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American Journal of  
Food Science and Technology (AJFST)

COVID-19 Concerns and Resources Allocation on Smallholder Rice Farms 
in Cote D’ivoire

Innocent Daniel Gniza1*

Volume 3 Issue 2, Year 2024
ISSN: 2834-0086 (Online)

DOI: https://doi.org/10.54536/ajfst.v3i2.3505
https://journals.e-palli.com/home/index.php/ajfst

Article Information ABSTRACT

Received: August 15, 2024

Accepted: September 19, 2024

Published: November 02, 2024

Covid-19 has caused a great deal of  concern among the population, particularly 
in rural areas. Smallholder farmers have reacted in various ways to these concerns 
and to the psychological stress associated with the spread of  the virus. This study 
analyzes the correlation between producers’ concerns about COVID-19 and 
productive resource allocation decisions on smallholder rice farms in Côte d’Ivoire. 
Using cross-sectional data obtained from 1359 households in the Poro, Tchologo 
and Tonkpi regions through a stratified random sampling technique where each 
village was considered a stratum, the study uses descriptive statistics, chi-2 tests 
and Pearson correlation tests to achieve the objectives. The results indicate that 
producers’ concerns are related to family, travel, finances and product availability. 
These concerns vary from region to region. In addition, the results indicate that 
concerns about COVID-19 led producers to reduce inputs used on the plots and to 
increase labor for cultivation activities. The study makes several recommendations, 
including the psychological consideration of  small-scale producers in rural areas 
and the effective subsidization of  inputs during health crises to avoid food 
insecurity problems.

Keywords

Covid-19, Concerns, Resource 
Allocation, Small Producers

1 Training and Research Unit in Economic and Management Sciences (UFR-SEG), Jean Lorougnon Guédé University (UJLoG) 
of  Daloa, Ivory Coast
* Corresponding author’s e-mail: gnizadaniel@gmail.com

INTRODUCTION
In March 2020, the World Health Organization (WHO) 
declared the outbreak of  the COVID-19 virus, which 
first appeared in China and then spread to the rest of  
the world, a global pandemic (INS, 2020). By the end of  
September 2020, the virus had infected nearly 30 million 
people worldwide and caused nearly one million deaths 
(WHO, 2021). Although there has been a delay in the 
number of  cases in sub-Saharan Africa, the continent has 
not been spared, which has exacerbated the vulnerability 
of  already precarious health systems (Akinyemi et al., 
2022).
Several researchers associate COVID-19 with economic 
downturns and increased poverty rates in many 
developing countries, particularly in Africa (Laborde 
et al., 2021; Zeufack et al., 2020). In response to this 
global challenge, African countries have complied with 
WHO recommendations such as containment, travel 
restrictions, sheltering, physical separation, and certain 
hygiene procedures to control the spread of  the virus 
and save their health infrastructure (Durizzo et al., 2021). 
These measures have led to an increased risk of  food 
insecurity, leading to Covid-19 being considered a hunger 
pandemic (Tabe-Ojong et al., 2022).
In Côte d’Ivoire, the first case of  Covid-19 was recorded 
in March 2020, prompting the government to adopt a 
series of  measures to reduce the risk of  contamination. 
These measures ranged from closing schools to isolating 
the economic capital (Abidjan) from the rest of  Côte 
d’Ivoire, with all of  its corollaries (OIM, 2020).

In rural areas, the population has been informed of  
the pandemic, largely through traditional information 
channels such as television and radio. Household anxiety 
about Covid-19 is very high and this feeling of  anxiety 
among heads of  households appears to be somewhat 
higher among women than men (INS, 2020). Due to the 
rapid increase in the number of  new cases, Covid-19 has 
caused panic, uncertainty, anxiety, income and expenditure 
pressures, resulting in psychological and socioeconomic 
disorders (Zhang & Ma, 2020).
In addition to inadequate health infrastructure and 
personnel, rural populations face uncertainties regarding 
the availability of  food, production inputs, and several 
other agricultural needs due to Covid-19-related 
concerns, hence the research question of  this study: is 
there a correlation between producers’ concerns and the 
allocation of  productive resources? The overall objective 
of  this study is to identify the links between smallholder 
producers’ perceptions of  the spread of  Covid-19 and 
the resources used on rice farms at the onset of  the 
health crisis. More specifically, this study aims to :

