POVERTY-ENVIRONMENT REPORT:
PESTICIDE USE IN THE MEKONG DELTA, VIETNAM
By
Craig Meisner, DECRG-IE
I. BACKGROUND
Vietnam began using pesticides as early as the 1950s, when agricultural production was limited
to cooperatives, collective farms, and state farm enterprises. Under this regime, little knowledge
existed of the hazards of pesticides, and with no regulation system. Application was centrally-run
by brigades of 4-5 farmers who worked in conjunction with the Plant Protection Department
(PPD). The PPD supplied pesticides at subsidized prices and recommended spraying on a
calendar basis, with little or no attention to field conditions. Brigade interventions generally
resulted in high costs but had little effect on pests and diseases (Pincus 1995; Chung and Dung,
2000).
By 1988, farming units were recognized as autonomous production units giving households the
control over all stages of production, including the pesticide use decision. As a consequence of
past excess, coupled with a new lease on production, the dependency on pesticides grew and as
Figure 1 shows, this growth peaked in 1998 at over 40,000 tons (FAO, 2004). 1
Figure 1: Consumption of pesticides, 1990-1999.
45,000
40,000
35,000
Metric tons
30,000
25,000
20,000
15,000
10,000
5,000
0
1991
1992
1993
1994
1995
1996
1997
1998
1999
In addition to the alarming increase in pesticide use, there is also significant evidence suggesting
that the substances being used are harmful to human health and the environment. In a nationwide survey conducted by the PPD in 2000, 2,500 kg of banned pesticides were found
(methamidophos, DDT and other chemicals), along with 4,753 liters and 5,645 kg of illegally
1
Since 1986, the pesticide market has been liberalized and there are currently nine state-owned and four
privately-owned formulation plants in Vietnam. However, the government is no longer granting licenses for
new formulation plants as there is an apparent overcapacity for Vietnam's current needs.
imported or counterfeit pesticides (PPD, 2000). Another survey in August 2000, among 480
farmers in four provinces in the South, found 96.6% of farmers used pesticides excessively and
not in compliance with the instructions on the labels, and nearly 95% of farmers pour residual
spray into canals and ditches or reapply on other plants or over-apply on the same crop just to
get rid of it (Huan et al., 2000). In regards to the health impacts of pesticide use, hospital
admission records in Vietnam trace nearly 11% of all poisonings to pesticide misuse: or
approximately 840 poisonings in 53 cities and provinces in 1999 (Poison Control Center, Vietnam
Ministry of Health, 2000).2
Given the rather dire picture of pesticide use in Vietnam, two broad questions that this section of
the Poverty-Environment Nexus report attempts to answer are:
1) Are the poor using more, toxic pesticides?
2) Is pesticide use, and its attended health consequences, impacting the poor to a greater extent
than the non-poor?
In developing a response to these questions, the World Bank team constructed detailed pesticide
use and medical surveys to be implemented on a representative sample of poor and non-poor
farmers in Vietnam. A description of the survey, sample and the results follows.
II. DESCRIPTION OF THE WORLD BANK SURVEY AND SAMPLING STRATEGY
A survey of Vietnamese farmers was carried out by the Research Department of the World Bank
in the winter of 2003. Structured questionnaires were used to collect information on farming
systems, pesticide use and practices, applicator precautions/ averting behavior and health/
environmental effects. The survey was divided into two major sections, one dealing with the
economics of pesticide use (e.g. production, pesticides used, protection, training, etc) and
another strictly dealing with the health of the farmer (e.g. questions on self-reported health
ailments/ health-related habits, a general physical exam, and patch-skin and blood tests for
pesticides). The survey was constructed by the World Bank team in collaboration with the
University of Economics in Ho Chi Minh City3 (for the socio-economic portion of the survey) and
the Centre of Occupational and Environmental Health (COEH) of the Vietnam Association of
Occupational Health (VINOH) for the medical survey. 4 Each of these World Bank counterparts
were responsible for the implementation of the survey. To minimize any possible reporting bias,
the survey was conducted under the agreement that the team would not reveal the identity of the
farms surveyed or the respondents.
2
Reliable data on the incidence of pesticide poisoning is rare in most countries. At best it is an
underestimate because the source of the surveillance data from which these figures are derived is health
facilities. These figures represent only a small fraction of pesticide poisoning for a number of reasons. First,
only a small number of severe poisonings are present in the health care system. These usually are suicide
attempts. The bulk of cases - milder, occupationally related - will not necessarily report to the health care
system due to costs, inaccessibility of services, or fear of reporting and loss of employment. Finally, those
few farmers that do present themselves for treatment are often misdiagnosed because pesticide poisoning
mimics other health problems.
Therefore the magnitude of occupational pesticide poisoning is
underestimated which makes it difficult to inform policy (FAO, 2001).
