Although Wetherall <9I2.@ attributes the disparity of about 50% between his flgw'e and that of UFD to an inherent positive bias due to the sampling of primarily major landing sites by UFD, even his estimates based on the fIrst quarter of the year could give a considerable negative bias, although not in the order of 50%, because this is the period when the catches are generally low. This is true for our survey (conducted during the fIrst quarter of the year) where a correction factor for seasonality was necessary.
The disparity in the retention characteristics of the same mesh size gillnet in different water bodies may be due to either wrong information on the mesh sizes used and/or use of mixed mesh size nets, fishing method (e.g. beating of water) or the state of the flShery (e.g. stunting). In Lake Wamala, the smaller size retention characteristics of the 76.2 mm mesh nets for - Oreochromis species compared to the Minor Lakes was apparently due to the stunted flSh stocks of Lake Wamala (Okaronon, unpublished data). The difference between the retention characteristics of the 127.0 mm and 203.2 rom mesh nets for O. niloticus in lakes Victoria and Kyoga was probably due to the use of mixed mesh size nets in Lake Victoria (127.0 mm and 152.4 mm for what was reported as 127.0 rom); this would not apply to the Lwampanga area of lake Kyoga where these nets were used as (during the survey) the canoes landed in areas specified for particular mesh size nets. I Apart from the Minor Lakes and Lake Wamala, canoes from landings in category B and to a certain extent C, flShed from more distant areas, 10' kIn on average, from the landings. This would indicate that the fIshermen travelled these distances in search of good flShing grounds and I' would most likely practice active flShing while guarding the flShing gears. This may partly explain the high catches per unit of effort in the category B landings. The flShermen from Malembo and Kanagisa both category A landings on Lake Kijanebalola, a minor lake, were observed to flSh for the haplochromines only, thus the Jow catch per unit of effort in the I category A landings. I I II Il I 16
I 3.5 Conclusions and recommendations I 3.5.1 Conclusions The survey field work was generally successfully carried out according to the objectives and schedule. The data collected from the catch assessment part of the survey has resulted in some I useful indices. (i) At least 17 fish species groups were encountered during the survey, with Lake Albert having the highest species diversity, at least 11 species. Oreochromis niloticus and Clarias I species were recorded in all the water bodies surveyed; Schilbe, Alestes and Hydrocynus species were only recorded in Lake Albert. I (ti) The size structure of the fISh stocks harvested in the various water bodies varied with the species and the sizes of gear used. In most cases the popularity of gears used in a water body reflected the relative abundance and/or the commercial importance of the particular stocks I within the size range heavily harvested by the given gear. (iii)The highest and lowest catch of 59.04 kg/canoe and 10.64 kg/canoe were recorded from Lake Kyoga and the Minor Lakes, respectively, while the highest and lowest catch of 12.14 I kg/net of 76.2 mm t and 0.31 kg/net of 76.2 mm mesh were observed from Lakes Wamala and Albert, respectively.
(iv) The total fISh production from the national waters during 1989 was estimated at 131,954 I mt. The highest proportion of 45.91% of the total production came from Lake Victoria. The Tilapiine species contributed the bulk (67.33%) of the national fish production.
I (v) The commercial gill nets used during the survey period ranged from 50.8 rom to 304.8 rom mesh. Gillnets of 127.0 rom and 203.2 rom mesh were the most popular on Lakes Kyoga and Victoria (for harvesting O. niloticus and L. niloticus), while in Lake Albert, the 50.8 mm I and 63.5 rom mesh were popularly used (for Hydrocvnus and Alestes species). The above indices will form a basis for further surveys.
I 3.5.2 Recommendations
On the basis of the observations made from the catch assessment survey, the following are I recommended: (i) Another Frame Survey is needed to rectify the controversy over the number of canoes I operating on the various water bodies, especially Lake Victoria (Uganda). (ti) There is need to establish the Catch Assessment Survey (CAS) programme on at least a quarterly basis, alongside the Uganda Fisheries Department CUFD) statistical collections, as I neither system by itself is likely to be adequate. If the quarterly CAS cannot feasibly be done in any single year, then this could be done over a number of years, provided a different quarter is covered each year.
I ALTERNATIVELY, consideration could be given to either
(a) A complete census of the fISheries parameters, whereby both the Frame Survey and I the Catch Assessment Survey could be conducted in all the waters over a limited period of time; OR
I (b) The statistical collection systems should be strengthened in order to cover most of the landing sites in the various water bodies and to collect accurate statistics.
(iii)There is an urgent need for a Stock Assessment Survey (SAS) to determine the available I stocks and relate these to the catch levels in 2(ti) above. I - I
17 1 4. ASPECTS OF ARTJSANAL FR;IBERIES IN UGANDA I by Drs. A.B. Frielink jr4
4.1 Introduction 1 4.2 Fleet size and structure 4.3 Fishing gear in use 4.4 Seasonality aspects I 4.5 Fishing effort, catches per Wlit of effort and total catches 4.6 Selected socio-economic aspects 4.7 Processing and marketing 1 4.8 Opinions of fIshermen 4.9 Conclusions 1 4.1 Introduction
The information obtained from interviewing the fIShermen, using Form A is presented here. 1 As not all the Questions asked resulted in satisfactory answers, a comprehensive and coherent analysis could not be made. Instead, a number of topics was selected, which together show important aspects of the fIShing industry in Uganda. This information, which is rather basic, 1 points the way to further research on fISheries in the country.
4.2 Fleet size and structure 1
The information presented in this Chapter comes from Question 1: Do you own this canoe?, Q. 4: What type of canoe? (planked or dugout), Q. 5: Does your boat generally operate with an 1 engine?, and Q. 24: How long does a canoe last? Analysis and results 1 The fIShing fleet in Uganda comprises approximately 10,000 planked fIShing canoes and 2,000 dugout canoes (MAIF 1989), 7,725 of which were covered by the sample survey. Of these, 11% were found to be motorized (Table 4.2.1). Only 5% of the fIShermen that were interviewed 1 worked on dugout canoes. On average, of those fIshermen that were interviewed, 65% owns the canoe they work on (Table 4.2.1). However, there are large differences between the lake areas, with Lake Wamala the highest canoe ownership, and Lake Victoria the lowest. :'1
Table 4.2.2 shows the age structure of the fleet. On average, 42% of the canoes is less than one year old. Lake Victoria and the minor lakes have made large investments in the fIShing sector in 1988, while Lake Albert did invest little in that year, but did so in 1987. Fishermen on 1 Lake Kyoga have invested little in recent years. In general, 61% of the planked canoes were found to be in a "fair" state, while 73% of the dugout canoes were in a "poor" state. 1 Table 4.2.3 shows the life expectancy of canoes. For all waterbodies, canoes have a life of 3.7 years, with a standard deviation of 2.7. There are marked differences between lake areas, with Lake Kyoga and Lakes GeorgelEdward having the longest living canoes, and the Minor Lakes the shortest living canoes. 1 1
4project Coordinator of the EEC funded Artisanal Fisheries Rehabilitation Project 1 I II I
I 18 I Discussion The percentage of motorisation of the fIshing fleet in Uganda of 11% is in line with MAIF 1989, which fOWld 10% motorised. 40% of the interviewed did not answer this question, as it I was not deemed relevant by them, which means that they do not operate an engine. The percentage of dugout canoes fOWld (5%) is much lower than that in MAIF 1989 (16%). This is due to the method of selection of vessels for interview, employed by the teams, which I had an built-in bias against dugout canoes. The outcome of 5% is therefore probably not representative. I The ownership structure has given some surprising results. While it is in the line of expectation, that on the whole 65% of the canoes are owned by active fIshermen (and not by absentee-owners), with a percentage of 52 on Lake Victoria, it does not seem true that this fIgure is as high as 81% on Lake Edward/George. This fIshery is characterized in general by a I high percentage of absentee fIshermen. The cause of this bias in the survey is not known.
One of the most salient features of the fIshing fleet in Uganda is the age structure, and the I low life expectancy. While most canoes are made of the best tropical hard wood, they are not used longer than 3 to 4 years. After that period, they sink, or are left to rot on the shore. It was generally observed that one of the main reasons for this is the poor quality of workmanship on the side of the boatbuilders, the lack of proper nails and glues, and the lack of knowledge about I modern construction techniques. This aspect of the fIshing industry should be taken seriously, as it not only represents a serious drain on Uganda's forest reserves, but it also makes the I fIsheries expensive. With relatively small improvements, canoes should last at least 10 years. While the fIshermen on Lakes Victoria, Wamala and Minor Lakes have been renewing their fleets in recent years, the fIshermen on Lake Kyoga have clearly suffered from the insecurity situation which existed there especially in 1987/88. The picture on Lakes GeorgelEdward is not I so clear, but it is suspected that the answers given related to officially licensed canoes only, while the illegal ones have a much shorter life span, due to the activities of the Fisheries I Department, amongst others. In conclusion, the Ugandan fIshing fleet has a low degree of motorization, with canoes that I are made of the best timber available, and have a very short life span. 4.3 Fishing gear in use
I The information presented in this Chapter comes from Question 21: What was the most important gear used on that trip?, Q. 22: How many of this (gear) were used on that trip?, Q.25: How long does a gil/net last?,' Q.27: Do you mend gill nets?, Q.28: If no, why not?, and Q. 29: I Where are the nets for this boat usually bought? Analysis and results
I The survey concentrated mainly on the gill net fIshery. A negligible percentage of those interviewed used hooks, traps or weirs. More than 90% of the canoes were f?Wld to use nets (Table 4.3.1). Nets generally do not last longer than 9 months (Table 4.2.3) with variations from I 5.7 months on Lake Wamala to 12.8 months on Lake Albert. Less than 40% of the fIshermen in Uganda mend their nets (Table 4.3.3). Nearly 60% of those that do not mend their nets (Le. 37% of the total number of fIshermen who use gillnets) claim that they do not have the knowledge of how to mend a net. There is a large variation between water bodies, and between different I categories of landing. I I J I I 19
Fishermen generally buy their' nets in a shop "elsewhere" (i.e. not near the landing) or at the local shop. Lakes Wamala and Lakes George have strongly benefitted from a Government I, project, while the Lake Albert is least well served by the local shop, and importing an important percentage of the nets from Zaire. . I Table 4.3.1 analyses the total number of nets in use in Uganda. The average number of nets per canoe is 19.2, with large differences between Categories of landing, between waterbodies, and between canoes on each landing (see Chapter 4.5 and Table 4.5.1). Using the number of canoes for each landing and the total number of landings from MAIF 1989, the estimated number of I nets in Uganda amounts to 190,000. With an average life of 8.8 months, this means that the total demand for nets in Uganda can be estimated at over 250,000. 1 Discussion While the gill net is the predominant gear in use in commercial fisheries in Uganda, other I gears are also commonly used, such as hooks, traps, weirs, beachseines (illegal), beating of water, and poison (also illegal). The survey, however, had a bias towards commercial (and legal) fishing. Therefore, the real p,ercentage of the canoes that use gill nets, might be lower than the 90% which was encountered in the survey. I
One of the more interesting aspects of the gill net fishery is that there is a large variation between lake areas in the number of fishermen who do mend their nets. When comparing the I figures in Table 4.2.3 (for nets only) with those in Table 4.3.3, then there is a strong correlation between the percentage of fishermen who mend their nets and the average life expectancy of the nets. On Lake Wamala, only 10% of the fishermen mend their nets, and consequently the . average life of the net is less than 6 months. By contrast, on Lake Albert, 68% mend their nets I with an average net life of over 1 year.
Of those that do not mend their nets, nearly 60% of the fishermen claim that they do not I have the know-how, this reason being' far more important than the lack of materials to do so (12%). This points to the need and necessity of an education programme for fishermen, to teach them how to mend nets. The example of the difference between Lake Wamala and Lake Albert I shows that mending nets could double the life of a net, and hence reduce the cost of fIShing gear, even taking into account costs of materials and labour.
The number of gill nets estimated to be in use in Uganda (190,000) is in line with the I fmdings of MAIF 1989, and corresponds well with the total catch estimates (see Chapter 4.5). However, in certain so-called controlled areas (especially the Lakes George and Edward), the true number of nets in use might be substantially higher. Allowing for this, the total number of I nets in use could be up to 250,000 pieces, with an annual requirement of about 340,000 pieces. If the nets were to be properly repaired and maintained, then the annual requirement could be as low as 250,000 pieces, saving the country an estimated 1 million US$ on imports. I 4.4 Seasonality aspects I The information in this Chapter came from Question 16: Is the season at the moment poor, fair, good or excellent?, and Q.17: Indicate for the two most important species for which you fish, how good a season it is for each month (good, fair or poor). I Analysis and results
The analysis was only done for the first species given by the fIShermen, as the information on I the second most important species was incomplete. For each water system a separate analysis was done. For each of the major species of the relevant water system, it was determined what I I I I
I 20
percentage of the valid5 answers was "good", what percentage "fair" and what percentage "poor". I The results of this are shown- in Table 4.4.1.
To translate the three percentages for each month, into an index figure, each of the percentages had to be assigned a weighing factor. The weighing factor for a "poor" catch was I 70%, and for a "good" catch was 130%, where an average ("fair") catch is 100%. Although not based on actual data, these factors were thought to be able to clearly bring out seasonal I patterns, Without exaggerating catch differences over the year. These weighing factors were applied to each of the percentages for each month, and added up. For example, JanuarylNile Perch in Lake Victoria, the three percentage might have been 58, I 23, 19. The calculation would have been (58 x 0.7) + (23 x 1) + (19 x 1.3) = 146.30. The resulting column of figures was then translated into an index figure, with the average number for January and February (the months of the interviews) as 100. Tables 4.4.2 and following I show the resulting index figures in column B. To make the seasonal index figures easier to read, they were recalculated, with the average for the whole year as 100 (column C) of the relevant tables. In the same tables, this has been I also graphically presented.
I Discussion The analysis and results presented in this chapter, are based on the opinion of the fishermen. That is, they are not based on actual observations. However, one can presume, that flShermen I are well aware of the seasonal patterns. This is confirmed by the fact that the answers given by the flShermen are fairly consistent.
In general, the answers given for the second species, were fairly weak and inconsistent. This I is probably explained by the fact that most flShermen are specialized in one, or at the most two, species.
I In constructing the seasonal index figures, the explicit assumption has been made that "good" catches are 30% higher than "fair" catches, and that "fair" catches are 30% higher again than . "poor" catches. The choice of these figures is arbitrary, and could not be confirmed by the I answers which were given on the actual catches of the day. The reason for this is, that one· flShermen might fmd 10 kg "fair" while an other fishermen will fmd the same 10 kg "poor".
Changing the above percentages (for example to 20%) would not change the pattern, nor I column C of the relevant tables. It would, however, change Column B, which will be used later, to correct the catch figures from the months of January and February.
I A sensitivity analysis has been done for some water bodies, to see what impact a change in the "30%" would have on the average correction factor for the whole year. Table 4.4.8 shows that for Lake Victoria, a change of 10 percent points in the 30% assumption, results in a change of 4 percent points in the correction factor over the whole year. For the lakes Kyoga and Albert, this I is 2 percent points. The sensitivity analysis shows that, within certain limits, the seasonal index figures for the catches can be used to correct the catch figures from the survey, so as to be able I to compute annual catch figures. The results for Lake Victoria are presented in Table 4.4.2. The patterns for the two species, I 5 a "valid" answer was 1, 2 or 3; in some cases, answers were given for some months, but were left blank (= 9) in others, so that the total number of valid answers, for each species I would differ from month to month. I I I
21 I
Tilapia spp and Nile Perch, are dear. It should be noted that there is a large difference in the index between December and January. This is explained by the fact that during the interview I period, the catches were poor, and naturally seemed much poorer than in the previous months. The same phenomenon can be seen in other water bodies, such as Lake WamaIa. 1 In a number of other water systems, the season was good at the moment of the interview. Hence, the season seemed at that point much better than recollected from the previous months. Which again explains the differences between the index figure of December and January, as is the case with Tilapia spp in Lake Albert (Table 4.4.4). I
4.5 Fishing effort, Catches per Unit of Effort and Total Catches I
The information presented in this Chapter comes from the analysis of question no 19: How many kgs did you catch on the last trip?, Q. 21: What was the most important gear used on that trip?, Q. 22: How much of this gear was on this trip? and Q 13: At the moment how many times I per week does the canoe go out to fish? The data was analyzed for nets only, hooks and traps were disregarded. I Analysis and results The analysis· of the data was done for those canoes using nets only. The number of canoes I interviewed using hooks (longlines), traps or 9ther methods was too low to' be analyzed.
The average catch per trip for the whole country was 30.7 kg per canoe per trip (Table 4.5.1), with the highest catches appearing on Lakes Victoria and Kyoga and the lowest on the minor I lakes. The high values of the Standard Deviation indicates wide variations in catches per trip. The number of nets used per trip per canoe varies from only 4 on Lake Wamala to 43 on Lake Albert. Here again, the values of the Standard Deviation are relatively large, with the exception ,I of Lake GeorgelEdward, and Lake Wamala, both controlled lakes.
The fishing effort was expressed as the number of times per week that the canoe goes out. Table 4.5.2 shows the results. The average is just under 6 days per week for the whole country, I with lakes GeorgelEdward having the most active fishery. The least active are those on Lake Kyoga (4.7 days per week). Standard Deviations are relatively small. I Table 4.5.3 gives an estimate of the total catches in Uganda. First the figure for the nets are calculated, then for the hooks, giving a catch of approximately 110,000 and 10,000 mt for 1989, a total of 120,000 mt not corrected for seasonal variations. I
Discussion I Although not based on observations, but on interviews, the catches per trip, and the number of trips are the practically the same as those found by Okaranon and Kamanyi (Chapter 3). This serves to underline the validity of the data obtained from both forms used. The high Standard I Deviations obtained for the Catches per trip and the Number of nets per trip are caused by the fact that it is not possible to have negative numbers of gear or negative catches, but it is possible to have very high ones. This causes a skewed distribution with high standard deviations. Some fishermen in Lake Albert for example use fleets of 200 nets. I
The total catch estimated to be 120,000 mt, is slightly less than that of Okaranon and Kamanyi. This is caused by small differences in the catch per unit of effort, which multiply over I a whole year. Total catches are not much affected when corrected for seasonal variations, as this would lower the catch in some lakes, and increase this in other lakes. The total effect would probably less than a 5% increase. I
-~ I ~ I
I 22
I In line with the discussion by Okaranon and Kamanyi in Chapter 3, it is believed that the figure of 120,000 mt might be an under-estimate caused by inaccurate information on the number of canoes which are actively fishing. This applies especially to Lake Victoria. MAIF 1989 gives 3,400 vessels for the lake, while some sources in the Fisheries Department put this figure I at 7,000. There is no indication at present that the figure is as high as that. Assuming a total of active flShing vessels on Lake Victoria of 4,500, then the total catch in Uganda would be not I more than 160,000 mt. • ' I 4.6 Selected socio-economic aspects The information in this Chapter groups together some social and economic information regarding flShermen in Uganda. The results from the survey did not warrant a coherent socio I economic analysis, and instead a number of selected topics are presented. Analysis and results
I The average number of flShermen per canoe, non-motorized gillnet flShery only, is 2.2, pointing to a total number of fishermen (in the survey area) of 23,000 (Table 4.6.1). The average age of the Ugandan flSherman is around 35 years, with the youngest flShermen on Lake Victoria and the oldest flShermen on the Minor Lakes (Table 4.6.2). On most lakes, the average age of I the flSherman declines with the increase of the size of the landing. A notable exception to this is Lake Kyoga, where the flShermen on the smallest landings are the youngest flShermen in I Uganda (average). Fishermen tend to have 1 or 2 wives, with a statistical average of 1.4. The number of children per flSherman is almost 6 on average, with a Standard Deviation of 6.5. The highest number of children are on Lakes GeorgelEdward, Albert and Kyoga, where the number of wives I is also slightly higher (Tables 4.6.3 and 4.6.4). In addition to wives and children, another 4 persons on average are staying in the house of the fisherman and are dependent on them. This makes the total number of dependents on average 11. A total of 79% of the flShermen has their I family staying with them.
Most flShermen in Uganda have gone to school and have at least reached Primary School I Standard (85%, Table 4.6.7). Of those that went to school, 80% reached primary school standard, 18% 0 level standard and 2% A-level standard.
85% of the flShermen in Uganda have flShing as their primary income source, while for 15% I flShing is a secondary source of income. Agriculture is by far the most important secondary source of income (Table 4.6.8).
I Various systems of renumeration of the crew are used in Uganda (Table 4.6.9). The most widely used is that the crew gets a share of the proceeds (32%), followed by a share of the catch (26%) and a wage (20%). Marked differences exist between the different lakes, with a high percentage of wage earners on Lakes Albert and Victoria. Also large differences between I waterbodies exist as to which share system is employed (Table 4.6.10). I Discussion Fishermen in Uganda are on average relatively young, which might indicate that access to the industry (whether through the father or from outside) is relatively easy, which creates a healthy I situation of a dynamic sector. The majority of the flShermen have their family staying with them, which is an indication of a low degree of mobility. Only on Lake Victoria, the flShermen I seem to be more mobile, presumably to follow the flSh stocks. I I I I 23
Fishermen in Uganda are also well educated. The fact that 85% of the fIShermen have gone to school, is very favourable in an international context. In many other couhtries, this percentage I often is below 50%. This means that the management of the fISheries should be easier than . usual, as fIShermen are more literate, and will be able to understand better the needs and means of fISheries management. I Unfortunately, it was not yet possible to extract information on incomes and costs from the survey. This can only be done, after certain information has been checked in the field. I 4.7 Processing and marketing I The information in this Chapter comes from Question 41: How is the processing organized?, Q.42: What processing method is used?, Q.43: At what prices do you sell the following species?, Q.44: To whom do you usually sell your fish?, Q.45: If you sell to a trader, do you always sell to I the same trader? Analysis and results I A quarter of the fIShermen in Uganda organize the processing of the fISh themselves (table 4.7.1). The highest percentage is for Lake Albert, where 60% of the fIShermen sell processed fISh, while the fIShermen of Lake Wamala sell only fresh fISh. The larger the landing, the more likely I that fishermen process their own flSh. The most popular processing methods employed are smoking (Lake Victoria and Lake Kyoga), sun drying (Minor Lakes and Lake GeorgelEdward) and salting and drying on Lake Albert (Table 4.7.2). I Table 4.7.3 shows the average producer prices for the various waterbodies. Prices range from 500 to 800 Ushs per kg of fresh Nile Perch on Lake Victoria to 30 - 60 Ushslkg of fresh Tilapia on Lake Wamala. Here, the extreme prices (high and low) which were only quoted once or I twice, have been eliminated to show the more common price range. Fishermen usually sell their catch to fISh traders and fISh mongers6 (91%, see Table 4.7.4). In I total, 13% of the fIShermen usually sell to the same trader/monger. This figure varies from 0 on Lake Wamala to 20% on Lake Kyoga, and is 31% at landings of Category A.
Discussion I
It is a common practise in Uganda for fISh to be auctioned at the landing site. This results in a relatively large variation in producer prices. These prices depend largely on the quantities I caught that day, and a number of other factors, such as the availability of transport, and the amount of cash available in the area. For Lakes GeorgelEdward and Lake Albert prices are determined by the demand in neighbouring Zaire. I As a result of the auction system, fIShermen are less dependent on their buyers than is the case with artisanal fIShermen in other countries. Only 13% usually sell to the same traders, with 31% for Category A landings, where it is less likely that there is an auctioning system. This I could indicate that a Ugandan fIShermen is less likely to be dependent on traders for the supply of credit for fIShing inputs, which usually results in a price squeeze to hide high interest rates charged. I I
61n Uganda there is no practical difference between fish trader and fISh monger. Only a formal difference exists, depending on the licensing authority. I I I I ..
