7. REFERENCES

Bayley, P.B., 1988. Accounting for effort when comparing tropical fisheries in lakes, river-floodplains and lagoons. Limnol. Oceanogr., 33(4, part 2): 963–972.

Bernacsek, G.M. and S. Lopes, 1984. Mozambique. Investigations into the fisheries and limnology of Cahora Bassa Reservoir seven years after dam closure. FAO/GCP/MOZ/006/SWE, Field Doc. 9. Rome: FAO, 145 pp.

Crul, R.C.M., 1992. DIFRA. Database on the inland fisheries resources of Africa. CIFA Occas. Pap., No. 17. Rome: FAO, 21 pp.

Fox, W.W., 1970. An exponential surplus yield model for optimizing exploited fish populations. Trans. Am. Fish. Soc., 99:80–88.

Hanson, J.M. and W.C. Legget, 1982. Empirical prediction of fish biomass and yield. Can. J. Fish. Aquat. Sci., 39(2): 257–263.

Henderson, H.F. and R.L. Welcomme, 1974. The relationship of yield to Morpho Edaphic Index and number of fishermen in African inland fisheries. Relation entre la production, l'indice Morpho-Edaphique et le nombre de pêcheurs des pêcheries des eaux continentales d'Afrique. CIFA Occas. Pap./Doc. Occas. CPCA, (1): 19 p.

Jackson, D.A., H.H. Harvey and K.M. Somers, 1990. Ratios in Aquatic Sciences: Statistical Shortcomings with Mean Depth and the morphoedaphic Index. Can. J. Fish. Aquat. Sci., 47: 1788–1795.

Kapetsky, J.M., 1984. Coastal lagoon fisheries around the world: some perspectives on fisheries yields, and other comparative fishery characteristics. In: J.M. Kapetsky and G. Lasserre (eds.), Management of Coastal Lagoon Fisheries. Stud. Rev. GFCM, 61(1):97–140.

Marshall, B.E., 1984. Towards predicting ecology and fish yields in African reservoirs from pre-impoundment physico-chemical data. CIFA Tech. Pap., (12): 36 pp. Rome: FAO.

Melack, J.M., 1976. Primary productivity and fish yields in tropical lakes. Trans. Am. Fish. Soc., 105:575–580.

Moyle, J.B., 1956. Relationships between the chemistry of Minnesota surface waters and wildlife management. J. Wildl. Manag., 20: 303–320.

Northcote, T.G. and P.A. Ryder, 1965. Indices of productivity in British Columbia lakes. J. Fish. Res. Board. Ca., 13: 515–540.

Oglesby, R.T., 1977. Relationships of fish yield to lake phytoplankton standing crop, production and morpho-edaphic factors. J. Fish. Res. Board Can., 34(12): 2271–2279.

Quiros, R., 1990. Predictors of Relative Biomass in Lakes and Reservoirs of Argentin a. Can. J. Fish. Aquat. Sci., 47: 928–939.

Rawson, D.S., 1952. Mean depth and fish production of large lakes. Ecology, 33:513–521.

Ryder, R.A., 1965. A method for estimating the potential fish production of north-temperate lakes. Trans. Am. Fish. Soc., 94: 214–218.

Schaefer, M.B., 1954. Some aspects of the dynamics of populations important to the management of commercial marine fisheries. Bull. Inter-Am. Trop. Tuna Comm., 1(2): 25–56.

Schlesinger, D.A. and H.A. Regier, 1982. Climatic and Morphoedaphic Indices of Fish Yields from Natural Waters. Trans. Am. Fish. Soc., 111: 141–150.

Toews, D.R. and J.S.Griffith, 1979. Empirical estimates of potential fish yield for the Lake Bangweulu system, Zambia, Central Africa. Trans. Am. Fish. Soc., 108: 241–252.

Vanden Bossche, J.-P. and G. Bernacsek, 1990a. Source book on the inland fishery resources of Africa. Vol I. CIFA Tech. Pap., 18/1: 411 pp.

Vanden Bossche, J.-P. and G. Bernacsek, 1990b. Source book on the inland fishery resources of Africa. Vol II. CIFA Tech. Pap., 18/2: 240 pp.

