always births, individual weight gains, and deaths, but the inputs tend to offset the outputs. This equilibrium-based model of biomass was described by Versulst-Pearl (1925).
The surplus production model seeks to analyse the influence of fishing effort on the state of one stock, and hence on catches. This model is therefore used to estimate the fishing effort, ‘F M’, that would maximize a catch.
The evolution, over time, of the stock biomass is characterized by the derivative: dB/dt. According to the assumption of the model, this evolution depends only on the biomass function g (B).
dB/dt = g (B)
Where
B = biomass
Bv = biomass at the virgin state of the stock
G (Bv) = 0
If B < Bv then g (B)>0 the stock tends to increase reaching its biomass at equilibrium
B > Bv then g (B)<0 the stock regresses to the equilibrium biomass
B = 0 then g (B) = 0
The simplest mathematical formulation of biomass function, g (B), satisfying the above- mentioned conditions, is the following:
g (B) = H*(B-Bv)*B, where H is a negative constant
This leads to the basic equation:
dB/dt = H*(B-Bv)*B
or
1/B *dB/dt = H*(H-Bv); with 1/B *dB/dt = instantaneous increase rate dB/dt = H*B2-K*B with K= Bv*H Biological modelling 35
The rate of increase of biomass reaches maximum for Bv = B/2
Thus the dynamics of the stock without fishing activities can be represented by the 2 graphics in Figure 12 below.
Figure 12: Relationship between increase rate (absolute and relative) and biomass in the model from Versulst-Pearl
If there is a fishing activity, the stock will interact with fishing pressure. Its ability to produce is positively, or negatively, affected. If an effort is applied to the stock (see Figure 13 below), the stock will produce the capture. According to an internal process dependent on inherent characteristics of the species, the biomass is more or less affected. Surplus production models take into account the stock as a whole (as a black box) and do not take into account the demographic structure of the stock (age or length).
36 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Figure 13: Schematic representation of the surplus production model
STOCK FISHING EFFORT CATCH
The main assumptions of the surplus production model include:
The unity and isolation of the stock: this implies that immigration and emigration do not affect biomass. This is the case in the Kapenta fishery, where there is only one stock in the entire lake;
The fact that the stock can be described by its biomass;
An increase in biomass is only dependent on the level of the current biomass (Bt);
There is a maximum biomass that the system can support (K = Carrying Capacity);
The relative growth rate of the biomass is maximum when Bt is close to zero, and this rate is zero when Bt approaches K;
Maximum stock surplus production is produced at an intermediate level of biomass between zero and K (see Figure 14 below).
Figure 14: Overall growth rate of the biomass of a stock
In general, the longer the time series of catches are, the better the expected results are from this type of model.
Biological modelling 37
The abundance index (e.g. the CPUE) must reflect a trend and, at the very least, vary significantly during certain periods. Changes in the abundance index must be related to those of catches, otherwise the model will not fit very well with the data. In other words, fishing activity should be sufficiently important as a factor of change in the stock, which should be reflected in the relationship between the abundance index of the stock and the harvest levels attributed to fishing.
Mathematical formulation
The variation of the biomass can be found by the following equation:
By+1 = By + g(By) - Cy
Where
By = Population biomass in year y
g(By) = Production function of biomass in year y
Cy = Total catch in the year y
The corresponding differential equation is:
dBt/dt = g (Bt) - q*ft*Bt
dYt/dt = q*ft*Bt (instant catches)
U = q * B (CPUE)
Where
q = coefficient of catchability and ft = fishing effort in year t
F = fishing mortality = q*ft
At equilibrium, the variation is zero, therefore g (Bt) - qftBt = 0; in other words, fishing activities must catch exactly what the stock is able to produce.
There are several types of surplus production models: Schaeffer, Fox, Pella and Tomlinson. The selection of the most appropriate surplus production model to meet the overall objectives of this study was based on multiple criteria:
Operational ability of the tool: it is important to have a tool that fits with existing data at this stage of data preparation;
Development capacity of the tool, improvement capacity and use by beneficiaries:
o Use of the tool in the region and by the scientific community;
o Relevant to management requirements;
o Accessibility for users: ease of data assimilation and use by operators.
Ability to answer the objectives of the study, and in particular, the ability to achieve short-, medium- and long-term forecasts. 38 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
The Pella and Tomlinson surplus model of production (Pella and Tomlinson, 1967) was used in the work undertaken by Loveness Madamombe in 2002 on the bioeconomic modelling of the Kapenta fishery. For more detail on this model, its mathematical formulation and the results obtained, please refer to this study.
The other surplus production models that are commonly used, the Schaeffer model (Schaefer, 1954) and the Fox model (Fox, 1970), will be presented hereafter.
3.3.2 Surplus production models: Schaefer and Fox
The Schaefer production model (Schaefer, 1954)
This model assumes there is a symmetric production function (relationship between stock size and production), and that production depends on the unexploited population size K (or carrying capacity), and the intrinsic growth rate r.
In the graphic below, K determines the width of the graph, whilst r determines the height. The height of the curve's peak gives the MSY (maximum sustainable yield), which always occurs at a biomass of K/2 for Schaefer models. In order to be able to obtain accurate estimates of r and K, data must be available for a range of stock sizes on both sides of the curve.
In the Schaefer model, the dynamic function of variation of the biomass can be written in the following format: g (Bt) = r * Bt * (Bt-1 / K)
Where
r = intrinsic growth parameter (growth depends on the intrinsic characteristics of the stock and its density);
K = biomass level before harvesting operations (or carrying capacity).
The detailed mathematical formulation of the Schaeffer model is given in Annex 3.
Biological modelling 39
The Fox production model (Fox, 1970)
The Fox model is similar to the Schaefer model in that production is related to r and K. However, the relationship between stock size and production is of a somewhat different form: the graph being much flatter on the right side of the peak, rather than symmetrical. The position and height of the peak in production are determined by r and K, and the data requirements for a reliable estimation of these parameters are similar to those for the Schaefer model.
In the Fox model, the dynamic function of variation of the biomass can be written in the following format:
g (B) = H*(Log B-Log Bv) *B
Where
Bv = biomass at virgin state, and
H = constant (negative)
g (B) = H* Log (B/Bv)*B
g (B) = (H Log B – K)*B
Where
K = H Log Bv
The detailed mathematical formula of the Fox model is given in Annex 3.
The shape of the production function of a Fox surplus production model is given in the following graphic.
Fox production Function
Ye
Ye 25 000
20 000
15 000
10 000 Annual production (in tonnes) (in production Annual
5 000 Effort expressed by a multiplier of effort mf (compared to current effort = 1) - 0,00 0,50 1,00 1,50 2,00 2,50 40 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Data required for a surplus production model
The data required to run a surplus production model mainly consists of:
The total catches of the stock; and
An index of abundance of the harvested species. The CPUE of the most representative fleet can be used as an indicator for the index of abundance; a second option would be to obtain an index of abundance calculated from experimental fishing.
Fishing effort can also be used, taking into account that the CPUE = Total catches/Fishing effort.
The longer the time series of catches and CPUE are, the better the expected results are from this type of model.
Choice of model
To develop the bioeconomic model of the Kapenta fishery, the Fox surplus production model was chosen. The main reasons for this choice are:
The Schaeffer model assumes that catches are zero beyond a certain level of fishing effort. In the case of the Kapenta fishery, this assumption is not very realistic due to the strong, known resilience of the Kapenta resource;
The Fox model uses time series of catches and effort; thus with this model it could be possible to simulate the impact of illegal trading (taking into account non- recorded catches from illegal trading based on the same level of fishing effort);
Variants of the Fox model have been developed (Freon, 1991) to test the effects of the environment. This dimension is of particular interest in the Kapenta fishery, where the productivity of the stocks appears to be affected by various climatic factors (rainfall, temperature, etc.);
Finally (and probably the most important aspect), the model fits well with the data available.
For the Fox surplus production model (1970) that was used, the input data were:
The total catches of the fishery; and
The corresponding fishing effort.
3.3.3 Results of the biological modelling
A Fox surplus production model developed on an Excel spreadsheet was used for the biological part of the bioeconomic model of the Kapenta fishery.
Biological modelling 41
Data inputs for the model included annual statistics on catches and fishing effort. For the Kapenta fishery, these data are expressed in tonnes and in number of nights fished. The effort in the model is then expressed by a multiplier of effort ‘mf’ (compared to the current effort of the year of reference = 1).
In order to take into account differences of fleet efficiency, the fishing effort was standardized (see Annex 2). Furthermore, analysis of CPUE trends in Zambia found that the value of the CPUE in 2007 appears to be abnormally high (see Figure 15 below). This value was corrected by applying the mobile average using the following formula:
CPUE Zambia 2007 (corrected) = (CPUE Zambia 2006 + CPUE Zambia 2008)/2 = 0,165
Figure 15: CPUE trends of the Zimbabwean and Zambian fleets
CPUE Kapenta Fishery CPUE Kapenta Fishery Zimbabwe & Zambia Zimbabwe & Zambia 0,400 0,400
0,350 0,350 0,300 0,300 0,250 0,250 0,200 ZWCPUE (T/night fished) 0,200 ZWCPUE (T/night fished) ZACPUE(T/night fsihed) ZACPUE(T/night fsihed) mT/night Fished mT/night Fished 0,150 0,150
0,100 0,100
0,050 0,050
0,000 0,000 198219841986198819901992199419961998200020022004200620082010 198219841986198819901992199419961998200020022004200620082010
The graph on the left presents the official statistics. The graph on the right shows the same trends with the corrected 2007 CPUE for Zambia.
The model calculates the observed catch per unit of effort (Uo), as well as those calculated in theory at equilibrium (Ue) according to the model’s mathematical formula. The Excel solver function enables an estimation of catches at equilibrium (Ye), seeking to optimize the gap between the observed CPUE and those calculated at equilibrium (Uo-Ue)2.
The main outputs of the model are: the yields curve and CPUE curve at equilibrium based on variations of fishing effort. These outputs are presented as graphs that enable a comparison of the current situation relative to the situation of the fishery at equilibrium. The model provides the following indicators:
Maximum Sustainable Yield: MSY
Fishing effort at MSY: F MSY
Current Yield/MSY: Cur Y/MSY
The model was fitted with two sets of data (catches and effort) for the period 1974 - 2011 (see section 3.2 Data available and Annex 2).
The results of the biological modelling with the Fox surplus production model presented in Tables 2 and 3 and Figures 16 and 17 below. 42 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Table 2: Results of the biological modelling using the Fox surplus production model, time series N°1.
Excess of Estimated MSY Estimated F MSY Cur Y/ Data* F cur fishing effort (tonnes) (nights fished) MSY (% F cur) Time series N°1** 21,032 108,701 196,450 45 0,869 * see Annex 2. ** The time series N°1 data was provided by LKFRI and DoF during the Economic Survey.
Figure 16: Evolution of CPUE observed and at equilibrium according to a multiplier of effort
Time series N°1. Current effort in 2011 = 1
Figure 17: Evolution of yields observed and at equilibrium according to a multiplier of effort
Time series N°1. Current effort in 2011 = 1
In Figures 16 and 17 above the fishing effort is expressed by a multiplier of effort (X-axis). For each level of fishing effort (harvesting rate in number of nights fished), the biological model calculates the corresponding level of sustainable yield (Ye) at equilibrium.
Biological modelling 43
Table 3: Results of the biological modelling using the Fox surplus production mode, time series N°2
Excess of Estimated F Estimated MSY fishing effort Data* MSY (nights F cur Cur Y/ MSY (tonnes) (% F cur) fished) Time series N°2 22,585 116,538 196,450 40 0,809 * see Annex 2 The time series N°2 data was provided by LKFRI and DoF during the Economic Survey.
Figure 18: Evolution of CPUE observed and at equilibrium according to a multiplier of effort
Time series N°2. Current effort in 2011 = 1
Figure 19: Evolution of the yields observed and at equilibrium according to a multiplier of effort
Time series N°2. Current effort in 2011 = 1
The maximum sustainable yield (MSY) in the Kapenta fishery ranges between 21,000 and 23,000 tonnes per year (these figures seem to be consistent with previous assessments: 1992, 1996, 2009 and 2010). 44 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
It should be noted that these estimates do not take into account any of the production that is illegally traded on the Lake at night. This why it can be reasonably assumed that production levels based on the fishing effort are higher than those recorded in official statistics. Thus, the MSY of the fishery is certainly higher than these figures. Therefore, the real MSY estimate depends on the proportion and intensity of illegal trading; it could be around 30,000 tonnes/year? However, this does not change the diagnosis of the status of the fishery given below.
The effort corresponding to the MSY should be in the region of 110,000 nights fished per year. However, the current level of effort used (170,000 nights fished in 2011) is much higher than that required to achieve the MSY (F Cur > F MSY). The current production yield represents 81 percent of the MSY (there are a losses of catches). The excess of fishing effort relative to the effort needed to achieve the MSY is approximately 40 percent. This confirms that the Kapenta fishery was overexploited in 2011.
This diagnosis was undertaken in 2011 however it is possible to update the results with the relevant data for the following years. Moreover, the model can predict the evolution of the yield at equilibrium if the fishing effort continues to increase. It is also possible to develop several scenarios based on different assumptions.
3.4 Economic modelling
Various economic studies were conducted on the Kapenta fishery in the early 90s as part of development projects. These works and their main results are summarized below. These studies are already relatively old and the economic context of the fishery has changed considerably. Thereafter, the results of the Economic Survey conducted in 2013, in support of the bioeconomic modelling exercise, are presented and analysed.
3.5 Previous work 3.5.1 Economic Survey of the Kapenta Fishery 1991, IFIP Project
An economic appraisal of the pelagic fishery of Lake Kariba was undertaken in 1991 with the support of the IFIP Project (Horemans & Hoekstra, 1992). This appraisal was based on data provided by the Lake Kariba Fisheries Research Institute (LKFRI) of Zimbabwe and the Department of Fishery in Zambia, as well as on interviews with fishing operators in both Zimbabwe (Kariba, Chalala) and Zambia (Siavonga). The various variables of the operating accounts (sales, variable costs, and fixed costs) were identified and estimated. The economic performance of the industry was then assessed based on the estimates of: production margins, net profit, value added, as well as on some indicators of the technical and economic efficiency of the fleet. This was done by stratification, taking into account the size of the individual company (number of rigs) and its licensed fishing area. Economic modelling 45
In 1990 it was noted that Zimbabwean boats operate 264 nights a year (an average of 22 nights per month), which was 28 percent more than Zambian boats (206 nights a year, 17 nights per month). Concerning the cost structures, it was estimated that fuel costs, expressed in a percentage of sales value, ranges from 8 to 11 percent in Zimbabwe and from 7.6 to 11.3 percent in Zambia, depending on the size of the fishing enterprise. Wages represent 21 percent of sales value in Zimbabwe and 14.7 percent in Zambia. The financial profitability was also assessed by calculating the return on investment: this appeared to be very low (an average of 3 percent in Zimbabwe and 1.5 percent in Zambia). With regard to the value added, the gross value added, calculated as a percentage of sales, was on average 55.7 percent in Zimbabwe and 62.3 percent in Zambia. This shows the important role of this fishery for the economies of these two countries. The estimated value added of the fishery in 1990 was US $10.3 million. However, the economies of scale that must exist were not clearly identified. This economic appraisal underlined the differences of technical efficiency between the Zimbabwean and the Zambian fleets (in 1992, the CPUE was almost two times higher in Zimbabwe than in Zambia). The report also highlighted the difficulty of appraising the performance of the fishery in a highly fluctuating economic environment (inflation, currency depreciation, etc.).
3.5.2 An Economic Assessment of the Lake Kariba Fishery in Zambia and Zimbabwe. SADC Fisheries Project Report N°14. 1992
This economic assessment report aimed to achieved four main objectives: (i) to provide an overview of the Kapenta fishery in its national and regional context; (ii) to produce a financial and economic analysis of the fishery; (iii) to support and advise fishery management processes; (iv) to review other selective issues such the institutional structure of the industry and other possible project ideas.
The assessment estimates the contribution of the Kapenta industry of Lake Kariba to the economies of Zambia and Zimbabwe in terms of value added. The contribution of the catching sector of the Kapenta industry to GDP is of the same magnitude in both countries: approximately US $3 million per year. However, the estimates only concern the catching sector and contributions from the distribution sector (trade and marketing) were not taken into account. The assessment stresses that the industry makes an important contribution to regional employment (1,700-1,800 directly employed in Zimbabwe and about 2,000 in Zambia), giving a total of at least of 3,800 jobs around the Lake. In terms of the financial analysis, the assessment indicated that - based on a sample of rig operators17 - the industry is financially profitable but found a declining trend from 1989 to 1992.
17 28 operators were visited in Zimbabwe and 14 in Zambia. These operators were visited at least twice. About 300 rigs were given questionnaires but not all of them were returned: data was collected from companies representing 90 Zambian rigs and 100 Zimbabwean rigs. 46 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
The economic analysis underlined the fact that both governments earn rent in the form of taxes and rates. The Zambian economic rent is estimated at between US $1.6 million and US $3.2 million. The Zimbabwean economic rent is estimated at about US $2.6 million. With regard to fisheries management issues, the assessment points out that biological management advice indicated that the principal influence on fish stocks in the early 90s was the environment and not fishing activity. Thus analyses concerning increased fishing effort need only depend on the cost of fishing and any crowding effects on the Lake on the one hand and the benefits from fishing on the other. The assessment also underlined that the economic performances of the Kapenta fishery are strongly affected by the macro- economic environment. It was also stressed that various uncertainties (global economic situation (inflation), illegal trading on the lake, security of tenures, etc.) reduced the willingness of Kapenta operators to invest in onshore facilities.
3.6 Available data
Most of the work undertaken on the Kapenta fishery concludes that this fishery is mainly driven by economic considerations, in particular the profitability of operational fishing vessels. However, despite the importance of the economic dimension of the management of this fishery, economic data is not collected on the Kapenta fishery on a regular basis. Partial data on costs and prices are available in various technical reports (see supra and Reference section) but this data is not up-to-date. Economic data on ex-vessel prices is not routinely collected and there is no regular monitoring of production costs and the profitability of fishing units.
Thus, a decision was made to conduct an Economic Survey in both countries with the support of the FAO SmartFish Project. A questionnaire was designed for this purpose (see Annex 4) and two national consultants were hired to undertake the survey: Mr Kefasi Nyikahadzoi, Senior Lecturer, Economist/CASS for the Zimbabwean side and Mr Charles Mwula, Principal Planner, Economist/MAL for the Zambian side. Mr Newman Songore, an FAO Fisheries Officer, who took part in a field mission in November 2012, also participated in this Economic Survey with Mr Nyikahadzoi on the Zimbabwean side.