I 24 I 4.8 Opinions of fishermen The information in this Chapter came from Question 58: Did you hear' about the AFRP before now?, Q 59: Has he bought anything from the AFRP?, Q.60: What did he buy from ihe AFRP?, Q.61: When did he buy this?, Q.62/63: Did it help to increase his fishing activitieslcatches?, I Q.64: Was the quality of the inputs good, fair or poor?, Q.65: Would he buy from the project again?, Q.66: If no, why not?, Q.67: In your opinion, what is the equipment that the project should provide?, Q.68: If he did not buy anything from AFRP, why not?, Q.69: Would he like to I buy from the project?, Q.70: If yes, what items would he be interested in?, Q.71: What are the biggest problems confronting fishermen? and Q.72: How should the government assist in solving I the problems of the fishermen? Analysis and results
I A total of 901 answers were given for Question 71, and 906 answers for Question 72 by 335 fIshermen; only 1 fIsherman did not answer. This means that on average, each fISherman could identify more than two problems (see Tables 4.8.1 and 4.8.2 for a summary of the answers I given). • Overall, the largest problem identilled by the fIshermen was the shortage of fIShing nets and high prices of those nets. In many cases, the fIShermen combined these two into one problem. I This indicates that fIShing nets might be available, but at unacceptably high prices. It is thought, that the way this question has been brought up by the fIShermen, indicates that fIShermen have become used to the Government being a cheap supplier of fIShing nets. Especially between 1986 I and 1988, nets were sold by the Artisanal Fisheries Rehabilitation Project, at prices which were far below their real value.
The second biggest problem identilled, was that of the theft of nets. This is a common I occurrence in Uganda, and should be considered a serious problem. A fISherman loosing his nets could be set back for months, adding to the instability of the industry rather than to the I development of the industry. The third biggest problem is the high income tax. This is a relatively new problem for the fIShermen. Since the fU'st of July 1989, all fIshermen are required to pay 40,000/· UShs income I tax, before they are allowed to buy a fIshing license. In certain areas, this has led to an uncontrollable situation, whereby the JPBjority of the fIshermen is not licensed. I Table 4.8.3 presents the problems o(fIshermen, by waterbody, and by category of landing. On Lake Victoria, the fourth most important problem is the lack of outboard engines, while the shortage of spare parts for outboard engines is not mentioned. For the whole of the lake, I lack of transport/poor roads are not above the average for the whole country, but for the small landings, this seems to be an important problem. Most of these small landings, covered by the survey, are situated on the islands, indicating the problem of inter-island transport.
I On Lake Albert, the lack of social services, such as schools and dispensaries, was thought to be the third problem, larger even than the shortage of engines. Presumably, engines can be obtained (illegally) from Zaire. The problem of marketing is more pronounced on Lake Albert, I than in other areas.
The Minor Lakes have different problems altogether. After the problem of the inputs, there I are problems with floating islands and the high income tax. For the smallest landings (category A), the high income tax, poor catches and no roads are the dominant complaints.
I The theft of nets is the single most important problem· on Lake W~ dominating the I
I -~- , I I, 25 scarcity or the prices of the inputs. High income tax and the occurrence of hippos is mainly a problem of the small landings. 1
On Lakes George and Edward. the lack of nest and the theft of nets are the biggest problems, dominating all other types of problems. Poor catches, high income tax and the 1 shortage of spare parts for outboard engines are other problems which are frequently mentioned.
Lake Kyoga experiences a problem with both nets and outboard engines, while the theft of nets has a high occurrence. I
Suggested solutions 1 In line with the problems identified by the fIshermen, the suggested solutions centre around the provision (by the Government) of inputs, the reduction of the prices of inputs, improvement on the marketing infrastructure, and a change in the income tax system (Table 4.8.2). 1
Table 4.8.4 presents the results by water body. There are no marked differences between the suggested solutions and the problems identified, albeit that there is a much stronger emphasis I on the issue of availability and prices of nets. The percentage of fIshermen that heard about the AFRP before the interview is 83%, with I ranges from 70% on Lake Albert to 99% on the Minor Lakes (Table 4.8.5). In contrast, the percentage of people that heard of the AFRP and actually bought something from the project is 31%, with variations from 0 on the Minor Lakes to·88.% on Lakes Edward and George. I Seventy percent of the fIShermen responded that the items they bought from the AFRP increased their activities and/or catches. Only 7% thought that the quality of the goods was poor, while all would buy again, with a strong preference for gillnets, twine and outboard 1 engines.
Those asked why they did not buy anything from the AFRP claimed that the project never came to their place, or that they did not have the money. High prices or wrong equipment were I not a consideration.
Discussion 1
The importance of various types of problems identified by the fIShermen, differs from water body to water body. The overriding complaint, however, is the availability and prices of nets. 1 The fact that many fIShermen combined these two problems in one answer, might indicate that fIShing nets might be available, but at unacceptably high prices. The way in which this question has been brought up by the fIShermen, might indicate that fIShermen have become used to the Government being a cheap supplier of fIshing nets. Especially between 1986 and 1988, nets were I sold by the Artisanal Fisheries Rehabilitation Project, at prices which were far below their real value. This has created an atmosphere of expectations of fIShermen which might not be realistic. ,I Whereas the policy of the Government is to leave an important number of activities to the private sector, the fIShermen in general expect the Government not only to supply fIShing inputs, but also at a low price. Whether current prices of fIShing inputs on the market are too high in relation to the earnings of the fIShermen, is something which should be analyzed. I
The theft of nets is a common occurrence in Uganda, and a big problem on some water bodies, and should be considered a serious problem. A fISherman loosing his nets could be set 1 back for months, adding to the instability of the industry rather than to the development of the industry. Although 17% of the fIShermen, have said that the Government should do something about it, they did not suggest as to how the Government could go about this. It is not altogether I I .1 I
I 26 I clear whether or not the Government is in a position to assist here. Surprisingly, the provision of credit is not mentioned by the fIshermen, while this is one area, where the Government could assist fIShermen (through donor funded projects, or through the use of existing institutions). Presumably, there is an efficient and well operating informal I credit system, or the fISheries sector is highly liquid.
The last important issue in this context is also of an economic nature: the income tax. At I present, the income tax for fIShermen is a flat rate of 40,000/· per year. While this might be a relatively modest amount for some fIsheries (Edward/George and Kyoga), it might be a prohibitively large amount for fIShermen in other areas.
I It appears that the activities of the Artisanal Fisheries Rehabilitation Project have been well publicised, as the majority of the fIShermen in Uganda had heard about the project. The percentage of 83 can be considered high under the circumstances. It is clear however, that the I project has not been able to reach all fIShermen on an equal basis. Those best served are on the controlled lakes, George, Edward and Wamala. While the knowledge about the project was highest on the Minor Lakes (which might point to a high degree of organisation), the sample did I not fmd any fISherman who was served. Comparing the different categories of landing, it appears that fIShermen on larger landings had a slightly bigger chance of being served, than those on I smaller landings. 4.9 Conclusions I Based on interviews with 336 commercial fIShermen in Uganda, the sample survey has resulted in a number of conclusions. •
(i) The degree of motorization of the flShing fleet in Uganda is low, about 11%. The average I life of a planked canoe (the most common type used in commercial fIShing) is 3.5 years. The most common gear, the gillnet, has an average life of less than 9 months, while less than 40% of the fIShermen mend their nets. On average, canoes have 19 nets each, with wide I variations between different canoes, and between different lakes. The estimated number of nets being used at any given moment is less than 200,000 giving an annual requirement of I 250,000 nets. (ii) Some 65% of the canoes in Uganda is owned by an active flSherman, the balance being absentee-owners.. Canoes go out 6 days per week on average, resulting in a catch per trip of 30.7 kg (net fIShery only), and an annual catch of approximately 110,000 mt for the nets I fIShery and 10,000 mt for the hook fIshery, a total of 120,000 mt, not corrected for seasonal variations. I (iii) The average life of the fIshermen in Uganda is 35 years, while he has 1 or 2 wives, 6 children, and an additional 4 dependents staying with him in his house. The education standard of the fIShermen is relatively high, with 85% having attended school. For most fIShermen, fIShing is the most important source of income, with agriculture being the second I source of income.
(iv) Most fIShermen (75%) sell their fISh as fresh fISh, at prices which show large fluctuations I between landings and between lakes. There seems to be little depedency of flShermen on traders, as they rarely sell their fISh always to the same trader. I I ,I I
27 I
(v) Overall, the shortage of fIShing nets, and the high prices of those nets are claimed to be I the biggest problem for fishermen in Uganda. The next biggest problem is the theft of nets, while the third problem is the high income tax. The suggested solutions centre around the provision of inputs by the Goverrunent, the reduction of prices of inputs and a change in the I income tax system.
(vi) The activities of the AFRP appear to have been well publicised, with 83% of the· fIShermen being aware of its existence. However, the project has not been able to reach all I fIShermen on an equal basis. The AFRP has had a negative impact on the fISheries, in the sense that it has accustomed flShermen to subsidized prices for inputs. On the other hand, fIShermen generally judge the programme in a positive way, enabling them to continue or I increase their fIShing activities. I
References .' I MAIF, 1983. Blue Print for Fisheries Development in Uganda. Ministry of Animal Industry and Fisheries, Fisheries Department, 16p. I MAIF, 1988. Estimated Fish Production for 1988, by water body. Ministry of Animal Industry and Fisheries, Fisheries Department (mimeo). I MAIF, 1989. Fisheries Survey 1988 Ministry of Animal Industry and Fisheries, Planning Department, mimeo, May 1989. I Marten, C.G., 1979. Impact of fIShing the inshore fIShery of Lake Victoria (East Africa). J. Fish. Res.Board Can., 36(8): 891-900.
Wanjala, B. and C.G. Mw-ten, 1974. Survey of Lake Victoria fIShery in Kenya EAFFRO I Ann. Rep. (1974): 81-85.
Wetherall, J.A, 1972. On the catch assessment survey (CAS) of Lake Victoria. EAFFRO I Occas. pap. No. 13, 58p. I
"-- I :1 I r I I I , . -, ------Table 2.2 Frame Survey and Sample Survey Background Information Table 2.1 Type and number of vessels covered by the survey
FS = Fnme Survey rcfen to MAIF 1989 AFRP = Presenl Sample Survq ~ Category of '-od;ng
A B C b'····Tola1· L..ah. Victoria No of landings in FS 653 ! No of landings DOl included in FS 61
Num~r of landing:s 136 91 42 269 Numl><:r planked canoes of 690 1225 1231 •. 3146 Tolal no IandiDgsin colmiry 8ycordingto FS . ·'714 Number of dugout canoes 7 22 76 1 . lOS
Ulke Alben
Number of landings 17 37 74 FS Basic data 20 Numba of planked cmlXS 117 487 691 I 1295 C Number of dugout caDOt:S 0 69 35 104 Category of lauding: A B All
Ulke GeorgdEdward Number of bmdings 266 22.'> 162 653 3,150 NUDlber of boots 1.436 5,719 10.305 _..:.)
DO ~r 5.4 14.0 35.3 Numbtr of landings 6 13 Average of boats landing 15.8 lIW; 25.4 Numb..:r of planked canoes 83 321 Standard deviation of 2.4 31
2~1 Same,excluding 1ar8"'" landing . 2.4 3.1 14.5 Number of dugour CHn~ 0 0
LaU WaJU.llha
Numb..:r of ~anrJ1Dgs
5 8 landin~ 15 Number of FS AFRP I AFRP ' % of % of Numl:k:r of phm.k.cd cantks 35 117 200 tidectioo sample total sclectioo 4~
Numb..:r of du~out canoes 0 0 I 0
266 191 12 4.5 6.3 Minor l.....ak.cs "B 225 186 13 5.8 7.0 I C 162 108 III 6.8 10.2
Nwn~r t)f Lindings 14 15 7 36 - _.. ------:------~----_. 1 Numhc::r of planked canoes 57 '237 241 535 ! 653 485 .36 5.5 7,4 Numhcl of dugoul canoes 60 0 0 60
(....ak.e KyU~Ol Number of boms FS % of % of
AFRP~:l selection sample total liClection Numb..:r or" landings 19 29 30 78
Num~r \)f planked canlXS 98 33~ 1119 1552 1,436 1,102 2.9 3.8 Numtkr of dugoul canoes 38 170 193 401 " 42] 3,150 2,745 JI8 3.7 4.3 C" 5,719 3,878 176 3.1 4.5 AU w8terbodi~s I ------10,305 7,725 336 3.3 4.3 NumbGr of landings 191 186 108 485 Number \1f pl.mked caolXS 997 2484 3574 7055 Numba of dugout can<:lieS 105 261 304 670 1 c"rcgorios of iJuJding: A: D-9UDOCS
B: JD-20 CJUJOt:S C: 2J unoes IUJdover
ITolaI~UDlber of vlOSSCb 1102 2745 3878 I 7725 Table 2.4 Number of people interviewed FORMA FORMB
A 8 C A 8 C
Lake Victoria 18 26 55 21 17 35 Lake Albert 4 35 14 4 12 14 Minor Lakes 8 19 30 5 12 17 Lake Wamala 5 12 13 4 6 6 Lake George/Edward 0 .18 30 0 12 17 Lake Kyoga 7 8 34 12 0 15 Total L. 42 118 170 T1 {SSe}! I 46 59 I
I .. I Table 3.3 Fish species encountered and analysed during the survey I Fish species Lake Victoria Lake Albert Minor Lam Lake Wamala LlIke Edward/George Lab Kyoga
HapJocbromis!>pp x x I ~!#Cu1C1Jtlis
O.lencctsticws x x 6j~).···· X X I O.IJiloticus X X X x x x Tii.pj. h7'fi . X
Bagrvs spp X X x ~.iPP. i.X X X x x
I XCIJOC/arias spp ~6jJp X x X
Latesspp X X x
,: ::~ .. ~t;s6jip • X
I Barbus spp x x x Labeo~ x MOTmyrus spp X x
I ~tlwJcT1Jusspp Scbilbe spp X ~lilSpp AJestes spp X I Hydnx;j7JJJsSPP X Others X I Total 8 11 5 3 7 8 I Table 3.4 Estimated fish production for 1989. I IImoWlts in mtltnc Ions Lates !>pp TiJapiiDe Hdroc)'Dus A/estes Bagrus Barbus CJariilS ProtQpterus IUplocbromis Otbcrw
I spp spp spp spp spp spp spp spp
Lake Albert 989 2,438 469 48\ 569 15 242 370$;~'1;ii 4.22
Lake Victoria 29,737 29,887 \31 325 222 137 45.91 Lake Kyoga 6,214 50,504 359 214 :~~~~~il 43.54 I Lab Edward\George 3,310 292 451 951 15 ·SMinor Lakes 255 6 379 182 :1' 0.62 I 576 . i668Jti~ TQt!Il 36,940 88,840 469 461 992 340. ··1;074 1,574 100 I Percentage of lotal 27.99 67.33 0,36 0.36 0,75 0,26 0,81 1.19 0,44 0.51 100 I I I I --_.-_.- I
I
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Table 3.5 Retention characteristics of the most popular gillnets I ------I Gillnel TotallFork Length (em) Weight (kg) rnesh ------_._------ Water Body (mm) Fish species n Average Minimum Maxirnum Average Minimum Maximum I ------_...._---~------'-'- ----- Lake ViclDria J:no O.niJocicU/; 58b 30.80 23.00 49.00 0.71 0.24 2.20 127.0 L. niJoticus 55 53.15 34.00 77.80 2.22 0.40 5.98 203.2 O. niJoticus 65 51.01 27.22 79.10 2.53 0.22 4.28 I 203.2 L.niJoticlLs 229 63.68 33.50 93.00 3.58 0.42 9.96
Lake Albert 50.8 Hydrocynus spp 149 25.61 19.80 32.30 0.21 0.08 0.72 63.8 AJestcs !>J1P 58 26.72 22.30 42.40 0.21 0.10 0.52 I 63.8 Hydrocynus !>J1P 114 25.44 20.30 30.30 0.20 0.12 0.33
Minor Lakes 76.2 O. vanabiJis 213 20.35 15.40 28.80 0.38 0.09 0.44 70.2 O. niJoticU/; 63 19.34 17.20 27.80 0.23 0.08 0.44 I
Lake Wamala 76.2 O.niJoticus 177 1g.40 14.50 24.00 0.13 0.04 0.26 889 O. niJoticlLs 245 18.10 15.20 22.70 0.12 0.06 0.20 I 88.9 Protopt.;rus spp 22 79.38 62.80 108.20 2.29 0.86 6.00
Lakes Edward 114.3 O.tliJoticus 668 29.12 22.70 38.90 0.52 0.28 1.20 George 114.3 Bagrus spp 156 45.08 25.50 76.00 1.02 0.20 5.90 I
Lake Kyoga 127.0 O.niJOtiCU5 265 29.78 22.00 39.00 0.61 0.30 1.72 203.2 O. oiJoticus 116 46.35 39.00 56.00 2.27 \.43 4.10 I ------n = number of specimens I I :,":
Catch per net per night per mesh size gillnet, Tabl~?7 Table 3.8 Landings surveyed, number of canoesinterviewed and per water body, January - March 1989 average number of days fished in a week.
Walec body Fish laDdings Calegory surveyed of Nwaberof Tolal Caleb Waler body GiIIncl IaodiDg (i) (li) (iii) meab c-.es DWDbcr of (kg) (mm) sampIcd _used per DCI Lake Victoria Buwuogc A 2 2 6.1 Kabagala A 2 1 6.1
G~ga . A 2 2 6.1 LAke Vicloria 101.6 3S 4.32 Kyoga A 2 5 6.1 127.0 343 2.46 Kamwaoyi A 2 3 6.1 .·1$2A 6> 88 3•.88 Kagonya A 1 7 6.1 177.8 10 209 1.51 Nabbak B 5 5 6.0
:,:.'.':. 203.2 . ·23" .6&5. 1~6T Nsazl B 5 7 6.0 Kasali B 5 5 6.0 228.6 1 15 1.50 * -~. 5 5.4 304.8 j ". .~ ·129 iPl Nangoma C 5 Waoyaoge C 6 7 5.4 LAke Albert 50.8 6 386 0.36 Kyagalanyi C 7 6 5.4 63.5 7 522 1.30 Musoli C 7 7 5.4 76.2 1 75 0.31 Kigungu C 10 10 5.4 101.5 5 32 I.SO u.tt Albert Kalolo A 4 4 6.0 114.3 7 51 2.40 KachunLaJreo Edward! 114.3 22 493 2.36 Nkony~ C 6 6 5.0 George 127.0 3 76 1.44 Ulres Edward/ KayonJa B 5 6 63 George \l a hyon, B 5 6 6.3 0:44 LAke Kyoga 50.8 1 16 K!:l(We C 15 15 6.5 76.2 2 11 2.55 L.Ue Kyoga lsalo A 2 4 5.0 101.6 2 15 uri Nkondo A 5 I 5.0 127.0 9 71 8.82 NS\Io:ampiti B 5 2 4.0 152.4 2 14 10.57 MsliQlll: B 5 0 203.2 4 40 10.09 Kigingi C 7 0 LwampaDga C 15 15 4.8
(i) Number of canoes that were to be interviewed
(il) Number of ~ that were acIIIa1Iyinterviewed (iii) Number of days fished in a week ------~ ------
Table 3.10 Catch per net per night for the various mesh size Table 3.9 Retention characteristics of the various mesh size gillnets, per fish species·, per water body, per fish species. and per water body. January - March 1989 January - March 1989
Catch GiUnet TolaIJFork Ieaglh (em) Weigh! (kg) . GiIIDllt Total mesh ------moob IIIIIDbcr of (kg) De1S petDet ~ (mm) used WaI£rBody Fish spoci~ (mm) 0 Avel1llge Minimum Maximum Average Minimum Waler body Fish b-pecies UIks Edward! Hllplocbromis spp 114.3 149 0.01 Lake Victoritt O. lIiloticu... 101.6 143 30.00 22.90 37.00 0.58 0.25 1.20 George O. niloticus 114.3 443 1.54
O. niJoticu.... 1J4.3 29 31.57 25.40 44.00 0.66 0.32 1.60 O. niloliclJS 121.0 76 0.52 O. niJoocus 117.0 586 30.80 23.00 49.00 0.71 0.24 2.20 &grusspp 114.3 478 0.61 O. nilOticus 152.4 84 37.62 27.00 48.20 1.32 0.59 2.26 &grusspp 127.0 76 . " 0.71 0. tlJ"Jot1cus 177.8 29 38.62 21.00 50.10 1.32 0.22 2.90 CIJuias spp 114.3 219 0.14 O. mlocicu.-," 203.2 65 51.01 27.22 79.10 2.53 0.22 4.28 CIJuias,,1'P 127.0 76 0.11 ut= 'PfJ. 1270 55 53.15 34.00 71.80 2.22 0.40 5.98 ProloplervG spp ]J4.3 245 0.60 Proloplerus _ utes 'pp. 203.2 229 63.38 33.50 93.00 3.58 0.42 9.96 127.0 57 0.09 Btubus spp 114.3 21 0.31 0.14 ('.64 127.0 35 0.80 u.l<~ Alben O. mJoiJCLJ5 101.6 63 24.90 20.80 34.50 0.58 Btubus spp 30' 0.02 O. mJooclL." 114.3 82 28.85 22.10 34.40 0.62 0.24 0.94 Mormyrus spp 114.3 1.28 &grusspp 88.9 16 36.81 28.00 50.20 0.45 0.24 4 1.15 u.u. Kyoga o.l~ 76.2 0.56 La~spp. 762 16 32.20 28.00 38.50 0.36 0.28 O. 1I2TiIJbilis 76.2 4 0.04 .. spp.·' 114.3 41 38.81 30.00 50.20 0.74 0.30 1.68 ute.. TibqWI zillij 76.2 11 0.27 0.52 AJ~·t6:.... ·j,.PP 63.5 58 26.72 22.30 42.40 0.21 0.10 TilBpiJJ zi1lii 101.6 12 0.32 Hydrocjnus spp 50.8 149 25.61 19.80 32.30 0.21 0.08 0.72 O. nilolicus 76.2 7 0.70 Hydrocynus spp 63.5 114 2549 20.30 30.30 0.20 0.12 0.33 O. nilOticus 101.6 15 0.57 o. niloticus 127.0 71 8.58 Minor l..akcs O. varilJbihs 76.2 213 20.35 15.40 28.80 0.38 0.09 0.44 o. nilolic/J$ 152.4 14 10.00 O. ajJolicus 76.2 63 19.34 17.20 27.80 0.23 0.08 0.44 O. niloliclJS 203.2 40 10.09 O. nilOlicus 114.3 17 28.36 24.50 36.20 0.49 0.30 1.00 Clarias spp SO.8 16 0.25 O. niJOl.jcu.-," 127.0 6 28.28 25.50 30.00 0.50 0.38 0.56 CUu-iJJsspp 101.6 IS 0.06 PrOlOplJ:rw" :,pp 88.9 20 8745 17.50 \27.30 2.82 0.53 9.SO CIJuias spp 127.0 6 0.72 ProIopterus spp SO.8 16 0.19 O. wJoIJCtL... 76.2 177 18.40 14.50 24.00 0.13 0.04 0.26 u.u Wamaia Proloplerus spp 76.2 4 0.80 o. aiJocicLls '88.9 245 1810 15.20' 22.70 0.12 0.06 0,20 ProIopterus spp 101.6 IS 0.24 Proloplerw.' .pp 88.9 22 79.38 62.80 108.20 2.29 0.86 6.00 utes spp 76.2 11 1.11 Lates spp 101.6 IS 0.14 o. nilolic/J$ 114.3 668 7.9.12 22.70 38.90 0.52 0.28 1.20 Lake Edward! Lates spp 127.0 25 0.49 O. niloticus 127.0 29.46 25.00 35.00 0.67 0.40 3.48 George 29 Lates spp 152.4 8 1.00 45.08 25.50 76.00 1.02 0.20 5.90 &grusspp. 114.3 156 Mormyrus spp 127.0 6 0.93 &lgrUSSPfJ· 1'27.0 15 51.61 42.00 8220 1.61 0.92 2.26
Ukc Kyoga O. leurosOcws 76.2 162 18.81 16.SO 20.00 0.14 0.12 0.18 O. niIoticus 76.2 148 18.34 16.30 21.80 0.14 0.10 0.22 O.niloIicus 101.6 149 19.57 16.10 29.SO 0.17 0.10 0.54 O. niIoticus 127.0 265 29.78 22.00 39.00 0.61 0.30 1.72 O.tUIolicus 1S2.4 59 39.75 28.00 49.60 1.43 0.54 2.65 O. niIoticus 203.2 116 46.3S 39.00 S6.00 2.27 1.43 4.10 • ,------ ------. .-._--~--_.- .--...... -.._,.....