Vanden Bossche, J.-P. and G. Bernacsek, 1991. Source book on the inland fishery resources of Africa. Vol III. CIFA Tech. Pap. 18/3: 219 pp.

Weisberg, S., 1980. Applied linear regression. New York. Wiley & Sons,. 283 p.

Welcomme, R.L., 1972. The Inland Waters of Africa. CIFA Tech. Pap., (1): 117p.

Welcomme, R.L., 1974. Some general and theoretical considerations on the fish production of African rivers. CIFA Occas. Pap. (3): 26 p.

Welcomme, R.L., 1975. The fisheries ecology of African floodplains. CIFA Tech. Pap. 3: 51 p.

Welcomme, R.L., 1979. The fisheries ecology of floodplain rivers. London, Longman, 317 pp.

Welcomme, R.L., 1985. River fisheries. FAO Tech. Pap., 262: 330 pp.

Youngs, W.D. and D.G. Heimbuch, 1982. Another consideration of the Morphoedaphic Index. Trans. Am. Fish. Soc., 111: 151–153.

CATCH VS AREA
71 African lakes and reservoirs

Figure 1. The relationship between surface area and total catch for 71 lakes and reservoirs (+). Lakes Victoria, Tanganyika, Malawi and Chad are plotted for comparison (.). (Broken lines are 95% confidence limits). Data from Table 3.

CATCH VS AREA
Subsets African lakes and reservoirs

Figure 2. The relationships between surface area and total catch for subsets of the 71 lakes and reservoirs based on surface area.

Subset 1	-	surface area > 1000 km²
Subset 2	-	100 km² < surface area < 1000 km²
Subset 3	-	10 km² < surface area < 100 km²
Subset 4	-	surface area < 10 km²
Data from Table 3.

CATCH VS EFFORT
58 African lakes and reservoirs

Figure 3. The relationship between number of fishermen and total catch for 58 lakes and reservoirs (Broken lines are 95% confidence limits). Data from Table 3.

CATCH VS DRAINAGE AREA
15 African rivers

Figure 4. The relationship between drainage area (km²) and total catch (t/y) for 15 African rivers (Broken lines are 95% confidence limits). Data from Table 6.

CATCH VS LENGTH
19 African rivers

Figure 5. The relationship between length (km) and total catch (t/y) for 19 African rivers (Broken lines are 95% confidence limits). Data from Table 6.

CATCH VS FLOODPLAIN AREA
14 African floodplains

Figure 6. The relationship between surface area (km²) and total catch (t/y) for 14 African floodplains (Broken lines are 95% confidence limits). Data from table 7.

Table 1. Existing models predicting potential fish production in African lakes and reservoirs

YIELD MODEL (corr. coeff)	DATA SET	SOURCE
Catch (kg/ha) = 8.7489 MEI^0.3813 (0.51)	31 African lakes and reservoirs	Henderson & Welcomme 1974
Catch per fisherman (t) = 0.7779 MEI ^0.37775 (0.74)	31 African lakes and reservoirs	Henderson & Welcomme 1974
Catch (kg/ha) = 14.3136 MEI ^0.4681 (0.69)	17 African lakes and reservoirs	Henderson & Welcomme 1974
Log Y (kg/ha/)=0.113 GP (gO₂/m²/day) + 0.91 (0.75)	8 African lakes	Melack 1976
Log Y (kg/ha) = 1.4071 + 0.3697 log MEI - 0.00004565 Area (0.81) ¹	17 African lakes and reservoirs	Toews and Griffith 1979
Log. Yield (t) = 0.76 log. Area + 3.57 (0.86)	17 African lakes and reservoirs	Marshall 1984
Yield (kg/ha) = 7.889 MEI ^0.59 (0.73)	11 African lakes	Marshall 1984
Yield (kg/ha) = 23.281 MEI ^0.447 (0.85)	11 African reservoirs	Marshall 1984
Yield (kg/ha) = 19.996 + 32.038 D_L/Mean Depth (0.97) ²	5 large African reservoirs	Marshall 1984
Yield (kg/ha) = 5.4250 MEI ^0.5825 (0.78)	11 African lakes	Bernacsek and Lopes 1984
Yield (kg/ha) = 23.3956 MEI ^0.4215 (0.76)	8 African reservoirs	Bernacsek and Lopes 1984
Log. (Yield+1) (kg/ha) = 7.30 i^0.5 - 2.37 i - 1.08 ³ (R²=0.75)	24 African lakes	Bailey 1988
Log. (Yield+1) (kg/ha) = 5.75 i^0.5 - 1.64 i - 0.59 ³ (R²=0.80)	7 African reservoirs	Bailey 1988
Log. (Yield+1) (kg/ha) = 7.30 i^0.5 - 2.37 i - 1.08 ³ (R²=0.75)	31 African lakes and reservoirs	Bailey 1988