Based on the 2011 frame survey, which provided information on rig distribution per basin/stratum, the sampling plan was designed to insure:
Representativeness in terms of geographical distribution and type of fishing business (companies, cooperatives, individuals);
Effectiveness and efficiency (receiving the most reliable information possible at a reasonable cost). The collaboration of fishing enterprises and producer associations was therefore sought (e.g. by targeting operators known to cooperate with research institutes in terms of submitting biological data). Operator participation in the economic frame survey was voluntary. Operators that were reluctant to participate were not forced to complete the survey tools. Economic modelling 47
It was noted in literature on the Kapenta fishery that the performance of fishing enterprises seems to be related to the number of boats owned. It was mentioned that four rigs were necessary to break even (November field mission). Attention was therefore paid during the sampling phase to select an even number of small enterprises (less than 4 rigs) and big enterprises (more than 4 rigs) to be interviewed.
Sampling plan, Zambia
Table 4: Distribution of Kapenta enterprises (companies, cooperatives, individuals) in Zambia
Sampling plan in % of the Stratum Stratum Nuumber of Enterprises Number of Rigs STRATUM IV (Siavonga) = 4 Companies 9%
IV 45 166 SRATUM III (Chipepo) = 4 Companies 25% III 16 26 STRATUM II (Sinazomgwe) 8= Companies 11% II 74 349
I 16 90 STRATUM I = 6 Companies 38%
Grand Total 151 631 TOTAL 22 15%
Source: Frame Survey, 2011
Of the 151 Kapenta fishing enterprises18 recorded (Frame Survey, 2011), 22 companies were sampled (14.5 percent). This represents a total of 126 rigs (20 percent of the total number of rigs recorded during the 2011 Frame Survey), and constitutes an acceptable sample size.
18 The term ‘fishing enterprise’ is used to designate a company, cooperative or an individual involved in the Kapenta fishery. 48 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
All sampled enterprises provided information for 2009, 2010 and 2011, notably information on operating and investment costs information’s requested in the questionnaire.
Sampling plan, Zimbabwe
Table 5: Distribution of Kapenta enterprises (companies, cooperatives, individuals) in Zimbabwe Basin Number of Enterprises Number of Rigs Sampling plan in % of the Basin 1 1 4 BASIN 5 (Kariba) = 8 Companies 25% 2 17 41 BASIN 4 4 Companies 25% 3 16 40 BASIN 3 4 Companies 25%
4 16 52 BASIN 2 4 Companies 24% 5 32 105 BASIN1 1 Companies 100% Grand Total 82 281 TOTAL 21 26% Source: Frame Survey, 2011
In Zimbabwe, a total of 17 fishing enterprises participated in the survey. This represents 20 percent of all fishing operators on the Zimbabwean side of Lake Kariba. This represents a total of 61 rigs, or 22 percent of the total registered number of rigs (281) from the 2011 Frame Survey. The consultants had planned to interview eight operators in Basin 5, however, some of the bigger companies were reluctant to take part in the survey. Only five enterprises were interviewed. The consultants only managed to interview three operators in Basin 3 (instead of 4), as they were not able to physically access the other big companies.
In both countries, the field data collected by the consultant was entered in a pre- formatted Excel sheet for easy analysis (see Excel files). Economic modelling 49
All costs and prices are expressed in US$. The reference year for all data (biological and economic) used to model the pelagic fishery of Lake Kariba is 2011.
As previously indicated, the period 1974 – 2011 is retained for the time series of catches, effort and CPUE used for the data input of the model.
3.6.3 Prices and demand
There is no regular reporting of ex-vessel prices therefore, there are no official statistics on ex-vessel prices for Kapenta in Zimbabwe or Zambia. Known prices relate to dried Kapenta, which is usually sold by harvesters in 20 kg bags. To estimate the price of ex- vessel fresh fish, a ratio of 3 is usually used. The ex-vessel price of fresh fish is obtained by dividing the price of dried Kapenta by 3.
It is reported in literature and confirmed by producers that Kapenta prices can fluctuate within a single year and also between years. The main factors determining the retail price are supply and demand as well as the rate of inflation. Kapenta production in Lake Kariba is also seasonal: August and November are peak production months (Bioeconomic Assessment, 1997). A good rainy season will reduce the retail price as well.
During periods of low production, prices increase, as experienced during the field mission in November 2011. At this time the price of dried Kapenta in Zimbabwe (Kariba) was US $6/kg (i.e. about US $2/kg for fresh fish); in Zambia (Siavonga), the price was ZK 42,500/kg for dried fish, or ZK 14,200/kg (US $2.68) for fresh fish. These prices are roughly halved when Kapenta is in abundance.
Table 6: Price ranges given by stakeholders in the Kapenta fishery (2011/2012)
Lowest price Highest price US $ / kg US $ / kg Dried Kapenta 3.00 6.00 Zimbabwe (Kariba) Fresh Kapenta 1.00 2.00
Dried Kapenta 2.40 9.50 Zambia (Siavonga) Fresh Kapenta 0.80 3.15
Exchange rate used US$ 1 = ZK 5,280
The estimated average ex-vessel price for 2011 was approximately US $1.33/kg in Zimbabwe (Kariba) and US $1,57/kg in Zambia (Siavonga).
Private operators indicated that Kapenta bought illegally on the Lake at night is usually sold at a lower price. This no doubt affects pricing on the market and its organisation.
The Economic Survey helped give a better idea of price evolution over these last three years, especially at the level of the main areas of production in Zambia (stratum) and Zimbabwe (basins). 50 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Prices in Zambia
Figure 20 shows the evolution of the retail price of dried Kapenta (ZMK/kg) in different stratum in Zambia over the period 2009 - 2011.
Figure 20: Evolution of the price of dried Kapenta (ZMK/kg) per stratum in Zambia, 2009 - 2011
Evolution of Current Prices of Dried Kapenta in ZMK/KG per Stratum in Zambia - Period 2009 - 2011 40 000,00
35 000,00
30 000,00 AVERAGE STRATUM IV 25 000,00 AVERAGE STRATUM III 20 000,00 AVERAGE STRATUM II 15 000,00 AVERAGE STRATUM I 10 000,00 AVERAGE (ZMK) 5 000,00 Prices of Dired Kapenta ZMK/KG
- 2009 2010 2011
The retail price of dried Kapenta and its evolution over these three years seems to be relatively homogeneous amongst the different stratum. Prices have increased substantially over the period under review (+58 percent on average).
The average price increased from US $4.3/kg in 2009 to US $7/kg in 2011: this could be due to high inflation rates (+9.9 percent in 2009 and +7,9 percent in 2010).
Figure 21: Spatial variation of the average price of dried Kapenta, Zambia, 2009 - 2011
Figure 21 shows the spatial variations in Average Price of Kapenta (ZMK/KG) the average price of dried Kapenta over per Stratum. 2009 - 2011 the period 2009 - 2011. Average prices AVERAGE STRATUM30 539,92 IV over the period 2009 - 2011 are 31 000,00 30 000,00 homogeneous within the different strata 29 000,00 (slightly lower for Stratum II). 28 000,00 27 000,00 26 000,00 Stratum IV recorded the highest average AVERAGE AVERAGE 25 000,00 STRATUM I STRATUM III price increase of Kapenta from 2009 to 30 085,89 30 125,00 2011, approximately 69 percent. The reason behind the high price of Kapenta in 28 645,83 Stratum IV could be due to its proximity to AVERAGE STRATUM II Lusaka, which is the main market for Kapenta followed by the Copperbelt Province.
The average price is US $5.7/kg. Economic modelling 51
Prices in Zimbabwe
Figure 22 shows the evolution of the retail price of dried Kapenta (US $/kg) in different basins over the period 2009 - 2011
Figure 22: Evolution of the price of dried Kapenta (US $/kg) per basin in Zimbabwe, 2009 - 2011
Evolution of Current Prices of Dried Kapenta US$/KG per Basin in Zimbabwe - periode 2009-2011
7,00
6,00
5,00 AVERAGE BASIN 5 AVERAGE BASIN 4 4,00 AVERAGE BASIN 3 3,00 AVERAGE BASIN 2 AVERAGE BASIN 1 2,00 AVERAGE (US$) Prices Dried Kapenta US$/KG 1,00
- 2009 2010 2011
Prices have increased over the period 2009 - 2010 in Basin 2 (+34 percent), Basin 4 (11 percent) and Basin 5 (20 percent). The price is highest in Basin 3 (approx. US $6/kg) and lowest in Basin 1 (US $4/kg). In the latter two basins prices have remained relatively stable over this three-year period.
Figure 23: Spatial variation of the average price of dried Kapenta, Zimbabwe, 2009 - 2011
Figure 23 shows the spatial variations of the average price of dried Kapenta over Average price of Kapenta (US$/KG) per Basin. 2009 - 2011 the period 2009 - 2011. The average price AVERAGE BASIN 5 of Kapenta by Basin ranges from US $4/kg 6,00 4,71 5,00 to US $6/kg. Basin 3 has the highest 4,00 3,00 AVERAGE AVERAGE selling price and Basin 1 the lowest. The 2,00 BASIN 1 3,97 4,83 BASIN 4 1,00 high price of Kapenta in Basin 3 could be - due to competition for this commodity amongst many small-scale Kapenta AVERAGE4,29 AVERAGE traders. The number of buyers in Basin 1 is BASIN 2 5,67BASIN 3 limited due to poor roads and access issues. The few buyers that frequent the area are able to successfully negotiate the price of Kapenta downwards.
52 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Price differences between Zimbabwe and Zambia
Table 7 below shows the evolution of Kapenta prices per annum in Zambia and Zimbabwe.
Table 7: Evolution of Kapenta prices per annum in Zambia and Zimbabwe, 2009 - 2011
2009 2010 2011 Average US $/kg US $/kg US $/kg US $/kg Zimbabwe Dried Kapenta 4.44 4.77 4.91 4.70 Fresh Kapenta 1.48 1.59 1.64 1.57
Zambia Dried Kapenta 4.29 5.62 7.03 5.65
Fresh Kapenta 1.07 1.45 1.75 1.88
Difference (%) 0.27 0.12 -0.07
Source: Economic Survey, 2013
Whilst ex-vessel prices were relatively similar in the two countries in 2009 (around US $1.4/kg for fresh fish), the price gap widened in 2010 and 2011. In 2011 there was a 43 percent price difference between the two countries.
In the absence of detailed time series on ex-vessel prices and based on information about the main factors that determine demand (product pricing, the price of product substitutes, consumer income and purchasing power, seasons, marketing, etc.), it was not possible to develop a detailed modelling of the demand in the bioeconomic model.
However, it was possible to undertake a sensitivity analysis by taking into account different types of price: minimum price, maximum price, average price, current price, import price of Kapenta from Cahora Bassa (if data available). This will enable a review of the impact of price sensibility on the economic performance of the fishery and fishing enterprises.
The weighted average price of fresh Kapenta was used as the price data input for the model:
Catches in ZIMB2011 x average price in ZIMB2011 + Catches in ZAMB2011 x average price in ZAMB2011 Catches in ZIMB2011 + Catches in ZAMB 2011
3.6.4 Costs
The nature and structure of production and investment costs in the Kapenta fishery have been detailed in various documents such as the Economic Assessment of the Fishery (Horemans, B. and Hoekstra, 1992 and Andrew Palfreman and Jarle Lovland, 1992) and bioeconomic modelling works (1992, 1997, 2003). The issue of cost was also discussed and validated during the field mission in November 2012 and within the scope of the Economic Survey that was conducted during the first quarter of 2013. Economic modelling 53
The main costs in the fishery were identified as follows:
Variable costs:
Fuel and lubrication;
Repairs and maintenance;
Supplies;
Wages;
Taxes (imports, property, etc.).
Fixed costs:
Depreciation;
Interest;
Insurance;
Administration salaries, administration supplies (management costs);
Licence fees.
Investment costs:
Boat/rigs including hull, engine (drive unit), generator for lighting, fishing gear (dip net);
Drying racks.
Variable costs
Variable costs vary according to the fishing intensity/effort. The Economic Survey estimated the average number of nights fished per rig: Zambia, 22 nights and Zimbabwe, 24 nights.
Fuel and lubrication
Fuel and lubrication costs were estimated based on consumption levels (fishing operations and lighting) and the market price for fuel and oil. An average was taken from estimates given from those enterprises who completed the questionnaire to take into consideration the fact that some boats are not mobile and even when they are equipped with a drive unit, most operators do not bring them back to the harbour every day.
Tables 8 and 9 below show the fuel and lubrication estimates for Zambia and Zimbabwe according to the different stratums and basins.
54 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Table 8: Average estimates of fuel and lubrication costs per basin, Zimbabwe
Fuel Number of Fuel Oil Cost fishing + Price of Price of consumption nights consumption consumption lighting fuel/litre oil/litre - fishing fished/rig/ - lighting (lt./rig/ (US$/rig/night (US $) (US $) operations month (lt./rig/night) month) fished) (lt./rig/night)
Basin 5 26 1.50 3.96 31 11 8.40 65.45
Basin 4 24 1.75 4.33 15 10 12.67 46.03
Basin 3 21 2.05 3.33 16.67 15.67 20 68.28
Basin2 24 1.55 4.75 18.25 12.50 24.25 52.56
Basin 1 24 1.5 4.45 20 15.00 7 53.80
Overall 24 1.67 4.17 20.18 12.83 16.46 57.22 average
Source: Economic Survey, 2013
Table 9: Average estimates of fuel and lubrication costs per stratum, Zambia
Fuel Number Fuel Oil Cost fishing + Price of Price of consumption of nights consumption consumption lighting (US fuel/litre oil/litre - fishing fished/rig - lighting (lt./rig/ $/rig/night (US $) (US $) operations /month (lt./rig/night) month) fished) (lt./rig/night)
Stratum IV 23 1.56 4.26 21.25 8.13 15.63 49
Stratum 23 1.57 4.91 18.13 10.63 4.88 46.37 III
Stratum 21 1.51 3.80 12.50 11 6.50 37.13 II
Stratum I 23 1.47 5.17 10.17 12.50 8.00 35 Overall 22 1.53 4.54 15.51 10.56 8.75 41.89 average
Source: Economic Survey, 2013
There is a difference of 27 percent between the fuel and lubrication costs in Zimbabwe and Zambia. This difference seems to be related to the level of consumption per fishing operation (especially in terms of lubricant, costs are double in Zimbabwe). Indeed, the price of fuel and oil per litre are almost the same in both countries. The level of fuel consumption for lighting (approx. 11 - 12 litres per night fished) is a little bit lower than it was in the early ‘90s (15 litres per night, Andrew Palfreman and Jarle Lovland, 1992). Economic modelling 55
The graphics in Figure 24 below show the spatial variation for fuel and lubrication consumption in the two countries.
Figure 24: Cost of fuel and lubrication (US $/rig/night fished) per fishing area, Zambia and Zimbabwe
Cost of fuel and lubrification (US$/Rig/Night Fished) per Basin in ZIMBABWE AVERAGE BASIN 5 70,00 65,45 60,00 50,00 40,00 30,00 AVERAGE BASIN 1 AVERAGE BASIN 4 20,00 46,03 53,80 10,00 -
52,56 AVERAGE BASIN 2 AVERAGE BASIN 3 68,28
Source: Economic Survey, 2013
Stratum III and IV in Zambia recorded the highest costs (approx. US $50/night fished/rig). These costs are much lower for Stratum I and II (approx. US $36/night fished/rig). In Zimbabwe, the cost of fuel and lubrication per rig per night fished range between US $46 in Basin 4 and US $68 in Basin 3. These costs are almost the same for Basins 1 and 2 (US $53/night fished/rig). The high cost of fuel and lubrication in Basin 3 is attributable to the high transport cost of ferrying commodities into the area. Unlike other basins that have easy access fuel and lubricants from formal markets, operators in Basin 3 have to rely on unscrupulous traders who bring fuel in on small pickup trucks.
The fishing strategy, in particular the remoteness of fishing zones on the Lake within the different geographical areas, appears to be one of the main factors of spatial variation in terms of the cost of fuel and lubricant consumption.
Repairs and maintenance
The economic survey calculated the estimates for repairs and maintenance costs on a monthly basis, and thereafter calculated them per unit of fishing effort (night fished/rig).
The graphics in Figure 25 below show the spatial variation for repairs and maintenance costs in the two countries.
56 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Figure 25: Cost of repairs and maintenance (US $/rig/night fished) per fishing area, Zambia and Zimbabwe
Cost of Repair and Maintennace Cost of Repair and Maintenance (US$/Rig/Night Fished) per Stratum (US$/Rig/Night Fished) per Basin In in ZAMBIA AVERAGE ZIMBABWE STRATUM IV 17,06 AVERAGE BASIN 20,00 5 16,00 15,00 14,00 12,00 8,91 10,00 10,00 8,00 5,00 AVERAGE BASIN 6,00 AVERAGE BASIN 1 4,00 4 AVERAGE AVERAGE 8,52 7,33 - 11,05 3,96 2,00 STRATUM I STRATUM III -
5,07 9,80 AVERAGE BASIN AVERAGE BASIN 2 14,02 3 AVERAGE Source: Economic Survey,STRATUM 2013 II
Repairs and maintenance costs are relatively homogeneous except for one Stratum (IV) in Zambia and one Basin (2) in Zimbabwe, where costs are twice as high as other fishing areas.
The average repairs and maintenance costs were estimated at US $11.31/night fished/rig in Zambia and US $8.09/night fished/rig in Zimbabwe.
Supplies
Regular supplies include fishing nets, salt 19 (in Zimbabwe), twine for repairing nets, packaging, bulbs, fire extinguishers and balusters.
The graphics in Figure 26 below show the spatial variation of supply costs in the two countries.
Figure 26: Cost of supplies (US $/rig/night fished) per fishing area, Zambia and Zimbabwe
Cost of Regular Supplies (US$/Rig/Night Cost of Regular Supplies
Fished) per Stratum In ZAMBIA (US$/Rig/Night Fished) per Basin in
AVERAGE STRATUM ZIMBABWE IV 10,00 AVERAGE BASIN 5 8,00 8,00 5,47 6,00 4,07 6,00 4,00 4,00 AVERAGE BASIN AVERAGE BASIN 2,00 6,25 AVERAGE STRATUM 1 2,00 3,52 4 AVERAGE STRATUM I 4,96 - III - 9,39
2,07
AVERAGE BASIN5,85 AVERAGE BASIN 8,42 2 3 AVERAGE STRATUM II
Source: Economic Survey, 2013
19 50kg of salt is usually used for one tonne of fresh fish. Economic modelling 57
In both countries, there is an important cost difference according to stratum/basin. Two major groups can be identified: high costs (Stratum II and III in Zambia and Basins 1, 2 and 5 in Zimbabwe) and low costs (Stratum I and IV in Zambia and Basins 3 and 4 in Zimbabwe). In Zimbabwe, it is not clear why the cost of regular supplies is low in Basin 3. An assumption was made that costs in this area might have gone up if the survey team had managed to interview the bigger operators based on the other side of Chibuyu Bay.
The average supply costs per night fished and per rig were estimated at US $6.71 and US $5 in Zambia and Zimbabwe respectively.