The World Health Organization estimates that there are 50 cases of poisoning for every case reported and
registered, which translates into nearly 375,000 cases each year in developing countries. The estimated
number of fatalities from agrochemical exposure are at least 20,000 every year, the majority in developing
countries (WHO, 1990; Kishi et al., 1995; Pimental et al., 1992; Rosenstock et al., 1991).
3 The socio-economic portion of the study was headed by Professor Nguyen Huu Dung of the Environmental
Economics Unit, University of Economics, Ho Chi Minh City.
4 The medical portion of the survey was headed by Dr. Khuc Xuyen of the Vietnam National Association of
Occupational Health (VINOH).
Although the survey was conducted in a single season (the winter of 2003), respondents were
asked detailed production and pesticide use questions that spanned the period of one year. Thus
the survey contained information for the three major growing seasons of Winter-Spring 20022003, Spring-Summer 2003, and Summer-Autumn 2003.
II A) DEFINITION OF THE POOR
As the primary focus of the study was to investigate the potential inter-linkages of pesticide use
and the poor, we adopted the poverty line set by the Ministry of Labor, Invalids, and Social Affairs
(MOLISA) Decision No. 1143 (MOLISA, 2001). This poverty line was also re-iterated in the
recent Comprehensive Poverty Reduction and Growth Strategy (CPRGS) signed by the
government in 2002.5 Applying this criteria to our sample of farmers, Table 1 presents the
number of respondents who fell into the category henceforth known as the ‘poor’.
Table 1: Sample poverty distribution6
Number
Percent
Poverty classification
Poor (≤ 1,200,000 VND/year)
79
13.1
Non-poor
524
86.9
Total
603
100.0
II B) GEOGRAPHICAL LOCATION OF THE SAMPLE
The main geographical area of interest is the Mekong Delta since it has been identified as the
most intensive with respect to production and pesticide use (see Map 1 for details). As a
sampling strategy, the team took the following into consideration when selecting candidate
districts and communes: 1) identifying areas where the poor versus non-poor comparison would
be possible (via the use of the latest poverty map from the World Bank, 2003); 2) areas where
rice production is dominant; 3) selection of farms that vary in geographical location (e.g. coastal
versus inland farms); and 4) focus on rural areas so as to target the rural poor (as opposed to the
urban poor in the cities).
5
The new poverty line published in 2001 is to be applied to poverty measurement in the 2001-2005 period in
which the new national poverty line developed by the National Poverty Reduction Program is defined at
different levels depending on specific areas/regions, based on per capita income: VND 80,000 per month for
island areas and rural mountainous areas, VND 100,000 per month for rural plain areas, and VND 150,000
per month in urban areas (pg. 17, CPRGS, 2002).
6 The low representation of poor farming households is primarily due to the definition of ‘poor’ (as 1,200,000
VND/ year); where the majority of farms exceed this poverty line where many of the poor do not own land
and thus do not make pesticide decisions (the primary focus of the survey).
Map 1: District location and number of poor/non-poor farms in the survey sample
Note: the low representation of poor farming households is primarily due to the definition of ‘poor’ (as 1,200,000 VND/ year); where the majority of farms exceed
this poverty line since many of the poor do not own land and thus do not make pesticide decisions (the primary focus of the survey).
III. SURVEY RESULTS
III A) GENERAL FARMER STATISTICS
Table 2: Farmer characteristics (%).
Ownership
Poor
5.1
Own < 100% of land
94.9
Own 100% of land
100.0
Total
Non-poor
3.8
96.2
100.0
79
524
Sample size (n=)
Farm size
< 0.50 ha
0.50-0.99
1.00-1.49
1.50-1.99
2.00-2.49
2.50+
Total
Poor Non-poor
36.7
9.4
31.6
26.3
20.3
23.7
6.3
17.2
1.3
8.6
3.8
14.9
100.0
100.0
79
524
Education
No schooling
Primary
Secondary
High school
Other
Total
Poor Non-poor
1.3
1.3
54.4
32.1
34.2
45.8
7.6
18.7
2.5
2.1
100.0
100.0
79
524
A large proportion of the farmers surveyed owned 100% of their land, with 89% of poor and 60%
of the non-poor farming less than 1.5 hectares of land. In terms of education, over half of the
poor had at least primary education, while a larger percentage of the non-poor had secondary
and high school education.
III B) PESTICIDE USE
Pesticide use is generally measured using a variety of key indicators such as number of
applications, the absolute amount used, as well as a measure of the relative risk of the pesticide.
The absolute measures of pesticide quantity are straightforward in interpretation, however,
factoring in the relative risk of each pesticide requires the adoption of a methodology that can
rank one pesticide as more toxic than another. The methodology we propose is elaborated
below.
