Determinants of knowledge, attitude and perception towards cage fish farming technologies among smallholder farmers in Uganda

Figures & data

Table 1. Description of explanatory variables and their expected signs.

Variable	Description	Unit of Measurement	Expected Sign
Age	Age of a farmer	Years	–
Gender	Sex of a farmer	Dummy: 1 = male; 0 = female	–/+
Education	Farmer’s level of educ.	Years in school	–/+
Extension	No. of visits by ext. officer	Number of days in a month	–/+
Experience	Duration in cage fish farming	Years	–/+
Social capital	No. of social ties a farmer has	Number of social contacts	–/+
Phone Access	Farmer owns Phone	Dummy: 1 = Yes; 0 = No	+
Tv Access	Farmer owns a TV set	Dummy: 1 = Yes; 0 = No	+
Farm Size	No. of cage units a farmer owns	No. of stocked cage units	–/+
Output	Amount Harvested fish per cycle	No. of kilograms	–/+
Fish Market	Nearest fish market	Distance in kilometres	+

Figure 1. Fish species reared in the study area.

Figure 2. Types of cage fish farming technologies used in the study area.

Figure 3. Gender of the respondents.

Table 2. Demographic characteristics of the sampled respondents (n = 384).

Variable	Obs	Mean	Min	Max	Stand. Dev
Age (years)	384	44.97	32	63	8.167
Education (years)	384	11.52	7	18	3.078
Distance to Market (km)	384	20.87	2	64	19.433
Number of cage units (No)	384	4.96	1	16	3.096
Fish output (kgs)	384	5518.32	1190	18848	3174.64

Table 3. Farmers’ knowledge on cage fish farming technologies.

Statements	Responses
	I don’t know	False	True	Total (384)
	%	%	%	%
1 Fish cage units are difficult to assemble.	11.72	66.15	22.14	100
2 Fish reared in cages take more than a year to mature.	6.51	83.07	10.42	100
3 Fish reared in cages are NOT given formulated feeds.	4.17	80.73	15.10	100
4 Cage fish farming is NOT a profitable venture.	5.73	69.53	24.74	100
5 Fish cage units are too expensive.	8.33	49.74	41.93	100
6 Cage fish farming is a labor-intensive job.	8.07	43.75	48.18	100
7 Cage fish technologies affect breeding sites for capture fish.	6.77	36.72	56.51	100

Table 4. Farmers’ attitudes towards cage fish farming technologies.

Statements	Responses
	Strongly Agree	Agree	Disagree	Strongly Disagree	Total (384)
	%	%	%	%	%
1 Cage fish farming is NOT a viable business	0.78	2.86	54.95	41.41	100
2 Cage fish farming is Not better than wild fishing	0.52	4.69	78.91	15.89	100
3 Cage fish farming does NOT address food insecurity	13.02	11.20	20.83	54.95	100
4 Cage fish farming technologies are very good innovations	54.17	35.42	6.77	3.65	100
5 Cage fish farming technologies give tangible results	42.19	50.78	6.77	0.26	100
6 I consider cage fish farming a desirable career option	8.59	46.88	37.76	6.77	100
7 Cage fish farming is a time-consuming job	2.60	7.81	53.65	35.94	100

Table 5. Farmers’ perceptions towards cage fish farming technologies.

Statements	Responses
	Strongly Agree	Agree	Disagree	Strongly Disagree	Total (384)
	%	%	%	%	%
1 Fish cage units require comparatively low capital to start	2.60	17.97	53.39	26.04	100
2 It is very easy to harvest fish reared in cage units	20.57	66.67	11.72	1.04	100
3 Easy to observe and sample fish reared in cage units at any time	54.17	37.24	7.55	1.04	100
4 Rearing fish in cage units is NOT labour intensive	9.64	44.01	40.62	5.73	100
5 Using cage units to rear fish is NOT risky (drowning) by the users	5.99	55.73	35.42	2.86	100
6 Cage fish farming technologies are NOT affordable to smallholders	4.95	39.06	41.93	14.06	100
7 Rearing fish cage units does NOT grow faster	8.07	24.74	36.72	30.47	100

Table 6. Results of the Multinomial logit analysis on the determinants of KAP outcomes.