, . Table 3.10 Catch per net per night for the various mesh size Table 3.10 Catch per net per night for the various mesh size per fish species, per water body, per fish species, per water body, January - March 1989 January - March 1989
GilInd ToIlI1 Caleb GiIIDat ToIlI1 e.tda meoh DIIIIIbor of (kg) ...... _ UIIlIdof (q) Wu::r body Fi.&h b-pecieo (mm) Dds UIlOd perDCt Wilier body Fi.&hspecies (mm) parllllt
Ute Viccaria O. J~tIIs 127.0 21 0.20 Ute Albert SyDodoIJIis6flP 114.3 24 0.10
O. variMbiJis 127.0 ltl 0.20 (~) Barbus 6flP 63.5 80 0.28 O. ni/oticus 1O\.6 35 3.96 Barbus 6flP 76.2 75 0.01 O. ni/oticus 127.0 251 2.58 Barbus 6flP 1O\.6 5 0.07 O. niJoticus 152.4 88 3.77 Barbus 6flP 114.3 6 0.77 O.niJoticus 177.8 179 0.81 Ubt>o6flP 50.8 22 0.03 O. ni/olicus 203.2 187 \.09 ScbiJbe 6flP SO.8 140 0.01 TilBpia zillii 127.0 73 0.06 ScbiJbe 6flP 63.5 72 .. 0.00 Til.opia ziJlii 152.4 10 0.11 AJcstes 6flP SO.8 246 0.06 8Itgrvs spp 203.2 30 0.15 AJcstes 6flP 63.5 442 0.32 CLuias spp 127.0 27 0.17 AlesIes 6flP 114.3 10 0.28 CJviMs >pp 177.8 29 0.84 Hydrocyuus 6flP 50.8 385 0.30 CJviMs spp 203.2 30 0.23 Hydrocynus 6flP 63.5 350 \.47 CUriIui spp 304.8 39 0.19 HydroCyuus 6flP 76.2 75 0.01 Utes'iPP 101.6 15 0.82 Hydrocynus 6flP 10\.6 5 0.09 UIeS .pp 127.0 278 0.64 Otber 6flP SO.8 22 0.01 Lst=' ..pp 152.4 68 0.14 Otber spp 1143 26 0.30 LsI&S >pp 177.8 159 0.91 Otber 6flP 203.2 9 0.17 utu Jipp 203.2 621 1.49 utes spp 228.6 15 \.50 MiDorLahs llaplocbromis 6flP 25.4 6 0.34 utes >pp 304.8 129 2.65 &pJoc/JrrJmi& 6flP 38.1 so 0.74 &rbusspp 203.2 71 0.09 lUpJocbromis 6flP 76.2 75 0.23 O. van.bilis 38.1 30 0.07 Ute Albert O. ni/olicWi SO.8 43 0.00 O. vm.biJis 76.2 104 1.01 O. nilotiCIJS 63.5 72 0.00 O. ni/olicus 76.2 lOS 0.21 O. niJoticus 76.2 75 0.01 O. niJoticus 127.0 3 0.97 O. IJiloticus IOU\ 27 1.58 Clariu spp 25.4 6 0.01 O. niJoticus 114.3 51 \.04 Cwus6flP 76.2 31 0.09 &grusspp 50.8 25 0.04 Clariu."" 114'.3 4 0.68 &gnzs.pp 63.5 72 0.00 I'rrK.oplluVs 6flP 76:2 52 0.44 &grusspp 76.2 75 0.03 ProlDptMJs 6flP 114.3 7 5.56 &grus.pp 101.6 5 0.16 1'rolDptNv$ 6flP 127.0 3 7.73 &grusspp 114.3 24 0.20 UteWlUlllllo O.ailoticus 76.2 17 12.02 Chuiuspp 114.3 4 0.80 O.ailoticus 88.9 43 4.78 Uli:Jsspp 50.8 163 0.03 ClarUs spp 76.2 4 0.13 Uli:Jsspp 63.5 150 0.00 ClarUs spp 88.9 9 1.12
~spp 76.2 75 0.23 1'roloplNvs OW 88.9 20 0.21 ~spp 101.6 5 0.74 ~spp 114.3 41 1.07 Z-spp 203.2 9 1.39 '.; ,. ,,-:." SyDodoatis spp 50.8 185 0.02 i $yDodotItia spp 63.5 244 0.65 $yDodotItia spp 76.2 75 0.04 I
Table 4.2.1 Fleet characteristics ------~ Percentage of interviewed tishermen that owns I the canoe they work on Lake ')f, Call1gory % I Lalal Victoria 5. A 82 Lako Albert 68 B 89 Minor Lakos 73 C 85 Lako Wamala 83 Lake George/Edward 81 I, Lake Kyoaa S9
Types of canoe, all interviews I
Dumber porcenlage porcenlago of tolal of 8Jl8wen
dugout II 3 5 I planked 208 62· 95 no answer-) 117 35
tOIll! 336 100 I Use of outboard engine, all interviews I porcentago number porccnlage o..t own oflOlal l.bo canoe
with .ngin. 37 11 97 .1 without engine 166 49 99 no answer-) 133 40 17 336 100 ,I
Observed condition of canoe
porc.ntag. poor fair good I
dugout 73 ol8 9 [plalllald 14 61 26 I I OJ No IUlSwor in Ibis caJJTable 4.2.2 Age structure of the canoe fleet
,,I
Number of C8IlOeS boughl in ~cb y~r as percoDIa~. of t.otaI DWIlber of CIlllOOS
Minor LaU Georgel LaU C Lake Lau II Year Victoria Alben lAkes Wamala Edward Kyoga All.1 A B C!
72 1 75 I 2 I 79 1 80 2 I' 81 J 82 7 2 83 6 5 II 4 2 4 ,I 84 8 5 11 1 5 85 8 11 3 4 14 14 8 9 10 86 10 14 6 4 5 25 8 11 10 87 12 44 12 32 27 7' IS 30 17 37 45 I 88 56 25 64 52 35 18 58' 89 12 6 6 4 8 4 6 tOO 100 100 100 100 100 100 100 100 I I :, 1 I ;'
I Table 4.2,3 Expected life of canoes and nets
I Expoctod lifo 9f. C8DOO Expoctod lifo of • IlOC ioyoan ill lIlOIIduI I A B c AU A B c ·AU
Lake Victoria 3.0 3.2 3.7 3.4 6.2 8.3 7.8 7.6 ,.0;1/(#$) ,'ii(i/'j\::N>'jji,)Q;#);;(1;pi;;::~pj·:;:;(~:9.1:
I Lake Wamala 5.0 2.7 3.0 3.2 4.4 4.7 6.9 5.7 "r(;ti6),'" .·(i?b< )&;#X:? r(?;iX ;)(l~~j:-;fti(-)J·~~,:.:,;Gtf:ll!
Minor Lakes 3.1 2.3 2.4 2.4 13.0 3.7 9.9 9.5 I ,(1//)(0)1) ,,>O{gj;(i;i)(:''f~!2).tii'J#J;(7;P);:i;;;t4~$i1::
Lake EdwlIrd/Oeorge 4.5 4.5 5.0 8.3 9.9 9.3
I (4;()) ",(~.q) ,;(7/(:))):{ <·&;~j(~ir)i! .:&j;~W Lake Alben 3.5 3.0 3.9 3.3 12.8 9.6 13.3 12.8
I (0;9) (0:P)(~,2)(J;S/./(~~ii)(i~*~j(j;ili.;;i;"g~Z;i Lake Kyoga 5.5 5.0 5.0 5.2 6.8 6.5
I (3.5) (2,2) "#/'1) /(,:1'6)' «~/'J>t?~?j All walorbOdie~ 3.6 3.4 3,8 3,7 7.4 9.4 8.7 8.8 I (2/)) (3S)(2'())" ·(2.7) "'rX6}p/,('l;6') (6.8)i(ztli.
I Fj$wl3$~b!ai:i.ftl't3i:fiWiX4(.MSuwdJlrd;DCviatiQn I I
I Table 4,3. 1 Estimated number of nets in Uganda I A B C AU A. Average number of 110111 per canoe 13.8 24.4 17.1 19.2 B. Aventge number of canOO/;/landing 5.4 14.0 33.6
C. Percentage of CllDoes with Dlllli 90.5 87.3 93.7 I D. Total number of Ia.ndin~ 293 249 172 714 IE. Estimated total number of nelli (AxBxCxD) 19,760 74,256 92,598 186,614 I F. Average life of Det, in mODthli 7.4 9.4 8.7 'I G. Total 1lDI1u.aJ requirelDCDI of net;;' 32,043 94,795 127,722 254,560
I Note: B: agd D. m from MAlE 1989 (fisilWps ~I'Y) .. I I I I , I I Table 4.3.2 Sources of fishing gear " I In percentage of number of answers
A B C Vic Albert MiDorWamaia I
Local shop 28 22 33 25 12 34 36 33 36 Fish trader 18 13 5 19 10 0 0 2 9 Visiting general trader 0 22 4 '10 18 9 0 7 4 I Shop elsewhere 38 29 35 29 27 55 32 9 38 From different country 5 10 2 0 24 2 0 4 0 From Government project 8 5 13 6 10 0 32 39 4 I Other 5 0 8 10 0 0 . 0 7 9 Total 100 100 100 100 100 100 100 100 100 I I
Table 4.3.3 Fishermen mending their nets I
As percentage of the towl number of lIWjWllr~ I A B C I Lake Victorill 27 23 52 Lake Wllmala 20 9 7 Millor l.llketi 20 II 33 Lake OeorgelEdward 22 30 I Lake Albert 100 73 50 Lake Kyoga 66 43
AU water!:lQdios 39 34 4tH I I Table 4.3.4 Reasons for not mending fishing nets Percentage of no of fishermen that do not mend thllir nell; I
Lake Lalu: Minor Lake Oeorglll
Yictorill Albert Lake~ Wlimaill Edwllru I Lac\( of knowledge 60 38 82 58 48 Lack: of twines 14 31 2 3 15 No people to mend 0 0 12 35 2 I Hard to mend II 23 0 0 2 Cosdy to mend 14 0 0 0 13 Net loses efficiency 0 0 0 0 2 No IU1.Swer 2 8 4 3 17 I 100 100 100 100 100 I I I ------
Table 4.4.] Basic data on seasons for the six water systems Table] (continued) Basic data on seasons for the six water systems
Percentage of lOtB1Dumber of 'llD.tiWCni for each mootb~ per spoc:ies
Lake Victoria, Tilapia spp. Lake Victoria, Nile Perch Lake Albert, Nile Perch Lake Albert, Hydrocynus
Month poor &ir good Month poor &ir good MOIIIh poor fair good MOIIIh poor fair good I 58 33 9 I 54 33 13 I 45 18 36 1 47 26 26 2 35 58 7 2 54 35 II "2 55 9 36 2 63 26 II 3 25 45 30 3 30 46 24 3 36 27 36 3 2ll 33 39 4 13 37 50 4 19 41 41 4 36 18 45 4 6 2ll 67 5 21 44 35 5 16 49 35 5 27 27 45 5 Il 39 50 6 27 48 25 6 49 32 19 6 36 45 18 6 17 56 2ll 7 42 43 15 7 54 30 16 7 18 45 36 7 17 56 28 8 30 47 23 8 24 51 24 8 18 45 36 8 22 39 39 9 28 40 32 9 14 . 57 30 9 18 45 36 9 22 44 D 10 24 48 2ll 10 U 49 30 10 18 18 64 10 33 33 33 II 31 30 39 II 30 38 32 Il 27 27 45 Il 28 50 22 12 37 30 33 12 45 29 26 12 27 27 45 12 39 33 28
Minor lakes, Tilapia spp Minor lakes, Haplochromis Lake Kyoga, Tilapia spp. Lakes George and Edward, Tilapia spp.
Mcmlb poor faU good Moolb ·poor fair good Moolb poor fair good MOIIIh poor fair good I 57 n 2J I 40 20 40 I 25 23 52 I 75 22 3 2 50 3b 14 2 30 30 40 2 20 23 57 2 56 28 17 3 25 43 32 3 50 40 10 3 II 20 68 3 19 58 22 4 4J 41 17 4 60 20 20 4 26 43. 31 4 II 42 47 5 31 45 24 5 40 40 20 5 40 45 14 5 14 42 44 6 7 25 68 6 20 60 20 6 50 38 12 6 39 28 33 7 7 4 89 7 0 20 80 7 52 29 19 7 58 22 19 8 7 30 63 8 10 20 70 8 33 35 33 8 39 47 14 9,. 26 52 22 9 20 40 40 9 21 30 49 9 19 53 2ll IO 70 26 4 10 70 30 0 10 12 30 58 10 3 39 58 11 63 33 4 II 80 20 0 II 21 21 58 II 14 36 50 I2 68 32 0 12 90 .10 0 12 18 34 48 12 50 .. 19 31 Minor lakes, Protopterus Lake Albert, Tilapia spp. Lake Warilala, Tilapia spp.
Moolb poor fair good Mouth poor fair good Month poor fair good I 75 17 8 I 17 25 58 I 15 35 50 2 58 33 8 2 17 33 50 2 12 35 54 3 33 25 42 3 8 33 58 3 27 27 46 4 33 25 42 17 25 58 :4 4 31 38 31 5 31 38 31 5 33 42 25 " 5 46 38 15 6 31 31 38 6 42 33 25 6 23 58 19 7 23 46 31 7 42 33 2S 7 19 58 23 II 38 38 23 8 33 42 2S II 15 3S SO 9 38 S4 II 9 33 33 D 9 31 42 n 10 46 23 31 10 17 42 42 10 46 31 Z3 11 S4 23 23 11 2S SO SO 11 69 19 12 12 77 IS 8 12 SO 42 42 12 6S 19 15 ------...
Table 4.4.3 Seasonal index for Minor lakes
Table 4.4.2 Seasonal index for Lake Victoria JUp/ocbromis A 8 C 130 LAKE VlCfORIA 1 99 104 Tu.pu spp. 114 TIW'LI. IlIIDl1Cl 2 101 108 120 3 87 92 8 ABC 112 .... 110 liD 4 87 92 :
5 93 98 ~
I 96 86 106 6 99 1~ e IDO 2 104 93 § 1116 7 122 130 ;
3 115 103 ~ 104 8 116 123 ! 00 4 125 112 :' 102 9 104 111
5 118 lOS ~ '00 10 78 83 eo
6 112 101 ~.. 11 75 79
7 104 93 ~.. 12 72 78 70 8 III 99 .:.. I • • • D • 7 • • sa ",C J2 94 100 If ...... 9 114 102 'l2 10 114 102 'lO \I 116 103 sa T;up;.spp. MINOR LAKES L I T1lapia app. 12 112 100· 1I6 iii iii i A 8 C 130 10 II 12 1 100 91 120 112 100 lIonth 2 100 91 "'0 3 114 105 liD 4 104 95 110 Nil. Perch 5 110 100 6 132 121 100 ABC LAKE VICTORIA 7 139 127 lDO lllU
2 100'00" 89 ::107 j PDCII I 10 : :::::,>90 82 : 3 113 101 :: n 11 92 84 .. 4 122 109 i\ 12 89 82 eo g:: IOZ 12'4~t5781101111
121 109 : :01~ 109 100 ......
8 :~:114 103 ;: :9 120. lOS !S eo A 8 C 'ELL .. V 10 117· lOS ... 1 1 97 85 ~ II 115 104 :, 2 103 91 104 12 108 97 :: i 3 124 109 8 I 2ii"" 3 .. :J t5 '7 • 9 10 11 12 4 124 109 i 100
III 100 "on'" 5 121 107 ~ ... 6 124 109 1 .. 7 124 109 = .. 8 116 102 .9 .. F.,r explanation sec text 9 11 0 97 eo 10 116 102 • 11 110 97 • 12 96 84 .. ,· •••• ' •• UUIl 114 100 -...
For expIanatlon.see text
Table 4.4.7 Seasonal index for Lake Wamala I TibpiM 6PP
A B c LAKE WAMALA ,14 TU.pI, IPP I 1 99 111 II' 2 101 114 '10 108
J •.95 107 108 4 "90 101 ~ 10; , loa I 5 81 92 lOll 6 89 100 .. 7 91 102 I .. 8 99 III ~ j 91 9 89 100 .. I 10 83 94 III \\ 74 83 12 76 86 ".. Bll , 3 9 10 II II • • 0 0 • • I 89 100 "oaUl PQr oxplaDatiOQ _ lox' I I Table 4.4.6 Seasonal index for Lakes George and Edward
Til.pill 6pp
A I B c LAKES GEORGE AND EDWARD T'll.~.. • pp I 94 78 ". 110 2 106 88 3 121 10\ uo I 4 133 III ¥ 100
5 131 109 " 100 6 118 98 ~
7 106 88 1 .. 8 III 93 ~ I
~ .. 9 123 103 s 10 140 117 eo 11 133 III ,0 12 124 103 ,. :1 • 10 11 18 120 100 \hRLA Pur explanutioQ Bee fOXI II I I Ii I I I I I I I Table 4.4.8 Sensitivity analysis
[n lbe CQnBtnlction Qf the seasQnal index, the ..sumpliQn has been lIllld. lbol a "good' catcb is 30% larger than a "fair" calch, and lbal a "fair' calch I is 30% larger than a •poor , catcb,
Below il is sbown, what lb. change i. in'th. aMusl correction faclor, if lbis porcentag. Qf 30 is changed, It is, however, b6lieved thaI 30% is. good averag., I and tiulilbe porcentages will nOI be> lowel lbon 20 or highor than 40. Collumn •A' shows lbe porcentage whicb is asswned; collumn '8' shQWS lb. I resulting correction "ctor fQr lbe yoal, Lake Victoria Lake Victoria I Ti/Mpt. app. Nile I'Drcb A B A B ------:._--- -... _-----_ .... ------ 10 104 10 103 20 108 20 107 I 30 112 30 III 40 116 40 116
I Lake Albert Lake Albert
TiJ6pUt spp Nil. Porch
A B • A B I ------_.------~------ 10 98 10 102 20 95 20 105 30 93 30 107 I 40 92 40 110 I Lake Kyoga Ti/Mpt. spp.
A 8 ------... ------...... --- I 10 98 20 97 30 95 I 40 94
I Table4.5.1 Catches per unit of effort
I Calcb por trip (ks> Number of ncls por trip Celeb por trip por 1101 (kg) C~ A B C r--"iil 'A Il C r--.AJi A 8 'I Lake Victoris 39.1 49,2 33,6 38.8 21./ 24, I 19.8' 21.l 1.9 2.0 1.7 1.8 (28;4) (35.0) (27.0) (30,3) (15.3) (15.1) (19.2) (17.6)
Lake Wamala 21.6 15.2 18.6 17,8 5.4 4.5 2.4 3.7 4.0 3.4 7.8 4.8 (/6.1) (7.0) (10.8) (11.0) (0) ~.5) (0.5) (2.7)
Minor lakes 4,2 4.7 11.0 8.1 5.6 58 11.7 9.0 0.8 0.8 0,9 0.9 I (4.1) (3.0) (7.5) (3.1) (3.6) (10.7) , (8.~) (8.7) Lake Edward/George 46.3 '24.9 33.7 220 21.1 21.5 2.1 1.2 1.6 (50.1) (18.8) (36.7) (6.7) (7;4) (7.1)
Lake Alben 32.0 26.0 10.0 21.8 10.7 45.0 48.7 43.0 3.0 0.6 0.2 0.5 I (23.0) (34;0) (10.8) (29.4) (6A) (48.0) (79.2) .(S7;~) Lake Kyoll", 15.7 65.3 56.0 8.0 8.3 8.2 2.0 7.9 6.8 I (8.7) (43.8) '. (44.1) (3;7) (4.1) '(4.1) jAil walerbodies 27,7 30.7 31,3 307 13.8 24.4 17, I 19,2 2.0 1.3 1.8 1.6 I (33.5) (37.0) (32.4) (31.5) (13.2) (30.9) (28.2) (28.1) ~'#.;litj#iii!-L~fflA:t!!!!:~!!4"iy!:Piiilpiiiif,.;;'..
I ~I Table 4.5.2 Fishing effort Table 4.5.3 Estimated catches in Uganda
Average number of trips per week, at time of interview Estimated landings, whole OOUDlry, nets only A B c AU
A B c AU Number of Iandingl; 293 249 172 714 Average no of boat!;lIanding • 5.4 14.0 33.6 Lake Victoria 6.2 6.2 5.6 5.9 Percenlllge boat!; with nets 90.5 87.3 93.7
(IA) (1i~ji /(-1,iffJ(¥5) Av no of boats/landing with nets 4.9 12.2 31.5 Calchftrip per boat in kg 27.7 30.7 31.3 Lake WamaJa 6.8 5.1 5.8 5.6 Catchlday per lauding in kg 135,4 375.2 985.4
.(OA) :(ldi) . ·a{~) . ·.(14)
TOCBIcaleh per day in kg 39,663 93,429 169,493 302~S85 Minor Laka; 5.4 5,4 5.9 5.7
(i.UJ) (1(4) ·(U) (i~3) [TOOl[caleh per year, in'ml - 14,437 34,008 61,695 110,141 I
Lake GeorgelEdward 6.8 6.7 6.7 (0:5) (0.8) (1).7) Estimated landings, whole counlry, hooks only Lake Albert 7.0 6.1 6.0 6.2 (1.0) .(j;7) (1,8) .. (J...7) A B . C AU Lake Kyoga 6.3 4.7 4.7 Number of landingI; 293 249 172 714 (3.8) .•'(1,5) (L5) Average no of boatsllanding 5.4 14.0 33.6 PercenLage of boats with hooks 9.5 13.7 6.3 Av DO of boatsllanding with hooks 0.5 1.9 2.1 AU waterhodies 6.0 6.0 5.7 5.9 Caleh per trip per boat in kg 7.7 17 47 (H) (1;5) .. 0.5) .•...... •(1.5) Caleh per day per landing in kg 4.0 32.6 99.5
Tow catch per day in kg 1,157 . 8,119 17,112 26,388 NotIJ; figlU{JS iiJ~ ·dMoie StllDdard Deviation ITow catch per year, rot 421 2,955 6,129 9,605 I
-., .~.~
-- - -~ ------_.------.. Table 4. 6.1 Average and total number of fishermen Table 4.6.2 Average age of the fisherman
For those canoes which employ gillnets
A. B C AU A B CI All Lab Victoria. 34.9 28.0 27.8 29.1 Lake Victoria 2.6 2.4 2.7 2.5
·..(17:')· (1Z3)'- "~J1j. '(1L3) (0;9) ".(0)8) . (1.1) (1.0) Lab Albert 46.5 36.6 32.8 363 Lab Wamala 1.0 2.0 1.0 1.4 •.. )y(isl"JJ· (mo);. ,tjO.(l) (11(6) (0.0) iol0jii> «O.(j) .. >(0101 Minor Lakes 48.7 41.8 39.1 40.8 MiDIK Lakes 1.0 1.0 1.0 1.0 ···········(J4/$) (J':UIJ·(14Y4) . (15;0) (O·W (010) .... . (Q;O) ·t«()ldJ. .Lab Wamaia 32.2 37.6 27.1 32.2 Lake Edward/George 2.8 2.7 2.7 (7A) (16.7) 'iii6) (/2;7) (0.7) (0.8) (0.8j Lake GeorgelEdward 49.0 30.9 37.7 Lab Albert 2.0 3.6 2.6 2.9 (13.J) (6.6) (11;0) (0;0) (1.1) ... (ui) (1.1) Lake Kyoga 26.1 43.6 40.2 38.8 Late Kyoga 1.8 1.5 2.1 2.0 (5;8) (12.9) (13;3) (13.S) (0.7) (0;5).' (0.5) (0.6)
AU waterbodies 36.8 38.0 32.8 35.2 [Allwaterbodies 2.0 2.4 23 2.2 (14.0) (14.4) (12.2) (13.5) (1.0) (El) . (1.0) (1.{)) 1~436 No of vessels (Table 2.2) 3,150 5,719 10,305 Figures in bracktns~ the StarJdiud Deviation
rNoof fishermen 2,872 7,560 13,154 22,671
fig~mbiaC~~t1Jo~d Deviatiim ------Table 4.6.3 Average number of wives per fisherman Table 4.6.4 Average '!u!TIber of children per fishennan
A A B C All B c AlI
UIke Victoria I.I 1.2 1.0 I.l UIke Victoria 5.9 4.5 3.5 4.2 @)~?l~J7i) (0.7) (O;9) ... ..(0;6) .' .·iO.7) ifa.'liJ
Lake Albert LWl Albert 3.0 1.7 1.4 1.7 25.5 5.6 6.0 7.2 ;/{~.7.r (1,2) ...... ·W: 9)?'(Q,7X·· ·«jj/PX . >Ki'/i) .(i5;i;. r~,'?J
Minor Labs Minor Lakeli I.3 1.5 0.8 1.1 3.5 9.0 3.1 5.1
(0.4) (019) .(0.7)" .. ·(f!}iJ :f >·ai9X·\i.8.Ji)i!<1~?it}i(f@
. UIke Wamala UIke Wamala 1.0 I.3 1.0 I.l 7.0 7.2" 2.8 5.3 (4;~j "(0.6) ····(0.;7) (0.4) . (Q)§) ·(4:2) (2;0) (4:~(J)
UIke GeorgelEdward 11.0 5.9 UIke George/Edward \.7 l.5 1.5 7.9 ($;J)'>< '·(4:8) (0.8) (0.6) (0.7) ($.S;
UIke Kyoga 3.1 9.0 7.4 7.0 Ute Kyoga 1.0 1.4 1.6 1.5 (0.0) (0.5) (1.2) (l.0) >(3;:0); >(i~i7i·\(6")9). (6;4).