- Identify the different perceptions of  smallholder rice 
farmers of  the possibility of  Covid-19 spread ;

- To measure the correlation between perceptions of  
Covid-19 spread and the amount of  resources used on 
rice farms in Côte d’Ivoire.
The relevance of  this study lies in the fact that most 
studies (Akinyemi et al., 2022; Adjet et al., 2022; Greteman 
et al., 2022) on Covid-19 have focused on people’s 
perceptions, knowledge, and application of  government 



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restrictive measures. However, almost no studies have 
examined the links between Covid-19 concerns and on-
farm resource allocation decisions. This study comes to 
fill that gap. Furthermore, the choice of  rice as a crop 
is highly suggestive given that rice has become the main 
food of  both urban and rural populations in Côte d’Ivoire 
(Gniza, 2023). As a result, rice is a strategic crop for social 
stability and food security in Côte d’Ivoire, especially in 
times of  health crisis.

Theoretical Framework : The Theory of  Planned 
Behavior
The theory of  planned behavior, according to Ajzen 
(1985, 1991), emerged in the field of  social psychology 
as a means of  predicting behavior. It is based on the 
observation that individuals make reasoned decisions, and 
that behavior is the result of  the intention to engage in it. 
The stronger the intention, the more effort the individual 
will make to move towards that behavior, and the more 
likely it is that he or she will engage in that behavior (Steg 
& Nordlund, 2013). The intention will not be expressed 
in the behavior if  it is physically impossible to accomplish 
or if  unexpected obstacles stand in the way.
Intention is a central factor in this theory. Intentions are 
supposed to reflect the motivational factors that influence 
behavior; they indicate the extent to which people are 
willing to invest themselves in a given behavior. Although 
some behaviors may in fact meet this requirement very 
well, most depend, at least to some extent, on non-
motivational factors such as the availability of  the 
necessary opportunities and resources (e.g. time, money, 
skills, cooperation from others) (Ajzen, 1985).
According to Ajzen (1991), there are three determinants 
of  behavioral intention : (i) Attitude (one’s own opinion 
of  the behavior): attitude is an individual’s favorable 
or unfavorable feeling towards a specific behavior. An 
individual will intend to adopt a certain behavior when 
he evaluates it positively. Attitudes are determined 
by an individual’s beliefs about the consequences of  
performing the behavior (behavioral beliefs), weighted 
by the evaluation of  these consequences (outcome 
evaluations). Thus, attitude is an individual’s dominant 
belief  about whether the outcome of  his or her behavior 
will be positive or negative (Fishbein & Capella, 2006); 
(ii) Subjective norm (others’ opinions of  behavior): 
subjective norms are assumed to be a function of  beliefs 
about whether individuals approve or disapprove of  
behavior. The beliefs underlying subjective norms are 
normative beliefs. Normative social influence is defined as 
the influence of  other people that induces us to conform 
in order to be liked and accepted by them (Aronson & 
Wilson, 2005). Although an action may not be accepted 
or approved by an individual, normative social influence 
exerts pressure on the individual to conform to the 
social norms of  the group. Normative social influence 
has been shown to exert a strong persuasive influence on 
individuals. An individual will intend to adopt a behavior 
when he perceives that important people think he should. 

Significant others may be a spouse, close friends or 
doctor, among others; (iii) The degree of  control over the 
perceived behavior (self-efficacy towards the behavior): 
refers to the perceived ease or difficulty of  performing the 
behavior and is assumed to reflect past experience as well 
as anticipated impediments and obstacles. This variable 
can influence the implementation of  the behavior either 
indirectly or directly.
The theory of  planned behavior has been able to explain 
various ecological behaviors. Steg and Nordlund, (2013) 
found that variables in this theory, especially perceived 
attitudes and behavioral control, could predict pro-
environmental behaviors. The ability of  this theory to 
predict behavior rises when other motivational factors 
are included in the model, such as personal norms and 
identity (Nigbur et al., 2010). Our work is based on Ajzen’s 
theory of  planned behavior.