	Knowledge Scores			Attitude Scores			Perception Scores
	RRR	z	P > z	RRR	Z	P > z	RRR	Z	P > z
Low (base outcome)
Medium
Experience	1.997	1.900	.058*	1.009	.090	.927	1.087	.120	.904
Gender	9.981	1.620	.105	0.723	−1.150	.251	9.129	1.090	.276
Age	0.993	−.130	.893	0.961	−2.690	.007***	0.962	−.370	.711
Education	0.795	−1.830	.067*	1.029	.680	.500	0.772	−1.220	.224
Cage type
HDE plastic	578658.1	0.01	0.991	.431	−1.49	0.136	1065553	0.03	0.979
Wooden	.170	−1.89	0.059*	3.679	2.93	0.003***	.007	−2.23	0.026
Extension visits	0.287	−2.980	.003***	1.088	.830	.406	0.065	−2.170	.030**
Social capital	1.887	1.910	.056*	0.805	−2.390	.017**	2.048	1.120	.265
Phone access	1.315	.340	.735	1.235	.890	.372	0.441	−.450	.656
Tv access	7.325	2.430	.015**	1.466	1.650	.099*	27.931	2.280	.023**
No. of cage units	0.992	−.070	.944	0.875	−3.180	.001***	0.916	−.300	.762
Farm location	0.924	−.250	.801	0.775	−2.480	.013**	0.881	−.240	.808
Market Distance	1.059	1.690	.091*	0.995	−.740	.457	1.072	1.270	.204
Target Market	0.877	−.280	.783	1.196	1.280	.201	0.348	−1.180	.239
High
Experience	1.780	1.500	.135	0.904	−.550	.579	1.400	.490	.625
Gender	12.555	1.720	.085*	1.986	1.590	.111	8.636	1.060	.288
Age	0.998	−.030	.973	1.016	.620	.537	.982	−.170	.864
Education	0.748	−2.080	.037**	0.990	−.150	.883	.781	−1.160	.245
Cage type
HDE plastic	3504320	0.01	.990	.287	−1.02	0.310	539754.4	0.03	0.980
Wooden	.0314955	−2.50	.012**	10.99	4.25	0.000***	.013	−1.96	0.05**
Extension visits	.331	−2.520	.012**	1.032	.190	.852	.058	−2.260	.024**
Social capital	1.885	1.800	.071*	1.022	.130	.896	1.920	1.020	.310
Phone access	1.032	0.040	.971	1.769	1.370	.172	0.419	−.470	.636
Tv access	2.510	1.050	.293	1.163	.370	.711	32.190	2.380	.017**
No. of cage units	0.988	−0.100	.923	1.024	.390	.699	0.897	−.370	.710
Farm location	1.152	0.420	.674	0.933	−.400	.689	0.908	−.190	.853
Market Distance	1.057	1.600	.110	0.990	−.860	.393	1.052	.920	.357
Target Market	0.590	−1.020	.309	1.080	.320	.745	0.444	−.900	.366

Asterisks: ***p < 0.01, **p < 0.05, *p < 0.1 Reference Category-Low KAP Scores.

Table

Observations	384	384	384
LRchi^2 (26)	82.83	61.45	104.69
Prob > chi^2	0.0000	0.0001	0.0000
Pseudo R^2	0.2019	0.0860	0.1683
Log-likelihood	−163.74732	−326.60602	−258.70952

Data availability statement

Data for this study are available from the Department of Agricultural Economics and Agribusiness Management, Egerton University in Kenya and the authors. Data can be accessed on special request with the department’s and the authors’ permission.