All waterbodies All waterbodies 1.3 1.6 I.3 1.4 7.0 7.1 4.7 5.9 (0.9) (1.1) (1.2) (J.J) (lao) (6.¥J> :.-(5Y4) (6.5) .....
l!i8~jD.~i~t1itJ·~ figuilismbi1tcfet$tkD.ot.ctbe SUrJdardDeviatiOD DeviatiOlJ ----_._------ ------
Table 4.6.6 Fishermen and their families Table 4.6.5 Average number of dependents other than wives and children
Percentage of those interviewed, A B c All who have their family with them
Lab Victoria 2.2 3.9 3.5 3.3
@;?X<@i§) . {4j(jj.(iii~)· Lake Victoria 55 Lake Albert 92 . Lake Albert 2.8 4.4 2.6 3.8 Minor Labs 84 (3:4).. &:,'-4).. .(2.3) .i(6t2) Lake WamaIa 90
Lake Geo~lEdward 95 2.8 2.7 2.3 2.5 Minor Labs Lake Kyoga 83 (H) (iJ;'j) ~.4) >/ '(2;.8).
A 69 2.6 5.0 3.1 3.8 Lake~amala B 85 ..(2.!!J.. (2.3) (i~J· ·'(3;1) C 77
Lab GeorgelEdward· 5.6 6.4 6.1 All waterbodies 79 (3/5) .(5.:/):(4.7)
Lab Kyoga 6.9 6.4 3.5. 4.5
(3.5)' (5jl). .(~.6):i ··.(S{#f
All waterbodies 3.2 4.4 3.7 3.9 (3.2) (5:1) (4.4) . (4.6)
{_Ui~.~~~l#Wj¥#~··· Table 4.6.8 Sources of income of fishennen
Table 4.6.7 Education of fishennen Figures as percenlage of total number of an:.wers
I A B C All Percentage of those interviewed, that went to school
Fishing 38 36 42 39 Fishing and farming JO 32 33 32 Lake Victoria 82 Fishing and eattle 3 3 I 1 Lake Albert 83 Fishing and fish tnIde 3 4 5 4 Minor Lakes 87 Fishing and ocher tIwIe 8 1 3 3 Lake Wamala 93 Fishing and bmdJord 0 2 2 I Lake George/Edward 91 Fishing and ocher 0 4 2 3 Lake Kyoga 75
lio --'--- :-,s1'?:«-:", - ",-, _..J$'. lTotalfisbiDg.s~~ -> 88 A 98 B 81 C 84 Fanning 3 I 1 I Fanning and fi.shiug 13 14 5 9 All waterbodies 85 Fanning and oIher 0 0 2 I Cattle and fishing 3 0 0 0 Other trading and fishing 0 4 1 2 Level reached Other tnIding and oIher 3 0 1 1 Other 0 0 2 I Primary School Standard 80 % .. a-level Standard 18 % IT0la.I other as primary fiO!IrC<: 20 19 12 . 1,5 A-level Standard 2 %
Tow 100 100 100 100
. ------.. Table 4.6.10 Share systems Table 4.6.9 Renumeration of crew
As percentage of !bose that employ a sbare system, vessels without outboard engine only As percentage of lolaI number of answers, vessels without outboard engine only
l.e Ub> MiDor I...ab Latt>o Ub> All !Au LaIc£ Minor LaIc£ Lakes l.ah Victoria AIbcrt Labs Wamaia GeoIEdw Kyogo Victoria Albert Lakes Wamaia GeoIEdw Kyoga i::';~: Sy&lCml 36 "'j, ~;, 26 A &bare of tbc CIIId:1 2D 51 20 11 SO ·24
Sjsfan2 60 i~ "$)'( :'36'0.: 'jg" 4tI 4 11 II. 20 A wage 3S ~ 2 Oibar""': ,'. 4" 43 23.::'
"'29 .<~; :32' A ....,.., of tbcproc:eCods 'rI 8 ..''ii' . 33
A wage plus.• ....,.., Syslem 1: After cIeduction of lhe C06lS from Ibe proceeds, tbc remaiDder of lhe proceeds IS <6. is divided betwoen owner aDd crew
.'~~~:l .. ~ Other 5 13 '11 :5 System 2: 1llc are divided belweea owner aDd crew, wbilc tbc ___ pays for tbc cosIs
i(~)l .j . ;;,~<.' Nomswer 3 '18
100 100 100 100 100 100 100 .... ------ -- -
I
~ \n~~ .'".;' \j&~i~s I ,.,'.,' Table 4.7.1 Processing of fish I Answers to the question: "How is the processing organized" as percentage of the number of valid OllBwers I A B A+B C D B Lake VictoriJI 5 14 20 0 79 1 100 I Lake Alben 34 26 60 2 34 4 100 Minor Lakos 17 6 22 2 74 2 100 Lake W IlIIUl1.a 0 0 0 0 100 0 100 Lake George/Edwor 15 0 15 2 81 2 100 1 Lakll Kyoga 8 15 23 0 75 2 100
Category A 17 24 40 0 60 0 100 I' Cawgory B 18 14 32 1 65 3 100 Cawgory C 10 7 17 1 80 2 100 1 All waterbodiea 13 12 25 1 72 2 100 1 Tht' foJl0wiDs are th/l po88ibJe answers tht' fisht'rmen could ChOO8A. Do it my S(j/f, with as.sisUlnce ofhired labour B. Do it myself, rogetht'r with my family 1 C. / SulrcODtract it D. I sell only fry,sh fish B. Other I 1
'.\." I
, . Table 4.7.2 Processing methods used by fishermen I As percentagll of thOSll doing their own procossing I'
sun drying smoking 1IB1tJdrying
Lake Victoria 0 100 0 100 1 Lake Albert 0 12 88 100 Minor Lakes 67 33 0 100 Lake George/Edward 100 0 0 100 Lakll Kyoga 0 100 0 100 1 .._- I All wowrbodies 13 62 25 100 I 1
~.~ III I I Table 4.7.3 Average producer prices of fresh and smoked fish, Jan-Mar 1989 Table 4.7.4 Market outlets for fishermen Fresh fish in Ushs/kg, smolced fish in Ushs per piece
price average standard fish usually sold to: Percentage range price deviation Lake Victoria Fish trader 38 Nile Perch, fre&h 50-800 200 144 Fish monger 53 Tilapia, fre&h 50-350 160 92 Local consumer 7 Cooperative Lake Albert o lnstilutions Nile Perch, smolced o 35-130 65 25 Other o Nile Perch, fresh 50-100 67 17 No answer 2 Tilapia, smoked 35-130 81 27
Tilapia, fre&h 40-100 69 20 . ~-."
Minor Lak:os Fish usually sold to same trader/IDODger? Tilapia, fresh 25-150 70 26 (percentage IllJSweriDg"yes") Lake Wamal.a Tilapia, fresh 30-60 39 12 Lake Victoria 17 Lake Albert Lak:os George/Edward 4 Minor Lakes 15 Tilapia, fre&h 20-80 42 20 Lake Wamala o Lakes GeorgelEdward Lake Kyoga 11 Lake Kyoga 20 Nile Perch, fresh 80-500 166 139 Tilpaia. fresh 25-150 82 40 Category A 31 Category B 10 Category C 11
'IAllwaterbodies 13 I
"'"
i I .. I ------! ------Table 4.8.3 Problems of fishermen, by waterbody Perceotagc: of llDSWers given. by type of landing
Lake Victoria Lake Albert Minor Lakes
Type of landing: A B C AU A B C AU A B C All
~ Table 4.8.1 Summary of problems of fishermen, all areas --- --- ------ ------ ------ ------ Lack of DelS 20 19 22 21 18 23 26 23 10 18 21 18 High prices of nelS 0 8 4 4 12 0 3 2 3 9 I 4 number percentage perceatoso Lack of nets AND nigh prices 13 10 JI II 18 7 5 8 14 II 15 14 ~ of answers , of fil>hermen Lack of engines II 15 10 Jl 6 10 10 10 3 2 0 I lA:k of spare part.' 0 0 4 2 0 I 0 I 0 0 0 0 Lack of nelS 186 56 21 Lb1:k of floalS 0 0 4 3 0 0 0 0 0 2 0 I High prices of oelS 35 10 4 Lock of fuel 0 0 2 1 0 3 0 2 0 0 0 0 Lack of nelS AND nigh prices 85 25 9 Poor RoadslTnmspon 18 6 4 7 6 10 3 8 7 4 13 9 Net lhefts 149 44 17 No markets 7 6 2 4 0 6 10 7 14 2 3 5 High inCome Ulx 87 26 10 Lou.' fish prices 4 2 3 3 6 0 0 I 0 4 6 4 Lad:: of engiDes 77 23 9 16 10 14 13 18 4 8 6 24 11 4 10 Poor roads/tnmspon 48 14 5 High InCome la, Then of nelS 2 17 13 12 18 23 26 23 7 9 3 5 Lack of markelS 37 11 4 2 0 2 2 0 I 0 I 0 7 8 6 Poor catches 40 12 4 Hippos Floating islands 0 0 I 0 0 0 0 0 0 13 17 12 Low fish price::; 26 8 3 No school/mspensar)' 2 4 I 2 0 13 10 11 0 0 3 I HJppos 20 6 2 Poor cl:llches 2 2 2 2 0 0 0 0 17 7 7 9 No school'SJdispeIL-.aries 26 8 3 Othc:r 2 0 2 2 0 2 0 1 0 7 0 2 Floauog islltods 23 7 3 30 9 3 Lack of engIne spare:; T otl:ll ~rccnlJll?e 100 100 100 100 100 100 100 100 100 100 100 100 Utek of floalS \4 4 2 Numbct of answers 45 48 140 233 17 III 39 167 29 45 72 146 Olhas IH 5 2
901 100 Lake George/ Lake Wamala Edward Lake Kyoga
Table 4.8.2 Summary of suggested solutions by fishermen, all areas Type of Lm. according to income: 78 23 Lack of tu.:1 0 0 0 0 6 0 2 0 0 0 0 Security against lhd't 58 17 .6 Poor Roadsff nll1spon . 0 0 0 0 8 0 3 15 0 0 2 Give loao.s fo fishermc-D 40 12 4 No markets H 6 0 3 4 0 2 0 0 7 5 Build schools/hospitals 29 9 3 Low fish prices 0 9 4 5 2 4 3 0 0 3 2 Proper administratioD 9 3 1 High income tax 25 15 10 13 '8 9 8 0 0 7 5 Olher 67 20 7 Theft of nelS 8 18 27 23 25 25 25 15 28 14 16 Hippos 25 9 0 5 0 0 0 0 0 0 0 906 100 Floating islands 0 0 0 0 0 0 0 0 I7 I 4 No scbool/mspeosary 0 0 0 0 0 0 0 8 0 0 I Poor catebe::; 0 0 11 7 4 10 8 0 6 4 4 Olher 0 0 0 0 0 0 0 0 0 0 0
TOlal percenlage 100 100 100 100 100 100 100 100 100 100 100
Number of answers 12 33 70 115 53 n 130 13 18 74 lOS
- -. ------. - - - -. r - ._- - - , , 1 ,", ',"""" I,
I Table 4.8.4 Solutions suggested by fishermen, by waterbody Percentage of the number of llIl6wers given, by type of landing, by waterbody I Type of landmg: A B C All A B C All A B CAlI Lake Victoria Lake Albert. Minor Lakes ------~-- ~------. I Provide enough inputs 39 31 31 33 27 30 28 29 38 30 32 32 Reduce prices of inputs 22 28 30 28 27 24 23 24 38 30 31 31 Tux according to income II 9 13 12 20 6 5 7 0 8 8 7
I Improve on trllIl6portJmarket~ II II 4 7 20 19 14 18 5 10 13 11 Build schoolsfbospitals 4 '2 2 3 0 3 2 2 5 3 5 4 Proper Ildministration 4 0 I I 0 3 2 2 5 0 0 1 Security ugllinst thefts 0 7 9 7 0 4 7 4 5 3 1 2 I Oive loans to fisbermen 4 7 4 5 0 4 7 4 5 5 0 2 Olbers 4 4 5 5 7 8 12 9 0 10 11 9 I Total percentage 100 100 100 100 100 100 100 100 100 100 100 109, Numl;>ers of UDswerS 46 54 135 235 15 107 43 165 21 60 88 169 I Lake Wamala Lakes George/ Lake Kyoga Edward I ------Provide enough inputs 36 34 27 31 33 33 33 26 19 29 26 Reduce prices of inputs 36 29 22 27 29 29 29 26 19 2S 24
I Tux lIccording to iDcom~ '27 II 10 12 '2 8 6 0 10 10 7
Improve on transporUmurk"ls 0 5 16 10 13 0 5 19 24 8 14 Build schools/ho~pitals 0 3 4 3 2 5 4 4 0 6 4 Proper udministrution .0 0 0 0 0 0 0 4 0 0 1 I Security aguinst lbefu 0 13 4 7 13 6 9 IS 24 6 12 Give loans to fishermen 0 0 2 I 9 9 9 0 0 6 3 Others 0 5 14 9 0 9 6 7 5 10 8
I TOUlI perc"nUlge 100 100 100 100 0 100 100 100 100 100 100 100 I Number of unswers 1\ 38 49 98 0 55 85 14Q 27 21 51 519
I Table 4.8.5 Reach of the AFRP
'Percentage of fiahonnen Percentage of those thaI beard that heard about the APRP about AFRP and bought something I before tbc interview from the project
UW> Victoria 74 21 I l,.a.Iw Albert 70 5 Minor Lakell 99 o Lake wamala 93 68 l..ltIce GeorgelEdward 98 88 I l,.a.Iw Kyoga 78 16
A 79 25 I B 84 29 C 84 33
I 31 All waterbodies 83 I I "
ANNEX 2 '.
Table A2-1 Master list of fish landings Table A2-2 Landings from which sample was drawn Table A2-3 Landings which were excluded from the survey
-,' ,-I 'l1. 5 47 ALBERT KAMUGA KAMUGA 35 BUNDIBUGYO \ Table 1. Masterlist of fish landings 13' 7 4S ALBERT WASA WASA 20 BUNDffiUGYO \, 14 0 . 49 ALBERT MULANGO MULANGO 20 BUNDffiUGYO
~ /988, PburniDg~t, 0 Fisheries Survey MAIF 50 ALBERT KIGUNGU KJGUNGU 40 BUNDlBUGYO 23 60 BUNDffiUGYO 53 0 No Lake Landing Village No DiSlrict DO of DO of 51 ALBERT NTOROKO NTOROKO 18 0 or of planked dIlgoul 52 ALBERT KANARA KANARA 2S BUNDffiUGYO 30 BUNDffiUGYO 23 0 river flrMn c:aDOe5 caDOelI 53 ALBERT RWANGARA RWANGARA 67 0 54 ALBERT SONGAKIY ANJA SONGAKlY ANJA 70 BUNDffiUGYO I KYOOA MUNAMI MUNAMI 15 LUWERO 6 I 32 10 55 ALBERT SANGA UCHAKI KATANGA 60 BUNDlBUGYO 2 'KYOOA ZENGEBE ZENGEBE 130 LUWERO 33 0 36 8 56 ALBERT RUKWANZI RUKWANZI 36 BUNDffiUGYO 3 KYOGA KISENYI KISENYI 30 LUWERO 15 0 0 39 59 NILE KAMDINI ATURA 39 !\PAC 3 KYOGA KAGWARA KAGWARA 224 SOROll :»3 0 41 0 60 NILE WANSOLO WANSOLO 41 !\PAC 4 KYOGA KASAMBYA KASAMBYA 50 LUWERO 21 0 18 0 18 61 NILE PALANGO AKETO !\PAC 5 KYOOA KJKOIRO KJKOJRO 50 LUWERO 9 0 0 19 62 NILE ALWOROCENG ALWOROCENG 19 APAC 6 KYOOA KJGULI KJGULI 160 LUWERO 22 0 17 0 63 NILE NAMWULU NAMWULO I7 !\PAC 7 KYOOA JRlMA IRJMA 40 LUWERO 19 0 32 !\PAC 0 32 64 NILE MUTUNDA ATURA 8 KYOOA KYALUSAKA KYALUSAKA 20 LUWERO 6 0 0 20 65 NILE AKAKA AKAKA 26 !\PAC 9 KYOGA WANGEDE LWAMPANGA 30 LUWERO 15 0 ALIDO 47 APAC 13 16 66 KWANL~ ALIDa 10 KYOGA LWAMPAGA LWAMPAGA 164 LUWERO 55 4 II 6 67 KWANIA ABEl ATULE 150 APAC II KYOOA KITYOBA KlMOLE 10 LUWERO 35 0 12 2 68 KWANlA ABEBE AYABI 12 APAC 12 KYOGA KYEBISIRE KIMOLE 30 LUWERO 21 I 10 I 69 KWANIA OOWIL OCWIL 10 APAC J3 KYOGA TUMBA KlMOLE 30 LUWERO 18 6 13 0 70 KWANIA OWINY OWINY 13 APAC 14 KYOGA KANSJRA KIMOLE 31 LUWERO 30 I 7 3 71 KWANIA ODWONG BUNG 7 APAC IS KYOGA KACHANGA DAGALA 20 LUWERO 40 0 9 35 72 KWANIA ABALI • ACENLWERO 100 !\PAC 16 KYOGA DAGALA KAZWAMA 50 LUWERO 24 0 27 "22 73 NILE ISUNGA KUNGU 27 -"PAC i7 KAYUMBU RWAMAKUBA MUROLAU 63 KABALE 0 i7 66 13 74 NILE KAYEI AKERE 79 !\PAC 17 KYOGA WAMUKOME WAMUKOME III 100 LUWERO 30 0 13 0 75 KWANIA WIGWENG WIGWENG 13 !\PAC 18 KYOGA NAMIKA NAMIKA 18 LUWERO 18 I 28 18 76 BISINA OMITO OMITO 30 KUMI 19 KYOGA KIKARAGANY A KIKARAGANY A 46 LUWERO 46 0 74 63 77 NYAGUNO ACIISA ATOOT 142 KUMI 20 KYOGA CHIKOBE KJMOLE 26 LUWERO 15 0 23 78 NYASALA OPOT ATOOT 74 KUMI 29 21 KYOOA MONI KIMOLE 20 LUWERO 12 0 79 BISINA KAKORO KAKORO 224 KUMI 93 29 22 KYOOA KELLE PORT KELLE 172 SOROTI 43 0 55 23 80 BISINA AKJDE AlUDE 121 KUMI 23 KYOGA KOTIRONO OTIRONO 239 SOROTI 23 0 38 2 81 BlSlNA AAKUM KAPOLIN 118 KUMI 24 KYOGA I3UGONDO BUGONDO 42 SOROTI 19 4 43 43 82 AGU OPELU NGORA 54 KUMI 25 KYOGA IRUKO AUULE 65 SOROTI 16 0 32 83 AGU AGU AGU 32 KUMI 32 26 KYOGA OCELAKUR OCELAKUR 124 SOROll 31 0 43 43 84 AGU OPEGl OPEGE 43 KUMI 27 KYOGA KOJ ETENY ANG OLUPE 52 SOROTI 14 4 34' 34 85 BISINA OMATENGA AGULI 15 KUMI 28 KYOGA MUGARAMA MUGARAMA lJ8 SOROTI 32 I 86 BISINA OSEERA OSEERA 97 KUMI 31 3 28 NILE ATURA ATURA . 40 APAC 2 38 0 87 KWANIKA AMAI ANYWALI 200 LIRA 26 29 KYOGA ACHOMIA ACHOMIA 112 SOROTI 28 0 6 88 KWANlA AWEI ARIDI 32 LIRA 10
30 BIGIN.~ KOKORIO AGULO 18 SOROTI 6 0 7 89 KWANlA BATA ARIDI 20 LIRA 20 31 KYOGA MULONDO MULONDO 77 SOROTI 25 0 7 3 90 KWANlA .~WIO ALWAR 10 LIRA 33 P!NGIRf PINGlRE PINGIRE 120 SOROTI 28 9 t7 2 91 KYOOA ADWAi ADONYOIMO 50 LIRA 34 ALBERT KATOLINGO KATOLINGO 33 BUNDlBUGYO 23 to 92 KYOOA KIRYANGA KlRYANGA 26 LIRA 17 6