MATERIALS AND METHODS
Sampling procedure and data
This study uses cross-sectional data from a survey 
commissioned by the World Bank among rice-growing 
households in Côte d’Ivoire as part of  an economic 
inclusion project in the rice sector in October 2020, i.e. 7 
months after the first case of  Covid-19 appeared in Côte 
d’Ivoire. Considering their importance in rice production 
and the diversity of  the agro-ecological zone, three 
regions were selected for design: the Tonkpi, Tchologo 
and Poro regions. Taking into account the intensity of  
rice production in the villages, 20 villages were selected 
in each region. In each village, 25 households were 
selected by stratified random sampling, where each village 
was considered a stratum. Thus, 1500 households were 
surveyed, but only 1359 households were selected for 
this study because they were growing rice in the current 
season.
The survey collected both quantitative and qualitative 
information. The quantitative information includes 
farmers’ socioeconomic and demographic characteristics, 
income, equipment, quantities of  inputs and labor used, 
area cultivated, and any other information that is relevant 
to this analysis. The qualitative survey examined farmers’ 
perceptions and concerns related to Covid-19. These data 
form the basis of  this study. Producer concerns were 
collected on six issues : family, employment, finances, 
mobility, transportation, activities, food, and products. 
Producers’ levels of  concern were measured on 5-point, 
fully anchored, bipolar Likert-type scales ranging from 
1 to 5 based on the degree of  concern : 1- not at all 
concerned; 2- not concerned; 3- neutral; 4- concerned 
and, 5- very concerned.
Table 1 summarizes the characteristics of  the producers 
in our sample for the quantitative survey. The majority 
of  producers in the sample are male (92%), belong to 
a group (62.55%), have an average age of  46.57 years, 
have participated in at least one rice training course, and 
have 2.95 and 17.70 years of  education and experience 
in rice production, respectively. On average, the sampled 



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farmers have a household size of  5.86 persons in which 
48.77% are children under 15 years of  age, 25.09% are 
young people between 15 and 24 years of  age, 22.25% are 
mature adults between 25 and 60 years of  age, and 3.89% 
are old people over 60 years of  age. After the start of  
Covid-19 in Côte d’Ivoire, smallholder rice farmers in the 
study area used an average of  8.20 kg/ha of  pesticides, 

43.30 kg/ha of  urea, 88.43 kg/ha of  NPK and 81.01 
kg/ha of  rice seed. In terms of  labor, farmers used an 
average of  88.85 man-days/ha from their household and 
126.41 man-days/ha from outside the household. The 
average cultivated area is 1.13 ha and these plots received 
an average of  1 visit from the extension agents.

Table 1: Description of  the producer, household, farm and productive resources
Variable Mean Std. Dev.
Producer characteristics
Sex (male = 1) 0.920 0.272
Age 46.574 12.08
Education (in years) 2.953 4.146
Experience (in years) 17.701 12.268
Number of  participation in rice training 1.064 1.944
Membership in a group 0.625 0.484

Household characteristics
Household size 5.866 2.801
Number of  Kids in the household 2.861 2.075
Number of  Youth in the household 1.472 1.283
Number of  Mature in the household w1.305 0.940
Number of  Old in the household 0.228 0.500
Inputs allocation
Amount of  pesticide applied (kg/ha) 8.202 6.463
Amount of  Urea applied (kg/ha) 43.301 51.333
Amount of  NPK applied (kg/ha) 88.433 86.801
Amount of  Seed used (kg/ha) 81.013 66.714
Labor allocation
Amount of  family labor used (man-day/ha) 88.853 86.122
Amount of  non-family labor used (man-day/ha) 126.415 104.001
Plot characteristics
Plot size 1.132 1.082
Number of  extension visits received 1.131 2.136
Observations 1359
Source : Author

Data analysis
Cronbach’s alpha statistical test was performed to ensure 
the internal consistency of  the factorial construction 
and the reliability of  the scale with a cut-off  value of  
0.7 (De Vaus, 2002). Next, the data were summarized 
using frequencies and percentages. Cross-tabulations 
were performed to assess the relationship between levels 
of  Covid-19 concern and different regions of  origin of  
producers. A series of  Chi-2 tests were performed to test 
for differences in responses. Finally, a series of  Pearson’s 
correlation tests were conducted to test the relationship 
between growers’ concerns and the allocation of  
productive resources in smallholder rice farms in the 
midst of  a health crisis.

