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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

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Front. Environ. Sci. Eng.    2024, Vol. 18 Issue (4) : 43    https://doi.org/10.1007/s11783-024-1803-8
RESEARCH ARTICLE
Endosulfan residues and farmers’ replacement behaviors of endosulfan in the north-west inland cotton region
Shuyan Zhou1, Yang Zhang2(), Jingjing Wang3, Shikun Cheng1, Fuyan Zhuo4, Yun Hong3()
1. School of Energy and Environmental Engineering, University of Science and Technology Beijing 100083, China
2. Foreign Environmental Cooperation Center, Ministry of Ecology and Environment of the People’s Republic of China, Beijing 100035, China
3. United Nations Development Programme in China, Beijing 100600, China
4. National Agro-Tech Extension and Service Center, Beijing 100125, China
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Abstract

● The situation of endosulfan residues in cotton fields were assessed.

● A KAP survey was carried out for cotton farmers.

● Endosulfan sulfate was the main endosulfan residue in the soil.

● Cotton farmers scored low on knowledge about the phase-out of endosulfan.

We assessed the situation of endosulfan residues in cotton fields after the endosulfan ban came into effect and the current knowledge, attitude, and practice (KAP) of cotton farmers on the phase-out of endosulfan and the application of alternative technologies. Topsoil samples (n = 91) of cotton fields were collected from the major cotton-producing areas in China, namely the north-west inland cotton region, and the endosulfan residues were analyzed. A KAP survey was carried out for cotton farmers, and 291 questionnaires were distributed. The influences of gender, age, education background, cotton planting years, publicity and training, income sources, and other factors on cotton farmers’ KAP were analyzed. The results showed that endosulfan sulfate was the main endosulfan residue in the soil, followed by β-endosulfan and α-endosulfan, the average residual contents were 0.569, 0.139, and 0.060 µg/kg, respectively. The results of the KAP study showed that cotton farmers scored low on knowledge about the phase-out of endosulfan and the application of alternative technologies but high on attitude and practice. The number of family members, years of cotton planting, age, and the cotton-planting area had different degrees of influence on KAP scores. The training could significantly improve the KAP scores of cotton farmers; training should be more targeted and designed reasonably for key groups, such as men and the population under 30, followed by training them to use pesticides safely. For large-scale cotton growers, training should focus on green prevention and control technologies.
Keywords Cotton fields      Endosulfan residues      Farmers      KAP survey      Replacement behaviours     
Corresponding Author(s): Yang Zhang,Yun Hong   
Issue Date: 15 December 2023
 Cite this article:   
Shuyan Zhou,Yang Zhang,Jingjing Wang, et al. Endosulfan residues and farmers’ replacement behaviors of endosulfan in the north-west inland cotton region[J]. Front. Environ. Sci. Eng., 2024, 18(4): 43.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-024-1803-8
https://academic.hep.com.cn/fese/EN/Y2024/V18/I4/43
Compound Mean SD GM Min Max Percentiles DF(%)
P25 P50 P75 P90
α-ES 0.060 0.103 0.021 N.D. 0.655 N.D. N.D. 0.074 0.164 49.5
β-ES 0.139 0.216 0.050 N.D. 1.197 N.D. 0.061 0.179 0.237 60.4
ES-sulfate 0.569 0.991 0.151 N.D. 6.170 N.D. 0.189 0.707 0.698 71.4
∑ESs 0.769 1.294 0.260 N.D. 8.022 0.072 0.274 0.914 1.864 83.5
Tab.1  Basic statistical parameters of endosulfan residues in cotton field soil (n = 91, µg/kg)
Survey region Year α-ES β-ES ES-sulfate ∑ESs References
North-west inland cotton region, China 2020 0.142 ± 0.123 0.281 ± 0.257 0.828 ± 1.155 0.946 ± 1.518 This research
Daiyunshan region of Fujian, China 2009 0.81 ± 1.78 2.50 ± 9.05 6.93 ± 27.33 – Qu et al. (2013)
Pearl River Delta, China 2009 0.19 ± 1.74 1.11 ± 5.45 3.81 ± 22.58 Dou & Yang (2015)
Wuhan, China 2009 0.34 ± 1.25 0.94 ± 3.89 – 1.28 ± 4.25 Zhou et al. (2013)
Huizhou City 0.56 ± 1.30 0.94 ± 1.08 10.11 ± 59.61 Ma Jin et al. (2010)
Shanghai, China 2007 0.13 ± 0.3 0.19 ± 0.54 BDL Jiang et al. (2009)
Campania, Italy 2011 0.41 ± 0.96 0.50 ± 1.32 1.06 ± 2.60 1.96 ± 3.75 Qu et al. (2017)
Himalayan region of India 0.22 ± 0.16 0.20 ± 0.29 0.19 ± 0.28 0.62 ± 0.73 Devi et al. (2015)
Punjab, Pakistan 2011 0.33 ± 0.41 3.0 ± 1.3 Syed et al. (2013)
Kathmandu, Nepal 2015 4.21 Pokhrel et al. (2018)