35 EDWARD KAY,~NJA KAYANJA 27 KASESE 27 0 93 KYOOA BURUBURU AKOL 20 LIRA 8 0 36 EDWARD KATWE KATWE 66 KASESE 62 0 12 I 94 'KWANH ~JOKDONG CHAKWARA 28 LIRA 37 EDWARD KAY ANJA B KAYANJA IS KASESE 15 0 95 KYOOA AMUK CHAKWARA 36 L1Ri\. II 0 38 RWEITERA RWEITERA RWEITERA 20 KABAROLE 15 0 24 LIRA 8 0 96 KWANL~ ADERO CHAKWARA 39 WABICERE WABICERE MUKONOMORA 18 KABAROLE 18 0 98 KYOOA MUNTU MUNTU 30 LIRA 20 3 40 SAAKA SA AKA SAAKA 9 KABAROLE 9 0 99 KWANlAlNIL KlGA NAKITOMA 23 LIRA 18 I 41 KAYANJA KAYANJA KAYANJA 17 KASESE 17 0 102 KYOOA KITALEBA NAMBIATA 115 LIRA 21 9 42 SEMLIKI NKARARA NKARARA 30 BUNDlBUGYO 0 21 103 KYOOA GOJWE GOJWE 51 LIRA 17 0 43 SEMLlKJ KUAJABA KlJAJABA 48 BUNDffiUGYO 18 0 104 KYOOA KAYAGO lRIGWE 18 LIRA 28 I 44 SEMUKJ RWENDEGEYA RWENDGEYA 30 BUNDlBUGYO 0 20 105 KYOOA lENGKO WABUNWA 48 LIRA 21 3 45 KANY ATETE KANYATETE A KANYATETE 23 KASESE 23 0 106 KYOOA BIKO MUKAWA 57 LIRA 23 0 46 KANYATETE KANYATETE B KANYATETE 14 KASESE 14 0
------~ 161 VICTORIA MASIRIGA MASIRIGA 60 IGANGA 18 0 120 LIRA 24 2 107 KYOGA MUCORA MUCORA 162 VICTORIA MULWANDA MULWANDA \5 IGANGA 15 0 108 LIRA 45 2 108 KYOGA MAKATIN ALEMERE 163 VICTORIA BUMERU BUMERU 31 IGANGA .31 0 18 IGANGA 14 0 109 VICTORIA KABANGAZA KABANGAZA 164 VICTORIA LWANIKA BUDHALA 26 IGANGA 26 0 10 0 109 KONG-GORO KONG-GORO :n LIRA 0 KYOGA 165 VICTORL~ BULUTA-MALIN BULUTA 18 IGANGA 9 BANGALDEC 35 LIRA 35 0 110 KYOGA BANGALDEC 166 VICTORIA JAGUZI-3 JAGUZI 20 IGANGA 20 0 15 0 NAVEYO NAVEYO 36 LIRA 10 0 III KYOGA loi VICTORl.~ llUGOMA BUGOMA II IGANGA ALYECMEDA 240 LIRA 43 0 112 KYOGA fITWE 168 VICTORIA BWEMBE BWEMBE 13 IGANGA 13 0 NALUBWOYO 250 LIRA 31 I 113 KYOG,\ NALUBWOYO 170 VICTORIA WJGUBI MUGUBI 12 IGANGA 12 0 KIBUGU % LIRA 12 16 0' 114 KYOGA KAMPALA 171 VICTORIA IlI;lUTA BULUT A-KITOGO 16 IGANGA 16 AGWENG ) LIRA I 115 KWANIA ,-\GWENG • 172 VICTORIA IIL:GOTO BUGOTO 41 IGANGA 41 0 AKURlLUBA LIRA 7 0 116 KWANIA ADWAI 173 VICTORIA BUKASERO BUKASERO 9 IGANGA 9 0 KACHUNG 120 URA 27 0 117 KWANIA KACHUNG 174 VICTORIA MUSOLI MUSOLI J7 IGANGA 37 0 ADEKNINO 36 LJRA J6 0 -.;\7 118 KWANIA ADEKNINO 175 VICTORIA MASAKA MASAKA 2J IGANGA 0 ADERO 13 LIRA IJ 2 119 KWANIA ADERO 176 VICTORIA NAKALANGA IMANYIRO 30 IGANGA 22 0 APYERIAGO 25 l.lRA 22 J 120 KWANIA AOA 177 VICTORIA LUFUDU LUFUDU 10 IGANGA 6 0 27 9 KWANIA APENYOWEO APENYOWEO 34 LIRA 41 0 121 178 VICTORIA BUKOBA BUKOBA 41 IGANGA IZIGWE 23 LIRA 23 0 '. 122 KYOGA NAMASALE 179 VICTORIA lUGALA LUGALA 5 IGANGA 5 0 NACHAKA 23 KAMULI 23 22 123 NILE ,'\IACHAKA 180 VICTORIA KABUKA KABUUKA 26 lGANGA 26 0 NAKATENTE (: KAMULI 6 6 124 NILE ,'NDEREA 181 VICTORIA NAKIRIMIRA NAKJRIMlRA 21 IGANGA 10 0 NABIGAGA 11 KAMULI II 2 125 KYOG.~ ,'\IABIGAGA 182 VICTORIA IlUT.-\NIRA BUTANIRA 14 IGANGA 14 0 IRUNDU 9 KAMULI 9 9 126 NAKUHA NANGALA 183 VICTORIA UKANGAWA UKANGAWA 12 IGANGA 12 0 KASODO g roRORO 0 0 127 MPOLOGOMA KASODO 184 VICTORl.A MAJANJI MAJANJI 14 TORORO 14 0 IRUNDU 10 KAMUU 10 3 128 ~AKUHA BUDIPA 185 VICTORIA NAMONI NAMONI 28 IGANGA 28 0 NGOLE < KAMULI 5 0 129 KYOGA NCOLE 186 VICTORIA HI',!' IlUTE 30 IGANGA 30 0 MUKEERI \" IGANGA 14 0 130 VICTORl,\ \1UKEERI 187 ViCTORIA :GOR.AFA MASAK..~ 13H "\CTORI.~ GOI(AI-/\ 19< VICTORI.A io\.:\;\1W ..\:\Yl KAMWANYI J 3 0
:GANGA ~ 0 ~
MATOlO ~ 13~ VICTORI,\ 'IATU!U 196 VICTORIA \~iSE;~Yi MISE:-iYI MASAKA 0 14 0 '(EllIe YEBE I" iGANGA \1.:.\ 1.1 :' 140 VICTOR!.A 19'/ VICTORI.~ KJTOHO 5 MASAKA 0 ,le IGANGA 35 G
VICTORI.~ MATIKO
MATIKO ~.~KlljAi"l..i.", 141 19~ v'ICTORI.·\ :-iAKIBAJ'GA 9 MASAKA 13 0 7 0 WALUJJO • WALUJJO 2G iGANGA "'! VICTORI,\ 199 VICTORI.·\ '..I~GA LlNGA J MASAK..-\ 3 0 BULIGI :.' :GANGA 24 0 143 VICTORIA K"BANDO 200 VICTOR!.·\ HI'I.I~Gt BULI.'iGI, 39 MASAK..A 39 0 BUMALENGE GANGA II a 144 ViCTORIA BUMALENGE 201 VICTORIA \4ASOl'KA FFUNVE 2 MASAKA 2 0 0 BUSUI llUSUI :C ,GANGA 20 '~G:' 1.5 VICTORIA VICTORI.~ ",I !,'NGI' KILUNG1.. J MASAKA J 0 BWONDHA .," :GANGA 44 0 BWONDIIA ~ ·~jl:SGA 146 VICTORIA 203 VICTORI.~ GUNGA , MASAKA 0 0 IGULUIIlI IGULUIBI !l IGANGA 21 ~ALlNDI 14) VICTORIA 204 VICTORI.~ MALANGA g MASAKA 8 0 ';8 IGANGA 33 0 148 VICTORIA SSIRO SSIRO 205 VICTORIA "A BUtA MPUGWE 2 MASAK..A 2 0 11 IGANGA II 0
BUMBA .MASAK..~ 149 VICTORIA BUMBA 206 VICTORIA ~lUWUNGE BUWUNGE 3 3 0 1 IGANGA 7 0
LWENG~ LWENGE 150 VICTORIA 207 VICTORIA ;;.~SENYI(MPUG MPUGWE 3 MASAKA 3 0 o IGANGA 6 0 151 VICTORIA IAGUSI JAGUSI 208 VICTORIA aUKAKATA KABASESE 34 MASAKA 30 0 15 IGANGA 15 0 152 VICTORIA NANGO NANGO 209 VICTORl..\ "AIJAGALA BUNYAMA 3 MASAKA 3 0 IGANGA II 0 153 VICTORIA DEMBE DEMBE II 210 VICTORIA \1 AZ1GO BUNYAMA 3 MASAKA 3 0 0 ~O iGANGA 20 154 VICTOR!.-\ IAGUSI JAGUSI 211 VICTOR!.A "A.\1JREMBE NAMIREMBE 19 MASAKA 19 0 KANDEGE 23 IGANGA 2J 0 155 VICTORIA KANDEGE ~E VICTORIA \lASALAGWA MASALAGWA 6 MASAKA 7 0 10 0 156 VICTORIA BAWETE SAGITU o IGANGA 213 VICTOR!.-\ .\11'KERO KIRUGU 2 MASAKA 2 0 20 0 157 VICTORIA JAGUZI-4 JAGUZI 20 IGANGA 214 VICTORIA \lISEENKE KIRUGU 3 MASAKA 3 0 19 0 158 VICTORIA WALUMBE WALUMBE 15 IGANGA 2\5 VICTORIA BUSINDI BUSINDI 5 MASAKA 5 0 24 0 159 VICTORIA BULUBA BULUBA 24 IGANGA 216 VICTORIA KM'IIUNGWA KACHUNGWA 4 MASAKA 4 0 97 IGANGA 28 28 160 VICTORIA WAKAWAKA WAKAWAKA .------ 217 ViCTORIA LUJWABA LUlWABA 4 MASAKA 4 0 270 VICTORIA BANDA BANDA II MASAKA II 0 218 VICTORIA DIMO KYESIGA 2J MASAKA 2J 0 271 VICTORIA BUYANGE BUYANGE II MASAKA J1 0 219 VICTORIA KAWAFU KUYE 1 MASAKA 2 0 272 VICTORIA KYOGA LULAMBA 4 MASAKA 4 1 220 VICTORIA MIRINDI KUYE 3 MASAKA 3 0 273 VICTORIA LUWUNGULU LUWUNGULU 9 MASAKA 9 0 221 VICTORIA KACHANGA 5 MASAKA 5 0 274 VICTORIA NAMATABA BUKONE 4 MASAKA 4 0 222 VICTORIA NJUBA K1RUNGU 3 MASAKA 3 0 275 VICTORIA MPATA MPATA 3 MASAKA 3 0 276 VICTORIA MALEMBO MALEMBO 14 14 1,p VICTORIA MAWAALA FFUNVE 1. MASAKA 2 0 MASAKA 0 224 VICTORIA LUBUNGO LUBUNGO 1 MASAKA 3 0 277 VICTORIA NJOGA NlOGA 7 MASAKA 7 0 ill VICTORIA KASSAGAZI BUTULUME ; MASAKA 9 0 278 VICTORIA MISENYI MISENYI 5 MASAKA 5 0 279 VICTORIA 226 VICTORIA KUUSU MPUGWE j MASAKA 5 0 NAMISOKE BULEGA 10 MASAKA 10 0 280 VICTORIA 227 VICTORIA MUSAVE BUBEMBE(lSLAN J MASAKA 3 0 KACHANGA KACHANGA 6 MASAKA 6 0 228 VICTORIA NANKULU NANKULlJ 1 MASAKA 3 0 281 VICTORIA NALUKANDUDE NALUKANDUDE 7 MASAKA 7 0 229 VICTORIA LUTOBOKA KJIZI 2 MASAKA 2 0 282 VICTORIA LWAZI LWAZI 5 MASAKA 5 0 283 VICTORIA 2J0 VICTORIA KAZIRU KAZIRU 10 MASAKA 10 0 MUSONZI MUSONZI 16 MASAKA 16 0 23\ VICTORIA BBAALE BBAALE 8 MASAKA 8 0 • 284 VICTORIA KABlRANGO KABIRANGO 9 MASAKA 9 0 232 VICTORIA KASISA SSERINYA J MASAKA 3 0 285 VICTORIA SSEMUGANJA SSEMUGANlA 3 MASAKA 3 0 133 VICTORIA MUKALANGA MUKALANGA 5 Mr\SAKA 5 0 286 VICTORIA KIBIBI KIBIBI 4 MASAKA 4 0 234 VICTORIA LUBAAGA KJRUGU(lSLND) J MASAKA 3 0 281 KIJANEBALO BBALE BBALE 9 RAKAI 9 0 287 VICTORIA KM'AESE KAMESE 14 MASAKA 14 0 235 VICTORIA MUGERA MUGERA 3 MASAK.~ J 0 288 vICTORIA KISOKO KJSOKO 236 VICTORIA LUYUYA BUSWA J MASAKA 3 0 4 MASAKA 4 0 289 \'ICTORIA KJSOKO KANYOGOGA 2 MASAKA 237 VICTORIA KAWUWA KUYE(lSLAND) 2 Ml\SAKA ! 0 2 0 290 VICTORIA KIKWAYI' KJKWAYU 6 MASAKA I) 238 VICTORIA KYAGALANYI KYAGALANYI 22 tv1ASAKA 1'1 II . 0 29i VICTORIA BUNGO BUNGO 7 MASAKA 7 239 VICTORIA KIWUNIKA NKOSE(ISLAND) 5 MAS/,K;.. :'> Ii 0 292 VICTORIA MBUGWE 240 VICroRIA NANKUTA BUGABA J Mf\S/\K/\ , (/ MBUGWE 3 MASAKA J 0
292 KUANEBALO MALEMB.~ 241 VICTORIA LUSERERA LUSERERA MASAK/\ .\ II MALEMBA 6 RAKAI 6 0 242 VICTORIA SSENERO KAGULUBE 4 MASAKi\ 4 II 292 VICTORIA TUBI TUBI 12 MASAKA 12 I 243 VICTORIA KIWUNGU KIWUNGlJ ) MASAKA j 0 293 VICTORIA LUKU BUGOMA II MASAKA II 0 244 VICTORIA KAGONYA BUNYAMA 3 MASAKA 3 0 293 KIJANEBALO KAKUNYU KAKUNYU 7 RAKAI 7 0 294 VICTORIA BUGOMA BUGOMA 245 VICTORIA KJKEEKA FFUNYE 2 MASAK" 2 0 2 MASAKA J. 0 ?96 VICTORIA 246 VICTORIA KYESERWA KYESERWA 4 MASAKA 5 0 KANAMUMBALA KANAMUMBALA 5 MASAK,\ 5 0 297 VICTORIA KJBANGA KJBANGA 4 MASAKA 247 VICTORIA KANGWE DAJJE j MASAKA 5 0 4 0 298 VICTORIA MISISJ MISISI 248 VICTORIA KABIRA KURUNGULU 5 MASAKA 5 0 24 MASAKA 24 0 299 VICTORIA SEMAWUNDO 249 VICTORIA NGABO NGABO I MASAKA 7 0 SEMAWUNDO 5 MASAKA 5 0 300 VICTORIA KAAYA KAAYA 250 VICTORIA KIREBO BlJYINDI IJ MASAKA 13 0 5 MASAKA 5 0 301 VICTORIA 251 VICTORIA BWAMBA BWAMBA 4 MASAKA 4 0 TABALIRO KALANGALA 2 MASAKA 2 0 302 VICTORIA NGABU KJJZI 252 VICTORlA .NKESE NKESE 17 MASAKA 24 0 4 MASAKA 4 0 303 V1CTORIA 253 NILE MALUGUYA MALUGUYA 34 KAMULI 34 31 MWENA-LUNSJ KJIZI 2 MASAKA 2 0 304 VICTORIA 253 VICTORIA KASENYI BUMANGI 4 MASAKA 4 0 MWENA-LUY ANl KALANGALA 3 MASAKA 3 0 305 NILE 254 VICTORIA MUTAMBALA MUTAMBAUi 8 MASAKA 12 0 IZANIRO IZANIRO 7 KAMULI 7 7 306 NILE NABABJRYE 255 VICTORIA KAGOLOMOLO KJZlRA 5 MASAKA 5 0 NABABIRYE 7 KAMULI 7 7 307 NILE NABWEYO 256 VICTORIA MUVO MlKALANGA 6 MASAKA 6 0 NABWEYO 56 KAMULI 38 /0 J08 KYOGA 257VJCTORIA NAKASENYI BUYOVU 2\ MASAKA 2 0 KAPJOKOLO BUKUNGU 40 KAMULI 26 I 309 KYOGA KJWANTAMA 258 VICTORIA KAAZI BUGABA 2 MASAKA 2. 0 KJWANTAMA 16 KAMULI 22 3 3/0 NILE NSANGABWlRE 259 VICTORIA DDAJE DDAJE 2 MASAKA 2 0 KABAGANDA 5 KAMULI 22 18 312 KYOGA BU1347 KYOGA KAMUGOY." KAMUGOYA ~ KAMULI 12 I 404 NILE KAMWOGORO KIIGYA II MASINDI II 0 348 IKANDA IKANDA 21i KAMULI 20 5 405 NILE KlKAITO KYOGA KIKA/TO 5 MASINDI 0 5
BWITANYINI BWITANYINI ~ KAMULi (' 406 NILE KANKOBA 350 KYOGA '> KANKOBA 17 MASINDI 0 17 KJ\BETO KABETO la KAMULI 12 407 NILE KAPUNDO 351 NILE 2 KANKOBA .12 MASINDI 4 8 KALAMA KABAGANDA 6 KAMULI 6 5 408 NILE WAKISO 352 NILI: KlRYAMPUNGUL 7 MASINDI 0 6 409 NILE KABONYI 353 NILE ;-IAMAKOBA NAMAKOBA 26 KAMULI 25 22 KlRY AMPENGUL 5 MASINDI I 4 KAKINDU 7 0 7 410 NILE ALERO 354 NILE KAKINDU KAMULI ALERO 2· o MASINDI {) 9 KIGULU 0 22 411 NILE MASINDI PORT 355 NILE KIGULU 22 KAMULI MASINDI 24 MASINDI 24 13 NAWAMPITI IRUNDU 33 KAMULI 9 4 412 ALBERT KAWAIBANDA 356 KYOGA KAWAIBANDA 43 MASINDI 17 0 357 NILE NANKANDULO NANKANDULO o KAMULI 0 0 412 NILE KARUMA D1MA o MASINDI I 5 358 NILE KlSEKYE KISEKYF o KAMULI 0 0 413 ALBERT FOFO FOFO 38 HOIMA 12 0 359 NAKUHA SAAKA SAAKA 10 KAMULI 0 0 413 NILE MUTUNDA BOMA o MASINDI 0 10 360 NILE LWANYAMA LWANYAMA o KAMULI 0 0 414 NILE ALERO ALERO I· o MASINDI 1 7 361 NILE KYAMATENDE NAMAKOBA 33 KAMUU I 20 415 ALBERT KATALA BULIISA 19 MASINDI 10 0 362 ALBERT SENJOIO BUHUKA 44 HOIMA 12 0 416 ALBERT BOOMA BOOMA 22 MASINDI 12 0 363 NGUSE NGUSE 55 HOIMA 14 0 417 ALBERT SONGA ALBERT PIIDA 52 MASINDI 20 0 364 KACHUI'GE BUHUKA 61 HOIMA 20 0 418 ALBERT KIINA PIIDA ALBERT 39 MASINDI 15 0 365 ALBERT KliNA BUHUKA 24 HOIMA 0 420 ALBERT KAMPALA 22 BOOM A 32 MASINDI 21 0 366 ALBERT NSONGA NSONGA 22 HOIMA 21 0 420 ALBERT KAMPALA BooMA 32 MASINDI 21 0 367 ALBERT KYAKAPERE NSONGA 36 HOIMA II 0 421 ALBERT TUGOMBIRJ TUGOMBIRI 22 MASINDI 11 0 368 ALBERT BUSIGE NSONGA 28 HOlMA 10 0 422 ALBERT KARAKABA KITAHlJRA 14 MASINDI 6 20 369 ALBERT BULINGA BULINGA 29 HOIMA 9 0 423 ALBERT KABOLWA KABOLWA 143 MASINDI 65 0 370 ALBERT NKONDO NKONDO 116 HOIMA 35 0 424 ALBERT BUGOIGO BUGOlGO 162 MASINDI 78 0 371 ALBERT NYAIWAGA NYAIWAGA 31 HOIMA II 0 425 ALBERT NYUMIKUTA NYAMUKUTA 49 MASINDI 20 0 372 ALBERT SSEBAGOLO SSEBAGOLO 34 HOIMA II 0 426 ALBERT KAMAGONGORO KAMAGONGORO 21 MASINDI 11 0 373 ALBERT KYEVUNDA KYEVUNDA 21 HOIMA 7 O. 427 ALBERT WALUKUBA WALUKUBA 33 MASINDI 16 0 374 ALBERT KYAMPANGA KIHORO 10 HOIMA 4 0 428 ALBERT BUIll' BUBE 18 MASINDI 9 0 375 ALBERT KIHORO KIHORO 33 HOIMA 12 0 429 ALBERT POKULU WALUKUBA' 21 MASINDI 18 0 376 ALBERT SONGAMORJE SONGAMARIE 36 HOIMA 12 0 430 ALBERT SONSIO SONSIO 30 MASINDI 10 1 377 ALBERT MUZIZI MUZIZI 19 HOIMA 6 0 431 ALBERT KALOLO KALOLO 12 MASINDI 6 0 378 ALBERT SSUSA BUTENDE 23 HOIMA 7 0 432 ALBERT WANKENDE WANSEKO 20 MASINDI 20 0 379 ALBERT ,KYENYANJA NSONGA 23 7 0 433 ALBERT WANSEKO HOIMA WANSEKO 79 MASINDI 48 /2 ------489 VICTORJA KICANGA KACANGA 13 MUKONO 13 0 434 VICTORIA GUNDA MI'ATA 13 MUKONO 13 0 490 VICTORIA KIGAYA LWERU II MUKONO II 0 435 VICTORIA WABISASIRO BULAGO 19 MUKONO 7 0 491 VICTORJA KlSILONGO ZIBA 3 MUKONO 3 0 436 VICTORIA KlSIGALA KISIGALA 13 MUKONO 6 3 492 VICTORJA KIZIZE MUVO 13 MUKONO 13 0 437 VICTORJA NSONGA NSONGA 24 MUKONO 12 3 493 VICTORJA MALUBYA MALUBYA ,7 MUKONO 7 0 438 VICTORJA NGAGA NGAGA 17 MUKONO 7 0 494 VICTORJA SAASA SAASA 10 MUKONO 4 0 439 VICTORIA WALWANDA BUBANZI g MUKONO 8 0 495 VICTORIA NANGOMO MPUNGE 20 '1UKONO 19 9 440 VICTORIA OUGAZI A BUGAZI 24 '1UKONO 9 0 496 VICTORJA BUI'OKA KJYrNDI 61 MUKONO 19 0 441 VICTORIA KASALI B BUWANGA iJ MUKONO 13 0 497 VICTORIA KJBUNGO KIBUNGO 20 MUKONO 8 0 442 VICTORIA OULEEBI BULEEBI 20 MUKONO 9 0 498 VICTORIA SANGO BUGOMBE 8 MUKONO 5 0 443 VICTORIA OATWALA BATWALA 28 MUKONO 10 0 499 VICTORJA NKONE MBAZI 20 MUKONO 8 0 444 VICTORIA KlWULUGUMA NKOMBWE 7 MUKONO 7 0 500 VICTORJA NABBAALE LWERU 12 MUKONO 16 0 445 VICTORJA KlMMI KlMMI&SAAMA 24 '1UKONO 5 0 501 VICTORIA BUKWAYA BUKWAYA 87 MUKONO 31 0 446 VICTORIA KlBANGA OUGOMBE 17 MUKONO 8 2 502 VICTORJA KAMWAANYI. KAMWAANYI 366 MUKONO 18 0 447 VICTORIA KlBlBI KIBIBI 7 MIJKONO 7 0 503 VICTORJA KASAALI MUVO 13 MUKONO 10 0 448 VICTORIA LUFU BUBANZI 18 MliKONO 17 0 504 VICTORIA KAMWANYI BUKULA 4 MUKONO 4 0 449 VICTORIA KlGUGO KIGUGO 14 MliKONO 14 0