RESULTS AND DISCUSSION
Producers’ concerns related to the possibility of  
Covid-19 spread
Producer concerns were collected on 2 groups of  criteria: 
concerns about family and displacement and concerns 
about finances and product availability.

Concerns about family and displacement
Observation of  Table 2 shows that there is a significant 
difference in how producers perceive the possibility of  
Covid-19 spreading depending on where they live. The 
vast majority (over 50%) of  producers in each region are 
very concerned about their family being infected or a 
family member losing their job to Covid-19. 



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Table 2: Producer concerns related to covid-19 with respect to family and transportation
What do you think about the possibility that, 
due to the spread of  Covid-19:

Frequencies and percentages (in brackets) by region
Poro Tchologo Tonkpi Total P-value

You or your family could be infected? 0.000
Not at all concerned 20 (4.38) 26 (4.91) 0 (0.00) 46 (3.38)
Not concerned 7 (1.53) 27 (5.09) 4 (1.08) 38 (2.80)
Neutral position 11 (2.41) 28 (5.28) 2 (0.54) 41 (3.02)
Concerned 120(26.26) 209(39.43) 90 (24.19) 419 (30.83)
Very concerned 299(65.43) 240(45.28) 276(74.19) 815(59.97)
You or a member of  your family will lose your job ? 0.000
Not at all concerned 17 (3.72) 30 (5.66) 5 (1.34) 52 (3.83)
Not concerned 17 (3.72) 51 (9.62) 15 (4.03) 83 (6.11)
Neutral position 13 (2.84) 33 (6.23) 9 (2.42) 55 (4.05)
Concerned 158(34.57) 210(39.62) 115(30.91) 483(35.54)
Very concerned 252(55.14) 206(38.87) 228(61.29) 686(50.48)
You may have to stay home for several weeks 0.000
Not at all concerned 1 11 (2.41) 24 (4.53) 0 (0.00) 35 (2.58)
Not concerned 2 16 (3.50) 55 (10.38) 2 (0.54) 73 (5.37)
Neutral position 3 3 (0.66) 9 (1.70) 4 (1.08) 16 (1.18)
Concerned 4 159(34.79) 212(40.00) 115(30.91) 486(35.76)
Very concerned 5 268(58.64) 230(43.40) 251(67.47) 749(55.11)
You cannot use public transportation and roads as 
you usually do ?

0.000

Not at all concerned 11 (2.41) 17 (3.21) 0 (0.00) 28 (2.06)
Not concerned 6 (1.31) 34 (6.42) 3 (0.81) 43 (3.16)
Neutral position 0 (0.00) 8 (1.51) 4 (1.08) 12 (0.88)
Concerned 154(33.70) 216(40.75) 122(32.80) 492(36.20)
Very concerned 286(62.58) 255(48.11) 243(65.32) 784(57.69)
Total 457 530 372 1359
Source : Author

In the Tchologo region, less than half  of  producers (45% 
and 38% respectively for the risk of  infection and job 
loss) are very worried. The same is true for concerns 
about travel restrictions. A total of  55.11% of  producers 
said they were very worried about staying at home for 
several weeks, while 57.69% said they were very worried 
about not using public transportation. A significant 
proportion (at least 13%) said they were not worried 
about a household member losing their job (table 2). This 

could be because these producers own their own off-
farm activities (food trade, farm machinery repair, etc.).

Concerns about finances and product availability
Table 3 reveals that a total of  55.04% and 56.73% of  
producers respectively said they were very worried that 
they would not be able to carry out their activities as usual 
and that their finances would be negatively affected.