By simply summing all pesticides (measured as kg of active ingredient) used in crop production,
this implicitly assumes that all pesticides are alike in terms of their toxicity. However, it is
common knowledge that different substances have different levels of toxicity to both humans and
the environment. To do this, we constructed risk-weighted measures by defining risk as the
relative toxicity or lethality of each active ingredient. Risk-weighted measures place a greater
weight on more toxic substances and provide a more convenient measure of comparing the use
of one pesticide over another from a health-hazard perspective. To gauge the relative toxicity of
each active ingredient, a measure called the LD50 (or lethal dose 50%) is used. LD50 is a
statistical estimate of the number of milligrams (mg) of toxicant per kilogram (kg) of bodyweight
required to kill 50% of a large population of test animals. Pesticides with a lower LD 50 value are
more toxic, thus in the calculations used for this study, each pesticide load was multiplied by
1/LD50 to account for its relative toxicity and giving greater weight to more toxic substances.
Figure 1: Mean pesticide application, risk-weighted amount
and number of applications by poor/non-poor (kg).
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Application amount *** Risk-weighted amount
No. of applications **
*** - difference
is statistically
significant at the 1% level;
(kg)
(per 100 kg)
** - difference is statistically significant at the 5% level.
Poor (n=79)
Non-poor (n=524)
Returning to the pesticide use indicators in Figure 1, we see that the mean application amount
over the three growing seasons is higher for the non-poor, as well as in terms of the number of
applications. However, when we account for the relative risk of the pesticides used, the poor are
using more toxic pesticides in absolute terms.7
To better understand the extent of risk exposure, make use of a widely-known categorical method
developed by the World Health Organization (WHO) and also based on the LD 50 measure.8
Pesticides are divided into 4 major hazard groups: Category Ia & Ib (extremely hazardous),
Category II (moderately hazardous), Category III (slightly hazardous), and Category U (least
hazardous or unlikely to present acute hazard under normal use). Field evidence suggests that
human poisonings correlate reasonably well with these toxicity ratings (Levine and Davies, 1982).
In Table 3, we provide a summation of the total amounts of pesticides under the WHO
classification system. Note that over one-half of the pesticides under use in this sample are
moderately hazardous. In Figure 2 we see further evidence of the earlier result that the poor are
using more toxic pesticides by the higher mean use of WHO Ia & Ib pesticides.
Table 3: Total amount pesticides applied by WHO classification
Category
Total (kg a.i.)
Percent
Ia (extremely hazardous)
0.0
0.0
Ib (extremely)
5.4
0.5
II (moderately)
594.7
55.5
III (slightly)
187.3
17.5
U (Unlikely)
283.8
26.5
Total
1071.1
100.0
7
Mean application amounts on a per hectare basis (both un-weighted and risk-weighted) possess the same
pattern where the non-poor applied at a higher per hectare rate and in terms of the risk-weighted measure,
the poor registered a higher rate.
8 The WHO toxicity rating is based on the lowest published oral LD , typically tested on rats. While WHO
50
ratings generally reflect acute toxicity, they also take into account other toxic effects such as reproductive
and developmental toxicity (WHO, 2002).
Figure 2: Mean application of WHO Ia & b
and common pesticide classes by poor/non-poor9 (kg).
0.14
0.12
kilograms
0.10
0.08
0.06
0.04
0.02
0.00
WHO Ia & Ib
Carbamates
Organophosphates Pyrethroids ***
***
Poor (n=79) Non-poor (n=524)
*** - difference is statistically
significant at the 1% level;
Yet another important method of classifying pesticides is by their chemical class. The chemical
class is quite simply a pesticides’ chemical structure, which will dictate the properties that a
pesticide holds. Perhaps the two of the most important properties to human and environmental
health are toxicity and degradability. Certain chemical classes have been identified as being
highly toxic and persistent in the environment. Epidemiological studies have linked so-called
carbamates, organophosphates and pyrethroids with fetal death, hormonal changes, DNA
damage, birth defects, and abnormal sperm, ovaries and eggs. 10 11
Among the sample farmers in the Mekong Delta, we see a consistent pattern in Figure 2 among
the non-poor, where they are using significantly higher average amounts of carbamates,
organophosphates and pyrethroids. Once again we note in the first bar of Figure 2 that it is the
poor who are using the most toxic pesticides according to the WHO classification system. Thus
when setting priorities, one must first select a measure upon which a policy benchmark can be
formulated. For example, if one was concerned with banning only WHO Ia & Ib pesticides, then
the evidence suggests that focusing on poor farmers may better address this issue than strictly
focusing on all farmers in general.
9
Similarly, on a per hectare basis, the poor applied a higher amount of WHO Ia & b pesticides.