Republic of Korea 2015 0.06 0.09 0.63 0.77 Kim et al. (2020)
Tab.2  Comparison of endosulfan residues in soil from different region and countries (µg/kg)
Fig.1  The value of α/β-endosulfan.
Fig.2  The socio-demographic characteristic of participants.
Fig.3  The lowest, highest, and average scores for each KAP section.
Category Variable Classification Mean Standard deviation P value
Knowledge Gender Male 4.03 1.794 0.262
Female 4.28 1.816
The main labor force of cotton planting in the family Yes 4.06 1.767 0.249
No 4.38 1.958
Join a cooperative Yes 3.93 1.691 0.111
No 4.28 1.888
Cotton planting is the main income source of the family Yes 4.13 1.851 0.705
No 4.03 1.618
Attitude Gender Male 4.27 1.908 0.002
Female 4.91 1.487
The main labor force of cotton planting in the family Yes 4.43 1.807 0.275
No 4.74 1.759
Join a cooperative Yes 4.45 1.771 0.724
No 4.52 1.829
Cotton planting is the main income source of the family Yes 4.47 1.812 0.731
No 4.56 1.764
Practice Gender Male 6.13 2.111 0.003
Female 6.84 1.797
The main labor force of cotton planting in the family Yes 6.32 2.005 0.382
No 6.60 2.176
Join a cooperative Yes 6.34 2.063 0.783
No 6.40 2.017
Cotton planting is the main income source of the family Yes 6.25 2.049 0.054
No 6.81 1.934
Tab.3  T-test results of participants’ basic information, knowledge, attitude and practice
Category Variable Classification Average value Standard deviation P value
Knowledge Education level Below primary school 4.00 1.732 0.745
Primary school 3.46 2.145
Junior middle school 4.10 1.704
Senior middle school 4.20 1.772
Junior college or above 4.15 1.892
Age 20–30 3.44 1.826 0.074
31–40 4.02 1.893
41–50 4.19 1.794
51–60 4.37 1.659
More than 60 1.00 0.000
Cotton planting area 50 mu and below 4.17 1.977 0.179
51–100 mu 4.23 1.654
100–499 mu 4.13 1.684
500 mu and above 3.29 1.648
Permanent resident population of the family 1–2 4.68 1.911 0.002
3–4 4.14 1.728
5 and above 3.40 1.777
Cotton planting years 0–9 3.82 1.983 0.039
10–19 3.91 1.802
20–29 4.44 1.599
30-39 4.76 1.480
40 and above 3.86 1.864
Attitude Education level Below primary school 3.67 3.215 0.510
Primary school 4.85 1.519
Junior middle school 4.20 1.933
Senior middle school 4.53 1.768
Junior college or above 4.69 1.670
Age 20–30 3.59 1.760 0.042
31–40 4.33 1.768
41–50 4.68 1.680
51-60 4.66 1.974
60 and above 3.00 0.00
Cotton planting area 50 mu and below 4.65 1.641 0.025
51–100 mu 4.75 1.679
100–499 mu 4.02 2.068
500 mu and above 3.90 1.972
Permanent resident population of the family 1–2 5.02 1.545 0.006
3–4 4.50 1.768
5 and above 3.85 2.000
Cotton planting years 0–9 3.92 1.979 0.003
10–19 4.56 1.638
20–29 4.90 1.464
30–39 4.97 1.880
40 and above 4.57 2.507
Practice Education level Below primary school 5.00 2.646 0.245
Primary school 7.23 1.423
Junior middle school 6.10 2.223
Senior middle school 6.47 1.871
Junior college or above 6.43 2.066
Age 20–30 5.78 2.063 0.347
31–40 6.20 2.174
41–50 6.44 1.983
51–60 6.61 1.984
60 and above 8.00 0.00
Cotton planting area 50 mu and below 6.79 1.790 0.012
51–100 mu 6.30 1.989
100–499 mu 6.02 2.214
500 mu and above 5.48 2.502
Permanent resident population of the family 1–2 6.92 1.441 0.107
3–4 6.27 2.129
5 and above 6.19 2.133
Cotton planting years 0–9 5.47 2.282 0.000
10–19 6.69 2.013
20–29 6.84 1.613
30–39 6.90 1.520
40 and above 7.43 0.976
Tab.4  ANOVA results of participants’ basic information, knowledge, attitude and practice
Category Variable Classification Average value Standard deviation P value
Knowledge Relevant government departments have organized trainings on green prevention and control technologies Yes 4.31 1.761 0.000
No 3.30 1.757
Relevant government departments have publicized green prevention and control technologies Yes 4.20 1.801 0.025
No 3.45 1.697
Attitude Relevant government departments have organized trainings on green prevention and control technologies Yes 4.93 1.445 0.000
No 2.68 1.964
Relevant government departments have publicized green prevention and control technologies Yes 4.77 1.556 0.000
No 2.36 2.104
Practice Relevant government departments have organized trainings on green prevention and control technologies Yes 6.81 1.682 0.000
No 4.61 2.379
Relevant government departments have publicized green prevention and control technologies Yes 6.64 1.818 0.001
No 4.27 2.375
Tab.5  T-test results of government departments’ trainings and publicity on KAP
Category Knowledge Attitude Practice
Knowledge 1
Attitude r = 0.368 1
p = 0.000
Practice r = 0.212 r = 0.619 1
p = 0.000 p = 0.000
Tab.6  Pearson correlation coefficient between knowledge, attitude and practice
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