S05 VICTORIA KJY1NDI KlI'INDI 50 ~1UKONO 15 0 450 VICTORJA MUBALE I BUWANGA It> MI;KONO 14 0 506 VICTORIA GOMBOLOLA GOMBOLOLA 54 MUKONO 13 0 451 VICTORIA KASAL! A BUWANGA 14 '1UI-:ONO 14 0 507 NILE BANDA BANDA 12 MUKONO 4 1 452 VICTORIA NAMUSENI'I BUNANKANDA 60 MUKONO 17 2 508 NILE LWABI'ATA LWABI'ATA 5 MUKONO 3 2 453 MUBEI' A MUBEI'A BUGOBA 15 MUKONO 14 0 509 NILE BUDALI BUDALI 7 '1UKONO 0 7 454 VICTORIA SINDIRO SlNDlRO " M1JKONO 6 0 510 KYOGA BUSUNGIRE BUSUNGIRE 7 '1UKONO 7 3 455 VICTORIA KlRYOWA KIRI'OWA 15 MIIKONO S 0 . 51 I 1':ILE BWEYALE BWEYALE 45 MUKONO 17 0 456 VICTORIA NYENDA NYENDA .,4 MI,KONO 24 0 512 KYOGA KITWE NTIMBA 30 MUKONO 13 0 457 VICTORIA LUKALJ, BUWANGA : I ,\1UKONlJ 10 0 513 KYOGA KALENGE KALENGE 14 MUKONO 7 0 458 VICTORIA NDOTWE BUWANGA L \;\',KONO 17. 0 514 KYOGA KYEDICHO KYEDICHO 19 MUKONO 19 0 459 VICTORIA MUNYAMA BUWANGA 1(, '1I.'KONO 16 0 515 NILE KlNAMAWANGA KINAMAWANGA 12 MUKONO 4 2
460 VICTORIA KULWE MAGYO 7 ~1\JKONO 7 0 517 KYOGA KlKOTA K1KOTA 48 MUKONO 21 0 461 VICTORIA SANGA BUWANGA 18 MlJKONO 38 0 518 KYOGA KIBUYE KIBUYE 14 MUKONO 7 0 462 VICTORJA KIFULU BUBANZI IJ MUKONO II 0 519 NILE LUKUNYU LUKUNYU 3 MUKONO 3 0 461 VICTORIA YUBWE BUWANGA 26 MUKONO 25 0 520 NILE NAMALERE NAMALERE 5 MUKONO 5 0 464 VICTORIA NAMUSENYU BUSAGAZI 17 MlIl-:ONO Ii 0 521 KYOGA KAWONGO NTIMBA 70 MUKONO 37 0
465 VICTORIA KITAMIRO BULIBA I~ ML'KONO 9 0 522 NILE KJWENDA KJWENDA \2 MUKONO 6 0 466 VICTORIA LUBEMBE LUBEMBE 11 MtJKONO II 0 523 NILE KILUBO KlLUBO 4 MUKONO 4 I 467 VICTORIA NAMBULA KIYINDI 19 MUKONO II 0 524 NILE MISANGA MISANGA 14 MUKONO 3 3 468 VICTORIA LUKALU 2 BWEMA 12 MUKONO 12 0 525 NILE KAMBATANE KAMBATANE 20 MUKONO 15 0 469 V.ICTORIA SERJNYA SERINYA II MUKONO II 0 526 VICTORIA NAKUKUTA KlZAALA 3 MUKONO 7 0 470 VICTORJA LUKALU I LUKALU 12 MUKONO 12 0 527 VICTORIA MUSENYI MUSENYI 15 MUKONO. 7 0 471 VICTORIA TAAVU TAAVU 10 MUKONO 5 0 528 VICTORIA KAKUNYU KAKUNYU 54 MUKONO 18 0 472 VICTORJA KATOS] KATOSI 20 MUKONO 6 0 529 VICTORJA BUGUNGU BUGUNGU II MUKONO 11 0 473 VICTORIA LWAZI LWAZI 18 MUKONO 7 0 529 CHAHAFI RWAMAKUBA MUROLA 6 KABALE 0 6 474 VICTORJA KIGIKO KlGIKO 40 MUKONO 21 7 530 GEORGE KASHAKA KASHAKA 42 BUSHENYI 30 0 475 VICTORJA LWANGA LWANGA 30 MUKONO . II 0 531 EDWARD KAZlNGA KAZINGA 90 BUSHENYI 10 0 476 VICTORJA BUWANZI BUWANZI 31 MUKONO 13 0 532 NY ABIHOKO NYABTHOKO NYABIHOKO 38 BUSHENYI 38 0 477 VICTORIA LlNGIRA KYOYA 50 MUKONO 21 0 533 NYAMUSINGI NYAMUSlNGIRJ K1CHWAMBA 27 BUSHENYI 7 478 VICTORIA BANGA BANGA 37 MUKONO 37 0 534 NYAKlYANJA NYAKIYANJA NYAKIYANJA 19 BUSHENYI 19 479 VICTORIA NSAZI NSAZI 46 MUKONO 17 0 535 EDWARD KlSHENYl KlSHENYI ISO BUSHENYI 480 VICTORIA BUWUJJA BUWUJJA 25 MUKONO II 0 24 536 KAZINGA KATUNGURURU KATUNGURU 60 BUSHENYI 481 VICTORIA MAALA MAALA-DAMBA 28 MUKONO II 0 26 0 537 KlBWERA KlBWERA KlCHUAMBA 5 BUSHENYI 0 5 482 VICTORIA BULUBA SENYI 13 MUKONO 13' 0 540 BUNYONYI TUSINGUIRE BUFUKA 17 KABALE 483 VlCTORJA LING IRA LING IRA 300 MUKONO 54 0 0 6 541 GEORGE KAHENDERO KAHENDERO 106 KASESE 51 484 VICTORJA KISU DAMBA 34 MUKONO 13 0 0 541 BUNYONYI RUTASIKWA MURANDI 63 KABALE 485 VICTORJA BUWERA BUBWA 7 MUKONO 7 0 0 13 542 BUNYONYI RUTASIKWA RUCHARAMBIKO 52 KABALE 0 486 VICTORJA NAMBU NAMBU 48 MUKONO J7 0 13 543 GEORGE HAMUKUNGU HAMUKUNGU 24 KASESE 24 487 VICTORJA NAMAZIINA KIGUGO 9 MUKONO 9 0 0 S44 GEORGE KASENYI KASENYI 20 KASESE 20 0 488 VICTORIA NKOBWE NKOBWE 10 MUKONO 7 0 ------c, ------....- - - 603 ALBERT 545 KAZINGA KATUNGURU KATUNGURU 45 KASESE 14 0 OGAL OGAL 12 NEBBI 10 0 604 ALBERT 547 GEORGE MANYORO MANYORO IJ KABAROLE 13 0 MUNYWA MUNYWA 44 NEBBI 25 0 605 ALBERT 548 GEORGE KAYINJA 0 50 KABAROLE II 0 ANGUMU ANGUMU 26 NEBBI 24 0 606 ALBERT 549 VICTORIA BUKAYANJA BWEMA 8 MUKONO 8 0 KAYONGA KAYONGA 33 NEBBI 0 0 607 ALBERT 550 vleTIRIA NALUBALE MPATA o MUKONO 10 0 SING LA SING LA 17 NEBBI 17 0 608 ALBERT 551 VICTORIA WABUZIBA 8ALVA 6 MUKONO 9 0 !\BOK ABOK 17 NEBBI 17 0 552 VICTORIA BWANIKA MALlJJA II MUKONO II 0 609 ALBERT NYAMOTAGANA PANYIMUR 7 NEBBI 7 0 610 ALBERT 553 VICTORIA SE"YI SENYI 9 MUKONO 9 0 DEI PANYIMUR 48 NEBBI 41 0 611 ALBERT 554 VICTORIA LUBYA LUBYA 40 MUKONO 34 0 BORO BORO 40 NEBBI 16 4 612 NILE 555 VICTORIA MAKALAGA MAKALAGA II MUKONO 10 0 JAKOK JAKOK 12 NEBBI 9 0 613 ALBERT 556 VICTORIA NAMITl NAMITl II MUKONO 10 0 GANDA KIVUJE 21 NEBBI 21 0 614 ALBERT 557 NAKlWALE RUKlNGA RUHINGA 30 MBARARA 25 0 WANGKADU WANGKADU 24 NEBBI 24 I 615 NILE 558 NAKIWALE KASHOJO KASHOJO 40 MBARARA 19 0 WICHAWA WICHAWA 17 NEBBI 16 0 616 "ILE 559 MBURO RWONYO RWONYO 27 MBARARA 27 0 KABAKA . MANGALE 13 NEBBI 6 6 617 NILE 560 NAKIWALE KAHIRlMBI KAHIRIMBI 18 MBARARA 18 0 MUGOBE MUGOBE 20 NEBBI 15 0 618 NILE 561 KASHERA NYANGA NYANGA 13 MBARARA 13 0 LEMBAWE PAKECH 5 NEBBI 1 4 619 562 MISHER.'\ MISHERA MISHERA J7 MBARARA 17 0 NILE KALOLO KALOLO 9 NEBBI 9 () 620 NILE ';63 VICTORIA NAKIWOGO NAKJWOGO 78 MPIGI 19 0 IUPALUNYA JUPALUNYA 24 NEBBI 15 I 564 VICTORIA KIGUNGl.' KlGUNGU 102 MPIGI 37 0 621 NILE PAKECH PAROKETHO 8 NEBBl 3 5 622 NILE 565 VICTORIA KlTUBULl.' KlTlJBULlJ 90 MPIGI 26 I MANGELE MANGELE 8 NEBBI 8 0 623 NILE 566 WAMALA LUNO"I MAMBA 6 MPIGI 6 0 OPOTAN OPOTAN 12 ARUA 0 12 624 ALBERT NILE OJlDIRlKU 56; VICTORIA BUGONGA BUGONGA 66 MPIGI 18 0 OJlDRlKlJ . 14 'ARUA 0 14 625 NILE 568 WA.\1ALA MAMB.'\ MAMBA 13 MPIC! 13 0 PARRA PARAA 17 ARUA 2 15 2:1 626 NILE ONARI LUBA 56~ VICTORIA KASENY' NKUMfJA MP,IGl J! 0 22 ARUA 0 22 627 NILE 57C WAMALA BUTAMI BUTAAMI 8 MUBENDE 8 0 RHINO CAMP RHINO CAMP 23 ARUA 0 17 628 ALBERT NILE ODOYI 57: W,\MAI.I\ LUBAJj,\ KIMUL! 14 MUBENDE II 0 ODOYI 12 ARUA 0 12 629 ALBERT NILE NDARA 572 WAMALA BUGOLO BUGOLO 8 MUBENDE 20 0 PALAI J3 ARUA 0 l3 630 ALBERTNILE FUNDO 573 WAMALA KATAKUL,\ KIMULI 14 MUBENDE 9 0 FUNDO 34 ARUA 0 15 631 VICTORIA 574 WAMALA NKONYA NKONYA 15 MUBENDE 21 0 ZINGA ZINGA 3 MUKONO 3 () 632 VICTORIA 575 WAMALA BUKANAGA BUKANAGA 10 MUBENDE 7 0 WAKlKERE WAKIKERE 6 MUKONO 6 0 633 VICTORIA 576 NILE KlLYORA KILYORA 13 MUKONO 6 3 KASENYI KIBO 12 MUKONO II 0
634 VICTORIA ~1UBALE 576 WAMAL.i\ BUTEBI BUTEIlI 22 MUBENDE 27 0 2 MUBAL£ 6 MUKONO 6 0 635 VICTORIA 577 WAMALA GOMBE GOMBE 51 MUBENDE 20 0 LWAZI BUTALE II MUKONO 9 0 636 VICTORIA 578' WAMALA KATIKO KATIKO 13 MUBENDE 13 0 MUKOONA WAKlISA 2 MUKONO 2 0 637 VICTORIA 579 WAMALA KALYANKOKO KALYANKOKO 45 MUIlENDE 18 0 KADININDI KADININDI 8 MUKONO 2 0 638 VICTORIA 580 WAMALA KJMULJ KJMULI 16 MUBENDE 12 0 KUAKA KlJAKA 9 MUKONO 8 0 639 VICTORIA 581 WAMALA BUZIBAZI MAMBA 5 MUBENDE 5 0 KlZIBA NABUSIZI 16 MUKONO 15 0 640 NILE PAKOU 582 WAMALA LUSALIRA MAMBA 10 MUBENDf 10 0 PATWE PAKOLJ 60 NEBBI 0 10 641 NILE 583 VICTORIA KYABASIMBA KYABASIMBA II RAKAI II 0 JALURNGA 50 NEBBI 0 10 642 NILE PACHENGO 584 KACHERA LWEBIRIBA LWEBIRIBA 45 RAKAI 45 0 MUBOGO 65 NEBBI 0 14 643 NlLl: MUKOBE 585 KACHERA LWANGA LWANGA 80 RAKAI 50 0 MUBOGI 50 NEBBI 0 18 644NlLE 586 KlJANEBALO KYEMPEWO KYAMPEWO 6 RAKAI 5 0 KlBORO PAKWACH .28 NEBBI I 7 645 NILE PUJWANG 588 KUANEBALO KANAMUZINZI KANAMUZINZI 6 RAKAI 6 0 AMOR 12 NEBBI 0 5 646 NILE MBOGOK 589 VICTORIA KASENSERO KASENSERO 16 RAKAI 16 0 MBOGO 18 NEBBI 6 66 647 NILE 10PUGULU 590 VICTORIA SANGOBAY SANGOBAY II RAKAI II 0 AMOR 18 NEBBI 0 16 648 NILE PAJOBI PAKWACH 591 KACHERA KATETE KATETE 17 RAKAI 17 0 20 NEBBI 3 2 650 NILE RlMBO 594 VICTORIA LUKUNYU LUKUNYU 8 RAKAI 8 0 LOBODEGI 50 NEBBI 3 8 651 RIVER JACAN 595 KlJANEBALO NTOVU NTOVU 33 RAKAI 33 0 PAVORA 50 NEBBI 12 0 652 RIVER JUPABlRA 596 KUANEBALO KASERERE KASERERE 15 RAKAI 15 0 JUPABIRA 50 NEBBI 0 0 653 NILE CWERE 597 KlJANEBALO KAKUNDi KAKUNDI 17 RAKAI 17 0 NYALUCI 40 NEBBI 0 14 654 NILE JAKOLE 598 NILE WILYECH MANGELE o NEBBI 0 0 PUVUNGU TOKA 20 NEBBI 2 8 659 NILE PAJABAR 599 NILE TUBALING MANGELE 3 NEBBI 3 0 WANGKAWA 35 NEBBI 2 3 900 RIPON 600 NILE PUVUNA PARALA o NEBBI 0 0 ROKO 25 JlNJA 10 901 VICTORIA WAlRAKA WAIRAKA 601 NILE PAJAU PAKALA 2 NEBBI 2 () 30 JINJA 4 0 902 VICTORIA MASESE MASESE 602 NILE MBAGORO KIYAYA 36 NEBBI II I 96 JlNJA 32
9. . 418 ALBERT MASINOI B IS KIINA IS 0 Table 2. Landings from which sample was drawn I 422 ALBERT MASINDI B 16 KARAKABA 6 20 392 ALBERT HOIMA B 16 SONGANGI 16 0
4T1 ALBERT MASINOI B. 16 WALUKUBA 16 0 48 ALBERT BUNOIBUGYO B 17 WASA 13 7 No Lake District No of Landing No of No of I 412 ALBERT MASINDI B 17 KAWAIBANOA 17 0 or category planked dug QUI I 389 ALBERT HOIMA B 17 WAKI J7 0
nY~r vess.t:ls canoes canoes 43 SEMLIKI BUNOIBUGYO B 18 K1JAJABA 18 0 429 ALBERT MASINOI B 18 POKULU 18 0 52 ALBERT BUNDlBUGYO B 18 KANARA 18 0 386 ALBERT HOIMA B 18 MBENGU 18 0 432 ALBERT MASINOI B 20 WANKENOE 20 0 383 ALBERT HOIMA A 4 KABUKANGA 4 0 I 417 ALBERT MASINOI B 20 SONGA 20 0 374 ALBERT HOlM A A 4 KYAMPANGA 4 0 1 0125 ALBERT MASINOI B 20 NYUMIKUTA 20 0 392 ALBERT HOIMA A 4 SONGA-RAU 4 0 I 364 ALBERT HOlM A B 20 KACHUNGE 20 0 385 ALBERT HOIMA A 5 KABANDA 5 0 37 388 ALBERT HOlM A A 6 K1SEGE 6 0 I 4'20 ALBERT MASINDI C 21 KAMPALA 21 . 0 377 ALBERT HOIMA A 6 MUZIZI 6 0 I 366 ALBERT. HOlM A C 21 NSONGA 21 0 431 ALBERT' MASINDl A 6 KALOLO 6 0 I 399 ALBERT HOIMA C 22 TOONYA 12 0 378 ALBERT HOIMA A 7 SSUSA 7 0 I 365 ALBERT HOIMA C 22 KIINA 22 0 379 ALBERT HOlM A A 7 KYENYANJA 7 0 I 3&4 ALBERT HOIMA C 22 KUANGI 22 0 373 ALBERT HOIMA A 7 KYEVUNOA 7 0 I 390 ALBERT HOIMA C 2J RUNGA 23 0 381 ALBERT HOIMA A 8 RWEBIGONGORO 8 0 I 53 ALBERT BUNDIBUGYO C 2J RWANGARA 23 0 397 ALBERT HOlM A A 8 NAN A 8 0 I 50 ALBERT BUNOIBUGYO C 23 K1GUNGU 23 0 428 ALBERT MASINDI A 9 BUBE 9 0 I 47 ALBERT BUNOIBUGYO C 25 KAMUGA 22 5 369 ALBERT HOIMA A 9 BULINGA 9 0 1 394 ALBERT HOIMA C 27 HOIMO 27 0 398 ALBERT HOIMA A 9 KIRY AMBOGA 9 0 I 34 ALBERT BUNOIBUGYO C 28 KATOLINGO 23 10 402 ALBERT HOlM A A 9 BUHUMA 9 0 I 387 ALBERT HOIMA C 28 KAlSO 28 0 396 ALBERT HOIMA A 9 RWENTALE 9 0 1 370 ALBERT HOIMA C 35 NKONDO 35 0 17 -- ------ I 55 ALBERT BUNDIBUGYO C 37 SANGA UCHAK! 32 10 400 ALBERT HOlM." B 10 NY AMASOGA 10 0 I 56-\LBERT BUNDlBUGYO C 40 RUKWANZI 36 H 368 ALBERT HOIMA B 10 BUSIGE 10 0 I J33 c'LBERT MASINDI C 49 WANSEKO 48 '2 415 ALBER' :vt.-\SINDI B 10 K."TALA 10 0 I 51 ALBERT BUNOIBUGYU C 53 NTOROKO 53 0 44 SEMLIKI BUNDlBUGYO B 10 RWENOEGEY A 0 10 I ·'2] ALIlERT MASINOI C 65 KABOLWA 65 U 42 SEMLIKI BUNDlBUGi:O B II NKARARA 0 II 1 54 ."LBERT BLJNDIBUGYO C 67 SONGAKIYANJA 67 0 430 ALBERT MASINDI B II SONSIO 10 : I J::!~ -\LBERT MASINDI C 78 BUGOIGO 78 U 371 ALBERT HOIMA II II. NY AIWAGA II 0 20 ·121 ALBERT MASINOI B II TUGOMBIRI II 0 74 1295 104 367 ALBERT HOIMA ,B II KYAKAPERE IJ 0 426 ALBERT MASINOI B II K.,\MAGONGORO II 0 372 ALBERT HOIMA B II SSEBAGOLO II 0 2 j4~ GEORGE KABAROLE B II K....YINJA 0 375 ALBERT HOIMA B 12 KIHORO 12 0 2 531 EDWARD BUSHENYI B 10 KAZINGA 10" 0 416 ALBERT MASINDI B 12 BOOMA. 12 0 2 545 KAZINGA KASESE B 14 KATUNGURU 14 0 362 ALBERT HalMA B 12 SENJOJO 12 0 2 547 GEORGE KABAROLE B 13 MANYORO 13 0 376 ALBERT HOIMA B 12 SONGAMORIE 12 0 2 544 GEORGE KASESE B 20 KASENYI 20 0 391 ALBERT HOIMA B 12 KIBIRO 12 0 2 37 EDWARD KASESE B 15 KAYANJAB 15 0 393 ALBERT HOIMA B 12 HOIMO 12 0 6 413 ALBERT HOIMA B 12 FOFO 12 0 2 35 EDWARD KASESE C 27 K.-\ YANJA 27 0 49 ALBERT BUNDlBUGYO B 14 MULANGO 14 0 2 530 GEORGE BUSHENYI C 30 KASHAKA 30 0 401 ALBERT HOIMA B 14 KlTEBERE 14 0 2 36 EDWARD KASESE C 62 K.'\TWE 62 'l> 382 ALBERT HOIMA B 14 NOAIGA 14 0 2 536 KAZINGA BUSHENYI C 26 KATUNGURURU 26 0 363 ALBERT HalMA B 14 NGUSE 14 0
_. _._. - - - - ._.. ------_._--~. 4 41 KAYANJA KASESE B 17 KAYANJA 17 0 2 543 GEORGE KASESE C 24 HAMUKUNGU 24 0 4 560 NAKIWALE MBARARA B 18 KAHIRlMBI 18 0 2 535 EDWARD BUSHENYI C 24 K1SHENYI 24 4 KABAROLE B 18 WABICERE 18 0 2 541 GEORGE KASESE C 5\ KAHENDERO 51 0 39 WABICERE B 19 KASHOJO 19 0 7 ------ 4 558 NAKIWALE MBARARA 534 NYAKlYANJ A BUSHENYI B 19 NYAKIYANJA 19 13 327 0 4 15 4 45 KANYATETE KASESE C 23 KANYATETE A 23 0 3 581 WAMALA MUBENDE A 5 BUZIBAZI 5 0 4 557 NAKIWALE MBARARA C l5 RUK1NGA 25 0 3 566 WAMALA MPIGI A 6 LUNONI 6 0 559 MBURO MBARARA C 27 RWONYO 27 0 3 570 WAMALA MUBENDE A 8 BUTAMI 8 0 4 4 595 K1JANEBALO RAKAI C 33 NTOVU 33 0 3 573 WAMALA MUBENDE A 9 KATAKULA 9 0 4 BUSHENYI C 38 NY ABIHOKO 38 0 3 575 WAMALA MUBENDE A 7 BUKANAGA 7 0 532 NYABIHOKO 4 584 KACHERA RAKAI C 45 LWEBIRIBA 45 0 RAKAI 50 LWANGA 50 0 3 578 WAMALA MUBENDE B 13 KATIKO 13 0 4 585 KACHERA C --- ---- ----_ ... _._----- .. 7 3 579 WAMALA MUBENDE B 18 KALYANKOKO 18 0 535 60 3 572 WAMALA MUBENDE B 20 BUGOLO 20 0 36 3 577 WAMALA MUBENDE B 20 GOMBE 20 0 3 568 WAMALA MPIGI B 13 MAMBA 13 0 3 580 WAMAL", MUBENDE B 12 KIMULl 12 0 5 127 MPOLOQOMA TORORO A o KASADO 0 0 3 571 WAMALA MUBENDE B II LUBAlJA II 0 5 359 NAKUHA KAMULl A o SAAKA 0 0 3 582 WAMALA MUBENDE B 10 LUSALlRA to 0 5 323 KYOGA KAMULI A 4 lGOOLA 4 0 5 262 KYOGA KAMULI A 4 lKAMA-KALUMB 4 . 6' 3 576 WAMALA MUBENDE C 27 BUTEBI 27 0 5 326 KYOGA KAMULI A 5 KAKOOGE 4 2 3 574 WAMALA MUBENDE C 21 NKONYA 21 0 5 129 KYOGA KAMULI A . 5 NGOLE 5 0 --- ---- ----- .. --- 2 -- ------ - ..------ ----- 5 350 KYOGA KAMULI A 6 BWITANYINI 6 0 \5 200 0 5 338 KYOGA KAMULI A 6 NTAMIA 6 0 5 8 KYOGA LUWERO A 6 KYALUSAKA 6 0 4 537 KlBWERIl IlUSHENYI A 3 KIBWERA 0 5 5 1 KYOGA LUWERO A 7 MUNAMI 6 1 KAMULl A 7 NAMUSITA 5 4 4 540 BUNYONYI KABALE A 3 TUSINGUIRE 0 6 5 321 KYOGA 4 529 CHAHAFI KABALE A 3 RWAMAKUBA 0 6 5 345 NAKUHA KAMULI A 8 ISALO 5 5 7 4 586 KIJANEBALO RAKAI A 5 KYEMPEWO 5 0 5 334 KYOGA KAMULl A 8 NJWEJWE 2 8 KITEGA 4 292 KUANEBALO RAKAI A 6 MALEMBA 6 0 5 3JI NAKUHA KAMULI A 8 0 4 588 KIJANEBALO RAKAI A 6 KANAMUZINZI 6 0 5 71 KWANtA APAC A 90DWONG 7 3 4 542 IlUNYONYI KA8...U A 7 RUTASIKWA 0 13 5 325 KYOGA KAMULI A 9 KASEBETI 7 3 4 541 BUNYONYI KABALE A 7 RUTASIKWA 0 IJ 5 336 KYOGA KAMULI A 9 NKONDO 9 0 4 293 KIJANEBALO RAKAI A '7 KAKUNYU 7 0 5 5 KYOG,\ LUWERO A 9 KlJ(OIRO 9 0 4 533 NYAMUSINGI flUSHENYI A 7 NY AMUSINGIRI 7 5 61 NILE APAC A 9 PALANGO 0 18 4 594 VICTORI." RAKAI A 8 LUKUNYU 8 0 19 4 17 K,\ YUMBU KABALE A 9 RWAMAKUBA 0 17 5 62 NIl.E ,\PAC B 10 ALWOROCENG 0 19 4 287 KIJANEBALO RAKAI A 9 BBALE 9 0 5 65 NILE APAC B 10 AKAKA 0 20 4 40 SAAKA KABAROLE A 9 SAAKA 9 0 5 69 KWANI.A APAC B IIOGWIL 10 I 7 14 -- ------ 5 J44 NAKUHA KAMULI B 1I BUTAMBALA 7 356 KYOGA KAMULI 9 4 583 VICTORIA RAKAI B II KY ABASIMBA II 0 . 