Wages
Wages related to Kapenta fishing were also estimated and include: the wages of the captain; wages of the crew; and the salaries of daily labourers (fish processing). Payrolls were calculated per night fished and per boat.
Tables 10 and 11 below provide wage estimates for Zambia and Zimbabwe according to the different Stratum/Basins.
Table 10: Average estimates for wages in the Kapenta fishery, Zimbabwe
Average Average Average Number Total Number Number monthly monthly monthly of daily wages of nights of salary - 1 salary salary - labourers (US$/rig fished/rig fishermen crew daily Captain at landing /night /month /rig member labourer (US$) site fished) (US$) (US$)
Basin 5 26 2 302.40 270.80 8 157.40 35.15
Basin 4 24 2 166.67 160.00 17 176.67 33.25
Basin 3 25 2 234.53 215.40 12 167.03 34.20
Basin2 25 2 234.53 215.40 12 167.03 34.20
Basin 1 24 4 200.00 157.00 8 130.00 42.40 Average 25 3 227.63 203.72 12 159.63 35.84
Source: Economic Survey, 2013 The number of fishermen per boat is homogeneous across all fishing areas (Stratum/Basin). However, crew numbers are twice as high in Zambia (1 captain and 3 crew) compared to Zimbabwe (1 captain and 1 crew).
Employment at the processing sites (daily labourers) is much higher in Zimbabwe than in Zambia. This could be due to the size of the enterprise (number of rigs20), the productivity of rigs, and differences in fish processing.
20 It was noted in the economic survey that enterprises with 2 rigs had an average of 2.25 daily labourers in Zambia, versus 3.8 in Zimbabwe. These figures become 2.5 in Zambia, and 8.33 in Zimbabwe for enterprises with 3 rigs. 58 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Table 11: Average estimates for wages in the Kapenta fishery, Zambia
Average Number Average Average Number monthly of daily Number of monthly monthly Total wages of nights salary - 1 labourers fishermen/ salary - salary daily (US $/rig/ fished/rig crew at rig Captain labourer night fished) /month member landing (US $) (US $) (US $) site
Stratum IV 23 4 84.13 69.79 3.75 87.07 15.50
Stratum III 23 4 280 106.24 5.25 84.80 31.08
Stratum II 21 4 171.82 160.79 6.13 76.32 33.94
Stratum I 22 4 245.59 182.74 3.83 53.41 29.12
Average 22 4 195.46 129.91 4.74 75.41 27.41
Source: Economic Survey, 2013
In Zimbabwe, remuneration for the crew is incentive-based (crew receive a fixed amount per tonne of fish plus a bonus above a certain quantity), whilst in Zambia, companies provide a minimum fixed salary, which is also augmented by incentives. It should be noted that salaries for crew in both countries are below the minimum legal wage. This situation was different in the early 90’s where fishermen’s wages were above the minimum wage. If the minimum legal wage is considered an indicator of the opportunity cost of labour, this gives an indication of the economic situation of the fishery and in terms of fishery rent generation.
The graphics in Figure 27 below show the variation of labour costs according the different fishing areas.
Figure 27: Cost of wages (US $/rig/night fished) per fishing area, Zambia and Zimbabwe
Cost of Wages (US$/Rig/Night Fished) Cost of Wages (US$/Rig/Night per Stratum in ZAMBIA Fished) per Basins in ZIMBABWE AVERAGE AVERAGE STRATUM IV 40,00 BASIN 5 50,00 35,15 30,00 40,00 15,50 20,00 30,00 10,00 AVERAGE 20,00 AVERAGE 42,40 AVERAGE AVERAGE BASIN 1 33,25 BASIN 4 - 10,00 STRATUM I STRATUM III - 29,12 31,08
33,94 33,73 AVERAGE35,71 AVERAGE AVERAGE BASIN 2 BASIN 3 STRATUM II
Source: Economic Survey, 2013 Economic modelling 59
Remuneration for labour in the Kapenta industry per night fished and per rig is relatively homogenous for each Basin in Zimbabwe (between US $33 and US $42). In Zambia, there is a huge gap between Stratum IV, where the labour cost is twice as low as that of the other stratums. The low remuneration rates in Stratum IV appear to be standard across those fishing companies that were interviewed.
On average, labour costs in the Kapenta industry are approx. US $36/night fished/rig in Zimbabwe, compared to US $27 in Zambia. The difference between the two countries is mainly due to the low cost of labour cost in Stratum IV in Zambia. In 2011, wages represented 25 percent of total costs in both Zimbabwe and Zambia. Wages amounted to 30 percent and 16 percent of production value in Zimbabwe and Zambia respectively. In the 1992 Economic Assessment (Andrew Palfreman and Jarle Lovland, 1992) the proportion of wages against the production value ranged between 13 and 25 percent.
The rate of crew turnover in the fishery industry is an indirect measure of several key variables. Firstly, it reflects wealth accumulation as a crew member will only stay in the fishery if the wages are comparable to, or better than other jobs. Secondly, crew longevity often means crew members reside in the community, and thus their earnings stay in the community and are spent locally, rather than being sent away in the case of itinerant or immigrant crews. Thirdly, experienced crew develop specialized knowledge and refined skills that make harvesting more efficient, so the fishery is better able to reach its wealth- generating potential. Finally, many crews will stay in the fishery if they believe its future is worthwhile and that they will have the means to progress to the position of captain.
The graphics in Figure 28 below provide information on crew experience in both countries.
Figure 28: Crew experience, Zambia and Zimbabwe
Crew experience In ZIMBABWE
4%
17% 17% More than 20 years 5-20 years 31% 3-5 years 31% 1-3 years 0 full years of experience
Source: Economic Survey, 2013
In Zambia and Zimbabwe the proportion of crew with less than 5 years’ experience is more than 60 percent. In Zambia, the percentage of crew members with less than 3 years’ experience in the Kapenta fishery is very high: 41 percent versus 31 percent in Zimbabwe.
60 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
The 1992 Economic Assessment Report (Andrew Palfreman and Jarle Lovland, 1992) already underlined the high turnover of fishermen due to the heterogeneous labour market and the hard physical work involved in Kapenta fishing.
Taxes
Very little information was collected on taxes in the Economic Survey (no data available in Zimbabwe and only three responses were given out of the 22 fishing enterprises interviewed in Zambia). In Zambia, the average amount of various taxes per rig and per night fished is estimated at about ZMK 14,800 (US $3).
This item will have to be discussed in more detail during the Technical Bioeconomic Working Group.
Fixed costs
Fixed costs in the Kapenta fishery are mainly related to:
Investment strategy (depreciation and financial interest);
Access (licence fees);
Management costs.
Whilst the access costs are similar for all users, costs related to investment and management vary according to the fishing enterprise (age, size of company, etc.), and its strategy (management, in particular with regards to its depreciation policy). As a consequence, fixed costs are much more difficult to estimate; the figures provided in this section should therefore be taken with caution.
Investment strategy
Depreciation
Data collected from the Economic Survey enabled estimates of the investment cost of a fully equipped rig (hull, engine/drive unit, generator, fishing gear). The lifespan of each component of the rig were also assessed during the survey, allowing for estimates of the depreciation cost/year21. The depreciation cost was then calculated per night fished and per rig.
The investment and depreciation costs of the fish processing equipment (mainly drying racks) were also estimated. Tables in Annex 5 provide detailed information on these figures in the different fishing areas (Stratum/Basin) in Zambia and Zimbabwe for the different fishing enterprises interviewed.
Overall, it was found that there is a fairly large variability in investment costs and depreciation periods based on the lifespan of equipment.
21 This was mostly estimated by observing the practices of fishing operators and based on the lifespan of certain equipment (not based on accounting regulations). Economic modelling 61
The total investment cost for a fully equipped rig (hull + engine + generator + fishing gear) ranges between US $ 8,000 and US $45,500 in Zimbabwe, (an average of US $17,000). In Zambia, these figures are a little lower and range between US $36 000 and US $5 500 (an average of US $14,700).
Two relatively distinct investment strategies seem to coexist in the fishery. Some operators prefer to make a costly investment (hull and engine between US $20,000 and US $25,000) that has a relatively long period of depreciation (10 years); whilst others prefer to minimize their investment costs (between US $10,000 to US $15,000 for a hull with engine), which have a shorter depreciation period (5 years). Both strategies have a similar impact in terms of depreciation costs: US $2,000 to US $3,000 per rig and per year.
The graphics in Figures 29 below show the spatial variation of depreciation costs in the two countries.
Figure 29: Depreciation costs (US$/rig/night fished) per fishing area, Zambia and Zimbabwe
Depreciation Cost (US$/Rig/Night Depreciation Cost (US$/Rig/Night Fished) per Stratum in ZAMBIA Fished) per Basin in ZIMBABWE
AVERAGE STRATUM IV AVERAGE BASIN 5 25,00 20,00 15,67 20,00 16,11 15,00 15,00 10,00 10,00 AVERAGE BASIN 1 AVERAGE BASIN 4 5,00 5,00 10,35 8,28 AVERAGE AVERAGE 18,40 - 10,67 - STRATUM I STRATUM III
10,15 AVERAGE BASIN 2 15,33 AVERAGE BASIN 3
AVERAGE23,40 STRATUM II Source: Economic Survey, 2013
The depreciation costs calculated per rig and per night fished in the two countries and in the different fishing areas (stratum/basin) are relatively homogeneous in Zimbabwe and Zambia at around US $10 to US $16 per rig per night fished, except in Basin 4 and Stratum II where these estimates are less than twice the cost (US $8) and twice as more (US $23).
Financial costs
Very few answers were provided in terms of financial interest costs in the survey. This cost item is always very difficult to assess in such an exercise. However, there were some qualitative trends that should be highlighted.
Exchanges with the industry confirm that very few adapted banking facilities exist for investment in the Kapenta business (long-term loans with a grace period during rig construction). 62 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
In Zimbabwe, interest rates range from 15 to 21 percent, for short-term loans (six months). In Zambia different interest rates exist: the cheapest is 7 percent for a national loan, but they can reach up to around 22 percent.
The Frame Survey carried out in 2011 and the Economic Survey in 2013 confirmed that a large part of the fleet in the fishery over 5 years old. It is therefore probable that this part of the fleet is no longer concerned with short-term loan refunding.
The graphics in Figures 30 below provide information about the source of capital in the Kapenta industry.
Figure 30: Source of capital for investment in the Kapenta fishery, Zambia and Zimbabwe
Source of Capital for Investment in ZAMBIA Source of Capital for Investment in ZIMBABWE
0% 0% 0% 4% 0% 0% Own capital Own capital 0% 5% 17% Unsecured business loans from banks 6% Unsecured business loans from or venture capital banks or venture capital Secured business loans from banks 6% Secured business loans from banks 5% 56% Loans from banks secured by personal 5% Loans from banks secured by (not business) assets personal (not business) assets 91% 5% Government subsidized private Government subsidized private lending lending 0% Government-run loan programs Government-run loan programs 0%
Source: Economic Survey, 2013
In both countries, the large majority of investors confirmed that they had invested in the fishery with their own capital. For this reason, no financial costs were included in the fishing cost estimate as part of the modelling exercise.
Access fees
Access fees are paid according to the fishing regulations. In Zimbabwe access fees are US $1,500/rig per year. Access fees are lower in Zambia at ZMK 2 million per rig per year (the equivalent of US $411.5 in 2011).
Calculated in relation to the number of night fished, access fees work out to be an average of US $6.76 per rig per night fished in Zimbabwe and US $1.54 per rig per night fished in Zambia.
Fishing enterprises were asked whether they paid their licence fees in full at the beginning of the fishing season (see Figure 31 below).
The ability of fishing enterprises to pay access fees fully at the start of the season is an indicator of the situation of the rent of the fishery (largely dissipated). Deferred payment of the licence and payment in arrears are also good indicators of the economic and financial situation of the fishing enterprises.
Economic modelling 63
Figure 31: Licence fees payments in Zambia and Zimbabwe
License fees paid in once before the License fees paid in once before the fishing season - ZAMBIA fishing season - ZIMBABWE
18% 37%
YES YES 63% NO NO 82%
Source: Economic Survey, 2013
Fishing entities were not able to provide any reliable data on insurance costs. Discussions with Kapenta operators during the field mission in November 2012 confirmed that no insurance policy was covering risks on boats.
Management costs
The economic survey tried to estimate monthly management costs per rig per night fished, taking into account special variation (see Figure 32 below).
Figure 32: Management costs (US $/rig/night fished) per fishing area, Zambia and Zimbabwe
Management Cost (US$/Rig/Night Fished) par Stratum in ZAMBIA Management Cost (US$/Rig/Night AVERAGE STRATUM IV Fished) per Basin in ZIMBABWE 1,40 AVERAGE BASIN 5 1,20 30,00 1,00 0,74 29,24 0,80 25,00 0,60 20,00 0,40 1,25 15,00 0,20 AVERAGE AVERAGE 0,84 - AVERAGE BASIN 1 10,00 AVERAGE BASIN 4 STRATUM I STRATUM III 5,00 9,77 24,875 -
10,58 1,22 12,89 AVERAGE STRATUM II AVERAGE BASIN 2 AVERAGE BASIN 3
Source: Economic Survey, 2013
Although difficult to estimate, it appears that there is a wide disparity for management
costs for different fishing areas in Zimbabwe. Management costs are the lowest in Basins
2, 3 and 4 (about US $10/rig/night fished) compared to Basins 1 and 5 (between US $25 and US $29/rig/night fished). In Zambia these costs appear to be much lower ranging from US $0.20 to US $1.40. 64 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
3.6.5 Summary of the cost analysis
The input for the economic part of the model will be the cost per unit of effort (per rig/night fished). For practical reasons, the average cost per unit of effort will be used. However, as illustrated in the above-mentioned cost estimates, there is cost variability according to spatial distribution. Thus, the estimated average cost will be weighted taking into account the spatial distribution of rigs on the basis of data of the last frame survey (2011).
Another issue is the potential variability of costs according to the size of the various fishing enterprises. On the one hand, there are operating costs that are shared according to the number of rigs owned by a fishing enterprise, e.g. transportation costs on the Lake for crew, fish and fuel. On the other hand, a company that owns several rigs might have greater/different management costs than a smaller company. According to the industry there is a threshold/scale effect starting from the ownership of 4 rigs. The synthesis below on estimated costs tries to address these two issues.
Cost sensitivity based on fishing area
In Zimbabwe the variable costs (operating costs) range between US $91 and US $115 per rig per night fished, giving an average estimate of US $106. These figures are consistent with estimates made by LKFRI in 2008 (Itai H. Tendaupenyu in 200822). In 2008, the annual operating costs for a Zimbabwean rig were estimated to be US $23,344, based on an average of 22 nights fished per month, giving an average operating cost of US $88 per rig per night fished in 2008.
More recently, Zimbabwean Kapenta operators tabulated their expenditure, within the framework of negotiations for fishing permit fees for 2013. Expenditure estimates total approximately US $3,612 per rig per month. If 4 rigs make an average of 24 trips per rig/month, the cost per rig per night fished would be approximately US $150. This is also consistent with estimates made from the last Economic Survey data.
Fuel and lubrication costs, as well as wages, represent 54 and 34 percent of the operating costs respectively (40 percent and 25 percent of total costs respectively).
Table 12 and 13 below illustrates the variation of cost estimates in Zimbabwe and in Zambia according to the different fishing locations.
22 Itai H. Tendaupenyu, Statistical and Economic Analysis of Kapenta Catches on Lake Kariba, 2008. Economic modelling 65
Table 12: Estimates of average costs per rig per unit of fishing effort (night fished) in the different basins in Zimbabwe (US $)
Basin 1 Basin 2 Basin 3 Basin 4 Basin5 Average Variable costs Fuel & lubrication 53.80 52.56 68.28 46.03 65.45 57.22 Repairs and 3.96 14.02 5.07 8.52 8.91 8.09 maintenance Supplies 6.25 5.85 2.07 3.52 5.47 4.63 Wages 42.40 35.71 33.73 33.25 35.15 36.05 Taxes 0 0 0 0 0 - Sub Total 106.41 108.14 109.15 91.31 114.97 106.00 Fixed costs
Depreciation 10.35 10.15 15.33 8.28 15.67 11.96 Financial Interest 0 0 0 0 0 - Licence fees & 6.94 6.12 8.45 7.05 5.26 6.76 insurance Management costs 24.875 12.89 10.58 9.77 29.24 17.47 Sub Total 42.17 29.16 34.35 25.09 50.16 36.19 TOTAL 148.58 137.30 143.50 116.40 165.14 142.18 Source: Economic Survey, 2013
As in Zimbabwe, fuel & lubricants and wages are two major costs in the Zambian Kapenta industry. They represent 47 and 31 percent of the total variable costs respectively (and 38 and 25 percent of total costs: almost the same proportion as Zimbabwe).
Table 13: Estimates of average cost per rig per unit of fishing effort (night fished) in the different stratum in Zambia (US $)
Stratum 1 Stratum 2 Stratum 3 Stratum 4 Average
Variable costs Fuel & lubrication 35.08 37.13 46.38 49.00 41.90 Repairs and 7.33 9.80 11.05 17.06 11.31 maintenance Supplies 4.96 8.42 9.39 4.07 6.71 Wages 29.12 33.94 31.08 15.50 27.41 Taxes 0.97 3.75 - 4.43 3.05 Sub Total 77.46 93.05 97.90 90.07 89.62 Fixed costs
Depreciation 18.40 23.40 10.67 16.11 17.15 Financial Interest - - - -
Licence fees & 1.52 1.61 1.53 1.51 1.54 insurance Management costs 0.84 1.22 1.25 0.74 1.01 Sub Total 20.75 26.23 13.44 18.36 19.70 TOTAL 98.21 119.28 111.35 108.44 109.32 Source: Economic Survey, 2013
66 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
A study on the sustainability and management of the Lake Kariba Kapenta fishery, undertaken in 2010 (Mudenda H. G., 2010), estimated fuel and lubrication costs at US $ 13,450 for fuel and wages at US $8,650 (fishermen & land-based crew) per rig for a period of 18 months. Taking an average number of 22 nights fished per rig per month in Zambia, estimates per rig per night fished came to US $34 for fuel US $22 for wages. These figures are consistent with estimates from the 2013 economic survey.
However, this study takes into account management costs in the budget of a Kapenta enterprise. Such costs mainly concern managers’ salaries (rig and processing). These wages were estimated at US $15,000 for 18 months, representing approximately an estimated cost of US $38/rig/night fished. This figure is much higher than that calculated in the 2013 Economic Survey (US $1.54/rig/night fished). Management costs estimated for the Zambian Kapenta Industry Study in 2008, seem more consistent with estimates made in Zimbabwe. This point needs to be further discussed during the Bioeconomic Working Group.
Sensitivity of costs according to the size of fishing enterprises
Tables in Annex 6 provide information on each cost item according to the size of the fishing enterprise sampled (number of rigs) in Zambia and Zimbabwe. A summary of these results is given in the graphics in Figure 33 below. Fishing enterprises were grouped into categories according the number of rigs owned. Average costs (variable, fixed and total) were calculated for each category in order to compare the situation between each category. Based on the assumption of economies of scale, a reduction of costs with increased size of the enterprise was expected.