Organophosphates as a class of insecticides have been linked with Non-Hodgkin’s lymphoma, leukemia,
and lung cancer. In children, organophosphates have been linked to aplastic anemia, which is the failure of
the bone marrow to produce blood cells, and leukemia. Children with asthma may have severe reactions to
organophosphates in particular (Zahm et al., 1997).
11 Repeated assaults to the nervous system from cholinesterase inhibitors (organophosphates and
carbamates) may lead to chronic polyneuropathies (damage to the brain). There are other groups such as
pyrethroids and organochlorines that are suspected to alter hormonal balance and the reproductive system
because such effects have been identified in animal populations. Given these potential long-term effects, it
is important to monitor the use of these products and to inform those who use them of the potential hazards
they may pose (FAO, 2001).
10
Figure 3: Mean application of insecticides, herbicides,
fungicides and others by poor/non-poor (kg).
1.20
1.00
kilograms
0.80
0.60
0.40
0.20
0.00
Insecticides
***
Herbicides
***
Fungicides
***
Acaricides Nematicides Bactericides Rodenticides
***
Poor (n=79)
Non-poor
*** - difference is statistically
significant
at the(n=524)
1% level;
Pesticides can also be classified by their intended use, for example, the use of insecticides for
insect attacks, herbicides for weeds, and fungicides for the mitigation of plant mildew and root rot.
Results of the survey reveal in Figure 3 that the non-poor use insecticides, herbicides and
fungicides at a rate more than double that of the poor. These averages also closely coincide with
those of other studies of pesticide use in the Mekong Delta, where insecticides dominated the
group averages (Dung and Dung, 2000).
Figure 4: Misuse of pesticides: Do farmers use any pesticides recommended for other
crops, on rice?
Non-poor (n=524)
Poor (n=79)
No
94.9%
I don't
know
3.8%
Yes
1.3%
No
90.1%
I don't
know
5.3%
Yes
4.6%
The misuse of pesticides, or using pesticides on a crop for which it is unintended, is not a very
common practice among the farmers in the survey; although, nearly 5% of non-poor farmers
mentioned misuse of some kind.12 These results also highlight the importance of confirming selfreported information in the field.
The overuse of pesticides is also a concern from health and environmental perspectives. In the
survey farmers were asked their pesticide dosage rates which were then compared to the
recommended dosages on the label. A statistical analysis of the extent of pesticide overuse
12
The non-poor were more likely to misuse pesticides; significantly different at the 5% level of significance.
revealed that the poor have a significantly lower probability of overusing. 13 Other variables that
contributed to the determination of overuse were farm size, income and the proportion of WHO
class II pesticides used in production. Overuse was also found to be more prevalent in the
provinces of An Giang and Cantho.
Table 4: Perceived risk of pesticides by poor/non-poor.
Poor
Non-poor
Perceived risk
High risk
Medium risk
Small risk
No risk
Don’t know
Total
Sample size (n=)
30.4
25.3
27.8
13.9
2.5
100.0
79
21.0
26.3
31.1
20.4
1.1
100.0
524
Increasing a farmer’s awareness of pesticide risk is an important first step in designing any
program on safer alternatives, however, do farmers think that the pesticides they are using pose
any significant risks to their health and the environment? According to the study’s results in Table
4, only 42% of the poor and 52% of the non-poor think the pesticides they are using pose small or
no risks. In addition, the low level of uncertainty (“Don’t know”) leads us to believe that a high
percentage of farmers regard their pesticide use as hazardous, however, it may be the case that
they are unaware of the exact health consequences.
III C) PESTICIDE CONTROL MEASURES AND AVERTING BEHAVIOR
As an alternative to pesticide use, other pest management methods are possible and appear to
be gaining traction in Vietnam. The main appeal to alternative methods is that the farmer
decreases his/her use of pesticides and it is therefore considered a more “safer” alternative.
Examples of other methods include Integrated Pest Management (IPM: an ecologically-based
approach to control of harmful insects and weeds 14), biological control (use of natural or modified
organisms, genes, or gene products to reduce the effects of pests and diseases), or organic
methods (farming that avoids the use of synthetic chemicals and genetically modified organisms
(GMOs)).
Among the farmers sampled in the Mekong Delta, approximately 32% of the non-poor practice
alternative pest control methods and 21% among the poor (Figure 4). 15 The most popular control
method across both the poor and non-poor is Integrated Pest Management (IPM), then
mechanical-physical techniques (such as manual weeding) and biological control measures. The
level of adoption appears to be higher for the non-poor leading to a possible explanation that the
non-poor may have better access to information and training on alternative methods. This was
indeed verified when 54% of the non-poor (versus 37% for the poor) stated that they had received
formal training on IPM methods. Thus if the policy goal is to train that segment of the population
that is relative more unaware, one could either include more poor people in the program or have
a program that targets them solely.
