1. RANDOMIZED CONTROL TRIAL EVALUATION OF SHORT-TERM EFFECT OF HEALTH
TRAINING INTERVENTION ON THE PRODUCTIVITY OF CROP FARMERS IN NIGERIA
Babatunde, R.O. and Olowogbon, T.S.
University of Ilorin, Ilorin, Nigeria

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Outline
 Motivation
 Research question
 The intervention
 Methodology
 Results
 Conclusion

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 The need to produce more food to combat the ravaging hunger in Africa necessitates the use of labour
saving technologies including use of agrochemicals.
 However, increased and persistent use of agrochemicals can have long-term negative consequences for
farmers health, such as respiratory disease, cancers and poisoning.
 This can ultimately exacerbate the low labour productivity in Africa. Hitherto the region has the lowest
agricultural productivity in the world.
 Occupational hazards in agriculture range from simple condition like heat exhaustion to complex
disease like respiratory disease, zoonotic disease, and poisoning from agrochemicals (IFPRI, 2011).
 ILO shows that the agricultural sector is one of the most hazardous to health worldwide accounting for
up to 25% of all disability-adjusted life year lost (DALYs) and 10% of deaths in low-income countries
(Gilbert et al., 2010).
Motivation

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 Estimate by WHO showed that globally 30 million people surfer severe chemical poisoning annually
and 25 million of these occur among agricultural workers in developing countries (Kuye et al., 2008).
 In spite of these numbers, issues of occupational health in general and in agriculture in particular,
remain neglected in most developing countries (IFPRI, 2011).
 Health is viewed as a major tangible asset in the production process which is important for agricultural
labour supply, quality and productivity (Asenso-Okyere et al., 2011).
 In many developing countries such as Nigeria with large endowments of labour, improving labour
productivity is an important way to improve the nation’s agricultural food sector.
 Nevertheless, studies that examine the nexus between agricultural health training and productivity
are limited in Nigeria.

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In this study, we used a simple RCT design that combines mobile technology
to examine the short-term effect of an agricultural health training
intervention on productivity, production time loss, safety knowledge and
safety attitude among cassava farmers in Nigeria.
Research question

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Intervention
Programme
elements
One-time village level
agricultural Health
Training
Follow up mobile phone
Safety Text Messaging
for three months
The health training intervention

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 Peer developed
training modules
 Focused on safe
agrochemical use and
ergonomics
 One time training
engaging a blended
training approach
The training component

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 Follow up mobile phone
safety text messaging
 For 3 months (twice a
month) a total of 6 safety
messages
The SMS component

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Intervention framework

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 The study used a randomized control trial approach
focusing on agricultural health intervention for
cassava farmers in Nigeria.
 It was carried out in Kogi and Kwara States in the
cassava-producing region of North-central Nigeria.
 A total of 480 farmers from 24 cassava growing
communities and consisting of 200 in the
intervention and 280 in the control group were
randomly selected for the study.
 The sample size was estimated using the optimal
design approach with a power of 80% and 5%
significant level to achieve the expected minimum
detectable effect.
Study design and setting
Fig. 2: Map of Nigeria showing Kogi and Kwara States.

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 Descriptive Analysis, Ordinary Least Square regression and Difference-in-
difference (DID) estimator were used to analyze the data.
 The main outcomes of the intervention are labour productivity (tons/man-days),
production time loss due to ill health (days), farmers safety knowledge (points)
and safety attitude (points).
 For instance, we want to examine whether cassava farmers that were given the
intervention are able to improve their labour productivity, whether the
intervention help to reduce the production time loss due to ill health, whether
the intervention increase their safety knowledge and safety attitude.
Analytical techniques

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The DID model used to estimate the effect of the health training intervention
on the farmers productivity outcome is stated thus:
Where Yit is the outcome variable for an individual i at time t, α is the constant, Treati is the dummy equals
1 if treated and 0 if not treated, and Postt is a dummy equals 1 if data is collected at post intervention and
0 if at baseline, β1, β2 and β3 are coefficients.
Constant 𝛼 measures the average treatment outcome before the intervention, 𝛽1 measures the difference
between treatment and control before the intervention (selection effect), 𝛽2 measures the changes across
time in the outcome variable common to both groups and 𝛽3 measures the average treatment effect of the
programme on the outcome variable.
The Difference-in-difference estimator compares the mean value of the outcomes between the treatment
group and the control group over time, at baseline and end line.