Figure 33: Average cost per rig per night fished in according to the size of the fishing enterprise, Zambia and Zimbabwe
Average cost per night fished and per rig in Average cost per night fished and per rig in ZIMBABWE according to the size of the ZAMBIA according to the size of the fishing fishing enterprise enterprise
Average 2 rigs Average 1 rigs 180,00 150,00 160,00 Average 19 rigs Average 2 rigs 140,00 120,00 100,00 100,00 80,00 Average 14 rigs Average 3 rigs 60,00 50,00 Average 11 rigs Average 3 rigs 40,00 Fixed Costs Operating Costs 20,00 - Operating costs - Fixed Costs Average 12 rigs Average 4 rigs Total Cost Total Costs
Average 11 rigs Average 5 rigs Average 7 rigs Average 4 rigs Average 9 rigs Average 8 rigs
Source: Economic Survey, 2013
Even though the sampling plan did not cover the full size range of fishing enterprises, the results found that there is no apparent correlation between differences in production costs and the size of a company or the number of rigs a company owns. Thus, no economy of scale could be clearly demonstrated. Economic modelling 67
This situation seems to be similar to the results found during the 1992 Economic Assessment (Andrew Palfreman and Jarle Lovland, 1992), where it was not proved that scale (number of rigs) was an important determinant of productivity or profitability in Zimbabwe. However, in 1992 in Zambia, the larger companies did appear to be more profitable.
Only a few large companies were sampled within certain size categories, in particular in Zimbabwe. Therefore, the low cost of production in Zimbabwe, in the category ‘11 rigs’ cannot be considered as a trend as only one company was sampled. Economies of scale assessments require accurate and detailed economic information and a broad sample of companies (in particular larger fishing companies). The cost of production per rig seems rather to depend on the age of the rig, cost of labour (crew) and distance from the harbour to the fishing grounds (fuel consumption).
3.6.6 Added value and profit
Based on the cost analysis and estimated sales of Kapenta in 2009, 2010 and 2011, it is possible to establish an average operating account for one rig. Due to a lack of detailed information on annual cost variations, the estimated costs were considered constant for this period.
Due to the constant overall decrease in production, which was not compensated by an increase in price in 2011, Zimbabwean fishing enterprises made losses in 2011. The value- added rate23, estimated at around 60 percent in 2009/2010, decreased in 2011 to 40 percent. This value-added rate is fairly close to estimates in the economic appraisal of the pelagic fishery of Lake Kariba from 1992 (B. Horemans & M. Hoekstra, 1992): value- added as a percentage of sales was estimated at an average of 55.7 percent in the Zimbabwean fishery.
The fuel and wages costs represented 32 and 20 percent of the sales values in 2009/2010 respectively (47 percent and 30 percent in 2011). These figures were 10 and 21.4 percent respectively in 1992 (B. Horemans & M. Hoekstra, 1992).
Table 14 below provides an estimated average monthly account of a Kapenta fishing enterprise in Zimbabwe.
23 Sales - intermediate costs/Sales 68 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Table 14: Estimated monthly account of a Kapenta fishing enterprise in Zimbabwe (US $)
Description 2009 2010 2011 Sales of wet Kapenta (dry) 4,169.23 4,237.97 2,890.72 Production (dry) 940.1 889.4 588.4 Average price (dry) 4.44 4.77 4.91 Variable costs
Fuel & lubrication 1,373.37 1,373.37 1,369.55 Repairs and maintenance 194.27 194.27 193.73 Supplies 111.14 111.14 110.83 Wages 865.15 865.15 862.74 Taxes
Sub Total 2,543.93 2,543.93 2,536.86 Fixed costs
Depreciation 286.96 286.96 286.16 Financial interest
Licence fees & insurance 162.30 162.30 161.85 Management cost 419.25 419.25 418.09 Sub Total 868.51 868.51 866.10 TOTAL 3,412.44 3,412.44 3,402.96 Profit/Loss 756.79 825.53 - 512.24 Value added 2,490.45 2,559.19 1,216.60 Rate of value added (%) 0.60 0.60 0.42 Source: Economic Survey, 2013
In Zambia the situation seems to be slightly different. Despite a continuous decrease in catches/rig, prices increased dramatically over these three years: offsetting the reduction in production. Fishing enterprises were therefore still making a profit but at a level that clearly indicates that the rent of the resource is also widely dissipated (see Table 15 below).
The calculation of value added as a percentage of sales gives a rate of 61 percent in Zambia. This rate went up to 65 percent in 2011 due an increase of the sales value. Here again, this estimate is consistent with previous economic works (B. Horemans & M. Hoekstra, 1992) that estimated the rate of value added in the Kapenta fishery at an average of 62.3 percent in Zambia.
Fuel and wages costs represented 24 and 16 percent of the sales values respectively in 2011: these figures were 10 and 14.7 percent respectively in 1992 (B. Horemans & M. Hoekstra, 1992).
Economic modelling 69
Table 15: Estimated monthly account of a Kapenta fishing enterprise in Zambia (US $)
Description 2009 2010 2011 Sales of wet Kapenta (dry) 3,394.44 3,418.20 3,817.96 Average price (dry) 790.4 608.7 543.1 Production (dry) 4.29 5.62 7.03 Variable costs Fuel & lubrication 935.28 935.28 935.28 Repairs and maintenance 252.50 252.50 252.50 Supplies 149.81 149.81 149.81 Wages 611.92 611.92 611.92 Taxes
Sub Total 1,949.50 1,949.50 1,949.50 Fixed costs
Depreciation 382.73 382.73 382.73 Financial interest Licence fees & insurance 34.43 34.43 34.43 Management cost - - - Sub Total 417.16 417.16 417.16 TOTAL 2,366.66 2,366.66 2,366.66 Profit/Loss 1,027.77 1,051.54 1,451.30 Value added 2,056.85 2,080.61 2,480.38 Rate of value added (%) 0.61 0.61 0.65 Source: Economic Survey, 2013 3.6.7 Estimated total cost per rig per night fished
In order to take into account the cost difference per unit of effort between the Zimbabwean and Zambian fleets, an average weighted cost was calculated based on the following formula:
∑ (No. of rigs Basin i x CPUE Basin i) + ∑ (No. of rigs Stratum i x CPUE Stratum i) (No. of rigs Zimbabwe + No. of rigs Zambia)
Number of rigs in each fishing area (basins/stratum) = Frame Survey, 2011
Cost per unit of effort in each fishing area (basins/stratum) = Economic Survey, 2013
Table 16: Estimated average cost per night fished (US $) in different fishing areas in Zambia and Zimbabwe and the number of rigs in these fishing areas
Zimbabwe Basin 1 Basin 2 Basin 3 Basin 4 Basin 5 Average cost/night fished (US $) 148.58 137.30 143.50 116.40 165.14 Number of rigs 4 41 40 52 105 Zambia Stratum 1 Stratum 2 Stratum 3 Stratum 4 Average Cost / night fished (US$) 98.21 119.28 111.35 108.44 Number of Rigs 90 349 26 166 Source: Economic Survey, 2013 70 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
These estimates give an average weighted cost of US $122.24 rig/night fished.
To be sustainable a business must generate what economists call a ‘normal profit’. A parallel can be drawn with employee remuneration: if an enterprise does not pay its workers, its activity will not be sustainable. In the same way, as employee wages are a cost for the enterprise, normal profit should also be considered as a cost. Thus, the total cost is estimated by adding the normal profit to activity costs (variable and fixed). In order to assess the total costs per rig, an estimate of the ‘normal profit’ in the Kapenta fishery was done.
This estimate was based on answers collected from the industry during the field mission in November 2012, the Economic Survey in 2013 (Question: expected rate of profitability of the invested capital in %), and by using the following formula :
Profitability of invested capital = Normal profit (net profit) Amount of investment
The average estimates for the profitability of the invested capital (opportunity cost of capital) were 31 percent in Zimbabwe and 44 percent in Zambia.
The estimated average investment cost (fully equipped rig and drying racks) was US $18,556 in Zimbabwe and US $15,515 in Zambia (see Table 17 below).
Table 17: Estimated investment costs (US $) in the Kapenta fishery, Zambia and Zimbabwe
Investment cost (US $)
Hull + engine + Hull + engine + Hull + engine + Hull + engine generator + generator+ gear generator gear + racks
Zimbabwe 13,492 14,657 16,289 18,556 Zambia 12,611 14,291 14,970 15,513 Source: Frame Survey, 2011 and Economic Survey, 2013
Thus, the annual normal profit was estimated to be:
US $5,752 in Zimbabwe (around US $20 per night fished)
US $6,826 in Zambia (around US $26 per night fished)
A weighted normal profit was also calculated, based on the repartition of the rigs between the two riparian countries (Frame Survey, 2011). The average weighted normal profit was estimated to be US $24 per night fished.
Therefore, the average total cost used as an input for the economic model was: US $142,27/night fished/per rig (US $122,24 + US $24).
Economic modelling 71
3.6.8 Estimated rent of the Kapenta fishery per rig per night fished
A rapid appraisal during the field mission in November 2012 24 seemed to indicate, according to some qualitative indicators, that the rent of the Kapenta fishery was widely dissipated.
The economic survey showed that some owners rent their rigs. Thus, a proxy estimate of the rent of the fishery could be this amount of rent. Indeed, the renter of the rig should, at least, stand to gain a normal profit; any extra profit (rent) could be considered as the amount for the rent of the rig (see Table 18 below).
Table 18: Estimated proxy for rent in the Kapenta fishery
Average rent/month No. nights Average rent per night of a rig (US $) fished/month fished (US $)
Zimbabwe 968.75 24.00 40.36 Zambia 740.66 22.00 33.67 Average 854.71 23.00 37.02 Source: Frame Survey, 2011 and Economic Survey, 2013
The bioeconomic model will enable an estimation of the value of the average rent per night fished in 2011 and allow for discussion on the consistency of this proxy.
3.6.9 Estimated rate of value added to costs
The total value added is calculated by finding the difference between the revenue of the fishery (turnover) and the intermediate costs (fuel & oil, repairs & maintenance and supplies).
Value added = Turnover – Intermediate costs
= Turnover – (fuel & oil + repairs & maintenance + supplies)
The total value added in the fishery is constituted by the sum of the value added created by the fishing activity (the value added in the cost) and the resource rent.
Value added in the cost = (turnover – rent – intermediate costs)
With
Rent of the fishery = Residual rent remaining at the private sector level (the extra profit) + rent extracted by governments (licence fees). Rate of value added in the cost = Value added in the cost. Turnover
The rate of value added in the cost was estimated to be about 50 percent.
24 Lionel Kinadjian. Bioeconomic Analysis of the Kapenta Fisheries, Lake Kariba, 2012. Zimbabwe & Zambia Mission Report N°1. GCP/RAF/466/EC SmartFish Project. 72 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
4. Bioeconomic model of the Kapenta fishery and its results 4.1 Previous work
This first section summarizes work that has already been done on the bioeconomic analysis of the Kapenta fishery on Lake Kariba, as well as the main results and gaps identified. 4.1.1 Sub-regional Workshop in Fishery Bioeconomic Modelling Zimbabwe, Kariba, 17-21 February 1992
A first bioeconomic modelling of the Kapenta Fishery was done in 1992 with the support of the regional project for Inland Fisheries Planning, Development and Management in Eastern/Central/Southern Africa (IFIP). This bioeconomic modelling was intended as a follow-up of the economic appraisal done by the project in 1991 and as an introduction to a bioeconomic analysis. The specific objectives of the workshop were (i) to provide a training exercise in bioeconomic modelling and (ii) to estimate the range, within the fleet size of the pelagic fishery, needed to ensure adequate economic returns. The main parameters estimated were the following: (i) the fleet size (number of rigs); (ii) total catch; (iii) the value of catch; (iv) employment; (v) value added; (vi) total profit; (vii) catch/rig; (viii) sales/rig; (viii) value added/rig; and (ix) profit/rig.
The model used during the workshop was on a specific computer software (BEAM1) developed by the FAO. This model is based on the Thompson & Bell yield per recruit biological model and a simple input-output micro-economic model. The growth functions of Kapenta (growth/length relationship and length/weight relationship) and related parameters are used in the model to monitor the evolution of a cohort from a given level of recruitment. The biological model considered that the age at first capture was 3 months (relationship between gear selectivity - mesh size - and Kapenta size).
Three main scenarios were tested:
The whole lake is exploited by a single fleet and natural mortality is constant; variation of recruitment due to the fact that recruitment in the Kapenta fishery is highly dependent on natural factors;
Reduction of natural mortality (M) as fishing (F) mortality increased;
Modelling of the two fleets separately, with one scenario consisting of an increase of 70 percent of the Zambian fleet (from 179 boats to 304 boats).
In 1990, the total number of vessels operating on the lake was around 400. Based on the assumptions made, the results of the various simulations found that the fishery appeared to be exploited close to optimum levels. Indeed, the value added was at the maximum for the number of rigs (ranging between 500 and 600) based on recruitment levels: the profit is maximum for 100 to 300 rigs. Modelling the two fleets separately gives a negative profit (loss) when the Zambian fleet is increased. Bioeconomic model of the Kapenta fishery and its results 73
4.1.2 Kapenta Bioeconomic Assessment Working Group, Lake Kariba, Zambia, 11-19 July 1997.
A Bioeconomic Assessment Working Group of the Kapenta fishery took place in 1997 under the framework of the Zambia/Zimbabwe SADC fishery projects. The objective of this working group was to update economic and biological data on Kapenta and to use this data to carry out a bioeconomic assessment to estimate optimum fishing effort and optimum economic rent. A Beaverton and Holt model was used for the biological part of the model (yield per recruit model). Biological parameters, obtained in the March 1996 Kapenta Assessment Working Group, were fed into the model. Simulations were done by varying natural mortality (M), recruitment levels, prices per kilogram and fishing costs (costs per rig). For the economic part, the yield was converted into revenue by multiplying its value by the appropriate price. The fishing effort was also converted in fishing costs. Due to differences of efficiency between the Zambian and Zimbabwean fleets, the total number of rigs was standardized to estimate the Zambian or Zimbabwean rig equivalent. Several sensitivity analyses were done with variations on the parameters of recruitment, natural mortality, annual running costs per vessel, and market price of Kapenta. The estimated MSY was 26,000 tonnes. There is a range within which MSY varies given variation in the input parameters used in the model. The rent at MSY was estimated to be US $6 million, but it was noted that the accuracy of this figure is uncertain due to the lack of detailed accounting information from the fishing companies. From the simulations done with the model, the optimum number of rigs was less than the number found in 1994 (i.e. 234 in Zambia and 345 in Zimbabwe); it also came to light that the optimum number of rigs is very sensitive to the relationship between fishing effort and fishing mortality. The working group also reported that the fishery production was found to be influenced by environmental factors, mainly river flow.
4.1.3 Economic Development of the Kapenta Fishery of Lake Kariba (Zimbabwe and Zambia), May 2002
This bioeconomic study on the Kapenta fishery was undertaken within the framework of a Master of Sciences in International Fisheries Management, by Loveness Maadamombe. The Pella and Tomlinson surplus production model (Pella and Tomlinson, 1967) was used, with inputs of historical catch and effort data in addition to individual growth parameters. Data from 1994 was used as the current data. Three reference points were used: maximum sustainable yield (MSY); maximum economic yield (MEY); and open access (OA) equilibriums. Prices and costs were varied to observe the sensitivity of the fishery to these two variables based on the reference points. MSY yield and effort, which were the same for both countries, were found to be 23,336 tonnes and 725 rigs (standardized for Zambia rig equivalent) or 517 (Zimbabwean rig equivalent) at the age of the first capture (four months). MEY yield and effort were found to be 21,854 tonnes and 475 rigs for Zimbabwe and 22,181 tonnes and 500 rigs for Zambia. Resource rent at MSY was US $27,300 and US $5,349 and at MEY was US $31,600 and US $5,760. 74 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Current (1994) effort levels were shown to be close to MSY effort levels. Other similar simulations were done with different ages of first capture (3 and 5 months). The report pointed out that the Kapenta presents a lot of potential in terms of resource rent for the fishery. The results of the model confirmed that the viability of the Kapenta industry is more dependent on economic parameters than biological parameters. However, the report also highlighted that there is need for both countries to implement a more comprehensive and workable data collection system, which provides accurate and reliable data. This was particularly true for the cost estimates that need to be improved.
4.2 Description of the model
Coupling the function of production (Fox surplus production model) and the economic function (revenue and cost) allows for the construction of bioeconomic model of the fishery.
The model was developed on an Excel spreadsheet and includes:
7 sheets (inputs): Price, cost, rent (cur), effort, employment, TS1 & TS2 (the 2 time series of effort, catches, CPUE that were selected to run the model);
1 sheet (output) including:
o Biological outputs of the bioeconomic model: Ye , Ue, MSY, fmsy, Cur Y/MSY
o Economic outputs of the bioeconomic model. The Economic outputs are developed
. At the macro level of the fishery: sales, value added in cost, total cost, rent, total value added, number of rigs, crew employment, employment (processing), total employment;
. At the micro level of the fishing enterprise (rig): catch/rig/year, CPUE (night fished), sales/rig/year, value added/rig/year, rent/rig/year.
o Various output graphics:
. Variation of Ue and Uo according to the variation of fishing effort;
. Variation of Ye, turnover (TO), total cost, value added, and rent according to the variation of fishing effort.
Input price:
This worksheet summarizes the results of the Economic Survey on the selling price of Kapenta in Zambia and Zimbabwe. Prices are calculated for fresh and dried fish. The conversion rate, dried fish/fresh fish, is 1 to 3 for Zimbabwe and 1 to 4 for Zambia. An average weighted price (fresh fish in US $/kg) is calculated for the whole Lake, taking into account the production of each country. This price is the input figure for the bioeconomic model used to estimate the revenue of the fishery (see cell ‘L84’ in the simulation worksheets). Bioeconomic model of the Kapenta fishery and its results 75
In the absence of detailed time series on ex-vessel prices and detailed information on the main factors that determine Kapenta demand (product prices, the prices of substitutes, consumer income and purchasing power, seasons, marketing, etc.), it was not possible to develop a detailed modelling of demand in the bioeconomic model.
The conception and implementation of a system of regular data collection on ex-vessel prices per main fishing area is an important action to undertake in the short term. Furthermore, more in-depth investigations and research on the value chain and marketing of Kapenta would be of interest to better understand the determining factors of the market (see Findings and recommendations section).
It was possible to carry out a price sensitivity analysis with the model by inputting various prices: minimum price, maximum price, average price, current price, or projected price according to recent proven evolution trends (e.g. inflation rate). This could enable the exploration of the impact of price sensitivity on the economic performance of the fishery and the fishing enterprises.