Notes:
¹ multiple corr.coeff.
² D_L= Shoreline development
³ i = EFFORT (in number of fishermen)

Table 2. List of African lakes and reservoirs used for models listed in Table 1.
((u) indicates an updated catch figure compared with figure given by Henderson and Welcomme 1974)
(l=lakes, r=reservoirs)

NAME	Henderson & Welcomme 1974 data sets		Melack 1976	Toews and Griffith 1979	Bernacsek & Lopes 1984 data sets		Marshall 1984 data sets				Bailey 1988 data set
LAKE/RESERVOIR	31 l/r	17 l/r	8 l/r	17 l/r	8 reserv.	11 lakes	17 l/r	11 l	11 r	5r	31 l/r
1 Albert	X	X	X (u)	X		X	X	X			X
2 Ayame (R)	X	X		X	X (u)		X		X (u)		X
3 Bangweulu lakes	X	X		X (u)			X	X			X (u)
4 Baringo	X	X		X		X	X	X			X
5 Chad	X		X			X (u)					X
6 Chilwa	X										X
7 Chiuta	X	X		X		X	X	X			X
8 Edward	X	X		X		X (u)	X	X			X
9 George	X	X	X (u)	X		X	X	X			X
10 Guiers	X	X		X		X	X	X			X
11 Kainji (R)	X	X	X (u)	X	X (u)		X		X (u)	X (u)	X
12 Kariba (R)	X								X	X	X
13 Kitangiri	X					X					X
14 Kivu	X										X
15 Kyoga	X	X		X			X	X			X
16 Maji Ndombe	X										X
17 Malawi	X										X
18 Malombe	X	X		X		X	X	X			X
19 Mwadigusha (R)	X	X		X	X (u)		X		X		X
20 Mweru	X	X		X		X (u)	X	X			X
21 Mweru Wa Ntipa	X										X
22 Nasser (R)	X	X		X			X		X (u)		X
23 Nzilo (R)	X	X		X	X		X		X	X	X
24 Rukwa	X										X
25 Tana	X										X
26 Tanganyika	X		X (u)								X
27 Tumba	X										X
28 Turkana (Rudolf)	X										X
29 Upemba	X	X		X		X (u)	X	X			X
30 Victoria	X		XX (u)								X
31 Volta (R)	X	X	X (u)	X	X (u)		X		X	X	X
32 Mcilwaine (R)					X				X
33 Nyumba ya M.(R)					X				X
34 Sennar (R)					X
35 Jebel Aulia (R)									X
36 Kossou (R)									X	X
37 Ihema
38 Rugwero
39 Naivasha
40 Ziway
41 Pool Malebo
42 Mantasoa (R)
43 Kyle (R)
44 Massingir (R)
45 Cahora Bassa (R)

Table 3. List of 71 African lakes (1–46) and reservoirs (47–71) used for updating the models