5 B II NAWAMPITI 4 128 NAKUHA KAMULI 12 BUD1PA 4 590 VICTORIA RAKAI B I I SANGOBAY II 0 5 B 10 3 125 KYOGA KAMULI B 11 4 561 KASHERA MBARARA B 13 NYANGA 13 0 5 12 NABIGAGA 2 LUWERO 4 46 KANY ATETE KASESE B 14 KANY ATETE B 14 0 5 21 KYOGA B t2·MONI 12 0 347 KYOG.A 4 596 KIJANEBALO RAKAI B 15 KASERERE 15 0 5 KAMULt B 13 KAMUGOYA 12 I 4 38 RWEITERA KABAROl.E B 15 RWEITERA 15 0 5 68 KWANI" ·\PAC B 13 ABEBE 12 2 4 589 VICTORIA RAKAI B 16 KASENSERO 16 0 5 70 KWANIA APAC B 13 OWINY 13 0 13 WIGWENG 4 562 MISHERA MBARARA B 17 MISHERA 17 0 5 75 KWANt... APAC B 13 0 5 126 NAKUHA KAMULt B 14 NANGALA 9 9 4 591 KACHERA RAKAI B I7 KATETE 17 0 " 5 67 KWANI.J\ APAC B 14 ABEl II 4 597 KlJANEBALO RAKAI B 17 KAKUNDl 17 0 6 ------6 259 VICTORIA MASAKA A 2 DONE 2 0 5 3 KYOGA LUWERO B 15 KISENYI 15 0 6 258 VICTORIA MASAKA A 2 KAAZI 2 0 5 9 KYOGA LUWERO B 15 WANGEDE 15 0 6 205 VICTORIA MASAKA A 2 KABlRA 2 0 5 20 KYOGA LUWERO B 15 CHIKOBE 15 0 6 219 VICTORIA MASAKA A 2 KAWAFU 2 0 5 330 NAKUHA KAMULJ B 15 PANYOLO 10 10 6 237 VICTORIA MASAKA A 2 KAWUWA 2 0 5 337 KYOGA KAMULJ B \5 MALIMA 15 0 245 VICTORIA MASAKA A 2 KIKEEKA 2 0 5 322 KYOGA KAMULI B 15 lKANDA-ISISI 15 0 6 MASAKA A 2 K1S0KO 2 0 64 NILE APAC B 16 MUTUNDA 0 32 6 289 VICTORIA MASAKA 2 LUTOBOKA 2 5 335 KYOGA K.-\MULJ B 16 K1WABA i4 4 6 229 VICTORIA A 0 264 VICTORIA MASAKA A 2 LWABALEGA 2 0 5 63 NILE APAC B 17 NAMWULU 17 0 6 201 VICTORIA MASAKA A 2 MASONKA 2 0 5 18 KYOGA LUWERO B 19 NAMlKA 18 I 6 223 VICTORIA MASAKA A 2 MAWAALA 2 0 5 329 KYOGA KAMULJ B 19 BUMOGOU 17 4 6 213 VICTORIA MASAKA A 2 MUKERO 2 0 5 7 KYOGA LUWERO B 19 IRIMA 19 0 6 303 VICTORIA MASAKA A 2 MWENA-LUNSI 2 0 5 324 KYOGA KAMULI B 19 IREMERIRA 16 6 6 6 257 VICTORl."t MASAKA A 2 NAKASENYI 2 0 5 59 NILE APAC B 20 K...MDlNI 0 39 6 240 VICTORIA MASAKA A 2 NANKUTA 2 0 5 314 KYOGA K...MULl B 20 KANGANY ANZA 20 0 29 -- ------ 6 301 VICTORIA MASAKA A 2 TABAURO 2 0 206 VICTORIA MASAKA A 3 BUWUNGE 3 0 5 143 NAK... Hl.iHA KAMULJ C 21 KYAFUBA 15 II 6 MASAK... A 3 GWAGALO 3 0 5 66 KWANIA APAC C 21 ALiDO 13 16 6 262 VICTORlJ\ 209 VICTORIA A 3 KABAGALA 3 0 5 4 KYOGA LUWERO C 21 KASAMBYA 21 0 6 MASAKA 5 340 KYOGA ! t~ ------6 527 VICTORIA MUKONO A 7 MUSENYI 7 0 6 217 VICTORIA MASAKA A 4 LUJWABA 4 0 6 526 VICTORIA MUKONO A 7 NAKUKUTA 7 0 6 269 VICTORIA MASAKA A 4 LWAZI 4 0 6 281 VlCTORIA MASAKA A 7 NALUKANDUDE 7 0 6 263 VICTORIA MASAKA A 4 MAKOKO 4 0 6 249 VICTORL,\ MASAKA A 7 NGABO 7 0 6 196 VICTORIA MASAKA A 4 MISENYI 4 0 6 438 VICTORIA MUKONO A 7 NGAGA 7 0 6 274 VICTORIA MASAKA A 4 NAMATABA 4 0 6 277 VICTORIA MASAKA A 7 NJOGA 7 0 6 302 VICTORIA MASAKA A 4 NGABU 4 0 6 488 VICTORIA ,'v1UKONO A 7 NKOBW£ 7 0 6 494 VICTORIA MUKONO A 4 SAASA 4 0 6 435 VICTORIA MUKONO A 7 W ABISASIRO 7 0 6 242 VICTORIA MASAKA A 4 SSENERO 4 0 6 142 VICTORIA IGANGA A 7 WALUJJO 7 0 6 272 VICTORIA MASAKA A 5 KYOGA 4 I 6 436 VICTORIA MUKONO A 8 KlSIG!\LA 6 3 6 221 VICTORIA MASAKA A 5 5 0 6 231 VICTORIA MASAKA A 8 BBAALE g 0 6 215 VlCTORIA MASAKA A 5 BUSINDI 5 0 6 549 VICTORIA MUKONO A 8 BUKAYANJA 8 0 6 300 VICTORIA MASAKA A 5 KAAYA 5 0 6 137 VICTORIA IGANGA A 8 KAAZ.,\ 8 0 6 248 VICTORl.,\ MASAKA A 5 KABIRA 5 0 6 204 VICTORIA • MASAKA A 8 KALiNDI 8 0 6 255 VICTORIA MASAKA A 5 KAGOLOMOLO 5 0 6 132 VICTORIA JGANGA A 8 !0 299 VICTORIA ~1ASAKA A 5 SEMAWUNOO 5 0 6 139 VICTORIA IGANGA A 9 MATOLO 9 0 6 471 VICTORIA MUKONO A 5 TAAVU 5 0 0 487 VICTORIA MUKONO A 9 NAMAZIINA 9 0 6 260 V!CTOR1-.. MASAKA A 6 KINYU 5 I 6 55:1 VICTORIA ).1UKONO A 9 SENYJ 9 0
6 265 VICTORIA ~lASAKA .'\ 6 BBOSA 6 0
a ,,51 VICTORI." ~'II!KO:-lO 9 WABUZIBA 9 0 6 IS I VICTORI.-\ IG,\NGA A 6 JAGUSI 6 0 " 0 I X~ \'ICTOI6 267 VICTORIA ~1ASAKA A 6' KAWAFU 6 0 0 /56 VICTORJ,\ 'GA."GA B 10 B...wET£ 10 0 In VICTOR!.·\ MASAKA ,,\ 6 KlKU 6 0 6 107 VICTORIA IGANGA B 10 BUGOMA 10 0 "6 290 VICTORIA MASAKA A 6 KlKWAYU 6 {) 0 503 VICTORIA MUKONO B 10 KASAALI to 0 6 IT! VICTORI... JGANGA A 6 LUfUDU 6 0 6 no VICTORIA \1... SAKA B j{) KAZIRU 10 0 0 250 VIC,'ORiA \IIASAK" ,-\ 6 MUVO 6 0 6 266 VICTORl." ,\1."S.-\K.'\ B 10 KITOBO 10 0 19J VICTORIA 'vlASAKA A 6 NAMIREMBE 6 0 0 >157 VICTOR!... MUKONO B 10 LUKALE 10 0 454 VICTORIA MUKONO A 6 SINDIRO 6 0 6 555 VICTORIA MUKONO. B 10 MAKALAGA 10 0 6 291 VICTORI." MASAKA A 7 BUNGO 7 0
~~ 1)0 V!CTORIA IGANGA B j{) MASULYA 10 0 6 485 VICTORIA MUKONO A 7 BUWERA 7 0 0 i3. V!,:TORIA !G.'\NG.-\ B j{) MWANGO 10 0 6 260 VICTORIA MASAKA A 7 DDEMIlE 7 0 <> l81 VI«:TORI-\ !GANGA B 10 "iAKIRlMIRA 10 0 6 447 VICTORIA MUKONO A 7 KIBIBI 7 0 0 550 V,CTORI,\ MUKONO B 10 NALUBALE 10 0 6 444 VICTORl." MUKONO A 7 KIWULUGUMA 7 0
G ~79 VICTORIA MASAKA B 10 NAMISOKE 10 0 6 460 VICTORIA MUKONO A 7 KULWE 7 0 556 VICTORIA MUKONO B 10 N,'\MITI 10 0 6 473 VICTORIA MUKONO A 7 LWAZI 7 0
"6 270 VICTORI." ~1."SAK.'\ B II BANDA II 0 6 150 VICTORIA JGANGA A 7 LWENGE 7 0 6 529 VICTOI------.... 6 902 VICTORIA JINJA C 32 MASESE 32 6 148 VICTORIA IOANGA C 33 SSIRO 33 0 6 554 VICTORIA MUKONO C 34 LUBYA 34 0 6 141 VICTORIA IOANOA C 35 MATIKO 35 0 6 478 VICTORIA MUKONO C 37 BANOA 37 0 6 564 VICTORIA MPIOI C 37 KIOUNOU 37 0 6 174 VICTORIA IGANGA C 37 MUSOLI 37 0 6 461 VICTORIA MUKONO C 38 SANGA 38 0 6 200 VICTORIA MASAKA C 39 BULINGU 39 0 6 319 VICTORIA KAMULI C 41 KIBUYE 31 19 6 172 VICTORIA 1GANOA C 41 BUOOTO 41 0 6 178 VICTORIA IOANOA C 41 BUKOBA 41 0 6 160 VICTORIA IOANOA C 42 WAKAWAKA 28 28 6 146 VICTORIA IOANOA C 44 BWONDHA 44 0 6 483 VICTORIA MUKONO C 54 LINOIRA 54 0
--- --~-- ~------42 -- ------ 269 3146 105
485 7,055 670 118 KWANlA LIRA ADEKNINO ADEKNINO 36 0 Table 3. l.aMings which were excluded from the survey 90 KWANIA LIRA AWIO ALWAR 7 3 117 KWANlA LIRA KACHUNG KACHUNG 27 0 94 KWANIA LIRA AJOKDONG CHAKWARA 12 I No Lake District Landing Village No of No of 120 KWANlA LIRA AOA APYERlAGO 22 3 or planked Dugout 88 KWANlA LIRA AWEI ARlDI 10 6 river C8DOCS CllJl<>eS 115 KWANlA LIRA AGWENG AGWENG 4 I 116 KWANIA LIRA ....DWAI AKURILUBA 7 0 99 KWANIA/NIL LIRA KIGA NAKITOMA 18 I 624 ALBERT NILE ARUA OllDIRIKU OJIDRlKU 0 14 87 KWANIKA LIRA AMAI ANYWALI 26 0 629 ALBERT NU..E ARUA NDARA PALAI 0 13 91 KYOGA LIRA ADWAI ADONYOIMO 17 2 628 ALBERT NU..E ARUA ODOYI ODOYI 0 12 107 KYOGA LIRA MUCORA MUCORA 24 2 630 ALBERT NU..E ARUA FUNDO FlJNDO 0 15 102 KYOGA LIRA KITALEBA NAMBIATA 21 9 627 NILE ARUA RHINO CAMP RHINO CAMP 0 17 106 KYOGA LIRA BIKO MUKAWA 23 0 626 NU..E ARUA ONARI LUBA 0 22 110 KYOGA LIRA BANGALDEC BANGALDEC 35 0 625 NU..E ARUA PARRA PARAA 2 15 92 KYOGA LIRA KlRYANGA KlRYANGA 17 6 623 NILE ARUA OPOTAN OPOTAN 0 12 104 KYOGA LIRA KAY AGO IRIGWE 28 I 900 lINJA RIPON ROKO 10 III KYOGA .L1RA NAVEYO NAVEYO 15 0 901 VICTORIA • JlNJA WAIRAKA ' WAIRAKA 4 0 98 KYOGA LIRA MUNTU MUNTU 20 3 341 NILE KAMULI ENDUDU ENDUDU 8 3 105 KYOGA LIRA LENGKO WABUNWA 21 3 352 NILE KAMULI KALAMA KABAGANDA 6 5 95 KYOGA LIRA A..\fU!< CHAKWARA II 0 342 NILE KAMULI IRINGA KAMENYA II 8 113 KYOGA LIRA :-;ALUBWOYO NALUBWOYO 32 I 124 NILE KAMULI ANDEREA NAKATENTE 6 6 108 KYOGA LIRA MAKATIN ALEMERE 45 2 317 NILE KAMULI KlIGA KlIGA 29 0 112 KYOGA LIRA TITWE ALYECMEDA 43 0 306 NILE KAMULI NABABIRYE NABABIRYE 7 7 109 KYOGA LIRA KONG-GORO KONG-GORO 10 0 310 NILE KAMULI NSANGABWlRE KABAGANDA 22 18 114 KYOGA LIRA K.o\MPALA KIBUGU 32 16 354 NILE KAMULI KAKINDU KAKINDU 0 7 122 KYOGA LIRA NAMASALE 1ZIGWE 23 0 351 NILE KAMULI KABETO KABETO 2 12 93 KYOGA LIRA BURUBURU AKOL' 8 0 355 NILE KAMULI KlGULU KlGULU 0 22 103 KYOGA LIRA GOJWE GOJWE 17 0 313 NILE KAMULI KlWONGOIRE KlWONGOIRE 16 I 410 NILE MASINDI ALERO ALERO 2' 0 9 361 NILE KAMULI KYAMATENDE NAMAKOBA I 20 403 NILE MASINDI KINYAMA KIIGYA 0 6 353 NILE K.o\MULI NAMAKOBA NAMAKOBA 25. 22 ~06 NILE MASINDI KANKOBA KANKOBA 0 17 253 NILE KAMULI MALUGUYA MALUGUYA 34 31 413 NILE :'-1ASINDJ MUTUNDA BOMA 0 10 305 NILE KAMULI rZANIRO IZANIRO 7 7 414 NILE MASINDI o\LERO ALERO I • I 7 123 NU..E KAMULI .NACHAKA NACHAKA 23 22 411 NILE l-IASINDI V.ASINDI PORT MASINDl 24 13 339 NU..E KAMULI KIIGE KlIGE 9 8 408 NILE MASINDJ WAKISO KlRYAMPUNGUL 0 6 307 NILE KAMULI NABWEYO NABWEYO 38 10 ~07 NIl.E MASINDJ KAPUNDO KANKOBA 4 8 83 AGU KUMI AGU AGU 32 32 405 NILE MASINDI KIKAITO KIK.o\lTO 0 5 84 AGU KUMI OPEGE OPEGE 43 43 404 NILE MASINDI KAMWOGORO KIIGY,\ 11 0 82 AGU KUMI OPELU NGORA n 43 412 NILE MASINDI KARUMA DIMA I 5 85 BISINA KUMI OMATENGA AGULI 34 34 409 NILE MASINDI KABONYI KIR YAMPENGUL I 4 86 BlsmA KUMI OSEERA OSEERA 31 3 510 KYOGA MUKONO BUSUNGIRE BUSUNGIRE 7 3 79 BISINA KUMI KAKORO KAKORO 93 29 521 KYOGA MUKONO KAWONGO NTIMBA' 37 0 80 BISINA KUMI AKIDE AKIDE 55 23 512 KYOGA MUKONO KJTWE NTIMBA 13 0 81 BISINA KUMI .>\AKUM KAPOLIN 38 2 51) KYOGA MUKONO KALENGE KALENGE 7 0 76 BISINA KUMI OMITO OM ITO 28 18 517 KYOOA c"-'MUKONO KIKOTA KIKOTA 21 0 77 NYAGUNO KUMI ACIISA ATooT 74 63 518 KYOGA MUKONO KIBUYE KlBUYE 7 0 78 NYASALA KUMI OPOT ATooT 29 23 514 KYOGA MUKONO KYEDICHO KYEDICHO 19 0 96 KWANIA LIRA ADERO CHAKWARA 8 0 453 MUBEY A MUKONO MUBEYA BUGOBA 14 0 89 KWANlA LIRA BATA ARIDI 20 7 523 NILE MUKONO KILUBO KlLUBO 4 I 119 KWANIA LIRA ADERO ADERO 13 2 524 :-.lILE MUKONO MISANGA MISANGA 3 'J 121 KWANIA LIRA APENYOWEO APENYOWEO 27 9 ------PAKOU PATWE PAKOU O. 10 525 NILE MUKONO KAMBATANE KAMBATANE 15 0 640 NILE NEBBI NEBBI JAKOK JAKOK 9 0 508 NILE MUKONO LWABYATA LWABYATA 3 "2 612 NILE PAVORA 12 0 520 NILE MUKONO NAMALERE NAMALERE 5 0 651 RIVER NEBBI JACAN 6 0 519 NILE MUKONO LUKUNYU LUKUNYU 3 0 30 BlGINA soRon KOKORIO AGULO AGULE 16 0 507 NILE MUKONO BANDA BANDA 4 I 25 KYOGA soRon IRUKO 53 0 5n NILE MUKONO BWEYALE BWEYAL£ 17 0 3 KYOGA SOROTI KAGWARA KAGWARA 43 0 576 NILE MUKONO KILYORA KILYORA 6 3 22 KYOGA SOROTI KELLE PORT KELLE 19 4 509 NILE MUKONO BUDALI BUDAU 0 7 24 KYOGA SOROTI BUGONDO BUGONOO MULONDO 25 0 522 NILE MUKONO KIWENDA KIWENDA 6 0 31 KYOGA SOROTJ MULONOO soROTI OCELAKUR OCELAKUR 31 0 515 NILE MUKONO KINAMAWANGA KINAMAWANGA 4 "2 26 KYOGA MUGARAMA 32 I 633 VICTORIA MUKONO KIBO KASENYI II 0 28 KYOGA soRon MUGARAMA ACHOMIA 28 0 635 VICTORIA MUKONO LWAZI BUTALE 9 0 29 KYOGA SOROTI ACHOMIA KOJETENYANO OLUPE 14 4 632 VICTORIA MUKONO WAKlKERE WAKIKERE 6 0 27 KYOGA SORon OTIRONO 23 0 631 VICTORIA MUKONO ZINGA ZINGA 3 0 23 K"YOGA soRon KOTIRONO PINGIRE 28 9 639 VICTORIA MUKONO KIZIBA NABUSIZI 15 0 33 PINGIRE SOROTI PINGJRE 636 VICTORIA MUKONO MUKOONA WAKIISA 2 0 ---- ------ 637 VICTORIA MUKONO KADlN1NDI KADININDI 2 0 160 634 VICTORIA ,MUKONO MUBALE 2 MUBALE 6 0 609 ALBERT NEBBI NYAMOTAGANA PANYIMUR 7 0 603 ALBERT NEBBI OGAL OGAL 10 0 610 ALBERT NEBBI DEI PANYIMUR 41 0 605 ALBERT NEBBI ANGUMU ANGUMU 24 0 611 ALBERT NEBBI BORO BORO 16 4 614 ALBERT NEBBI WANGKADU WANGKADU 24 I 607 ALBERT NEBBI SINGlA SINGLA 17 0 613 ALBERT NEBBI GANDA KIvUJE 21 0 604 ALBERT NEBBJ MUNYWA' MUNYW,\ 25 0 608 .ALBERT NEBBI ABOK ABOK 17 0 616 NILE NEBBI KABAKA MANGALE 6 6 654 O'-IILE NEBBI JAKOLE PUVUNGU TOKA 2 8
622 SILl' NEBBI ~ANGELE MANGELE 8 0
641 ~ILE O'-IEBBI 'ALURNGA 0 10 648 NlLE NEBBI PAJOBJ PAKWACH 3 2
601 ~IU' NEBBI .?AJAU PAKAlA 2 0
650 ~ILE NEBB! RIMBO LOBODEGI 3 8 617 :->ILE NEIlBl :v1UGOBE MUGOBE 15 0 653 SILl' NEBBI CWERE NYALUCI 0 14 619 NILE NEBBI K.ALOLO KALOLO 9 0 642 NILE NEBBI PACHENGO MUBOGO 0 14 ~UKOBE 643 ~ILE NEBBI MUBOGI 0 18 644 :->ILL NEBBI K1BORO PAKWACH I 7 618 "'ILl' NEBBI LEMBAWE PAKECH I 4 602 N1LE NEBBI MBAGORO KlYAYA II I 659 NILE NEBBI PAJABAR WANGKAWA 2 3 646 :'-IIL£ NEBBl MBOGOK MBOGO 6 66 645 NILE NEBBI PUJWANG AMOR 0 5 615 NILE NEBBI WICHAWA WICHAWA 16 0 599 NILE NEBBI TUBALlNG MANGELE 3 0 620 NIL£ NEBBI JUPALUNYA JUPAlUNYA 15 I ... 647 NILE NEBBI JOPUGULU AMOR 0 16 621 NILE NEBBI PAKECH PAROKETHO 3 5 ------I I I I I I I · . I I I I I I a ut:I0 ..4 y ut:I°4 I
sa.:I1:-eUUOt~sanO I I I I ,.., I I ~/. . . .- "---""---'-- ~~r.~-~:; \~t ......
".:A. 1
Interviever: Introduce yoursel.f' to the im;erviewcd pcr!;lon, I:lnd ' , h~lli: FOJ.U4 1>. oxplain ~bo~t the survey. also tell
I am now doin.:; to ask you SOLlC q,ueetions dJOUc YOlll' i:'iSll:U~ FI;:.H.:..tLS JU.,lV.3Y aSK~ne wa~n r~~son activities ~nd about yourself. The for .hese ~6 ~e ~Ltervicv Da~e of 1. /J J - J..J..J q,uee.ions .hat want to undersoana the proolewa dna .ria needs anyth~, of the f~shermen. r alii not prolllisiJ:lg but the answers will be used to try and LJake better projects in order to Code in~erviEmer 2. /J -/..J..J assist the fishermen. byprythin,g you say to wc is cOId'iucntial and I Code Supervisor 3. /J - IjJ will not ask for your name.
F.='oer· of Lc1.ndins 4. /..J J Inj;erviewer: iirite down the time at stal·t QuestioPJliiiI'(J nlLlher A(;0.388 5. ;jj..J..J ,of tl.L8 interviewl (:J?I:IIamJ I. ::'_;~~~:..;" ::..'j" '...... --.