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• Attrition rate for the study was 14% (28) for the treated group and
16% (45) for the control.
Reasons:
• Treated: inability to receive the follow up text messages leading to
uncompleted treatment
• Control: largely due to unavailability of respondents during post
intervention data collection.
Attrition rate

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Treatment Control
Variables Mean(SD) Mean(SD) t-stat for test of
diff. in means
Age in years 38(8.0) 39(8.4) 0.1(0.9)
Household size(numbers) 5.0(2.7) 5.3(2.3) 0.8(0.4)
Years of schooling (years) 13.6(2.5) 13.3(3.6) 1.3(0.1)
Farming Experience (Years) 13.7(7.6) 14.4(7.4) 0.3(0.8)
Farm size(Ha) 2.1(2.9) 2.4(2.4) 0.4(0.7)
Monthly health expenditure (naira) 1119(11187) 1135(1028) 0.1(0.9)
Daily duration of chemical spray 5.9(2.4) 6.2(2.5) 0.04(0.9)
Average years of chemical usage 9.0(2.6) 10.0(3.8) 0.5(0.6)
Ergonomic discomfort per week 2.0(3.3) 3.0(3.6) 0.4(0.6)
Production Loss time in days 5.0(3.5) 6.0(4.4) 0.2(0.8)
Number of recurrent pesticide poisoning
symptoms/season
13.0(2.5) 11.0(3.7) 0.8(1.2)
Table 1: Selected baseline characteristics of farmers in the intervention
Results

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Items Frequency Percentage
Hand washing after spraying
Yes 256 53
No 224 47
Cloth changing after spraying
Yes 336 70
No 144 30
Hand washing before eating in the field
Yes 64 13
No 416 87
Sprayer washing
Yes 304 63
No 176 37
Container management
Throw in the field 312 65
Bury in the soil 48 10
Burn in the field 48 10
Washed and re-used as household container 72 15
Table 2: Patterns of agrochemical application by farmers in the study area

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Items Frequency Percentage
Chemical measurement into sprayer
The use of chemical lid cap 288 60
Measured by experience 192 40
Reading of chemical label
Yes ( occasionally) 336 70
Yes (always) 29 06
No 114 24
Adherence to advice on chemical label
Yes (Sometimes) 254 53
No 226 47
Information read on chemical label
Expiration date 480 100
Safety instructions e.g Protective gear use 96 20
Re-entry time 24 05
General Instruction of use e.g mixing volumes 400 83
Understanding of safety instructions on label
Yes 144 30
No 336 70
Source: field survey, 2018

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0
10
20
30
40
50
60
70
80
21
4
29
72
35
11
54
11
18
11 11
39
4
75
18
14 14
4
7
4
17
Percentage
of
exposure
Chemical poisoning symptoms
Figure 3: Self reported agrochemical poisoning symptoms

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0
10
20
30
40
50
60
70
80
90
100
Neck Shoulder Elbow Wrist/hands Upper back Lower back Hip and
thigh
Knees Ankle/feet
64
96
53
43
82
85
11
14 14
P
e
r
c
e
n
t
a
g
e
e
x
p
o
s
u
r
e
Affected body parts
96% reported shoulder pain, 85% lower back pain, and 82% upper back pain.
Figure 4: Self reported ergonomic symptoms

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Difference-in-difference
estimates of production time
loss due to illness (days)
Co-efficient t-value
Treatment 0.11 0.28
Time trend -0.95 -2.39
DID(Interaction) -1.88*** -3.34
Constant 6.50 23.16
Table 3: Average intervention effect on production time loss
due to illness
Note: *** indicate the coefficient is significant at 1%, DID is the difference in
difference estimator showing the intervention impact on production loss
time

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Difference in difference
estimates of farmers’
safety knowledge
Co-efficient t-value
Treatment 0.43 1.60
Time trend -0.23 -0.64
DID (Interaction) 2.45*** 4.97
Constant 2.86 15.08
Note: *** indicate the coefficient is significant at 1%, DID is the difference in
difference estimator showing the intervention impact on farmers’ safety
knowledge
Table 4: Average intervention effect on farmers’ safety
knowledge

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Difference in difference estimates
of farmers’ safety attitude
Co-efficient t-value
Treatment 0.48 1.50
Time trend -0.29 -0.67
DID (Interaction) 2.65*** 4.39
Constant 3.29 14.66
Note: *** indicate the coefficient is significant at 1%, DID is the difference in
difference estimator showing the intervention impact on farmers’ safety
attitude
Table 5: Average intervention effect on farmers’ safety
attitude

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Difference in difference estimate of
Labour productivity (tons/man-days)
Co-efficient t-value
Treatment 0.008 -0.13
Time trend 0.10 1.42
DID(Interaction) 0.16* 1.77
Constant 1.10 21.83
Note: * indicate the coefficient is significant at 10%, DID is the difference in
difference estimator showing the intervention impact on labour
productivity
Table 6: Average intervention effect on labour productivity

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 The study concluded that farmers were engaged in unsafe agrochemicals application
practices exposing them to some health risks which negatively affect their productivity
and well-being.
 Farm safety education was found to have the potential of reducing farmer’s exposure
to health risks.
 The training model with farm safety text messaging used in this study was found to be
effective in improving farmers safety knowledge, increasing their safety attitude as
well as reducing farmers production time loss due to illness and improve their
productivity in the short term.
Conclusion

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 However, additional research is needed to establish the long-term intervention effects
and explore issues of cost effectiveness.
 We suggest the need for inclusive agricultural health policy that would provide
effective and timely agricultural health information, agricultural health surveillance and
agricultural health training for the farming population in Nigeria and other developing
countries where farmers currently practice unsafe agrochemicals application.

25. THANK YOU