Input cost:
This worksheet summarizes the results of the Economic Survey on the cost of production per unit of fishing effort. These costs are estimated for the different fishing areas on the Lake (basin/stratum). Based on the results of the Frame Survey carried out the two countries in 2011, an average weighted cost of production was calculated for the whole Lake, taking into account the number of rigs recorded in the different fishing areas. The normal profit per night fished, estimated according to the rate of profitability of capital invested expected by producers, is then added to this average weighted cost of production. This gives an estimate of the total cost of production per unit of fishing effort (per night fished). This cost is the input figure for the bioeconomic model used to estimate total costs in the fishery (see cell ‘L87’ in the simulation worksheets).
Input effort:
This worksheet estimates the average number of nights fished per month for one rig on Lake Kariba. The Economic Survey provides information on the average number of nights fished per month in the different fishing areas in the two countries. The number of nights fished per boat and per month is more or less the same for both countries. There is only a difference of 2 nights fished per rig and per month between Zimbabwe and Zambia. The number of nights fished per rig per month per country is weighted by the number of rigs in each country obtained from the 2011 Frame Survey. The average number of nights fished per month and per rig on Lake Kariba is then used in the simulation worksheet to estimate the number of rigs that correspond to the various levels of fishing effort. Several indicators based on economic performance per rig are also calculated based on this information (e.g. sales/rig, value added/rig, rent/rig). 76 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Input employment:
The Economic Survey found that on-board crew numbers in Zambia were double those of Zimbabwe. The average weighted on-board employment is calculated by taking into account the difference in the number of rigs between the two countries: giving an average number of crew per rig. This information is used in the simulation worksheet to estimate on-board employment numbers in the Kapenta fishery.
TS1 and TS2 data f, Y, CPUE:
These are the worksheets for the times series on effort, catch and CPUE, that can be used to run the model. Standardization of fishing effort was done to take into account differences of technical efficiency of the fleets in each country. A column has been added in these worksheets to allow for a simulation on the impact of the illegal trading of catches at night on the Lake: a coefficient of illegal trading is used to estimate catch totals (those declared by the fishing companies and those that are illegally traded on the Lake at night). The simulation worksheets can therefore use the same time series of fishing effort, either industry-recorded catches, or the estimated total catches, including illegal trading estimates.
Input rent:
This worksheet is used to estimate the residual rent of the resource at the industry level. These estimates are based on information collected during the Economic Survey. They are mainly used to calibrate the model and to assess its consistency with regard to estimates of normal profit.
Bioeconomic model/simulation worksheet (outputs)
The bioeconomic model can be described as follows:
Column A refers to the years of the time series chosen. In conformity with the assumption of the surplus model of production, the longest time series available was used (1974- 2011).
Columns B and C contain the inputs for the biological model. They concern the time series of total catches of Kapenta and the corresponding total fishing effort (in nights fished). This information comes from the TS1 or TS2 worksheets (Total catches - columns LK catch in tonnes) and standardized effort - column LKs effort corrected). It is, of course, possible to update these times series in worksheets TS1 or TS2, by adding recent years, or to create additional worksheets in order to add a new time series to feed the model.
Column D represents the scale of fishing effort. Fishing effort is measured by a multiplier of fishing effort. The model standardizes the effort at 1 for the year of reference (i.e. the current year = 2011 in this case).
Columns G and H calculate the CPUE: observed (Uo) and estimated by the model at equilibrium (Ue).
Bioeconomic model of the Kapenta fishery and its results 77
Column I calculates the squared difference between Uo and Ue. It is this difference that the model tries to optimize through the function solver in the Excel spreadsheet.
Column J shows the sustainable yield (Ye) for different levels of fishing effort. The formula for calculating Ye results from the biological modelling. This yield concerns only the Kapenta. The sustainable yield (Ye) varies according to the levels of fishing effort. It reaches a maximum known as the maximum sustainable yield (MSY), meaning that if the fishing effort is maintained at a level that can achieve the MSY, it is expected that the average production of the fishery will be close to or around the MSY.
Column L shows the revenue generated by the sustainable yield (Ye). The sustainable yield in column J is multiplied by a constant price for fresh Kapenta (see cell ‘L84’). The Economic Survey in 2013 indicated that bycatches in the Kapenta fishery are not very important and are not sold. Thus revenues from this fishery mainly come from sales of Kapenta.
Column M gives an estimate of the value added in the costs. This estimate is made by using a rate of value added from the results of the 2013 Economic Survey. The situation in Basin 3 in Zimbabwe, where it appears that fishing enterprises only earn a normal profit, was considered (estimated at around US $24/rig/night fished). In such a case, where the resource rent is fully dissipated, the value added in terms of percentage of sales is around 55 percent. The rate of value added in the costs is given in cell ‘O80’. This is a parameter that can be updated by regular economic monitoring of the fishery. The value added in the costs is then calculated by multiplying the value of Kapenta sales (column L) by the rate of value added in the costs (cell ‘O80’).
Column N calculates the total cost in the fishery by multiplying the standardized fishing effort (column C) by the estimate of total cost per unit of fishing effort given in cell ‘L87’. This last parameter can also be updated through regular economic monitoring of the fishery.
Column O estimates the rent (super profit) of the fishery by calculating the difference between Kapenta sales (column L) and total costs (column N).
Column P estimates the total value added in the Kapenta fishery and is calculated by adding the estimated rent (column O) to the value added in the costs (column M). This represents the total potential of wealth that the Kapenta fishery could generate for the economies of both countries. This potential fluctuates according to the level of fishing effort, however, this potential does not increase to infinity with increases in fishing effort, but is rather optimized at levels of fishing effort that correspond to the maximum rent generated.
Column Q estimates the number of rigs corresponding to the standardized fishing effort (column C), taking into account the average number of nights fished by an ‘average’ rig operating in the fishery (estimated in the Input effort worksheet). 78 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Column R estimates the number of employment on board (crew) by multiplying the number of estimated rigs (column Q) by the average number of crew on board (Worksheet: Input employment).
Column S provides an indicator of the estimated catch/rig and per year by dividing the sustainable yield (Ye calculated in column J) by the number of estimated rigs (column Q). This gives an indication of what the average production of a rig would be if the fishing effort is maintained at this position. Column U gives the information for the estimates of sales per rig per year by multiplying column S (catch/rig/year) by the average weighted price (see cell ‘L84’)
Following the same principle, Column T gives an estimate of the CPUE for one rig by dividing the estimated catch/rig/year by the average number of nights fished by one rig per year (worksheet Input effort).
Column V estimates the value added in the costs for one rig per year, and Column U estimates the rent for one rig per year. These indicators are calculated by dividing the value added in the costs and estimated rent at the scale of the Kapenta fishery (columns P and O). It is also possible to estimate these indicators per rig and per night fished which would probably give a better idea of the huge potential of this fishery in terms of wealth generation and economic growth.
All these indicators, on the socio economic performance of the Kapenta fishery, and that are a function of fishing effort, can be represented by various graphics that are available in Excel.
4.3 Results, potential use of the model for the Working Group, and perspectives 4.3.1 Results
A summary of the results of the bioeconomic model developed for the Kapenta fishery can be found in Table 19 and Figure 31 below.
Table 19: Results of the bioeconomic modelling at the fishery level
Fishery level results Total value Fishing Production Sales Rent Employment added effort Number - Ye (US $ (US $ (on-board (US $ (nights of rigs (tonnes) Millions) Millions) crew) Millions) fished) MSY 22,585 38.30 21.27 29.79 116,538 431 1,484
MEY 21,130 35.85 24.25 30.05 79,302 293 1,010
Cur Y (2011) 19,225 32.62 3.88 18.25 196,450 726 2,501
R=0 16,488 27.97 -6.50 10.73 235,740 871 3,001 Bioeconomic model of the Kapenta fishery and its results 79
The Kapenta fishery operating on approximately 196,450 nights fished (standardized effort at 1 in Figure 34 below) and has a current sustainable yield of 19,225 tonnes (compared to an estimated MSY of 22,585 tonnes). The total value added produced is approximately US $18.25 million per year, of which US $ 3.88 million is annual rent. Concerning employment, the number of on-board crewmembers is estimated at 2,501 for a total of 726 rigs.
Overfishing of the Kapenta resource, relative to an harvesting rate close to the MSY, resulted in 2011 to an annual loss of catches of about 3,000 tonnes per year and the dissipation of wealth based on the rent of the resource of about U.S. $20 million per year for the two countries.
If weakness and lack of access regulations continue, the rent of the Kapenta resource will fully dissipate and production losses will be more than 6,000 tonnes per year compared with the situation at MSY. This kind of situation would not be interesting or profitable for either country, the fishing industry, national consumers or the Kapenta resource.
The fishery will, of course, generate more on-board employment due to the presence of more fishing vessels (that will produce less fish): 3,000 rigs versus approximately 1,500 rigs at MSY. However, this consideration should be reviewed with regard to employment losses that will occur in processing activities due to the scarcity of Kapenta and production losses (6,000 tonnes). This situation should also be assessed with regard to losses of potential employment that could be created by using the economic growth potential offered by the important sustainable wealth (rent of US$ 20 million/year) that the fishery could generate, year after year, if well managed.
Figure 34: Bioeconomic model of the Kapenta fishery and presentation of various management situations, including the situation in 2011
80 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Table 20 details the economic performances that could be achieved by the industry at the rig level according to the above-mentioned management targets (MSY, MEY, current situation, and open access).
Table 20: Results of the bioeconomic modelling at the rig level
Rig level results Catch/ CPUE Sales/rig/year Value Rent/rig/nights rig/year (kg) (kg/nights (US $) added/rig/year fished fished) (US $) (US $)
MSY 52,452 194 88,988 44,494 183
MEY 72,116 267 122,349 61,174 306.5
Cur Y 26,487 98 44;936 22,468 19.8 (2011)
R=0 18,929 70 32,115 16,057 -27.68
The figures in the Table 20 clearly indicate that industry performance has strongly decreased with the increase in fishing effort. This is due, in particular, to the drop of the CPUE. The situation in 2011 indicates that the rent of the resources is strongly dissipated. Thus in 2011, the industry is in a position where it only earns a normal profit. In such a situation, the industry is vulnerable to any exogenous factors that could affect its profitability: price or cost fluctuations (fuel), environmental factors that could negatively affect Kapenta production, etc.
4.3.2 Potential use of the model for the Working Group
During the Bioeconomic Working Group, the model will allow the impact of a fluctuation of the harvesting rate (fishing effort per number of nights fished) to be assessed on parameters such as: sales, total cost, rent, value added in the cost, employment (fishing crew), fishing capacity (number of rigs), etc.
The following assessments could be done at the macro level of the fishery and at the micro level of the fishing enterprise (one rig): catches, sales, CPUE, values added, and rent.
In accordance with the mandate of the Bioeconomic Modelling Working Group set at the Fifth Technical Consultation Meeting held in Siavonga, in September 2012, the model will help develop the following scenarios with stakeholders:
Enable the fishing effort to continue to increase at the rate observed over the last 10 years;
Retain existing fishing effort (licensed and unlicensed rigs) at the current level;
Retain existing fishing effort of only licensed rigs – complete removal of unlicensed rigs and related illegal fishing; Bioeconomic model of the Kapenta fishery and its results 81
Return the overall fishing effort to a new optimum level based on information available;
Return the overall fishing effort on the Lake to 500 rigs.
Other scenarios and a sensitivity analysis on prices and costs could also be discussed during the Working Group.
4.3.3 Perspectives
The development of a bioeconomic modelling tool should be considered as a process to support fishery management. As shown in this report, such a tool can first help to clarify the issues of sustainable management of the fishery with the different stakeholders. Secondly it can help assess the impact of different management options in relation to various objectives of different public policies (economic growth, food security, employment, trade balance, etc.).
The development of such a tool on in Excel will enable different stakeholders to improve on the model and adjust it according to the different needs that will certainly appear throughout the management process of the Kapenta fishery.
Assessment of the impact of illegal trading at night on the Lake
A first question that arises with the use of the bioeconomic model is the question of the reliability of the statistics produced. Several biases can be pointed out:
Some fishing enterprises with fishing rights (licences) do not report or do not correctly report their data on effort and catches;
Vessels operate illegally (without a licence), and their production is not taken into account;
Illegal trading of Kapenta, that is legally harvested, takes place during the night on the Lake with the crews of the fishing companies. Thus, part of the production is not taken into account in the records of the licenced fishing enterprises. This would mean that for the same level of fishing effort, production is more important than it would seem.
Regarding the last point, the model allows for some assumptions on the volume of this illegal trade to show the impact of such practices. Figure 35 below shows an illustration with assumptions made on the percentage of illegal trade per volume of production landed. 82 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Figure 35: Bioeconomic modelling of the Kapenta fishery with a simulation of illegal trading effects
Assessment of the impact of the environmental factors
Some scientific research (Assessment Working Group 1996, SADC fisheries Project, Ndebele-Murisa et Al. 2011) indicates that the productivity of the Kapenta stock in Lake Kariba is significantly influenced by environmental parameters. The lake hydrology (water levels) as well as the maximum temperature, appear to influence Kapenta catches the most.
An analysis of the correlation between several environmental parameters (rainfall, water level, temperature, etc.) and the CPUE would provide data for an environmental index that could be used in the Fox surplus production model to estimate the influence of the environmental conditions on catch predictions. Such modelling has been developed in octopus fisheries in western Africa (Freon, 1991) to test the effects of the environment. Such a model could allow for the prediction of several patterns of production (good year, average, or bad year) according to different environmental conditions. Figure 36 below gives an example of what this type of modelling would look like for illustrative purposes.
Bioeconomic model of the Kapenta fishery and its results 83
Figure 36: Production function of the Kapenta fishery with environmental effects
Figure 37: Bioeconomic modelling of the Kapenta fishery with environmental effects
Figure 37 illustrates how environmental effects could be integrated in bioeconomic modelling to support fishery policy planning.
If the fishing effort is set at 1.4 (Sales = Total Cost and Rent = 0) to exploit with the maximum number of rigs and employment during a period where the environmental effect is good, fishing companies will experience a strong reduction in CPUE and economic hardship if environmental conditions worsen.
In fisheries that are strongly affected by environmental conditions a precautionary approach would recommend allocating lower levels of fishing effort.
84 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
In this example, a recommendation would be to allocate effort below a fishing effort of 1.2. By changing the effort allocations, environmental conditions will never affect the sustainability of the fishery.
Modelling of demand
The modelling of prices and demand were relatively limited in the bioeconomic model that was developed. This was mainly due to the lack of data on ex-vessel prices (by geographical area and also by month to assess seasonality). Thus the model uses a constant annual price.
There is a need for more information on prices and studies on the other factors affecting demand. Such factors include the price of substitutes (other fish, other animal products e.g. chicken, etc.), consumer revenue and purchasing power, marketing, etc. The information requested should be related to the importance of each substitute in order to estimate the elasticity of the demand.
However the model enables a sensitivity analysis to be developed with different price ranges, and projections can be made price evolution based on historical trends (notably by taking into account inflation rates). This will allow for the development of different scenarios and assessment of their impacts on the main socio economic parameters and on the performance of the fishery.
Modelling of an employment function in the Kapenta fishery (on-board crew + processing employees) based on the evolution of fishing effort
The harvesting system is characterized by its integration with processing activities. Thus, the Kapenta fishery is not only creating on-board employment (crew) through fishing activities, but also employment for processing activities (daily labourers involved in the drying process).
Whilst on-board employment directly depends on the number of vessels, and increases with the development of fishing capacity, the number of employees involved in processing activities will depend on the volume of production achieved. In fishing, the production does not increase indefinitely with increased capacity and fishing effort. When stocks are overexploited, the overall production tends to decrease with increased fishing effort. In such situations, there will be employment gains in the field of the catch if the number of boat increases, but there will be losses in processing activities due to a lack of produce. Fishing enterprises need to generate enough value added to properly remunerate both employees on-board and ashore.
If, during the Bioeconomic Working Group, better information is available on the ratio of numbers employed in processing activities based on the volume of catches landed, it will be possible to model both the employment function for fish processing and fishing activities and, thus, have an overall idea on how employment is evolving in the Kapenta fishery with the evolution of fishing effort. Bioeconomic model of the Kapenta fishery and its results 85
Modelling of fiscal arrangements (e.g. licensing fees) that may be established for an equitable sharing of the resource rent
The results of bioeconomic modelling in fisheries clearly illustrates that the economic performance of fishing companies depends on the level of fishing effort in the fishery. The Government of Zimbabwe recently initiated a process to revise the number of fishing licences in the Kapenta fishery. The bioeconomic model can be a very good tool to simulate the impact of new fiscal arrangements that can be taken in the fishery based on the level of fishing effort.
Changes in the amount of the access fees paid by fishing enterprises will affect total costs and the value of the remaining resource rent (if it exists). This could easily be simulated in the model by adding in a specific column for licence fees.
Modelling of the value of fishing rights at different levels of resource rent and for different levels of royalties paid for access to the Kapenta fishery
If access to the Kapenta fishery is limited (limited number of licences) and rent is being generated, then it will be possible to assess the value of the fishing rights (licence) by estimating the amount of rent capitalized in the rights according to a chosen/given discount rate.
This value of rights could also be simulated for different values of access fees. This type of modelling could be of particular interest if the management system moves towards territorial concessions (basin/stratum) with a limited number of licences per concession. The value of the rights could help determine the value of concession fees.
5. Findings and recommendations
Information and data available on the Kapenta fishery permitted the development of a bioeconomic model of the fishery. This analytical tool is, for the biological part, based on the Fox surplus production model. The model is powered by the official statistics for catch and effort. This data is provided by the fishing industry in the context of obligations that the industry must adhere to in order to obtain a fishing licence (right to fish). The model allows for predictions of the trend of yields at equilibrium (production function) according to different levels of fishing effort.
The economic part of the model was built on the results of the Economic Survey conducted in 2013 in both countries in the different fishing zones of the Lake. A range of fishing companies of various sizes were interviewed to estimate an average total cost per unit of fishing effort (night fished), and estimates of a normal profit. 86 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Results of the bioeconomic modelling show that in 2011 the Kapenta resource was overexploited with an excess of fishing effort of about 40 percent. The fishery was almost in a situation of open access equilibrium where the rent of the resource was fully dissipated. As a consequence, the fishing industry was achieving very poor economic results and was faced with many difficulties and low sustainability. Other main findings concern the performance of the Kapenta fishery as a whole: the potential of wealth for economic growth in terms of rent (estimated to be at about US $24 million per year) is completely lost for the economy of both countries; the productivity of the Kapenta resource and thus the production of the fishery is negatively affected due to overexploitation, this seems to obviously have a negative impact on food security amongst consumers who strongly depend on Kapenta for their diet. The fishery is generating some on-board employment but remuneration for these employees tends to be very low compared to the national standards for wages. Furthermore, declining production levels has probably had a negative impact on employment in the Kapenta processing activities that take place along the lakeshore. This needs to be further assessed during the Bioeconomic Working Group, based on information to be provided by the industry on processing activities.