NO NAME	AREA (km²)	CATCH (t/y)	PERIOD (yrs)		YIELD (kg/ha)	#FISHERMEN (number)	#F/KM2	Conduct. (μS/cm)	ALT (m)	LAT (°)	VOLUME (km²)
1 Turkana	7570	11737		76–86	15.5	800	0.1	3300	1136	2	224.83
2 Albert	5270	15000		85 mr ¹	28.5	15000	2.8	700	618	2	131.75
3 Mweru	5000	13860		75 mr	27.7	6000	1.2	80	930	9	32.50
4 Kyoga	2700	57000		88 mr	211.1	7000	2.6	128	1033	1	6.21
5 Rukwa	2300	5990		86 mr	26.0	1381	0.6	-	500	8	-
6 Edward	2240	14000		82 mr	62.5	4500	2.0	900	914	0	76.16
7 Chilwa	1750	9220		62–82	52.7	1740	1.0	1500	654	15	3.50
8 Mweru Wa Nt.	1600	11560		75–82	72.3	1100	0.7	600	928	9	4.80
9 Pool Mal.	550	4250		84 mr	77.3	5000	9.1	32	500	4	1.65
10 Upemba	530	5500		75–81	103.8	1000	1.9	200	575	8	0.90
11 Alaotra	600	3700		60–80	61.7	2872	4.8	165	750	17	-
12 Ziway	434	1085		79–83	25.0	500	1.2	400	1636	8	1.09
13 Malombe	390	5983		71–82	153.4	900	2.3	225	470	14	1.56
14 George	250	3171		50–88	126.8	600	2.4	223	914	0	0.60
15 Wamala	244	2000		83 mr	82.0	500	2.0	-	-	0	-
16 Guiers	210	650		88 mr	31.0	300	1.4	73	1	16	0.27
17 Chiuta	200	1045		71–82	52.3	200	1.0	150	620	14	1.00
18 Naivasha	150	385		78–86	25.7	200	1.3	330	1890	0	0.98
19 Kinkony	139	750		60–75	54.0	160	1.2	3379	-	6	0.49
20 Baringo	130	385		64–86	29.6	100	0.8	416	965	0	0.73
21 Rugwero	100	375		68–75	37.5	150	1.5	157	1350	2	0.21
22 Ihema	86	186		83 mr	21.6	42	0.5	107	1291	2	0.43
23 Mujunju	80	421		69,75	52.6	100	1.3	98	1280	2	0.47
24 Lushiwashi	80	319		78–82	39.9	163	2.0	-	-	13	-
25 Chisi	60	240		54–72	40.0	34	0.6	115	-	9	0.15
26 Kijanbalola	42	670		62–66	159.5	-	-	-	1126	0	-
27 Mugesera	39	300		75 mr	76.9	325	8.3	236	1360	2	0.12
28 Jipe	39	300		82–86	76.9	100	2.6	618	700	3	-
29 Kachira	37	1043		60–66	281.9	-	-	-	1230	0	-
30 Itasy	35	569		84 mr	162.6	1000	28.6	-	255	19	-
31 Nakivali	26	581		61–66	223.5	-	-	-	1230	0	-
32 Oubeira	21	80		74,81	38.1	8	0.4	-	1	36	-
33 Nasho	14	50		75 mr	35.7	25	1.8	105	-	2	0.06
34 Sake	14	175		73–75	125.0	100	7.1	182	-	2	0.06
35 Rwampanga	10	30		75 mr	31.6	30	3.2	108	-	2	0.05
36 Birira	5	70		75 mr	129.6	-	-	157	-	2	0.03
37 Madarounja	5	20		61 mr	40.0	90	18.0	-	353	13	-
38 Mulehe	5	28		69 mr	56.0	33	6.6	260	1750	1	-
39 Nyamisungire	4	53		69 mr	120.5	-	-	892	975	0	-
40 Kirimbi	2	20		73 mr	83.3	15	6.3	204	-	2	0.01
41 Gaharwa	2	23		68,73,75	100.0	26	11.3	192	-	2	0.00
42 Gashanga	2	30		73,75	130.4	13	5.7	134	-	2	0.01
43 Mirayi	2	20		75 mr	87.0	-	-	144	-	2	0.01
44 Kidogo	2	33		75 mr	150.0	8	3.6	129	-	2	0.01
45 Murago	2	10		75 mr	45.5	-	-	141	-	2	0.01
46 Cufada	2	5		82 mr	33.3	-	-	-	-	11	-
47 Volta	7394	38492		70–79	52.1	20000	2.7	65	85	8	140.49
48 Nasser	5811	33933		81 mr	58.4	9000	1.5	220	183	16	146.44
49 Kariba	5364	36749		88 mr	68.5	17000	3.2	72	485	17	156.63
50 Cahora Bassa	2665	4343		82 mr	16.3	1200	0.5	117	326	16	55.75
51 Kossou	1600	7500		80 mr	46.9	3200	2.0	80	204	7	22.88
52 Jebel Aulia	1500	8108		75,82	54.1	1550	1.0	220	377	15	3.45
53 Kainji	1270	4579		74.78	36.1	6320	5.0	55	142	10	13.97
54 Mwadingusha	393	3728		82–83	94.9	1410	3.6	217	1100	11	1.02
55 Roseires	290	1480		75,76,82	51.0	-	-	-	-	11	2.90
56 Nzilo	280	2800		57,60,61	100.0	830	3.0	400	1246	10	2.32
57 Massingir	151	400		81 mr	26.5	70	0.5	238	109	24	2.82
58 Sennar	150	1166		75,82	77.7	200	1.3	265	422	13	1.04
59 Nyumba Ya Mungu	140	3540		73–87	252.9	1500	10.7	854	663	3	0.84
60 Ayame	135	785		66–79	58.1	826	6.1	100	91	5	1.35
61 Khashm El G.	125	500		75 mr	40.0	100	0.8	-	-	14	0.85
62 Masinga	120	480		85 mr	40.0	-	-	-	-	0	1.56
63 Kyle	91	180		80 mr	19.8	200	2.2	52	1035	20	1.43
64 Robertson	81	500		76,84	61.7	250	3.1	-	1350	17	0.49
65 Tsiazompaniry	31	60		75 mr	19.4	68	2.2	-	1490	19	-
66 Mc Ilwaine	26	145		72–78	55.8	-	-	155	1364	17	0.25
67 Mantasoa	14	25		75 mr	17.9	7	0.5	22	1385	19	0.07
68 Ngwazi	5	25		72–78	49.0	15	2.9	-	-	8	-
69 IITA	1	9		75–79	112.5	-	-	26	20	7	0.00
70 Kerenge	1	10		54–63	127.5	-	-	319	-	5	-
71 Malya	1	8		77,78	114.3	4	5.7	-	-	2	-
V Victoria	68800	200000		87 mr	29.1
T Tanganyika	32000	85000		89 mr	26.6
M Malawi	30800	39400		70–86	12.8
C Chad	22000	90000		70	50.0

