Attitudes and practices of farmers with regard to pesticide use in NorthWest Ethiopia

Figures & data

Figure 1. Map of Lake Tana and surrounding study area (Tegegne et al., 2017).

Table 1. Socio-demographic characteristics of key informants around Lake Tana (n = 423)

Variables	Frequency	Percent (%)
Gender
male	302	71.45
Female	121	28.61
Age
18–24	206	48.7
25–39	134	31.68
40–54	83	19.62
Educational level
Illiterate (unable to read and write)	177	41.85
Can read and write(informal education)	161	38.05
Elementary (grade 1–8)	44	10.40
Secondary and above (>grade 9)	41	9.70
Farm sizes (ha)
>1 hectare	128	30.26
< 1hactare	295	69.74
Land tenure situation
Landowners	258	61
landholder	165	39
Cultivated Agricultural Products*
Cereals and legumes	423	100
Fruits and vegetables	304	71.87
Cash crops (khat and coffee)	92	21.75
Pesticides use frequency per year
Once	183	43.26
Twice	219	51.77
Three and more	21	4.96

*Multiple choices were possible; Source: filed survey 2018

Table 2. Summary of the assessments of pesticides (herbicides, fungicides and rodenticides) used in farms, around Lake Tana (n = 423)

Pesticides	Frequency	Percent (%)
Insecticides*	423	100
Endosalfan35%EC	302	71.39
Diazinone 60%EC	423	100
Diamthote 40%EC	169	39.95
Malathion 50%EC	215	50.82
Deltamethrine 25 g/l	7	1.65
Anti-fungal*	278	65.73
Mancozeb 64% WP	245	57.92
Rediomil gold 68WS	111	26.24
Apron Star 42WS	17	4.02
Tilt 250 EC	8	1.89
Herbicides*	407	96.4
2–4D Amine	407	96.4
Primagram Gold	73	17.26
Palace	2	0.47
Rodenticide
Zinc phosphide	60	14.18

*Stands for multiple choices were possible; Source: filed survey 2018

Table 3. Farmers’ knowledge, attitude and understanding about pesticide (n = 423)

Variables	Frequency	Percent (%)
Pesticides affect human health
Strongly agree	265	62.65
Agree	72	17.02
Disagree	61	14.42
Strongly disagree	25	5.91
Pesticides affect livestock
Strongly agree	167	39.48
Agree	21	4.96
Disagree	154	36.41
Strongly disagree	81	19.15
Pesticides affect the environment (fish)
Strongly agree	64	15.13
Agree	62	14.66
Disagree	221	52.25
Strongly disagree	76	17.97
Pesticides are indispensable for high crop yield
Strongly agree	159	37.59
Agree	137	32.39
Disagree	96	22.70
Strongly disagree	31	7.33
Read pesticides labels
Yes	79	18.68
No	344	81.32
How do pesticides enter the human body?*
Dermal	6	1.42
Inhalation	38	8.98
Oral	201	47.52
Eye contact	178	42.08
Do you know pesticides are banned or restricted for use?
Yes	321	57.89
No	102	24.11
Do you know the reasons for banning/restricting pesticides?
Highly toxic	111	26.24
Not effective	54	12.77
Expensive	223	52.72
Don’t know	35	8.27
Have been taken formal training on pesticides
yes	82	19.39
No	341	80.61
Informed about the proper use of pesticides
Informed	241	57.1
Non informed	182	43.03
Source of information*
Pesticides sellers	175	41.37
Local DA experts	35	8.27
Health care providers	31	7.33

*Multiple choices were possible

Table 4. Farmers’ practices on use, storage and disposal of pesticides around Lake Tana (n = 423)

Variable	Frequency	%
Store of pesticides
Open shed just for pesticides	2	0.47
Locked chemical store	55	0.13
Living house	257	60.9
Animal house	109	25.77
Mixing pesticides/dose according to the recommendation
yes	302	71.39
No	121	28.61
Place of pesticide mixing
Near a river/lake water sources	286	67.61
In the field	92	21.75
In home	45	10.64
Recommended frequency of spraying
Yes	22	5.20
No	401	94.80
Use personal protective during spray
Yes	32	7.57
No	391	92.43
What do you do with the unused leftover (mixed, diluted) pesticides?
Dispose of in the field	107	25.29
Dispose into	12	2.84
Apply on crops reputably	304	72
What do you do with old pesticides stocks?
Return to retailer	2	0.47
Apply on crops	265	62.65
In the field	150	35.46
Buy the only amount needed	6	1.42
Empty pesticide containers
Discard on-farm	181	42.8
Incinerate/bury on-farm	5	1.18
Reuse for other purposes	237	56.03

Table 5. Bivariate and multivariate analyses of factors associated with poor attitude and practice of pesticide use among farmers, around Lake Tana

Variables		Attitude and practice		Crude odds ratio(95%CI)	Adjusted odds ratio(95% CI)
		Positive	Negative
Sex	Male	39	263	0.99(0.41,2.43)	-
	Female	16	105	1	-
Educational level	Illiterate (unable to read and write)	7	170	5.25(1.14,24.27)	3.39(1.7,6.77)
	Can read and write(informal education)	52	109	2.06(1.78,5.41)	2.78(1.33,6.80)
	Elementary (grade 1–8)	10	34	2.46(1.99,6.11)	2.81(1.15,6.91)
	Secondary and above (>grade 9)	14	27	1	1
Farm size (ha)	< 1hactare	64	231	3.24(2.12, 4.93)	0.81(0.45,1.76)
	>1 hectare	42	86	1	1
Land tenure situation
	Land holders	71	94	7.25(3,17.6)	1.8(1.52,2.9)
	Land owners	112	146	1	1
Cultivated Agricultural Products*
	Cereals and legumes	138	285	1.52(1.07,2.16)	1.18(.48,2.91)
	Cash crops (khat and coffee)	12	80	1.20(1.89,1.61)	0.74(0.55,0.99)
	Fruits and vegetables	132	172	1	1
Read pesticides labels
	no	45	299	5.48(2.95,10.2)	3.69(1.62,13.64)
	Yes	10	69	1	1
Have been take formal training on pesticides	No	45	296	7.25(3,17.6)	2.97 (1.06,8.37)
	Yes	71	11	1	1
Source of information*	Pesticides sellers	75	99	0.90(0.12, 1.22)	-
	Local DA	23	12	0.74(0.48,1.16)	-
	Health care providers	17	14	1	1

*=multiple choices were possible

Tegegne, G., Park, D. K., & Kim, Y. O. (2017). Comparison of hydrological models for the assessment of water resources in a data-scarce region, the Upper Blue Nile River Basin”. Journal of Hydrology: Regional Studies, 14, 49–66. https://doi.org/10.1016/j.ejrh.2017.10.002

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