Table 3: Producer concerns related to covid-19 with respect to activities and availability of  goods
What do you think about the possi-bility 
that, due to the spread of  Covid-19 :

Frequencies and percentages (in brackets) by region
Poro Tchologo Tonkpi Total P-value

You cannot perform your normal activities
Not at all concerned 1 11 (2.41) 25 (4.72) 0 (0.00) 36 (2.65)
Not concerned 2 9 (1.97) 44 (8.30) 2 (0.54) 55 (4.05)
Neutral position 3 5 (1.09) 4 (0.75) 5 (1.34) 14 (1.03)
Concerned 4 168(36.76) 214(40.38) 124(33.33) 506(37.23)
Very concerned 5 264(57.77) 243(45.85) 241(64.78) 748(55.04)
Your finances will be affected ? 1 2 3 Total 0.000



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Not at all concerned 14 (3.06) 28 (5.28) 0 (0.00) 42 (3.09)
Not concerned 11 (2.41) 44 (8.30) 2 (0.54) 57 (4.19)
Neutral position 11 (2.41) 23 (4.34) 2 (0.54) 36 (2.65)
Concerned 147(32.17) 199(37.55) 107(28.76) 453(33.33)
Very concerned 274(59.96) 236(44.53) 261(70.16) 771(56.73)
Your ability to obtain food is limi-ted 0.000
Not at all concerned 1 16 (3.50) 33 (6.23) 1 (0.27) 50 (3.68)
Not concerned 2 42 (9.19) 66 (12.45) 5 (1.34) 113 (8.31)
Neutral position 3 3 (0.66) 6 (1.13) 2 (0.54) 11 (0.81)
Concerned 4 142(31.07) 199(37.55) 111(29.84) 452(33.26)
Very concerned 5 254(55.58) 226(42.64) 253(68.01) 733(53.94)
Your ability to purchase other goods and 
services is limited

0.000

Not at all concerned 1 11 (2.41) 28 (5.28) 0 (0.00) 39 (2.87)
Not concerned 2 8 (1.75) 54 (10.19) 3 (0.81) 65 (4.78)
Neutral position 3 4 (0.88) 11 (2.08) 3 (0.81) 18 (1.32)
Concerned 4 183(40.04) 215(40.57) 120(32.26) 518(38.12)
Very concerned 5 251(54.92) 222(41.89) 246(66.13) 719(52.91)
Total 457 530 372 1359
Source : Author
In addition, 53.94% and 52.91% of  producers said they 
were very worried about the ability to obtain food and the 
availability of  goods, respectively. The Tonkpi region has 
the highest level of  concern (64.78%, 70.16%, 68.01% 
and 66.13% respectively for activities, finances, food and 
goods), with the Tchologo region still having the lowest 
percentage (table 3).
In the end, not only is there a great deal of  concern 
among growers, but growers in the Tonkpi region (in 
the west of  the country) were found to be much more 
worried about the possibility of  Covid-19 spreading 
than those in other regions (Poro and Tchologo). This 
result is contrary to that of  Adjet et al. (2022) who show 
that in the eyes of  the respondents Covid-19 is a new 
pathology and could not spread in Côte d’Ivoire because 
of  a certain “genetic immunity”. This could be explained 
by the timing of  the survey (just after the first case was 
detected in Côte d’Ivoire). However, these results on 
regional differences in the perceived spread of  Covid-19 
are similar to those found by several authors (Strobel 
et al., 2020; INS, 2020; IOM, 2020; Mueller et al., 2021; 
Greteman et al., 2022; Akinyemi et al.) These authors have 
shown that concerns about Covid-19 could differ from 
one area to another depending on several factors such as 
access to good information, access to quality health care 
services, etc. In our study, in addition to these reasons, the 
concern of  producers in the Tonkpi region was amplified 
by the occurrence of  more cases of  contamination 
than in the other two regions (INS, 2020), the lack of  
adequate sanitary facilities, and the deterioration of  
road infrastructure leading to the country’s capital. In 
addition, producers in this region have an extroverted 
economy based on coffee and cocoa plantations. Thus, 
the restriction on travel and the isolation of  the economic 
capital (Abidjan), the main channel for the sale of  

agricultural products, has accentuated the concerns of  
producers in this region.