The main recommendations of this study concern the way forward to improve the bioeconomic model that has been developed. These recommendations are therefore mainly focused on improving the information required to feed and to develop the model: this will require strengthening partnerships with the industry.
5.1 Costs and prices
As the Kapenta fishery is mainly driven by economic considerations, its management must depend, above all, on sound economic valuation. Thus, there is an urgent need to develop and implement a system of regular data collection on ex-vessel prices and costs of fishing activities in the main fishing zones of the Lake. This economic observatory function of the fishery should be developed in strong partnership with stakeholders in the industry: it will also be useful for management decisions. Several options could thus be explored:
ask the industry to provide, on a regular basis, economic information on prices and costs, as is done with data on effort and catches for licensing procedures;
work with the Representatives of producer organisations and a sample of representative fishing industries which will be aware of the interest of economic information for the management of the fishery and whom would be willing to provide such data (based on the confidentiality of the analysis and its restitution);
undertake an annual economic survey as was done for this bioeconomic modelling exercise by using and/or improving the questionnaire and the preformatted Excel file that has been prepared to analyse the data. Findings and recommendations 87
5.2 Environmental factors
There is a need to request updated information on environmental parameters collected by the Zambezi River Authority, as well as information on climate conditions for the Lake Kariba area available from the Zambian and Zimbabwean Meteorological Departments.
A statistical analysis of these environmental parameters should be done to try to better assess the correlation between environmental factors and the Kapenta fishery production (catches, CPUE, etc.). It is recommended to start the analysis with those environmental parameters that have an influence on the water level of the Lake (inflow catchment, rainfall patterns, temperature, etc.). In fact, it is only on matters to do with the water level that management actions could eventually be undertaken in close partnership with the Zambezi River Authority.
If correlations between environmental factors and Kapenta production are clearly established, it will be possible to further develop the bioeconomic model: taking into account environmental effects and assessing any impacts. Based on the results of the bioeconomic modelling, and the bioecological knowledge of the Kapenta resources, options for an adjusted water management system could eventually be implemented in close partnership with the ZRA to increase the productivity and the performance of the fishery.
5.3 Illegal trading and illegal fishing
The illegal trading practices on the Lake at night and IUU fishing (unlicensed boats) are one of the most important, unknown parameters of the bioeconomic modelling. In order to calculate realistic estimates of the impact of these practises for the bioeconomic modelling exercise, it is necessary to stop such activities. These activities are the cause of huge losses, not only for the fishing enterprises, but also for the economies of both countries. However, this could lead to high competition for the Kapenta resource, which will become more and more scarce if the number of rigs continues to increase. Future consequences could include increased conflicts over resources leading undoubtedly to the loss of property and human life on the Lake.
5.4 Value chain and demand
The bioeconomic modelling analysis enables estimations of the economic potentialities of the Kapenta fishery for only the production and processing activities. The contribution of this natural resource towards the social and economic wellbeing of both countries should also take into account the distribution sector, as well as markets issues. Thus, there is a need for more in-depth investigations and research on the value chain and the marketing of Kapenta. This will help to better understand determining factors of demand. This area is currently a gap in the bioeconomic model that will need to be filled in due course for a better modelling of prices and demand. 88 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
5.5 Quality of data
There is a strong need in both countries to regularly validate data that are collected concerning catches (including bycatches) and fishing effort. If economic information on prices and costs of fishing operations are also to be collected from private operators in the near future, validation procedures will, of course, have to be applied. The biannual consultation meeting or the Technical Committee seems to be the appropriate consultative framework in which to validate data: the industry should be actively involved and participate in this validation process.
References 89
6. References
Andrew Palfreman and Jarle Lovland (1992). An Economic Assessment of the Lake Kariba Fishery in Zambia and Zimbabwe. Zambia/Zimbabwe SADC Fisheries Project Report N°14. 1992.
Anonymous (1992). Working Group on the Assessment of Kapenta (Limnothrissa miodon) in Lake Kariba (Zambia and Zimbabwe). Zambia/Zimbabwe SADC Fisheries Project, Report N°11.
Anonymous (1996). Working Group on the Assessment of Kapenta (Limnothrissa miodon) in Lake Kariba (Zambia and Zimbabwe). Zambia/Zimbabwe SADC Fisheries Project, Report N°41. 43 p.
Anonymous (1997). Working Group on the Bioeconomic Assessment of Kapenta in Lake Kariba and Zimbabwe. Zambia/Zimbabwe SADC Fisheries Project, Report N°50. Kafu River Hotel, 11-19 July 1997. 32 pp + appendix.
Chitembure, R. (1999). Unified data collection and storage system, Zambia and Zimbabwe. Experiences and management in research and management of shared inland fisheries resources. Zambia/Zimbabwe SADC Fisheries Project. Processing Workshop, 12- 14 July 1999. Cutti Sark Hotel, Kariba, Zimbabwe.
FAO (1990). Papers presented at the IFIP/SWIOP Workshop on the Economic Aspects of Fisheries Development and Management. RAF/87/099-TD/12/90 (En). Dar-es-Salaam, Tanzania, 30 October to 9 November, 1989. 124 pp.
FAO (2002). Report of the Ad Hoc Technical Consultation on the Development and Management of the Fisheries of Lake Kariba. FAO Fisheries Report n° 720. Siavonga, Republic of Zambia, 19-20 Nov 2002. 18 pp.
FAO (2004). Report of the Second Technical Consultation on Development and Management of the Fisheries of Lake Kariba. FAO Fisheries Report No. 766 SAFR/R766 (En). ISSN 0429-9337. Kariba, Zimbabwe, 30 November – 1 December 2004. 14 pp + appendix.
FAO (2006). Report of the Third Technical Consultation on Development and Management of the Fisheries of Lake Kariba. FAO Fisheries Report No. 824 SFS/R824 (En). ISSN 0429- 9337. Siavonga, Zambia, 26–27 October 2006. 10 pp + appendix.
FAO (2010). Report of the Fourth FAO (2010). Report of the fourth technical consultation on development and management of the fisheries of Lake Kariba (Pre-publication). FAO Sub-regional Office for Southern Africa. Kariba, Zimbabwe 18–20 May 2010. 15 pp + appendix.
FAO (2011). Lake Kariba Fishery Frame Survey Report Zambia (draft). Ministry of Agriculture and Livestock Department of Fisheries. Compiled by: Mbamwai Mbewe, Chijoka Mweemba, Ian Habulembe, Emmanuel Silwimba. 50 pp. 90 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
FAO (2011). Lake Kariba Fishery Frame Survey Report, Zimbabwe. 32 pp + appendix.
FAO (2012). Report of the Fifth Technical Consultation on Development and Management of the Fisheries of Lake Kariba (Pre-publication). FAO Sub-regional Office for Southern Africa. Siavonga, Zambia 11–13 September 2012. 23 pp + appendix.
Horemans, B. (1992). Report of the Sub-regional Workshop on Fishery Bioeconomic Modelling, 17-21 February 1992, Kariba, Zimbabwe. UNDP/FAO Regional Project for Inland Fisheries Planning (IFIP), RAF/87/099-TD/43/92 (En). 22 pp + appendix.
Horemans, B. and Hoekstra (1992). Economic Appraisal of the Pelagic Fishery of Lake Kariba. UNDP/FAO Regional Project for Inland Fisheries Planning (IFIP), RAF/87/099- TD/36/92 (En). 37 pp.
Itai H. Tendaupenyu (2008). Statistical and Economic Analysis of Kapenta Catches in Lake Kariba.
Itai Hilary Tendaupenyu, Hee-Dong Pyo, Chang-ik Zhang. A Comprehensive Analysis of Maximum Entropy and Analytical Models for Assessing Kapenta Stocks in Lake Kariba (in press).
Kinadjian, L. (2012). Bioeconomic Analysis of the Kapenta Fisheries. Mission Report No.1. Report/Rapport: SF-FAO/2013/09. SmartFish Programme of the Indian Ocean Commission, FAO, Ebene, Mauritius. 27 pp + appendix.
Loveness Madamombe (2002). The Economic Development of the Kapenta Fishery Lake Kariba (Zimbabwe/Zambia). Thesis submitted in partial fulfilment of the requirements for a Masters of Science in International Fisheries Management. Norwegian College of Fishery Science, University of Tromso. 28 pp + appendix.
Machena C. and V. Kanondo. (1991). A Review of the Fisheries of Lake Kariba and their Management. UNDP/FAO Regional Project for Inland Fisheries Planning (IFIP), RAF/87/099-TD/17/91 (En).
Machena, C. (1990). The Status and Management of the Lake Kariba Fisheries (Zambia and Zimbabwe). UNDP/FO Regional Project for Inland Fisheries and Planning (IFIP).
Mudenda, H. G. (2010). Sustainability and Management of the Lake Kariba Kapenta Fishery. Institute for Policy Studies (IPS). The Zambia National Farmers Union (ZNFU). March 2010. 29 pp + appendix.
Mvula, C. (2013). Bioeconomic Study on Lake Kariba Fishery Zambia. Synthesis report. 32 pp. + appendix.
Ndebele-Murisa, M.R.; Mashonjowa, E.; Hill, T. (2011). The Implications of a Changing Climate on the Kapenta Fish Stocks of Lake Kariba, Zimbabwe. Transactions of the Royal Society of South Africa (2011) 66 (2) 105-119. [DOI: 10.1080/0035919X.2011.600352] References 91
Ngalande, P. and Mhlanga, W. (1999). Bioeconomic Assessment of Kapenta in Lake Kariba Zambia/Zimbabwe. Experiences and management in research and management of shared inland fisheries resources. Zambia/Zimbabwe SADC Fisheries Project. Processing Workshop, 12-14 July 1999. Cutti Sark Hotel, Kariba, Zimbabwe.
Nobuhle Ndhlovu; Itai Hilary Tendaupenyu. The Kapenta Fishery of Lake Kariba and its Contributions to Food Security in Zimbabwe (technical paper to be published by the FAO). Zimbabwe Parks and Wildlife Management Authority. Lake Kariba Fisheries Research Institute.
Nyikahadzoi, K. Songore, N. Kinadjian, L. (2013). A Descriptive Analysis of the Bioeconomic Situation of the Kapenta Fishing Industry, Zimbabwe. 20 pp.
Sen, S. (1995). The Market for Fish and Fish Products in Zimbabwe. ALCOM. FAO Fisheries Technical Paper.
Zambia/Zimbabwe. 1999. A Protocol on Economic and Technical Co-operation Between the Government of the Republic of Zimbabwe and the Government of the Republic of Zambia Concerning the Management and Development of Fisheries on Lake Kariba and Trans- boundary Waters of the Zambezi River. 6 pp.
92 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Annex 1. Time series Kapenta fishery: analysis of consistency between various sources of data
In Zimbabwe In Zambia
9547 9305
97418 100053 6 410
7 655
9 993
7 967
9 116
6 460
6 004
5 913
4 937
4 290
5 645
8 579
1 134
7 960
5 927
5 728
6 736
7 178
8 526
7 672
7 284
6 948
7 753
6 319
5 858
8 226
7 422
5 959
0,219 0,298 0,214 0,245 0,256 0,184 0,190 0,222 0,186 0,185 0,170 0,184 0,197 0,150 0,159 0,159 0,181 0,024 0,187 0,184 0,138 0,144 0,180 0,134 0,158 0,156 0,165 0,145 0,097 0,091
4 4 136
18 874 874 18 670 16 832 27 304 30 163 32 755 31 218 33 897 34 413 37 284 39 004 45 416 46 402 36 797 44 033 36 170 37 965 43 186 48 801 45 701 30 098 31 248 34 812 32 921 44 906 40 400 58 426 48 734 68 284 79 243 70 4 965
615 1294 1833 3111 5903 12847 33516 40935 37776 38865 41234 41403 45790 52414 53403 54919 59193 62208 71066 68155 71249 75443 73524 75633 74770 64091 65625 59375 55000 68182 72792 78138 78144 78144 81048 87384
487 654 1050 1171 2772 4874 8395 12006 8450 8548 10394 14586 15747 15823 18366 20112 21758 19306 18931 19957 19232 15280 15423 17034 15288 11208 10500 9500 7150 7500 8735 10158 12503 10940 12157 9728
0,792 0,505 0,573 0,376 0,470 0,379 0,250 0,293 0,224 0,220 0,252 0,352 0,344 0,302 0,344 0,366 0,368 0,310 0,266 0,293 0,270 0,203 0,210 0,225 0,204 0,175 0,160 0,160 0,130 0,110 0,120 0,130 0,160 0,140 0,150 0,111 0,098 0,093
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Cpue (T/night Fished) (T/night Cpue
Effort Night Fished Fished Night Effort
Catches Tonnes Catches
IFIPProjectRAF/87/099-TD/13/90
IFIPProjectRAF/87/099-TD/36/92
Economic assessments Economic 1992
Kapenta assessment WG 1996 Kapentaassessment WG
Bio-économicassessment 1997
Workshop SADC FisheriesSADC Workshop Project1999
Bio economic report 2002 (Master) 2002 Bioeconomicreport
FAO TC meeting FAOTC 2002
FAO TC meeting FAOTC 2004
FAO TC meting 2006 FAOTC
FAO TC meeting FAOTC 2008
FAO TC meeting FAOTC 2010
Stock assessmentTendaupenyu Stock I.H. 2011
FAO TC meeting FAOTC 2012
Report ofnationalReport 2013 consultant
Références
References
Cpue (T/night Fished) (T/night Cpue
Effort Night Fished Fished Night Effort
Catches Tonnes Catches
IFIPProjectRAF/87/099-TD/13/90
IFIPProjectRAF/87/099-TD/36/92
Economic assessments Economic 1992
Kapenta assessment WG 1996 Kapentaassessment WG
Bio-économicassessment 1997
Workshop SADC FisheriesSADC Workshop Project1999
Bio economic report 2002 (Master) 2002 Bioeconomicreport
FAO TC meeting FAOTC 2002
FAO TC meeting FAOTC 2004
FAO TC meeting FAOTC 2006
FAO TC meeting FAOTC 2010
Stock assessmentTendaupenyu Stock I.H. 2011 FAO TC meeting FAOTC 2012 Annexes 93
Annex 2. Times series of data used to run the Surplus Production Model of Fox Time series N°1: Data provided by LKFRI and DoF during the Economic Survey
615,00 615,00
5 903,00 903,00 5
3 111,00 111,00 3
1 833,00 833,00 1
1 294,00 294,00 1
87 997,19 997,19 87
94 656,01 656,01 94
97 841,41 841,41 97
97 272,08 272,08 97
99 866,29 866,29 99
85 678,58 678,58 85
78 095,15 095,15 78
76 089,79 089,79 76
71 776,82 776,82 71
71 818,74 818,74 71
69 710,02 710,02 69
62 470,67 470,67 62
64 873,93 873,93 64
61 439,25 439,25 61
56 266,12 266,12 56
40 935,00 935,00 40
33 516,00 516,00 33
12 847,00 847,00 12
196 450,11 450,11 196
177 223,82 223,82 177
170 169,06 169,06 170
133 448,00 448,00 133
145 829,71 829,71 145
126 012,75 012,75 126
126 222,33 222,33 126
122 065,57 065,57 122
113 062,02 062,02 113
119 245,63 245,63 119
102 781,20 781,20 102
113 700,48 700,48 113
101 949,59 949,59 101
100 830,33 830,33 100
108 701,12 701,12 108
Corrected
LK Effort LK
96397
79806
82785
52400
67686
47869
48084
49274
44880
32997
35281
53621
38690
38930
26317
27306
33258
26592
29117
28800
23471
18902
21171
18374
19405
23920
21068
23640
22574
18490
Corrected
ZAM Effort ZAM
0,73
0,79
0,67
0,94
0,85
0,94
0,96
0,67
0,76
0,94
0,87
0,85
1,20
1,13
1,41
1,32
1,35
1,37
1,59
1,56
1,67
1,98
1,65
1,81
1,64
1,34
1,44
1,18
0,74
1,02
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
Standardization of fishingofStandardization effort
ZAM
ZIM/Cpue
Cpue
0,107 0,107
0,108 0,108
0,132 0,132
0,154 0,154
0,151 0,151
0,164 0,164
0,132 0,132
0,139 0,139
0,121 0,121
0,133 0,133
0,168 0,168
0,171 0,171
0,163 0,163
0,196 0,196
0,204 0,204
0,193 0,193
0,183 0,183
0,245 0,245
0,249 0,249
0,229 0,229
0,262 0,262
0,297 0,297
0,310 0,310
0,285 0,285
0,258 0,258
0,308 0,308
0,307 0,307
0,237 0,237
0,243 0,243
0,222 0,222
0,293 0,293
0,250 0,250
0,379 0,379
0,470 0,470
0,376 0,376
0,573 0,573
0,505 0,505
0,792 0,792
615
5 903 903 5
3 111 111 3
1 833 833 1
1 294 294 1
86 098 098 86
90 076 076 90
96 606 606 96
89 816 816 89
86 621 621 86
84 169 169 84
77 953 953 77
71 707 707 71
69 066 066 69
55 535 535 55
56 650 650 56
40 935 935 40
33 516 516 33
12 847 847 12
170 092 092 170
160 491 491 160
143 239 239 143
130 173 173 130
135 574 574 135
123 197 197 123
124 442 442 124
105 604 604 105
102 430 430 102
111 426 426 111
110 583 583 110
118 735 735 118
112 803 803 112
109 557 557 109
120 240 240 120
107 651 651 107
114 571 571 114
116 070 070 116
101 492 492 101
654
487
8 395 395 8
4 874 874 4
2 772 772 2
1 171 171 1
1 050 050 1
18 270 270 18
17 368 368 17
18 944 944 18
20 017 017 20
20 416 416 20
20 162 162 20
16 409 409 16
14 648 648 14
12 437 437 12
11 440 440 11
15 145 145 15
19 079 079 19
17 974 974 17
23 248 248 23
22 961 961 22
21 151 151 21
22 016 016 22
26 410 410 26
28 483 483 28
26 603 603 26
26 590 590 26
28 706 706 28
27 865 865 27
24 685 685 24
21 681 681 21
23 973 973 23
22 008 008 22
16 353 353 16
13 513 513 13
12 586 586 12
12 006 006 12
LKCatch (tonnes) LKCatch (nightsEffort LK fished) (T/night fished) CPUE LK
0,302
correction
before
data
Original Original
Auteur:
0,128 0,128
0,124 0,124
0,165 0,165
0,160 0,160
0,165 0,165
0,170 0,170
0,135 0,135
0,180 0,180
0,144 0,144
0,138 0,138
0,184 0,184
0,187 0,187
0,146 0,146
0,181 0,181
0,159 0,159
0,159 0,159
0,150 0,150
0,197 0,197
0,184 0,184
0,170 0,170
0,185 0,185
0,186 0,186
0,222 0,222
0,190 0,190
0,184 0,184
0,256 0,256
0,245 0,245
0,214 0,214
0,298 0,298
0,219 0,219
ZACPUE(T/night ZACPUE(T/night fished)
70 039 70 039
63 073 63 073
55 855 55 855
49 125 49 125
57 430 57 430
45 053 45 053
46 304 46 304
32 812 32 812
34 248 34 248
31 098 31 098
30 701 30 701
45 801 45 801
46 492 46 492
43 965 43 965
37 170 37 170
36 033 36 033
44 797 44 797
36 402 36 402
46 416 46 416
45 004 45 004
39 284 39 284
37 413 37 413
34 897 34 897
33 218 33 218
31 755 31 755
32 163 32 163
30 304 30 304
27 832 27 832
16 670 16 670
18 874 18 874
5 5 913
4 4 937
4 4 290
5 5 645
8 8 579
6 6 766
7 7 960
5 5 927
5 5 728
6 6 736
7 7 178
8 8 526
7 7 672
7 7 284
6 6 948
7 7 753
6 6 319
5 5 858
8 8 226
7 7 422
5 5 959
4 4 965
4 4 136
8 965 965 8
7 821 821 7
9 216 216 9
7 860 860 7
9 476 476 9
7 659 659 7
6 251 251 6
0,093
0,098
0,111
0,150
0,140
0,160
0,130
0,120
0,110
0,130
0,160
0,160
0,175
0,204
0,225
0,210
0,203
0,270
0,293
0,266
0,310
0,368
0,366
0,344
0,302
0,344
0,352
0,252
0,220
0,224
0,293
0,250
0,379
0,470
0,376
0,573
0,505
0,792
615
5903
3111
1833
1294
97418
87384
81048
78144
78144
78138
72792
68182
55000
59375
65625
64091
74770
75633
73524
75443
71249
68155
71066
62208
59193
54919
53403
52414
45790
41403
41234
38865
37776
40935
33516
12847
100053
9305
9547
ZWCatch (tonnes) (nights ZWEffort fished) ZWCPUE fished)(T/night ZACatch (tonnes) (nights ZAEffort fished)
2011
2010
2009 9728
2008 12157
2007 10940
2006 12503
2005 10158
2004 8735
2003 7500
2002 7150
2001 9500
2000 10500
1999 11208
1998 15288
1997 17034
1996 15423
1995 15280
1994 19232
1993 19957
1992 18931
1991 19306
1990 21758
1989 20112
1988 18366
1987 15823
1986 15747
1985 14586
1984 10394
1983 8548
1982 8450
1981 12006
1980 8395
1979 4874
1978 2772
1977 1171
1976 1050
1975 654
1974 487 Years 94 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Time series N°2: Data used by Itai Hilary Tendaupenyu, Hee-Dong Pyo, Chang-ik Zhang: A Comprehensive Analysis of Maximum Entropy and Analytical Models for Assessing
Kapenta Stocks in Lake Kariba, presented at the 5th Technical Consultation Meeting.