13
In the (probit) modeling exercise overuse was defined as the dose amount over the label then summed
across all pesticides used. It was then coded as =1 for positive amounts, 0 otherwise.
14 IPM uses better knowledge of local conditions to maintain yields while reducing chemical inputs. IPM
relies on natural parasites and predators to control pest populations whenever possible. Farmers in many
developing countries have used IPM to cut chemical pesticide use by limiting applications to areas where
natural controls are insufficient.
15 This difference is statistically significant at the 1% level of significance.
Figure 5: Prevalence of pest control measures by poor/non-poor.
Poor (n=79)
Non-poor (n=524)
IPM
8.9%
IPM
15.8%
Me chanicalphysical
te chnique s
7.6%
Me chanicalphysical
te chnique s
8.8%
Biological
control
3.4%
O the r
0.4%
Do not use
pe st control
78.9%
O rganic
production
0.4%
Biological
control
4.7%
Do not use
pe st control
68.2%
O the r
2.0%
Rotation of
crop
0.4%
Rotation of
crop
0.1%
O rganic
production
0.4%
The education of farmers on safer alternatives is one piece of a two-part initiative on raising
awareness and improving farmer productivity. The second part is to increase education on the
safe handling and application of pesticides. As was earlier discussed, even simple knowledge of
the relative toxicity may induce the farmer to take remedial or more protective actions in the shortterm. From our survey evidence, in Figure 6 we see that only about 29% of the poor and 34% of
the non-poor had any formal training on pesticide safety. This difference was not significantly
different leading us to believe that the poor have the same access to information as the non-poor.
Figure 6: Prevalence of basic training on safe handling
and application of pesticides by poor/non-poor.
Non-poor (n=524)
Poor (n=79)
No
70.9%
Yes
29.1%
No
65.8%
Yes
34.2%
The potential suppliers of pesticide information may enter at different stages of the pesticide use
decision. As mentioned above, one stage may be ex-post, or after the farmer has chosen what to
use and is now learning more about the pesticide they were using in the past. Another is at the
purchasing stage or retailer level. When purchasing pesticides, farmers may also have the
opportunity to learn more about the pesticide through pamphlets, brochures and other media.
This information may influence the pesticide decision and would be particularly helpful to the
farmer in terms of understanding what is needed to adequately prepare for the application (e.g.
how much to apply, what the pesticide can be used for or on, what protective measures are
necessary for avoiding exposure, etc).
Among the sample of farmers in this study, only 42% of the non-poor reported receiving safety
information and procedures on pesticide use versus only 39% for poor farmers. Of the farmers
who received information, the main source of the information was from the public media, pesticide
companies and agricultural officials (Table 5). The significant role of the public media for the poor
clearly indicates an opportunity for extension agents and local community groups to foster greater
communication and awareness among poorer farmers in the region.
Table 5: Main sources of pesticide information
by poor/non-poor (%).
Source
Poor Non-poor
Public media
38.8
40.9
Pesticide companies
17.8
18.5
Other
14.4
12.8
Agricultural official
17.7
11.6
Pesticide retailer
5.5
9.1
No information available
5.5
7.1
NGO
0.2
0.1
Total
100.0
100.0
Sample size (n=)
79
524
In addition to formal training and access to information on the health risks posed by pesticides,
protection habits are probably one of the most effective short-term measures that a farmer may
take to better ensure safety. In the survey, farmers were asked about the protective clothing they
wear during pesticide application. As Figure 7 shows, a high percentage of farmers use basic
protective measures such as shirts, and trousers, and about half use hats and masks, even
among the poor. However, with respect to increased protective measures such as gloves,
glasses and shoes, the average falls, especially among the poor. 16
Figure 7: Use of protective clothing while using pesticides (%).
100
90
80
Percentage
70
60
50
40
30
20
10
0
Shirt
Trousers
Mask
Poor (n=79)
Hat
Gloves
Glasses
Shoes
Non-poor (n=524)
When asked as to the reason why they did not use the protective measure, the most common
response for glasses, masks, shirts, trousers and gloves was that the item was uncomfortable,
which is often the case when the farmer gets overheated during the day (Table 6). In the case of
shoes, those who did not wear them felt that they were inappropriate, which may be the case for
paddy rice. The poor also consistently stated a higher percentage of unavailable items across all
clothing, and especially for hats and glasses.
16
Non-poor farmers use more protective measures and this is statistically significant at the 10% level.
Table 6: Stated reasons for not wearing the protective clothing measure
by poor/non-poor (%).