Correlation between Covid-19 concerns and 
productive resource allocation
Cronbach’s alpha is greater than 0.7 (alpha = 0.82), 
which confirms the adequacy of  the associated items in 
the concern level constructs. The relationship between 
Covid-19 concerns and productive resource allocation 
was tested using Pearson’s correlation coefficient.

Covid-19 Concerns and Input Allocation on Rice 
Farms
Table 4 presents the result of  the correlation test between 
farmers’ concerns and the allocation of  inputs such as 
pesticides (herbicides), basal fertilizer (NPK) and cover 
fertilizer (Urea). Overall, the results reveal a significant 
negative correlation between concerns about Covid-19 
and input allocation, especially for fertilizer. This means 
that farmers’ concerns about the possibility of  Covid-19 
spread partly explain the reduction of  inputs used on 
the plots durin g the pandemic period. This result is 
consistent with that of  Ebel et al. (2022) who showed 
changes in farm management due to Covid-19, based 
on the agricultural practices applied by producers. These 
authors found that 28% of  producers changed their 
fertilizer application practices in two rural U.S. states, 
Montana and South Dakota. In our study area, producers 
adopted adaptive and anticipatory strategies in light of  
changing conditions. This strategy has been to cut back 
on certain pockets of  spending that they do not feel 
are too critical to the success of  their operations. Thus, 
instead of  using 200 kg of  NPK per hectare, they prefer 
to reduce it to 100 kg/ha. Also, instead of  using post-
emergent herbicides, they prefer to do manual weeding.



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Covid-19 Concerns and Seed and Plot Allocation
Table 5 shows that there is no statistically significant 
correlation between levels of  concern and seed and plot 
allocation. Furthermore, the correlation coefficients are 
very low (close to zero), implying that seed and plot 
allocation during the pandemic period cannot be explained 
by farmers’ concerns. This result is consistent with 

Martey et al. (2022) who show that the effect of  Covid-19 
on land allocation is not significant on rice-allocated plots 
in Ghana. In our study area, this is explained by the fact 
that land is part of  farmers’ personal endowments while 
the vast majority of  seeds used by farmers come from 
previous harvests. Thus, the occurrence of  events does 
not a priori influence the allocation of  these resources.

Table 4: Correlation between Covid-19 concerns and input allocation
What do you think about the possibility that, due to the spread of  Covid-19: Phyto Urea NPK
you or your family could be infected? -0.072** -0.122*** -0.116***

Your finances will be affected ? -0.088*** -0.123*** -0.137***
You or a member of  your family will lose your job ? -0.065** -0.109*** -0.100***
You cannot use public transportation and roads as you usually do ? -0.021 -0.095*** -0.100***
You cannot perform your normal activities -0.046* -0.130*** -0.127***
You may have to stay home for several weeks -0.037 -0.122*** -0.135***
Your ability to obtain food is limited -0.043 -0.142*** -0.151***
Your ability to purchase other goods and services is limi-ted -0.048* -0.144*** -0.163***
Source : Author

Table 5: Correlation between Covid-19 concerns and seed and plot allocation
What do you think about the possibility that, due to the spread of  Covid-19: Seed Plots
you or your family could be infected? 0.011 -0.003

Your finances will be affected ? -0.017 -0.002
You or a member of  your family will lose your job ? 0.026 0.016
You cannot use public transportation and roads as you usually do ? 0.022 0.040
You cannot perform your normal activities 0.000 0.038
You may have to stay home for several weeks 0.014 -0.024
Your ability to obtain food is limited 0.044 -0.039
Your ability to purchase other goods and services is limited -0.025 0.014
Source : Author