615,00 615,00
5 903,00 5 903,00
3 111,00 3 111,00
1 833,00 1 833,00
1 294,00 1 294,00
92 039,23 92 039,23
84 180,94 84 180,94
83 342,42 83 342,42
79 301,97 79 301,97
82 206,80 82 206,80
76 174,18 76 174,18
67 971,77 67 971,77
71 788,58 71 788,58
67 177,16 67 177,16
49 403,86 49 403,86
40 935,00 40 935,00
33 516,00 33 516,00
12 847,00 12 847,00
196 450,11 450,11 196
177 223,82 223,82 177
177 148,66 148,66 177
133 448,00 448,00 133
115 181,32 181,32 115
126 012,75 012,75 126
126 222,33 222,33 126
127 575,58 575,58 127
136 191,27 191,27 136
116 538,46 538,46 116
112 500,00 500,00 112
121 018,75 018,75 121
115 298,61 298,61 115
122 807,08 807,08 122
110 323,66 323,66 110
109 721,09 721,09 109
118 269,74 269,74 118
104 257,97 257,97 104
101 356,53 356,53 101
103 567,69 567,69 103
Corrected Corrected
LK Effort LK Effort
96397
79806
89765
52400
37037
47869
48084
54784
68009
61538
53125
55394
51208
48037
34691
36197
42827
33009
33202
32502
29831
24988
28423
25899
29793
30384
26569
30555
28312
11628
Corrected
Effort Effort
ZAM ZAM
Standardization of Effort ofStandardization
0,73 0,73
0,79 0,79
0,70 0,70
0,94 0,94
0,85 0,85
0,94 0,94
0,96 0,96
1,00 1,00
1,00 1,00
1,00 1,00
1,00 1,00
1,00 1,00
1,17 1,17
1,11 1,11
1,34 1,34
1,26 1,26
1,29 1,29
1,32 1,32
1,54 1,54
1,52 1,52
1,57 1,57
1,80 1,80
1,53 1,53
1,63 1,63
1,47 1,47
1,33 1,33
1,41 1,41
1,15 1,15
0,78 0,78
1,01 1,01
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
#DIV/0!
ZAM
Cpue ZIM/Cpue ZIM/Cpue Cpue
6%
7%
4%
5%
2%
5%
6%
3%
3%
-1%
-2%
-6%
-7%
-2%
-3%
-9%
-4%
-1%
-1%
15%
19%
17%
14%
10%
12%
10%
25%
23%
21%
22%
90%
70%
42%
-11%
161%
118%
110%
0,107
0,108
0,131
0,154
0,186
0,164
0,132
0,120
0,110
0,130
0,160
0,160
0,163
0,196
0,204
0,193
0,183
0,245
0,249
0,229
0,262
0,297
0,310
0,285
0,258
0,304
0,304
0,237
0,242
0,223
0,293
0,250
0,379
0,470
0,376
0,573
0,505
0,792
615
5903
3111
1833
1294
98359
95699
96347
86310
78781
76470
60948
49462
40935
33516
12847
170092
160491
150332
130306
109565
123070
124394
127576
136191
116538
112500
121019
124051
128245
122069
119217
130824
114711
119386
120325
109027
104131
654
487
8395
4874
2772
1171
1050
18270
17368
19721
20017
20416
20162
16409
15309
14981
15150
18000
19363
20163
25110
24847
23016
23954
28142
29679
27589
28564
30943
30521
27273
24817
26196
23946
18096
14775
11051
12006
= 0,302
correction
before
data
Original Original
Auteur:
0,128
0,124
0,159
0,160
0,165
0,170
0,135
0,120
0,110
0,130
0,160
0,160
0,149
0,184
0,168
0,166
0,157
0,205
0,190
0,176
0,198
0,204
0,240
0,211
0,205
0,258
0,250
0,219
0,282
0,223
70039
63073
62948
49258
31421
44926
46256
54784
68009
61538
53125
55394
59960
53475
46436
45693
55381
43462
51231
49259
46819
44938
43440
42296
43933
40520
37378
35236
22083
11686
8965
7821
9993
7860
9476
7659
6251
6574
7481
8000
8500
8863
8955
9822
7813
7593
8674
8910
9722
8658
9258
9185
8907
8994
9360
7702
6227
2601
10409
10449
ZACatch (tonnes) (nights ZAEffort fished) ZACPUE(T/night fsihed) LKCatch (tonnes) (nights LK Effort fished) LK CPUE fished)(T/night Increase Effort
0,093
0,098
0,111
0,150
0,140
0,160
0,130
0,120
0,110
0,130
0,160
0,160
0,175
0,204
0,225
0,210
0,203
0,270
0,293
0,266
0,310
0,368
0,366
0,344
0,302
0,344
0,352
0,252
0,220
0,224
0,293
0,250
0,379
0,470
0,376
0,573
0,505
0,792
ZWCPUE fished)(T/night
615
5903
3111
1833
1294
97418
87384
81048
78144
78144
78138
72792
68182
55000
59375
65625
64091
74770
75633
73524
75443
71249
68155
71066
62208
59193
54919
53403
52414
45790
41403
41234
38865
37776
40935
33516
12847
100053
ZWEffort (nights ZWEffort fished)
654
487
9305
9547
9728
8735
7500
7150
9500
8548
8450
8395
4874
2772
1171
1050
12157
10940
12503
10158
10500
11208
15288
17034
15423
15280
19232
19957
18931
19306
21758
20112
18366
15823
15747
14586
10394
12006
ZWCatch (tonnes)
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974 Years Annexes 95
Annex 3. Mathematical formulations of the Schaeffer and the Fox models
Schaeffer model
The Schaefer model is a surplus production model that uses a function of biomass production and can be described as follows:
G (Bt) = H*(B-Bv)*B = H*B2- K*B dB/dt = H*B2- K*B – q*f*B = H*B2 - (K+q*f)*B dB/dt = H*B2 – (K+F)*B
At equilibrium: dB/dt= 0 H*B2 – (K+F)*B = 0
Be (f) = biomass at equilibrium is a function of fishing effort
Be (f) = K/H + q/H*f
f limit corresponds to a situation where Be (f) = 0 f limit = - K/q
Ue (f) = q*Be (f) according to U=q*B
Ue (f) = q*K/H + q2/H*f
96 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Ye (f)= f*Ue(f)
Ye (f) = q*K*f/H+q2*f2/H
Ye (f) = F*K/H + F2/H
fm is the effort that allows maximum production
fm = f l/2 = -K/2q
Fm = q fm = -K/2
Um= Ue(fm) = q*K/2H= qBv/2
Ym= Ye(fm) = -K2/4H
Ym = Maximum Sustainable Yield (MSY)
If f > or = fm, the stock is overexploited.
Annexes 97
Fox model
The mathematical formulation of the function of biomass production in the Fox model can be described as follows: g (B) = H*(Log B-Log Bv) *B
With Bv = Biomass at virgin state and
H = constant (negative)
g (B) = H* Log (B/Bv)*B
g (B) = (HLog B – K) * B
With
K = H Log Bv
dB/dt = 0
H/B *B + H logB – K = 0
H log B = K – H
H Log B = H * ( Log Bv – 1)
Log B = Log Bv/e
B = Bv/e
Bv = e K/H
dB/dt = H*(LogB/Bv)*B – q*f*B dB/dt = H*Log B – (K+F)*B dB/dt = 0 (equilibrium condition) q*f = H*(Log Be – Log Bv) 98 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
With
Be = Biomass at equilibrium
Log Be(f) = q*f/H + Log Bv
Be (f) = e (q*f+K)/H
Ue (f) = q * Be (f) = q * e (q*f+K)/H
Ye (f) = q *f*Be(f)
Maximum reached for Be (f) = Bv/e= e K/H /e
Be (f) = e K/(H-1)
f m = - H/q
U M = Ue (fM)
Y M = Ye (fM)
Annexes 99
Annex 4. Questionnaire - Economic survey
Questionnaire
Country: Zimbabwe: □ Zambia: □ Date of visit: ______
Name of company ______Contact person ______
How long have you been working in the Kapenta fishery (months or years) ______
How many rigs do you currently own? ______(1)
Do you own a landing and processing site: YES: □ NO: □
What are the key events that have affected the Kapenta fishery? (Please indicate the type of event and date) 1. ______date ______2. ______date ______3. ______date ______4. ______date ______
How long have illegal sales of night fishing been going on? ______
Average volume of Kapenta wet fish kg caught/rig/year (over the last 3 years: 2009- 2010-2011): 2009:______2010:______2011:______
Average catch of Kapenta wet fish kg caught/rig/per night (over the last 3 years: 2009-2010-2011):
2009:______2010:______2011:______
What are the main species of bycatch in the Kapenta Fishery? ______
What is the average volume of bycatch kg caught/rig/night? ______Is the bycatch: kept and sold by the crew □ Kept by the crew but not sold □ Kept and sold by the company □ Discarded alive □ Discarded dead □ Do not know □
What is the average price/kg of the main species caught as a bycatch: ______100 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Average sales of Kapenta dried fish kg/year (over the last 3 years 2009-2010-2011):
2009:______2010:______2011:______
Average price of Kapenta over the last 3 years 2009-2010-2011 (value per kg - dried
Fish): 2009: ______2010: ______2011:______
Variable costs
Fuel & lubrication
Price of fuel/litre: ______
Average fuel consumption, litres/rig/night for fishing operations: ______
Average fuel consumption, litres/rig/night for lighting: ______
Average oil consumption/rig/month (litres):
OR average oil consumption for all rigs (litres): ______
Price of oil/litre: ______
Maintenance and repairs
What are the main recurrent maintenance and repairs to be done per rig?
Main issues (e.g. oil Frequency (e.g. every Unit cost of specific change, paintwork, engine month, every year…) maintenance and repairs overhaul, change bulbs, maintenance of fishing equipment, etc.) 1. 2. 3. 4. 5. 6. 7. 8.
Has there been a change in the frequency of recurrent maintenance and repairs operations during the past 5 years: YES □ NO: □ If yes, Why? Annexes 101
What are the main regular supplies (other than fuel and oil) purchased for fishing and processing activities?
Description (e.g. Unit (Kg, No, etc.) Frequency (e.g. Cost per unit salt, bulb, fishing every month, gear, etc.) every year, etc.) 1. 2. 3. 4. 5. 6. 7. 8.
Wages
Number of fishermen per rig: ______
Crew wages: a) Fixed salary/month: YES: □ NO: □
If yes, what is the amount/month for the: Captain: ______Fishermen: ______b) Incentives (e.g. payment related to catch): YES: □ NO: □
If yes, what is the incentive (e.g. amount paid per kg of wet fish caught) for the: Captain: ______Fishermen: ______
c) Bonuses (e.g. amount paid related to catch objectives/month):
Captain: ______Fishermen: ______d) Other (please specify): ______e) Insurance:
Is there any form of insurance (health, accident, etc.) paid for the crew? YES: □ NO: □
If yes, please indicate the cost per person/year: ______What is the current average salary of the captain/month? ______
What is the current average salary of a fisherman/month? ______102 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Are crew wages higher than the minimum wage? YES: □ NO: □
Crew experience: More than 20 years □ 5-20 years □ 3-5 years □ 1-3 years □ 0 years of experience (mostly new crew each season) □
Daily labourers/processing workers’ wages
There are no daily labourers, fish processing is done directly by the wholesaler who buys the production □ Number of daily labourers at the landing site for the whole company: ______(2) and average per rig: ______(2)/(1) Do the wages of daily labourers include: a fixed amount □, incentives □, bonuses □, other □, please specify: ______, Is there any form of insurance (health) paid for the daily labourers? YES: □ NO: □
If yes, please indicate the cost per person/year: ______
What is the average salary of a daily labourer/month: ______
Are the wages of daily labourers higher than the minimum wage? YES: NO: □
Taxes - Taxes (import, property)
Import taxes YES: □ NO: □ If yes average amount per year: ______
Taxes for on-shore properties YES: □ NO: □ If yes, average amount per year: ______
Other taxes: please specify: ______Average amount per year: ______
Fixed costs
Investment costs Description Main specifications Cost Depreciation (Year) Rig (hull) Length Engine (drive unit) Number of cylinders Generator HP: Fishing equipment
Drying racks Other Annexes 103
Source of investment capital: Personal □ Unsecured business loans from banks or venture capital □ Secured business loans from banks □ Loans from banks secured by personal (not business) assets □ Government subsidized private lending □ Government-run loan programmes □ International aid agencies □ Micro credit □ Family/community-based lending □
If you have a loan: could you please indicate the borrowing rate (%) on loans made for the fishery industry: ______
Loan repayment period (number of years): ______
Age of facilities/functionality of capital
Average age of the key durable harvesting capital unit
Harvesting capital Quantity Average Age unit Rigs (e.g. 3 rigs) < 5 years Rigs 5
What is the average number of nights fished per rig/month?______
Insurance
Do you insure your assets? YES: □ NO: □
If yes, what is the cost of the insurance per rig per year? ______
Licence fees
Cost of licences fees: ______Do you pay your license fees up front before the fishing season? YES: □ NO: □
If no, how many payments for the licence fees are made during the fishing season? 104 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Management costs
Number of managers: ______(3)
Number of managers/rig: ______(1)/(3)
What is the average wage for a manager/month? ______
Number of secretaries: ______(4)
Number of secretaries/rig: ______(1)/(4) What is the average wage for a secretary/month? ______
Estimated amount for other management costs/month (office rent, telephone, office supplies, other…): ______
OTHER INFORMATION Do you rent out some of your rigs? YES: □ NO: □
If yes, how many: ______How long have you been doing this for? ______
What is the average rent/month? ______
As an investor, what was (is) the expected return on the capital invested in the fishery (rate of profitability of invested capital in %)? ______
Annexes 105
Annex 5. Estimation of investment and depreciation costs in the Kapenta fishery
Zimbabwe (US $)
260
100
200
500
200
300
250
750
750
900
240
200
614 614
1200
Year
333 333
537 537
143 143
2 167 167 2
2 167 167 2
Deprecialtion /
3
3
2
4
2
1
4
1
3
4
4
7
1
4
1
1
2,8
5,33 5,33 187
3,67 3,67
Drying Rack Drying
life(Year)
durationof
Estimateof
200
500
750
852
900
960
200
2250
1000
1200
(US$)
Amount
Drying racks racks Drying
110 6500
110 6500
447 1200
590 2600 10
500 800
500 800
300 300
500 3000
766 3000
65,5
250,4
Year
322 322 533
129 129
444 444
869 869
738 738
467 467
167 167
375 375
2 600 600 2
Deprecialtion /
8
6
7
8
3
4
1
1
1
1
2
2,5
1,5
0,6
1,22
life(Year)
durationof
Estimateof
Fishing Gear / Rig / FishingGear
655 10
766
978
700
Fishing
1 933 933 1
Amount
640 640
gear (US$) gear
425 1100 10
425 1100 10
450 5900 10
240 626
100 4000
130
130 900
100 4000
160 900
240
450 500
600 2600
500 100
400 750
1100 738
Year
167 167
243 243
553 553
167 167
Deprecialtion /
2
2
5
5
5
5
5
5
3
1
3
1
3
1
1
3,2
2,33 2,33
Generator / Rig / Generator
life(Year)
durationof
Estimateof
650
560
730
1700
6000 10
(US$)
Amount
Generator Generator
20 450
425 850
425 850
240 1200
800 500
244 650
800 500
900 800
400 500
800 500