Reasons
Not available
Uncomfortable
Inappropriate
Unnecessary
Other
Total
Sample size
(n=)
Shoes
NonPoor
poor
19.3 16.5
16.9 23.8
48.2 42.6
12.0 10.1
3.6
6.9
100.0 100.0
79
524
Hat
Glasses
Mask
Shirt
Trousers
Gloves
NonNonNonNonNonNonPoor
Poor
Poor
Poor
Poor
Poor
poor
poor
poor
poor
poor
poor
51.2
40.7 32.9 25.2 16.2 16.2 0.0 5.0 0.0 0.0 19.2
17.8
31.7
39.6 34.2 41.4 54.1 52.5 100.0 45.0 80.0 68.0 41.1
45.4
2.4
7.0 12.3 12.7 10.8 10.8 0.0 20.0 0.0 4.0 13.7
13.0
12.2
8.5 16.4 16.4 13.5 12.3 0.0 20.0 0.0 8.0 19.2
19.1
2.4
4.1 4.1
4.2 5.4
8.3 0.0 10.0 20.0 20.0 6.8
4.7
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
79
524
79
524
79
524
79 524
79
524
79
524
III D) HEALTH
The health consequences of pesticide use were recorded during the survey. Two types of data
were collected, farmer self-reported and medical test data. Self-reported data is often collected in
the absence of full medical test data, as test data may be either cost or time prohibitive. In this
study we opted to collect both self-reported and medical test data to facilitate a comparison
between the two and to test the reliability of farmer self reporting. It should be noted that the
medical survey covered only 482 out of the 603 farmers from the socio-economic survey. The
remainder of these were unreachable at the time of the medical survey.
Among the various self-reported health impairments after using pesticides, a high percentage of
both the poor and non-poor experienced acute (or short-term) health effects such as skin
irritations, headaches, dizziness, eye irritations and shortness of breath (Figure 8). Upon asking
the farmers whether they believed that these symptoms were related to pesticide use, 81% of
poor and 82% of non-poor farmers believed this to be true.
Figure 8: Self-reported health impairments after using pesticides (%).
70
60
Percentage
50
40
30
20
10
0
Skin
irritation
Headache
Dizziness
Eye
irritation
Other
problems
Poor (n=70)
Shortness of
breath
Fever
Non-poor (n=412)
Vomiting
Convulsions
Diarrhea
To further confirm the evidence of the extent of pesticide-related health problems, a team of
medical doctors17 were deployed to screen and test farmers for pesticide exposure. An initial
screening was conducted using several key indicators that are important in the identification of
potential pesticide exposure – prevalence of skin diseases, blood pressure, respiratory ailments
and heart disease. Tests revealed a fairly high prevalence of skin diseases among all farmers
(28-33%), with the poor showing a higher symptomatic rate for each of skin disease (‘other
dermatose’), heart disease, respiratory ailments and blood pressure (Table 7).
Table 7: Medical examination results for common problems relating to pesticide use by
poor/non-poor (%).
Skin
Non- Blood
Poor
disease
poor pressure
Normal
58.6
60.2 Normal
Irritant
2.9
4.6 Low
Allergic
5.7
6.8 High
Other
32.9
28.4
dermatose
Total
100.0 100.0
Sample size
70
412
17
Nonpoor
80.0
89.3
5.7
2.4
14.3
8.3
Poor
100.0
70
100.0
412
Respiratory
diseases
Normal
Pneumonia
Bronchitis
Non- Heart
poor disease
95.7
98.3 Normal
0.0
0.2 Positive
4.3
1.5
Poor
100.0
70
100.0
412
Nonpoor
98.6 99.3
1.4
0.7
Poor
100.0 100.0
70 412
Medical doctors from the Centre of Occupational and Environmental Health (COEH) of the Vietnam
Association of Occupational Health (VINOH), Hanoi, Vietnam.
Figure 9: Medical skin tests for the detection and reactivity
of commonly used pesticides, by poor/non-poor (%).
Skin test: Poor (n=60)
Skin test: Non-poor (n=153)
Positive
reaction
31.4%
Positive
reaction
38.3%
Normal
61.7%
100
90
7
8
7
10
80
Normal
68.6%
8
8
10
17
13
13
6
16
Percentage
70
60
50
85
84
75
79
77
78
Poor
(n=60)
Non-poor
(n=153)
Poor
(n=60)
Non-poor
(n=153)
Poor
(n=60)
Non-poor
(n=153)
40
30
20
10
0
Karate
Weak reaction
Basudin
Strong reaction
Padan
Extreme reaction
Building upon the initial screening exam, detailed patch-skin tests18 were conducted on the
sample of farmers to gauge dermal (skin) exposure to pesticides. Results show that
approximately 38% of the poor and 31% of the non-poor registered a positive reaction (Figure 10:
top).19 A second stage of skin testing resumed with three of the most commonly used pesticides
18
Selection criteria included the symptoms of contact dermatitis, such as skin itch, prurigo, skin irritation,
and allergy. The purpose of the test was to diagnose farmers suffering from some form of pesticide contact
dermatitis (irritant contact dermatitis and allergic contact dermatitis).