Covid-19 Concerns and Labor Allocation on Rice 
Farms
Table 6 presents the results of  the Pearson correlation 
test between producer concerns and labor allocation on 
rice farms. The results reveal that the Pearson coefficient 
is positive and statistically significant indicating a positive 
association between farmers’ concerns and labor 
allocation. The results further indicate a weak positive 
relationship between producers’ concerns and family labor, 
explained by the high proportion of  household members 
unable to work (more than 52% children and the elderly, 
see Table 1), while there is a strong positive relationship 
with non-family labor. These results are contrary to those 
of  Ebel et al. (2022) and INS (2020). Ebel et al. (2022) 
showed that 34/1% of  growers perceived a decrease 

in labor availability due to Covid-19 in two rural areas 
in the United States. The INS (2020) report also shows 
that 28.6% of  producers in Côte d’Ivoire experienced a 
reduction in agricultural labor. But, this result is consistent 
with Martey et al. (2022) who show that 46% of  producers 
said that Covid-19 resulted in increased family labor. In 
our study area, this is because producers use more labor 
to compensate for the reduction or elimination of  weed 
control products such as herbicides (hand weeding instead 
of  applying pre-emergent herbicides) and mechanization 
(hand plowing and threshing). In addition, membership 
in self-help groups (62.55% of  our sample) facilitates 
access to cheap labor for various cultivation operations. 
From the producers’ point of  view, labor and inputs are 
substitutable (Gniza, 2023).

Table 6: Correlation between Covid-19 concerns and labor allocation
What do you think about the possibility that, due to the spread of  
Covid-19 :

Family labor Non-family 
labor

you or your family could be infected? 0.084*** 0.126***

Your finances will be affected ? 0.092*** 0.129***
You or a member of  your family will lose your job ? 0.073*** 0.123***



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You cannot use public transportation and roads as you usually do ? 0.097*** 0.099***
You cannot perform your normal activities 0.086*** 0.121***
You may have to stay home for several weeks 0.067** 0.089***
Your ability to obtain food is limited 0.079*** 0.093***
Your ability to purchase other goods and services is limited 0.092*** 0.121***
Source : Author

CONCLUSION
This study examined producers’ levels of  concern 
about the possibility of  Covid-19 spread and possible 
correlations with resource allocations on smallholder 
rice farms in Côte d’Ivoire. The study showed that the 
vast majority of  producers were very concerned about 
the possibility of  the spread of  Covid-19 with respect 
to their household, travel, activities, and availability of  
food and goods. The study showed that producers in the 
Tonkpi region were more concerned than others at this 
time due to the deterioration of  the health system and 
road infrastructure and the occurrence of  more cases of  
contamination compared to other regions. In addition, 
the study found that there was a negative and significant 
correlation between producers’ levels of  concern and 
chemical input allocation and a positive and significant 
correlation with labor allocation. This confirms the 
suspicion of  substitutability between labor and chemical 
inputs from the producers’ perspective. 
From the above, the study makes four recommendations. 
First, promote awareness and information among rural 
populations to counteract rumors and false information. 
This could be done through extension agents who are 
already well integrated in the villages and who enjoy 
the trust of  producers. Secondly, encourage community 
mobilization by relying on existing local mechanisms and 
through the organization of  producers’ groups, which 
will help cushion the effects of  health crises. Third, 
begin to provide psychological support to producers in 
times of  health crises so that they can remain effective 
in their activities (Tagne et al., 2021). Finally, the study 
recommends an effective subsidy of  inputs during health 
crises to allow producers to access inputs without any 
stress and ensure good production and effectively fight 
against food insecurity.

REFERENCES
Adjet, A. A., Yao, K. A., Kouakou, Y. F., & Akpetou, K. 

K. R. (2022). Perceptions sociales liées à la COVID-19 
en milieu rural: Cas des populations du village de 
Tapeguhé/ Sous-préfecture de Daloa (Centre-Ouest 
ivoirien). REPRISS: Revue d’Etude et de Recherche 
Interdisciplinaire en Sciences Sociales; Numéro spécial, 02, 
140 – 162.

Ajzen I. (1991). The Theory of  Planned Behavior.  
Organizational behavior and human decision processes, 50, 
179-211.

Ajzen, I. (1985). From intentions to actions: A theory of  planned 
behavior. In: Kuhl, J., Beckmann, J. (eds) Action Control. 
SSSP Springer Series in Social Psychology. Springer, 

Berlin, Heidelberg. https://doi.org/10.1007/978-3-
642-69746-3_2

Aronson, E., Wilson, T. D., & Akert, R. M. (2005). Social 
Psychology. Pearson Education International: Upper 
Saddle River, NJ, USA.