¹ mr = most recent available catch figure

Table 4. New and updated models predicting potential fish yields of African waters

YIELD MODEL (coeff.of Determination)	DATA SET	SOURCE
CATCH VS AREA EQUATIONS LAKES AND RESERVOIRS
Catch = 8.32 Area^0.92 (R²=0.93)	(1) - 71 African lakes and reservoirs	SIFRA 1990, 1991
Catch = 8.93 Area^0.92 (R²=0.92)	(1A) - 46 African lakes	SIFRA 1990, 1991
Catch = 7.09 Area^0.94 (R²=0.94)	(1B) - 25 African reservoirs	SIFRA 1990, 1991
CATCH VS AREA EQUATIONS SUBSETS LAKES AND RESERVOIRS (based on surface area)
Catch = 28.58 Area^0.77 (R²=0.36)	(s1) - 15 African lakes and reservoirs (A>1000 km²)	SIFRA 1990, 1991
Catch = 0.86 Area^1.36 (R²=0.62)	(s2) - 22 African lakes and reservoirs (100<A<1000 km²)	SIFRA 1990, 1991
Catch = 7.10 Area^0.94 (R²=0.37)	(s3) - 19 African lakes and reservoirs (10<A<100 km²)	SIFRA 1990, 1991
Catch = 9.88 Area^0.79 (R²=0.57)	(s4) - 15 African lakes and reservoirs (A<10 km²)	SIFRA 1990, 1991
CATCH VS NUMBER OF FISHERMEN EQUATIONS LAKES AND RESERVOIRS
Catch = 2.26 Number of fishermen^1.02 (R²=0.88)	(1) - 58 African lakes and reservoirs	SIFRA 1990, 1991
Catch = 2.27 Number of fishermen^1.03 (R²=0.84)	(1A) - 38 African lakes	SIFRA 1990, 1991
Catch = 2.01 Number of fishermen^1.02 (R²=0.94)	(1B) - 20 African reservoirs	SIFRA 1990, 1991
CATCH VS DRAINAGE AREA AND LENGTH RIVERS
Catch (t) = 0.048 Drainage Area^0.93 (R²=0.91)	14 African rivers	SIFRA 1990/ Welcomme 1985
Catch (t) = 0.01 Length^1.81 (R²=0.85)	19 African rivers	SIFRA 1990/ Welcomme 1985
CATCH VS FLOODPLAIN AREA FLOODPLAINS
Catch (t) = 8.78 Floodplain Area ^0.90 (R²=0.87)	14 African floodplains	Welcomme 1985

Table 5 Confidence limits of a selected number of yields predicted by models on Catch vs Area and Catch vs Number of fishermen for African lakes and reservoirs.