=.=.=.=...--- _ -- -c- ..,- - ._._~- - ---~====_~,~_:...:.;:.:~
FO~l 'J2i: ....'-'-' US:C (;l.LY c"mUu"-~:'"(M~n:J I~ou -'7'/JJ ~ IntervieHer: If the answer ~3 no,'·' ...... -... '---'--'-l =nU=, '''''3u.m,o no. 'J ,
2. .i:1811 dl.d you ,llurchas" '';l"QS I canoe? 19___ I V,,:: /_/..J 3, How l!luch did you lXiy fo! _iT" I Old Shs I 'J,. /..JjjJjj New Shs /jjj..JJJ (Old=O,New=l) 10. Ij 4. l'ype of ;::anoe:plankea or' 'lugOU',: (dugou"=O,planked=l) u. /J 5. Does your boat generally ')peru tf; ~L dr~ine?(yes/no; w~th J./.:( /j -- .. _--' ._------_ .._--- I:
',,"tarv ...e'lor; (j ';3c.-ncr;;;..i 00001 tior, 'Jf "tn€ c",noo; (poor:J..f2.ir=2,goOd=31 ...--- ===-.: i .-' I 6. Ho\! :JilJ:<,y c<.noe s do .0\: ovm in ~oT.c.l·i /.../../.. '
7. Ho'" many vf "tno"" «.r-<:..
':;'.'('. C(i':;':-; ,-t1!ln.l:r~;·· -- _.------ ------.. .. -- -"- - - ------"
3 2 11. Interviewer: for the next question, ask Interviewer: Find out if any of these <.:::08::;-, what the biggest proble~s a.re for a are part of the sample. If so, take ~ht I boat owner, and let him ,give answers. appropriate number of queatinaaires, go ohrougb Only then go with him through the quostion 1-7 for each of them and contir,uF options, and let him rank them 1,2 and 3.
simul taneously wi th all of them 1. ~h ptiC8S of inputs ---::-c:-:--:- 2. high taxes 3. low fisb prices 19. /../
8. Do you resid~: (check one)
4. not enough good people --.~
1. at the landin.:; or in "~hc: vlll"-2,l as crew 20. nee-rby? /j 5. low catches 21. /.../
2. f,~ther away, but in the 6. marketing and transport same district 7. thefts of nets il1puts 3. district HQ or ...iaroebbl; 8. scarcity of 9. other(spscify) 4. el$ewhere :c6. '..J
9. How often do you visic the landil~ landing?(check one) 12. Do you operats the canoe
1. 3 times psr Heek or ~ore yourself?(y8s/no) 22 .. /../ 2. onee a week ~3. At the moment hOYI many times per 3. Once a Ulonth week dOes" the canoe so out to fiah? 4. Seldom '1"'. /.j 23'. /../
~4. In the low season, bow illany times? 10. ,Then you ,;ant to inves'c: 111 24. /../
fishiIlb equipment, such as ~5. In the good season, how lDllny a boat, in en,gine or nel;s, times? 25. what is your sources of 1.../ capital?(check one) 16. Is the season at the moment: 1. personal savings 1. pDor 2. fawily suvings 2. fair 3• loan froL1 bank 3. good 4. one) 4. loan from friends exccellent(check 26. /...1 ). loan from falliily 6. o"cher(spocify) 10. /../
....
~~:;" ">;~;;p;J~:~,";
-
-
19. 19.
.~~.\
18. 18. "iI~f"
"
r'" r'"
17 17
-
. .
• •
HOI.n,:;.ny HOI.n,:;.ny
2. 2.
1. 1.
~hi":
j. j.
(C:iCU. ~\;.
0",: 0",: 1;0', 1;0',
.:~
Qc', Qc', Sept Sept
Aue>, Aue>,
.r: Ju} Ju}
l l
Jill: Jill:
:'~~. Apr Apr
i~a!
1"(:<:
Jal: Jal:
N3L!C: N3L!C:
Species' Species'
it it
yoU yoU
\1=poor,2~fair,3=gooa) lilportapt lilportapt
Ililll.
•.
.' .'
::. ::.
I)cl'OI"e I)cl'OI"e
las las {es1.crd
~ i3 i3
'~rlp?
~<':::5
- ,
fish, fish,
c~
une) une)
c c
for for
1;<: 1;<:
lege lege
m@1t m@1t
your your
1 1
a ytls~~rd"Y
for
each each
how how y y
sp~cies
speci.,,:> speci.,,:> ,;pecJ.l!:O ,;pecJ.l!:O
did did
- .!.~~st
the the
good good you you
mon~h I·l~!j~l<.
1;\10
for for
c...: c...:
'. '. 3 3
- tC[.
moo moo scasop
which which
......
,1\'. ,1\'.
t t
0J. 0J.
enp" enp"
Species Species Name: Name:
-
1\ 1\
,2 ,2
! !
i i I I
I I :;'-. :;'-.
I I
\ \
I I
jj. jj. ,
I I
';!li. ';!li.
~So
4
"' ., .,
4.i. 4.i.
.,
.
)j )j SCI. SCI.
;5. ;5.
~.
~'~ .,- ..
J. J.
,
J J
. .
.. ..
-' -'
I I
. .
~ ~
-
I I
_/ _/
;' ;'
J J /...J
:..J :..J
IJ IJ
1.../ 1.../ /j /j
/j /j
-
...J ...J 1
I..J I..J /J /J
; -
../ ../
./ ...J ...J
./
/ /
.' .'
• •
.;''':" .;''':"
fiL. fiL.
!;
4.'.0 4.'.0
.' .' :~~
..,:.
.i4. .i4.
1/)
~ jC. jC. '. '.
;,"~
.. .. ,!.,) ,!.,)
~
- .,
. .
.. ..
'J 'J
/-1 /-1
I
I..J I..J
/J /J
oJ oJ
/..J /..J
/j /j
/_/
.J 1../ 1../
J J 1"-/ 1"-/
I
I
J J
(1r~¥~~~~J
- -
~
- 26.
25. 25.
24. 24.
23. 23.
~
22.
i
21. 21.
20. 20.
!
app!'ox. app!'ox. can can
"!;hc.. "!;hc.. -:;he -:;he
rn-,;ervie\'l~r: kst?
Ho~.. l':b~S"
::io~·.
.3(J~l .~u~.
i,;, :->0'" :->0'"
~i1':':
trip trip
',ne: ',ne:
GYlJ«
ca.lcu.2. ca.lcu.2.
d.~
:i",s :i",s
no.
-
flO'. flO'.
.i.OIl~. ~ -i..O~
lDr.:...~ "'C..:.i..L~
_1J,y _1J,y t
()~··T.o.ed?
i:rip? i:rip?
leIl6th) leIl6th)
of of
(chcct: (chcct:
enJ.s enJ.s
.)1' .)1'
w2. w2.
c.a.r::.t c.a.r::.t
10,,", 10,,",
~shc
1005 1005 '10e~
te kgs," kgs," ...
o:t'.-tillIS,-,wvr,,-~aQ:-OJ-
;t... ;t...
of of
cd';;ch cd';;ch
- if
'..:.!' '..:.!' ~,;li(:
Q;l:::'i'cren~
3.n 3.n
tIl>;: tIl>;:
Ull:'::)
d d
.~
so
~1Ti
:'105 :'105
USCt: USCt:
the the
;;;pprox. ;;;pprox. c~r~Ot
p.li.l1~t
o\.t
ttL .,-;.::.;. .,-;.::.;.
te te
t t
pe.rs,on pe.rs,on
t;ooara
.. ..
tC:;(IlllaO.:.:l' tC:;(IlllaO.:.:l'
l~c
')l~
impol'
- ',
in in
4. 4.
!. !.
3 . . 3
L L
i.:ls!? i.:ls!?
2" 2" 3. 3.
1, 1,
pcrso,: pcrso,:
t...l).il t...l).il
, ,
"'i2.
the the
t1o:: t1o:: \'Clei~'~
o~ner{specifY)
::'0
craps .;;.)~....:.
docs
~
poer
i"ail' ______
b:t.ulogJ,S" 300<1 300<1
-~
OA..
box box
:
_..::::..e _..::::..e
- :J
,.
h: h: '3
or· or·
nero nero ',it' ',it'
-....;~~
later
U';i,;
J.:ars J.:ars rl~t! 30""
.foar~
mow
-
-_.
______
I
i
I I
62. 62.
61.. 61.. ;,c./.../
jS~ 57. 57. ,o~
'1ii;~Ji;;
56. 56.
!jj
/.../..../
/.../..J /.../..J l..../j..J l..../j..J
1.../ _ _
/..../ /..../
;:: ;::
. .
~. _
. .------..----._-----'-- -, ~ ------7
~ 27". .Do YO1.\. rilond Gil~t6?(yeS/rlO 63. /J 28. If no, why not? ~ sh~re 32. If a systeill is 8wployed, hOi'" d0(;:~:
i t ~"ork? (check one) .'ner'(; are; 1;no r..:lts 1'0:1' 'i;his 29.
~ne deduc~ion 'chG COS'l; froD bo~t usually 00UJht?(chock onu) 1. After of
1" local shop proceeds, the re~inder is devit18C[ bet'.ll'en 2. . fisll trc..der owner ancr cree
3. visiting gerw::C3.1 trader 2. fhe proceeds devideCi.betwee:r, OIm",r are
cos~~
4. shQl) elsewhore and crew ~nd thu o,mer pays all
. 3. he r Jspecify)
co,-n~cl'Y 9t 5." fred 2..ii':f~r<.}r~c .0: 67. / J ,.. "). [roroJ. gov;:rr.w.(;.Li"(; [:ix'o 33. If the ere\! 3ets a ltage, how much is .J:..;~"G
this vel' trip: U.Shs, ·jrpu'
ot:lOr '. ?opEc .:' .L:'~;
7. ~
-'::;T-:~Q~:;l·· fi~j:~ U;' lnCOi:le ') <;> 2~ '/ () // -;:;~(·i:.o.("'.:....;
s(;'n.~j·i::'.l )- c· VJ.S1. T::~_l16 j : (RarJc m.;.;Y.ir.u.lIil 3)
4-. I. If. :::ls ....:r!i1c~c(:, ,; 3S. 1.h6liU1g( '-"--l"'~ :~:. _,.,J.:...... 1.i·]:·._'~\2!~, 01..<_ c 2. Panning ~., Jov3:cn!J.E:~i" ". ~ :co,,", ',_,-. .~: ~ ,:J c 3. Cattle breedlr~ 7. a-.,,-"_\s:J..;(;j.i:y. 6), /_/ 4. Fish trilding ~jll<'; 9~Op.Le 31. 00 '~,,8 lior!{i..:...5 5. Other trading
"\;11(;: 00&"1; o...:"t l (;EUCK J~'~ , 6. Lc?ndlord '~',i:' S~l~-CI_ l!lo '.~ Gh2 ...;_t \.\,.._
,,", 7. Otner(specify) ~- \{cJ,.di.. Ii,; • IJ
S_ru:il't:.' (11. ," ;~:~:. '.: 'OJ" a r'18i1i:l1..~: 35. hO'.. 101'6 do" s .;.. "rip \;c -'~!l2 :';cl,:;QS
~rO\ffid. ~_
.-._ ":J.gr :[)i\i: .. las,. clOur'~ I.../JI
~)l'~ ~;(." ~i~l. .;.... IlL
~.~:.. O:,::.:.O':-"i :~;; ..-.\...... \: ·~C ...... i.../ i
, ,
-- -
-
-~-
8. 8.
'. '. n--Af:tier-·yoL1.havc n--Af:tier-·yoL1.havc
..
2. 2.
J. J.
1. 1. 36. 36.
Ho>o: Ho>o:
4. 4.
3. 3.
2. 2.
.Do-
1. 1.
food food
'Wba
3. 3.
2. 2.
Other Other
1. 1.
I I
the the \7ho \7ho
I_ I_
\nt;-, \nt;-,
I I 3. 3.
2. 2.
of of
1. 1.
do do
i..e i..e (nets (nets
you you
never never
s"l1 s"l1 use use
less less
How How
t t suo suo
tno tno naarly naarly
dl.' dl.'
other other
crew
hired hired
o\-mer o\-mer
for for
crew? crew?
i.e, i.e, i'~
usually
one one
othor(spocify) (check (check
return return
stay stay
..y ..y
g g
ic ic (sp(:cify) (sp(:cify)
on on
much much or or
(;Ol.1;r~Cc
-tho -tho
;J:u.j ;J:u.j
uyself, uyself, thun thun
or~..l1l:la8)
"he "he
:themsel
PJ'ocossi:t\6 PJ'ocossi:t\6
f':;!Jj,
L::fs-.:;lf, L::fs-.:;lf,
2. 2.
throo throo
overy overy
fish: fish:
(specify) (specify)
OI' OI' l..'.:Jour l..'.:Jour
'-lith '-lith
aver£lc5e aver£lc5e
trip? trip?
fuel fuel
one) one)
fT",sh fT",sh
cre~on
to to
'that; 'that; buys
~.-- ly ly
suporvisor suporvisor
,nth ,nth
\ \
the the
the the
~T t:i.lnosjuook
day ves ves
do do
act act
co.sc·;':;he:r co.sc·;':;he:r
the the
ibn ibn
landing
netaMnee netaMnee
coo coo
you you
orgaIUzcd? orgaIUzcd?
a a
~ _Ii
assistance assistance
,~
the the
food food __
~p?'
do do
t t
. . usUAlly
~_.li
of of
gear
you you
for for
trcs trcs
It_';J It_';J
8 8
OJ OJ
-
•. •. _Uab
of of
fuel fuel
• •
76 76
rt. rt.
7-6. 7-6.
IJ IJ
'( '(
5. 5.
74. 74.
1.../ 1.../
73. 73.
7 .
/JJJj..Jj /JJJj..Jj
" c..
/_1 /_1
IJ IJ
/.../JJ /.../JJ /.../..1-1
• •
. .
~:'-
44.
43. 43.
._-' ._-'
42. 42.
place, place,
Intcrvic':lcr: Intcrvic':lcr:
• •
". ". 1. 1. <5. <5.
2. 2.
3.
.J. .J. you you
To To
Ales'tes
Hydrocynus Hydrocynus
Pro'top'teru.s
Claias !1iliC !1iliC
folloYiin.; folloYiin.; ~il2.:pia
lor lor
6. ':1);;, ':1);;,
5. 5.
4. 4.
2.
1. 1. :>. :>. used?
\~t
\:ho£lJ \:ho£lJ
go go
v'(::h,r{s~J~cify)
J..:.:.;j J..:.:.;j .::ooj.h;).'a'ti
t, t,
~oc~l fish fish ,{2.sn ,{2.sn
l'iah?(chcck
which which
. . O'oDer
5<:.1 drying
drying smoking smoking
sundI'ying
Perch Perch
.::.r" .::.r"
procoBsiDG procoBsiDG to to
t~
t t
do do
ID0n6
- 'tu"l::lor,l;~
trader
question question
,zh,:; ,zh,:;
drying
coneumcr
If If
species?
you you
(specify) (specify)
you you
v C1
- no
currom; currom;
€
'
sell sell
uau,111y uau,111y
one) processing processing
~thod
s
43
thc
(Har.,ge£,)
pri
-
$cl} $cl}
is
ce
9 9
C5
takes
• •
•
\
,
\
\
'j0. 'j0.
'l'j.lj
:>4. :>4. 0j.IJJjj
;32. ;32. :;l.I../J"/..I
[~o.
7':).
-
/.J../../.J /J../../../
/..J
lJ
, ,
Ij
/
.
./.../J j j
.J...I
-
/
I
.. jJ jJ ~O._:- • ·C'.:._ ,::, •.~.;})~~'~ "". ...... "f -,,!, .. Ij C-'-~'r '.~ '."). 45. - - - - -11 U 'tio you a - - - - - sell trnder, - -
1s it always the snmo trader? 54c B(w 1 'U'c;e. i3 :;our fani.~.y'~ (yes/no) 87. ____ ;ave~ /J' r 99. / -' -~_~.l.~,...~._'Jthi.!.rLl llho sta.v hero? (yes/no). I 88. I.J ...."-tJ:.. ,{l]U 101,
47. If yes, hOH mueh?_Shs/trip 189./J'J' .../'j ------_...... -..._--
~catch J :;,:>, I 90. iEL~2;.~:!~; Try Ol".d f':i1d. /J' out the I • approx.l.ln3.te at,-e of' the ~)eroor.~ I 48. Do you sometimes 'or e ----7 If Vt::S., lev1:~j CJ. U ::10...... :[·ec:.ch': );lay? _uahl J day 92. /J'J'.-IJ'.../ (CUO{)~: ,,'0;;: i tt.i..l!~r) P:ri.a;~u:y· ..';::'0.(,;; -L s
50. Do you work: -:: '- (i·_-.l.V·'l eJ.. s 1,G.t":'(~!··j alone ." 1. n-1.f:'1!€:l t;L3.!iQU· ..~
~ith ! 2. othor fisherfuen • c j.rU:;-~,i·tl~·L-,l.()i~[1.': 1.-:;v81 I one b02.t
in .'~
Ut"...i "\."'\:,).'8::" T;";l eve} 1 6. }Joy,..,:, i 3. w.;c ther '
~." ~-~ .------_._----~-_. -...... :". __ i ~!hen oo~ -cs fislllli& ~J • .IJ ! I c:..:.~: ~_ IntG:r·,,::1>;:-.~c'r.~ th.:;UC:Cj.\.lb (;J.+'la;..L -i-~i~~R ~P ~ act1.1}:~~'t-les 51. Arc you Bomber of a t~~e ...:.d"tli6 .. t
_--~ .fishormens Cooperative? -----_.--=---...... _----_...._- -~1 \ .( yes/no) 1 940 / J' l I
52. Ho,! much do you pay )0 e "".::i (10 h·C::i: a.cout ~:",F.1i'.".L to , .. -:I,·.':'tTU " ....·ll':· 1.3"-0:;;2/;,J.:)." : -~he coopcra'civo? 1 '1.")0 .I_J --.'..'- i. UShs/month ______
~-"-~--"--"~l 95. /.-IJ.-I.-I ---'<----~-~· .. e.Ioll3J: I ___ UShs/year J.rr\;"'r:r:. }_'f r.:G:; J::'f) ::. sU0B·1.i:~.O!} 95. /J'J'J'J' I 69 j /J'J'.../.../ • J __ ..J%tca-cch 970 /J'J' ----'_.,_.- ..~._------~ 53. Is your f:.::.roily stayi1tb \ ! "i tr, you?(yes/no) 9i). / J' I \' . ~ ....:.,.
':'-..-. . ~L .: . ~'~ .-.--'r(,;:-~.: - - .. .
.'•.-'-~ -' 12 _ -13 66. It no, why not? 59. Bas he boU8ht-a.nyth1Jl8 1. price too high from the ~(yes/no)- 2. wrong equipment 106. 1-1 3. bad quality Intorv1ewera U no, &Q to 4. prefer to buy on open market Q.uoat1on 68 ". 5. I' have no money 6. other (specify) 113. ij 67. In your OPinion:, What is the 60. ~ \1ha.:t did lul ~ i'rOlil equipment that the-project shoula provide(check two) (check one) 1. gillnots 1. glllnew 2. twine 2. twine 3. ongine 3. hooks 4. hooks 4. outboard engines 5. other 5. other(specify) 107 IJ 11 u. IJ CO_n~ Ihte~le~" "'til ./
q~ertion 71 61.. When did he buy this teo,ef, __ M) 108 68. ''t'hy rlid you nOT buy c!nYch~ng i I.../J from the A:mIP( check two) 62. Did. thia hel.1) to -
incre~ee hie fishing 1. they never came aotiv~~QY(YDQ/no) 2. prices too hig~ LOS /.../
3. \/no) pr~I-er 1:0 buy an "P"n UD IJ market
64. lias -Ghe quali r,y of '5. I have no mon~y -----"--/---
inputs;(check one) 6. I did not: know 'che:! ·.'Jf:'r~ I coming 1. good ------~ I I. 'J~her (spec.L i 2. f~ir ry 1'1 ?.. -.i.. 3. poor --1 111. / J I 65. Would he buy again
frc~ thb project? (yes/no) 112. I J
~ ------" -~-~------ ·'1 I , '.
':, I .' , 69. Would,he like to buy ;from I tho projeot?(yes!no) I 116. /J 70. If YG 8, wha t items ".'ould ho bo interosted in? I (oho<;lk tV/o) , l 1. gillnets I
I 2, tHinG 3. hooks I 4. outboo.rd
5. othcr(spocify) ~ I • I 117. /J 71. What arc the b1ggGst
I problems confronting fiahermon?(na~ throe) I I 72. How should tho gOVQ:crllil(;.:n'i:;
I assist in solving tho problems of thQ fishQ~~Qn? I I I I I I .' I I I
FamI B
1. How lOIl8 have you been at this lan~? yeo.rs I 206. /../../ S'GRVln' nSBEBIES
youdo~ Date of interview 201. 1././ 1././ 2. ,~t were before you came "Co thie landiD,g? Code. inter{1ewer 202. 1./ - /././
.4 ~~a..xw~~0Jl~ Code Superviaor . 20). /.../ - /!.../ same lake Number of Landirl& 204. 1./.../
2. .F~ OIl. 3.Il.Ot.b.er lake ~ [-C~..~ s Questionnaire n~ber I 20). 1.../.../J.../ 3. Fanrung
4. Doing busin~ss
FOR OPPle;:; USE ONLY • 5. G";;ner (specify} ~.i - - ~. J. un aver~e• .tw... lll1. ~ 2. dUSOU1; - 2v9. 1.../
5. .ih::.t is 1;!la 11,l~1OA ...... riWtrili <.J,O- j..1,.L d, ... "-. ~ ~ ~ ::.'AAt ~Qi\Jt1 4/1
1. .f~one ~ 2. b.p - j. 10-20 h,p I ~. 25 hp 21 1 .:.. 1.../ .:';'. I Pu.rC'=I..;...~-.;c:ti 7. ':'fb.ere \Yc:.5 r,he e:n.g~r.:.e ':" 1. local sho>, I
2. f~Sh tr5.dE:I"
--J. vis~~ gen'.::L·:-....i.. -cr::':.Q..:y"
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--- 9. .nterv1e~erl 8. "t~n
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7.
1. 5. 6. 4. 2. 1. 2. 3. 3. h=':)er Frora <'here
i.'ype
three
project
other from
fro!" country visiting eisel'here fish local hooks trader other Gillne"t
(specifJ')
....
on
Qf hO\1
\.as
for
-
of
thi.s catches,
~overnlJen(; differern
~ trader
shop (specify)
many
.fishcrmer:
one, Question tile
t
ge~ral
ca):1oe.
~~m;ljo.r}
--
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U I canoes
1;\'10 ,-
ChOOSD
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on If
"0 is
I
three
canoesl three.
thore
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this
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differ'ent
n
are
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-
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-
hooks g11Inets
other(spocify) hooks(no) other(epecify) gillnets(mesh)
t
t
disWllC'"
direction:
distance
was \~s
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landed.
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tihon, ~nri1ng(km) - of
200-50Om
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of
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ground.
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232 230. 22~. ". .:...... J~.
226. 228.I"'/JJ 227.
225. 224. 223.
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's--·~ 5 4 19. Is this catch cf yours; l5. If gillnets ~!ere employed, Hhat vias the typed of setting? 1. poor I II III 2. fair 1. bottom - - - 238. /J 3. good 272. / J 2. surface 239. /J 20. Rate your catch as poor(1),fair(2) 240. /J" I good{J) for eael., of th<:: folloYii.l10 16. ',jas part of Y01,U' ca"cch sold or 1of"t behi.nd year: (yes/no) 1 241. /J 1984 273. /-./ 1985" 274. /..J 17 • If-y~a:; ;sPoci'fy. 1986 275. /J' u Species code l~Uffiber \!cid:t (k \ 1987 276. /J 1. 242. /J'j 1988 2T,.. "/ ../
2. ~4j" 1..i.lJ 3. 21. In your opinion, since 1984 OT your 244. /./.j arrival h.J.ve the C£...~TJ'7 • /J-./ --' / 3. HO signii"icu.nt
, / change ,c"!6. Old. I:ndica te for 'cnc t;'o fJost i.;llpor'L~;"nt :3pc:cii'.< /..../ '-""' . {_~ ~.-lT-C .:J L. J. J. :~;11l~ru::n i~c 101" ';;hicD you ~ish, DV"j ,sood a 88<:,;,,01: i t 22. In yom' opinion ';[lC i'.l.ppr(;]Jri~l-i:l for each month type of ,:;e3.Ys :lr,a si~r.:s fOJ'
tr~s lake (yes/no) (1=poor,2=fair,3=good) 2"/ ~j. /J'
l1
23. Nol1 Lhep wh,::'t Lypu oi' '-:':C':'.:" cio yOl.l r,~COLJ.ijlcn(i 1 S])c<;j.c:;', t~ If Species l\""'G' l;oliv. /J' ..../ J...... L..l , ,-,,(.,. /' / . t./...... / for Lhis 1al:e? 2~u. Na~e: /-./ I , ",' 'j / / Jan c- ).1... -I er~in8s 1. outboard i~~O: Feb 252. ;i'; < /' / /j' -./.J. J hp: 280 0 /-./J Mar 2".)!~. /-./ ?5'J./J ',,-'! / / 2. gillnets ucsh: Apr 2~)6. /-./ c..) 1_, J 201. /-.1-./' l:l2.y 3. hOOks 25&. /-./ 259./-./ size 282. / J-!
Jun ;~60. /-./ 2.61./-./ 4. other - (specify) 2c)j. /-e/ Jul 262. C!6 /J 3./-./ (eh~cl{=1) Aug 264. /-./ 265./"'/ S0P 2S6. /J 267 p/.../ Oct 268. /./ 269./.../ .: Nov 270.- / •./ --271./J