19 A statistical analysis of blood test results revealed that there was no significant difference in mean
pesticide poisoning for poor and non-poor. A further regression analysis of blood test results revealed that
the key determinants of poisoning were pesticide amount, the proportion of WHO class Ia and Ib pesticides
used in production and the number of protection measures taken. The prevalence of blood poisoning was
also found to be higher in the provinces of Cantho and Tien Giang.
in the region, Karate (WHO category II), Basudin (WHO II) and Padan (WHO II).20 Test results in
the bottom of Figure 9 show that on average 15-25% of farmers experienced a ‘strong’ to
‘extreme’ reaction to these pesticides implying that exposure, from a dermal perspective, is
significant among those farmers tested. Only small differences in positive reaction were
observed between the poor and non-poor, with the largest being for Basudin (organophosphate)
at 4%.
Figure 10: Medical blood tests for the detection of acute and chronic pesticide poisoning,
by poor/non-poor (%).
Blood test: Poor (n=59)
Blood test: Non-poor (n=129)
Poisoning
32.6%
Poisoning
42.4%
Normal
57.6%
Severity of poisoning
Acute poisoning
Chronic poisoning
Total
Sample size
Normal
67.4%
Poor
40.0
60.0
100.0
25
Non-poor
42.9
57.1
100.0
42
A third stage of pesticide exposure testing resumed with detailed blood tests. 21 Results indicate
(Figure 10: top) that the poor experience a higher prevalence rate of blood poisoning than nonpoor (42% versus 33%). Among those who tested positive for blood poisoning (25 poor, 42 nonpoor ), fifteen or 60% of the poisoned poor were found to be chronic cases, and 24 or 57% of
non-poor (Figure 10: bottom table).
IV. CONCLUSION AND DISCUSSION
Pesticide use has been on the rise in Vietnam, notably over the past decade with a near doubling
of consumption from 1990-1998. Field evidence has suggested that farmers are also misusing
and overusing pesticides in order to maintain crop yields and production. As a consequence of
this growing dependence and hap-hazard use of pesticides, the prevalence of health impairments
and environmental damage are mounting.
The purpose of this component of the Poverty-Environment Nexus study is to answer two
questions relating to pesticide use in Vietnam, 1) Are the poor using more, toxic pesticides?, and
2) Is pesticide use, and its attended health consequences, impacting the poor to a greater extent
20
Karate 2,5 EC: Pyrethroid, Basudin 50 EC: Organophosphate and Padan 50 SP: Carbamate.
The test was a cholinesterase enzyme technique measuring active cholinesterase enzymes in erythrocyte
and plasma using the Test-Mate-Kit (EQMR-USA), with 0.03ml of blood from the fingertip. The purpose of
this test was to aid in the diagnosis and detection of persons suffering from pesticide poisoning (organicphosphates and carbamates).
21
than the non-poor? A World Bank survey of 603 farmers was conducted in the Mekong Delta,
Vietnam, an area known for extremely intensive agriculture. The survey comprised two major
sections, one to gather information on crop production and related pesticide use by farmers, and
one on the health and medical condition of the farmer.
In regards to the first major question, survey results indicate that although the poor are currently
using smaller amounts of pesticides (as well as on a per hectare basis), they are using relatively
more toxic pesticides. Several toxicity indicators were constructed to measure a pesticide’s
degree of relative toxicity. By weighting a pesticides’ active ingredient by its degree of lethality (or
by the LD50 value) and categorizing this measure according to the WHO risk classification system,
the poor are using a greater percentage of WHO Ia & Ib pesticides. Analogously, by classifying
pesticide use by its chemical class, it was found that the non-poor are using a greater amount of
carbamates, organophosphates, and pyrethroids.
In regards to the second question, farmer self-reported and medical test data were compiled and
compared. A large proportion (40-60%) of poor and non-poor farmers reported episodes of skin
irritation, headaches, dizziness, eye irritation and shortness of breath after using pesticides.
Upon closer examination by medical doctors, although few were suffering from heart disease,
respiratory ailments or large abnormalities in blood pressure, approximately 33% of the poor and
28% of the non-poor experience some type of skin disease (under the classification ‘other
dermatose’). Patch-skin tests for external pesticide exposure revealed that approximately 38% of
the poor and 31% of the non-poor farmers reacted positively to the presence of pesticides. Blood
tests for internal pesticide poisoning showed that the poor (25 out of 59 or 42%) had a slightly
higher prevalence rate than the non-poor (24 out of 153 or 32%), however this difference was not
statistically significant.