Catch vs Area model
Area (km²)	Predicted yield (t/y)	95% Confidence Zone (t/y)
1	8	2 –	33
10	70	18 –	271
100	585	152 –	2,253
1,000	4,908	1,264 –	19,055
10,000	41,523	10,441 –	165,214
Catch vs Number of fishermen
Number of fishermen	Predicted yield (t/y)	95% Confidence Zone (t/y)
10	23	4 –	129
100	244	46 –	1,303
1,000	2,536	475 –	13,544
10,000	26,635	4,829 –	146,825

Table 6. Data set on rivers

# RIVER	COUNTRY	LENGTH (km)	DRAINAGE AREA (km²)	CATCH (t)	PERIOD (yrs)
1 SABAKI	KENYA	560		300	60
2 UELE	ZAIRE	1210		3720	61
3 BENITO	EQ.GUIN.	365		300 ¹	79
4 OTI ²	TOGO	300		450	76
5 EWASO NGIRO	KENYA	200	15,022	300 ³	65
6 PENDJARI ⁴	BENIN	330	11,226	300	62,68,69,76
7 TANA	KENYA	800	42,217	433	85
8 WHITE VOLTA	BURK.FASSO	255	6,602	70	-
19 RED VOLTA	BURK.FASSO	270	6,871	390	-
10 MONO	BENIN, TOGO	360	22,000	533 ⁵	70
11 BLACK VOLTA	BURK.F.	650	45,324	1560	77
12 SASSANDRA	COTE D'IV	650	75,000	1518 ⁶	-
13 GAMBIA	GAMBIA	480	77,000	2781 ⁷	77–81
14 COMOE	CdI, B.F.	1160	78,000	2142 ⁶	-
15 BANDAMA	COTE D'IV	950	97,000	3408 ⁶	-
16 KASAI	ZAIRE	1735	342,116	7750 ⁶	-
17 UBANGI/UI	ZAIRE, CAR	1060	772,800	4670 ⁸	61,75
18 NIGER	7 COUNTRIES	4183	1,125,000	30000 ⁶
19 ZAIRE ⁹	ZAIRE	4700	4,014,500	75000 ⁶

Notes:
¹ including all inland catches Eq.Guinea
² Tributary of Volta River
³ including swamp
⁴ Pendjari River is name of Oti River in Benin
⁵ Catch in Benin only
⁶ Welcomme pers. comm.
⁷ Catch in Gambia only (total length Gambia River 1120 km)
⁸ Catch in Zaire and Centr. Afr. Rep.
⁹ rough estimate of catch, main river without tributaries (# 3, 17 and 18 tributaries of Zaire River

Table 7. Data set on floodplains

# FLOODPLAIN	AREA (km²)	CATCH (t/y)	#FSMEN	YEAR	SOURCE
1 Benue (Nigeria)	3100	9570	5140	1969	Welcomme 1985
2 Niger (Nigeria)	4800	14340	4600		Welcomme 1985
3 Niger (Niger)	630	4700	1314	1965	Welcomme 1985
	600	3200	3200	1982	Welcomme 1985
4 Niger (Mali)	20000	90000	54112		Welcomme 1985
5 Niger (Benin)	274	1805			Welcomme (pers. commm.)
6 Oueme	1000	6500	29800	1957	Welcomme 1985
7 Rufigi	1450	3589	3000		Welcomme 1985
8 Shire	665	9545	2445	1970	Welcomme 1985
	665	7890	3324	1975	Welcomme 1985
9 Yaeres	7000	17500			Welcomme 1985
10 Senegal	5490	30000	10400		Welcomme 1985
11 Pendjari	40	140	65		Welcomme 1985
12 Kafue	4340	8554	1112	1963	Welcomme 1985
	4340	6747	670	1970	Welcomme 1985
13 Pongolo	104	400			Welcomme 1985
14 Massilli	150	475			Welcomme 1985