900 400
1800 4500 10
6700 1100
1000 500
Year
648 648
493 493
667 667
2 013 013 2
Deprecialtion /
2
2
5
1
2
4
1
1
2
5
7
2
6
1
1
1
1
4,67 4,67 311
life(Year)
durationof
Estimateof
850
850
800
975
800
900
800
100
800
900
1200
1450
3450
2680
4000
6700
1000
Engine (drive unit) / Rig / (driveunit)Engine
18000 10
AmountEngine
(driveunit) (US$)
800
800
900
600
1300
1700
1475
1650
3000
1567
1400
1500
1500
1700
1200
Year
1 300 300 1
1 289 289 1
1 300 300 1
Deprecialtion /
5
5
8
5
8
5
5 10
life(Year)
durationof
Estimateof
Rig (Hull) Rig
6500
6500
8000 10
8000 10
9000 10
6000 10
8500
6000
17000 10
11800
16500 10
15000
15666 10
14000 10
15000 10
15000 10
(US$)
12 267 267 12 9,33 325 1 667 6 5,33 947 067 2 6,67 263 509 3 6,83
13 167 167 13 8,33 817 1 892
12 889 889 12
10 100 100 10 Amount Rig (Hull)AmountRig
Source: Economic Survey, 2013 Survey, Economic Source:
AVERAGE BASIN1 AVERAGE
Companie1
BASIN 1 BASIN
AVERAGE BASIN2 AVERAGE
Companie3
Companie2
Companie1
BASIN 2 BASIN
AVERAGE BASIN3 AVERAGE
Companie3
Companie2
Companie1
BASIN 3 3 BASIN
AVERAGE BASIN4 AVERAGE
Companie3
Companie2
Companie1
BASIN 4 BASIN
AVERAGE BASIN5 AVERAGE
Companie5
Companie4
Companie3
Companie2
Companie1
BASIN 5 BASIN BASINS 106 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
In Zambia (US $)
Year
864 864
103 103
617 617
146 146
190 190
185 185
309 309
257 257
103 103
324 324
103 103
411 411
576 576
206 206
198 198
137 137
118 118
185 185
350 350
74 74
41 41
10 10
48 48
51 51
27 27
Deprecialtion /
3
5
5
5
5
4
2
1
8
3
2
3
5
1
1
2
2
3
2
2
1
30
0,5
6,63
2,25
Drying Rack Drying
life(Year)
durationof
Estimateof
(US$)
Amount
Drying racks racks Drying
Year
62 62 309
77 77 206
93 93 411
82 82 370
781 781 742 3,92 285
720 720 370
206 206 086 3
329 329 51
760 760 610
171 171 761
257 257 370
514 514 309
171 171 057 2
658 658 144
432 432 103
720 720 309
276 276 478
494 494 514
370 370 411
103 103 576
136 136 411
102 102 342
132 132 237
103 103 350
3 292 292 3 432
3 155 155 3 823
Deprecialtion /
5
1
4
2
5
3
2
1
3
1
1
1
1
5
5
4
5
5
5
2,9
1,5
0,5
0,25
1,69
2,88
4,75
life(Year)
durationof
Estimateof
Fishing Gear / Rig / FishingGear
Fishing
Amount
gear (US$) gear
Year
4 115 115 4 309
1 029 029 1 247
974 974 703
535 535 823
329 329 720
180 180 309
540 540 411
144 144 646 1
557 557 453
480 480 370
309 309 514
411 411 679
Deprecialtion /
226 226 514
521 521 514
720 720 514
180 180 514
206 206 658
261 261 432
3 086 086 3 732 4
261 261 720
1 180 180 1 489
1 029 029 1 370
1 200 200 1 658
926 926 411
1 564 564 1 514
5
5
4
4
5
1
4
3
1
3
2
3
2
3
3
2
1
10
10
0,5
1,5
1,5
3,92
2,13
2,25
Generator / Rig / Generator
life(Year)
durationof
Estimateof
2525
(US$)
Amount
Generator Generator
Year
283 283 263 2
463 463 782
463 463 720
480 480 057 2
513 513 132 1
167 167 057 2
823 823 720
720 720 926
343 343 823
480 480 600 3
754 754 852 1
1 029 029 1 720
1 029 029 1 782
2 057 057 2 086 3
2 057 057 2 782
1 231 231 1
1 337 337 1 086 3
2 351 351 2 564 1
Deprecialtion /
8
2
1
4
3
1
3
8
5
2
6
6
3
3
0,5
5,25
0,42
3,105
life(Year)
durationof
Estimateof
Engine (drive unit) / Rig / (driveunit)Engine
AmountEngine
(driveunit) (US$)
Year
12 344 344 12 029 1
926 926 646 1 5 329 646 1
514 514 057 2 3 686 160 2
535 535 263 2
123 123 172 6
823 823 057 2
411 411 440 1
473 473 988
2 469 469 2 852 1 2 926 675 2
4 115 115 4 761 5 1 761 5 057 2
2 057 057 2 234 1 10 123 720
2 057 057 2 029 1 2 514 720
3 403 403 3 967 1 2,81 983 399 1 683 4,19 075 1
4 629 629 4 926
1 646 646 1 852 1
1 910 910 1 440 1
3 292 292 3 029 1
2 743 743 2 029 1
1 337 337 1 237 2
1 337 337 1 337 1
2 160 160 2 115 4
1 029 029 1 440 1
1 744 744 1 179 3
4 115 115 4 024 8
1 975 975 1 263 2
Deprecialtion /
5
2
5
5
2
1
5
7
3
3
2
6
5
5
10
20
10
30
10
10
20
15
7,63
10,5 7,75
life(Year)
durationof
Estimateof
Rig (Hull) Rig
(US$)
8 230 230 8
9 258 258 9
8 795 795 8
5 349 349 5
9 258 258 9
8 230 230 8
9 875 875 9
3 703 703 3
8 230 230 8
4 320,53 320,53 4
6 172 172 6
9 875 875 9
2 366 366 2
10 116 116 10 7,83 023 2 263 2 3,83 390 1 663 1
12 344 344 12
10 287 287 10
10 287 287 10
10 287 287 10
12 344 344 12
13 373 373 13
11 109,92 109,92 11
13 373,05 373,05 13
10 286,96 286,96 10
16 459,14 459,14 16
10 776 776 10
24 689 689 24 Amount Rig (Hull)AmountRig
Source: Economic Survey, 2013 Source: Survey, Economic
AVERAGE STRATUM I STRATUM AVERAGE
Companie6
Companie5
Companie4
Companie3
Companie2
Companie1
STRATUM I STRATUM
AVERAGE STRATUM II STRATUM AVERAGE
Companie8
Companie7
Companie6
Companie5
Companie4
Companie3
Companie2
Companie1
STRATUM II STRATUM
AVERAGE STRATUM III STRATUM AVERAGE
Companie4
Companie3
Companie2
Companie1
STRATUM III STRATUM
AVERAGE STRATUM IV STRATUM AVERAGE
Companie4
Companie3
Companie2
Companie1
STRATUM IV STRATUM
STRATUM STRATUM
Annexes 107
Annex 6. Cost sensitivity according to the size of the fishing enterprise
Zimbabwe (US $)
69,02 69,02
69,02 69,02
94,54 94,54
95,12 95,12
282,62 282,62
TOTAL
49,22 49,22
49,05 49,05
23,84 23,84
42,17 42,17
36,82 36,82
32,69 32,69
55,38 55,38
33,16 33,16
28,70 28,70
34,19 34,19
161,49
161,49
155,73
120,39
167,31
103,97
130,23
127,24
142,33
144,26
141,18
156,02
170,51
127,08
131,96 120,34
Sub Total Sub
3,50 3,50
3,50 3,50
9,89 9,89
9,89 9,89
6,92 6,92
1,92 1,92
9,13 9,13
4,81 4,81
7,58 7,58
4,10 4,10
18,26
18,26
30,68
30,68
28,09
14,99
23,85
20,75
13,26
16,09
29,72 29,72
32,95 32,95
99,76 99,76
12,18 12,18
24,88 24,88
15,64 15,64
17,31 17,31
12,50 12,50
16,67 16,67
14,42 14,42
17,31 17,31
137,46
6,41 6,41
6,41 6,41
6,41 6,41
6,41 6,41
5,75 5,75
6,41 6,41
7,58 7,58
5,77 5,77
4,81 4,81
4,81 4,81
5,77 5,77
5,81 5,81
6,94 6,94
5,68 5,68
4,81 4,81
7,95 7,95
8,01 8,01
9,92 9,92
4,49 4,49
5,77 5,77 11,56 11,56
FIXED COSTS FIXED
-
8,35 8,35
8,35 8,35
8,52 8,52
7,30 7,30
9,90 9,90
8,77 8,77
7,18 7,18
7,37 7,37
6,58 6,58
9,79 9,79
14,38 14,38
14,38 14,38
13,76 13,76
14,76 14,76
32,89 32,89
10,18 10,18
10,35 10,35
13,23 13,23
31,32 31,32
11,11 11,11
Depreciation FinancialInterest LicenceFeesInsurance & cost Management
50,77 50,77
50,77 50,77
92,30 92,30
88,98 88,98
70,69 70,69
79,04 79,04
93,92 93,92
86,15 86,15
130,81 130,81
130,81 130,81
106,51 106,51
118,26 118,26
145,15 145,15
109,48 109,48
103,40 103,40
100,16 100,16
131,00 131,00
104,36 104,36
123,33 123,33
115,14 115,14
103,26 103,26
Sub total Sub
1,38 1,38
1,38 1,38
5,77 5,77
5,77 5,77
3,74 3,74
6,38 6,38
5,06 5,06
1,85 1,85
3,30 3,30
3,19 3,19
5,29 5,29
6,07 6,07
6,25 6,25
7,05 7,05
4,92 4,92
4,66 4,66
2,44 2,44
1,92 1,92
5,88 5,88
6,92 6,92
6,15 6,15
58,57
58,57
38,54
38,54
22,68
27,12
30,32
11,06
14,04
27,38
30,58
42,61
42,40
57,27
28,14
39,30
30,96
59,17
27,14
43,08
36,15
VARIABLE COSTS VARIABLE
9,12 9,12
5,77 5,77
6,14 6,14
9,23 9,23
9,00 9,00
6,56 6,56
3,96 3,96
4,91 4,91
5,09 5,09
4,31 4,31
5,14 5,14
6,87 6,87
5,38 5,38
3,77 3,77
12,54 12,54
12,54 12,54
17,15 17,15
17,15 17,15
12,28 12,28
31,25 31,25
10,80 10,80
36,85 36,85
36,85 36,85
69,35 69,35
69,35 69,35
67,82 67,82
49,69 49,69
51,64 51,64
70,31 70,31
47,21 47,21
64,62 64,62
54,24 54,24
53,80 53,80
68,82 68,82
40,10 40,10
55,30 55,30
85,62 85,62
48,92 48,92
54,02 54,02
47,88 47,88
40,08 40,08
105,35 105,35
7
4
4
4
4
4
4
3
3
3
2
2
2
2
2 11
Source: Economic Survey, 2013 Survey, Economic Source:
Average 7 rigs 7 Average
Average 11 rigs 11 Average
Average 4 rigs 4 Average
Average 3 rigs 3 Average
Average 2 rigs 2 Average NumberofRigs Fuellubrification & (F&L) (R&M)Maintenance Repair and supplies Other Wages(import, Taxes property) 108 Report on the Bioeconomic Modelling of Kapenta Fisheries on Lake Kariba
Zambia (US $)
97,49 97,49
97,49 97,49
87,68 87,68
87,68 87,68
90,83 90,83
90,83 90,83
85,67 85,67
85,67 85,67
98,83 98,83
91,07 91,07
93,13 93,13
96,46 96,46
90,63 90,63
76,85 76,85
79,32 79,32
88,50 88,50
127,01 127,01
127,01 127,01
115,99 115,99
115,99 115,99
116,00 116,00
118,33 118,33
113,66 113,66
143,08 143,08
153,34 153,34
151,59 151,59
124,32 124,32
106,59 106,59
105,06 105,06
119,82 119,82
102,52 102,52
104,76 104,76
147,01 147,01
TOTAL
9,67 9,67
9,67 9,67
7,23 7,23
7,23 7,23
8,69 8,69
24,60 24,60
24,60 24,60
28,46 28,46
28,46 28,46
11,22 11,22
11,22 11,22
21,01 21,01
27,87 27,87
14,16 14,16
22,75 22,75
42,05 42,05
16,10 16,10
10,10 10,10
11,70 11,70
11,70 11,70
16,53 16,53
24,37 24,37
26,25 26,25
49,21 49,21
21,63 21,63
14,73 14,73
19,42 19,42
23,33 23,33
10,57 10,57
15,23 15,23
60,86 60,86
15,75 15,75
14,26 14,26
Sub Total Sub
-
-
-
-
-
0,70 0,70
0,70 0,70
0,55 0,55
0,55 0,55
0,73 0,73
0,73 0,73
0,75 0,75
1,03 1,03
0,47 0,47
0,73 0,73
1,14 1,14
0,54 0,54
0,50 0,50
0,88 0,88
0,88 0,88
0,89 0,89
0,89 0,89
0,71 0,71
0,34 0,34
1,07 1,07
0,93 0,93
0,82 0,82
1,03 1,03
2,07 2,07
1,47 1,47
2,81 2,81
1,68 1,68
2,34 2,34
1,63 1,63
1,63 1,63
1,63 1,63
1,63 1,63
1,56 1,56
1,56 1,56
1,49 1,49
1,49 1,49
1,53 1,53
1,43 1,43
1,63 1,63
1,63 1,63
1,90 1,90
1,49 1,49
1,49 1,49
1,56 1,56
1,56 1,56
1,56 1,56
1,56 1,56
1,49 1,49
1,56 1,56
1,43 1,43
1,48 1,48
1,37 1,37
1,63 1,63
1,43 1,43
1,49 1,49
1,57 1,57
1,56 1,56
1,63 1,63
1,56 1,56
1,56 1,56
1,56 1,56
FIXED COSTS FIXED
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
7,57 7,57
7,57 7,57
9,00 9,00
9,00 9,00
8,11 8,11
9,27 9,27
9,27 9,27
4,78 4,78
4,78 4,78
6,19 6,19
9,01 9,01
22,96 22,96
22,96 22,96
26,12 26,12
26,12 26,12
18,73 18,73
25,41 25,41
12,06 12,06
20,40 20,40
39,00 39,00
14,08 14,08
14,33 14,33
22,47 22,47
24,30 24,30
47,01 47,01
20,00 20,00
13,30 13,30
16,91 16,91
20,10 20,10
12,12 12,12
56,50 56,50
12,51 12,51
10,36 10,36
Depreciation FinancialInterest LicenceFeesInsurance & cost Management
69,03 69,03
69,03 69,03
78,01 78,01
78,01 78,01
94,98 94,98
90,46 90,46
99,51 99,51
79,13 79,13
79,13 79,13
78,44 78,44
78,44 78,44
82,30 82,30
82,22 82,22
82,38 82,38
78,81 78,81
70,61 70,61
80,89 80,89
78,41 78,41
85,34 85,34
73,13 73,13
80,07 80,07
61,63 61,63
86,15 86,15
63,57 63,57
74,23 74,23
102,42 102,42
102,42 102,42
104,77 104,77
104,77 104,77
120,33 120,33
111,29 111,29
135,49 135,49
114,22 114,22
Sub total Sub
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
4,43 4,43
4,43 4,43
0,97 0,97
0,97 0,97
1,25 1,25
3,75 3,75
Taxes (import,Taxes property)
8,17 8,17
8,17 8,17
8,39 8,39
48,93 48,93
48,93 48,93
39,49 39,49
39,49 39,49
52,52 52,52
52,52 52,52
36,98 36,98
27,39 27,39
46,57 46,57
24,88 24,88
31,78 31,78
34,47 34,47
39,01 39,01
39,01 39,01
27,09 27,09
27,09 27,09
27,84 27,84
27,84 27,84
27,84 27,84
23,94 23,94
16,05 16,05
34,27 34,27
16,89 16,89
28,56 28,56
21,79 21,79
30,46 30,46
22,34 22,34
15,69 15,69
25,03 25,03
15,45 15,45
6,87 6,87
6,87 6,87
3,04 3,04
3,04 3,04
6,36 6,36
6,36 6,36
2,37 2,37
2,37 2,37
5,09 5,09
9,14 9,14
1,03 1,03
9,14 9,14
7,32 7,32
4,53 4,53
2,19 2,19
2,19 2,19
8,21 8,21
8,21 8,21
8,75 8,75
5,87 5,87
7,54 7,54
4,36 4,36
6,27 6,27
2,45 2,45
7,34 7,34
2,69 2,69
5,00 5,00
9,96 9,96
9,68 9,68
9,35 9,35
15,57 15,57
11,62 11,62
17,10 17,10
VARIABLE COSTS VARIABLE
Other suppliesOther Wages
9,51 9,51
9,51 9,51
9,93 9,93
9,93 9,93
3,44 3,44
3,44 3,44
3,10 3,10
3,10 3,10
9,68 9,68
8,61 8,61
2,67 2,67
6,06 6,06
6,53 6,53
7,38 7,38
6,17 6,17
5,31 5,31
6,60 6,60
7,16 7,16
22,48 22,48
22,48 22,48
17,14 17,14
17,14 17,14
15,28 15,28
14,79 14,79
15,78 15,78
15,00 15,00
16,31 16,31
10,50 10,50
18,19 18,19
10,76 10,76
10,41 10,41
19,83 19,83
13,09 13,09
Repair and Maintenance (R&M) Maintenance Repair and
24,13 24,13
24,13 24,13
36,25 36,25
36,25 36,25
21,68 21,68
21,68 21,68
39,94 39,94
39,94 39,94
37,63 37,63
39,14 39,14
36,13 36,13
70,06 70,06
52,14 52,14
74,95 74,95
83,10 83,10
34,48 34,48
34,48 34,48
40,04 40,04
40,04 40,04
36,03 36,03
39,90 39,90
32,16 32,16
36,91 36,91
47,53 47,53
23,47 23,47
35,42 35,42
41,23 41,23
37,47 37,47
39,53 39,53
28,12 28,12
55,18 55,18
22,26 22,26
42,27 42,27
9
9
8
8
8
5
4
3
3
2
2
2
2
1
1
1
1
1
19
14
12
11 Average 5 rigs 5 Average
Source: Economic Survey, 2013 Survey, Economic Source:
Average 19 rigs 19 Average
Average 14 rigs 14 Average
Average 12 rigs 12 Average
Average 11 rigs 11 Average
Average 9 rigs 9 Average
Average 8 rigs 8 Average
Average 4 rigs 4 Average
Average 3 rigs 3 Average
Average 2 rigs 2 Average
Average 1 rigs 1 Average NumberofRigs Fuellubrification & (F&L)
The IOC-SmartFish Programme aims to contribute to an increased level of socio-economic and environmental development in the Eastern and Southern Africa and Indian Ocean (ESA-IO) region, inter alia through improved governance and more robust approaches to fisheries and aquaculture management. The SmartFish Programme is implemented by the Indian Ocean Commission (IOC) on behalf of Common Market for Eastern and Southern Africa (COMESA), Intergovernmental Authority on Development (IGAD), the Indian Ocean Commission (IOC) and the East African Community (EAC), in collaboration with Food and Agricultural Organization (FAO) and benefits from the financial support of the European Union (EU).
In this context, the programme has supported work on the bioeconomic modelling of the Kapenta fishery on Lake Kariba, conducted as part of a process of joint fisheries management between the Governments of Zimbabwe and Zambia.
This report details the various steps involved in creating a bioeconomic model of the Kapenta fisheries on Lake Kariba and presents highly detailed findings. In addition to a diagnosis of the Kapenta fisheries and the economic situation of the industry, this report illustrates the model’s potential for management purposes and its prospects for development.