V. REFERENCES
Comprehensive Poverty Reduction and Growth Strategy (CPRGS), 2002. Document No.
2685NPCP-QHQT, Hanoi, Vietnam.
Chung, D. K. and K. T. Dung. 2002. Pest Management in Rice Production in Vietnam: A Socioeconomic Assessment. Hanoi: Nha Xuat Ban Nong Nghiep.
Dung, N. H. and T. T. T. Dung, 1999. Economic and Health Consequences of Pesticide Use in
Paddy Production in the Mekong Delta, Vietnam. EEPSEA Research Report No.1, pp. 1-39.
Available at: http://www.eepsea.org/en/ev-8427-201-1-DO_TOPIC.html.
FAO, 2001. Farmer Self-Surveillance of Pesticide Poisoning Episodes: Report on One Month
Pilot: August 15-Sept 15, 2000. FAO Programme for Community IPM in Asia, Field Document.
FAO, 2004.
FAO-STAT.
http://apps.fao.org/default.jsp.
Statistics
downloaded
for
Vietnam.
Available
at:
Kishi, M., N. Hirschhorn, M. Qjajadisastra, L. N. Satterlee, S. Strowman and R. Dilts, 1995.
Relationship of Pesticide Spraying to Signs and Symptoms in Indonesian Farmers,
Scandinavian Journal of Work & Environmental Health, 21: 124-133.
Levine, R.S., and J.E. Davies, 1982. Pesticide Toxicity and Mode of Action. In J.E. Davies, V.H.
Freed, and F.W.Whittemore (eds.), An Agromedical Approach to Pesticide Management: Some
Health and Environmental Considerations. Miami, Fla., USA: University of Miami School of
Medicine in cooperation with the Agency for International Development (USAID) and
Consortium of International Crop Protection.
Nguyen Huu Huan, Le Van Thiet et al., 2000. Results of survey for confidence, attitude and
practices in safe and effective use of pesticides.
Pimental, D., Acquay H., Biltonen, M., 1992. Environmental and Economic Costs of Pesticide
Use. Bioscience, 42, 750-60.
Pincus, J. 1995. Pesticide Policy Survey. Unfinished report. FAO Intercountry Programme for IPM
in Rice in South and Southeast Asia, Hanoi.
PPD (Plant Protection Department), 2000, Report on Results of Nation-wide Inspection of
Pesticide Regulation Enforcement in 2000, Large-scale Inspection Meeting for 2000 in Hanoi,
31 October 2000.
Rosenstock, L., Keifer, M., Daniel W.E. et al, 1991. Chronic Central Nervous System Effects of
Acute Organophosphate Pesticide Intoxication. Lancet, 338, 223-7.
World Health Organization (WHO), 1990. Public Health Impact of Pesticides Used in
Agriculture, 1990.
WHO (World Health Organization), 2002. The WHO Recommended Classification of
Pesticides by Hazard and Guidelines to Classification 2000-2002, Geneva, World Health
Organization. Available at:
http://www.who.int/ipcs/publications/pesticides_hazard/en/
World Bank, 2003. Poverty Map for Vietnam. World Bank and the General Statistics Office,
Vietnam.
Zahm, S. H., M. H. Ward and A. Blair, 1997. Pesticides and Cancer. In: Occupational Medicine:
State of the Art Reviews. Vol. 12: Pesticides (Keifer, M., ed). Philadelphia: Hanley and Belfus,
Inc., p.269-289.
Appendix: Supporting regression work
Overuse regression
Variable
Coefficient
0.001
age
0.018
educ
0.000
owner
0.079*
farmsize
0.000*
income
-0.379**
prsppoor
-0.072
ptrain
-0.009
pwhoI
0.012***
pwhoII
0.001
pwhoIII
0.289*
AnGiang
0.670***
Cantho
-0.074
LongAn
-0.006
TienGiag
-1.207**
Constant
N
LR
Log likelihood
Variable
pestamt
pwhoIab
pwhoII
pwhoIII
pwhoU
ptrain
nprotect
ipmd
prsppoor
AnGiang
Cantho
LongAn
TienGiag
Constant
603
54.880
-384.52
Blood test regression
Coefficient
-0.087*
0.038**
0.002
0.008
0.006
0.261
-0.421*
0.097
0.203
-0.139
-0.844***
-0.467
-0.846***
-0.144
N
LR
Log likelihood
t-ratio
0.27
0.26
0.05
1.94
1.64
-2.11
-0.63
-0.61
3.18
0.37
1.64
3.71
-0.42
-0.03
-1.99
187
28.650
-108.992
t-ratio
-1.89
2.36
0.38
1.21
0.89
1.21
-1.76
0.42
0.87
-0.39
-2.55
-1.41
-2.59
-0.22