Durizzo, K., Asiedu, E., van der Merwe, A., van Niekerk, 
A., & Günther, I. (2021). Managing the covid-19 
pandemic in poor urban neighborhoods: The case 
of  Accra and Johannesburg. World Development, 137, 
105175.

Fishbein, M., & Cappella, J. N. (2006). The role of  theory 
in developing effective health communications. J. 
Commun., 56, 1–17.

Gniza, I. D. (2023). Analyse de l’efficacité allocative des 
ressources utilisées dans les petites exploitations de 
riz de bas-fond au centre-ouest de la Côte d’Ivoire. 
African Journal of  Agricultural and Resource Economics, 
17(4), 287-297. https://EconPapers.repec.org/
RePEc:ags:afjare:333984.

Greteman, B. B., Garcia-Auguste, C. J., Gryzlak, B. M., 
Kahl, A. R., Lutgendorf, S. K., Chrischilles, E. A., & 
Charlton, M. E. (2022). Rural and urban differences 
in perceptions, behaviors, and health care disruptions 
during the COVID-19 pandemic. J Rural Health., 
38(4), 932-944. https://doi.org/10.1111/jrh.12667. 

INS (Institut National de la Statistique). (2020). Mesure De 
l’Impact Socio-Economique Du Covid-19 Sur Les Conditions 
De Vie Des Menages En Côte d’Ivoire. Rapport final. 

Laborde, D., Martin, W., & Vos, R. (2021). Impacts 
of  covid-19 on global poverty, food security, and 
diets: Insights from global model scenario analysis. 
Agricultural Economics, 52(3), 375–390.

Martey, E., Goldsmith, P., & Etwire, P. M. (2022). 
Farmers’ response to COVID-19 disruptions: The 
case of  cropland allocation decision. Sustainable 
Futures, 4, 2666-1888. https://doi.org/10.1016/j.
sftr.2022.100088.

Mueller, J. T., McConnell, K., Burow, P. B., Pofahl, K., 
Merdjanoff, A. A., & Farrell, J. (2021). Impacts of  
the COVID-19 pandemic on rural America. Proc Natl 
Acad. Sci. USA, 118(1), 2019378118. https://doi.
org/10.1073/pnas.2019378118. 

Nigbur, D., Lyons, E., & Uzzell, D. (2010). Attitudes, 
norms, identity and environmental behavior: Using 
an expanded theory of  planned behavior to predict 
participation in a kerbside recycling programme. 
British Journal of  Social Psychology, 49, 259-284.

OIM (Organisation Internationale pour les Migrations). 
(2020). Evaluation Rapide De L’impact De La Pandemie 
Liee Au Covid-19 Sur La Cohesion Sociale En Côte 



Pa
ge

 
82

https://journals.e-palli.com/home/index.php/ajfst

Am. J. Food. Sci. Technol. 3(2) 75-82, 2024

D’Ivoire. Rapport.
Steg, L., & Nordlund, A. (2012). Models to explain 

environmental behaviour. In L. Steg, A. E. van 
den Berg, & J. I. M. de Groot (Eds.), Environmental 
psychology: An introduction (pp. 185-195). 

Wiley-Blackwell., Strobel, S., Danzi, B., Puumala, S., 
Kenyon, D., O’Connell, M., Wesner, C. (2020). 
Knowledge, Attitudes, and Needs: Assessing the 
COVID-19 Impact in Rural America. S. D. Med. 
73(11), 536-539.

Tabe-Ojong, Martin, P. J., Nshakira-Rukundo, E., & 
Gebrekidan, B. (2022). COVID-19 and food (in)
security in Africa: Review of  the emerging empirical 
evidence. IFPRI Discussion Paper 2121. Washington, 
DC: International Food Policy Research Institute. 

Tagne, N. A., Tachom W. B., Ngah E. H.C., & Mvessomba 
E. A. (2021). Perception du risque lié au COVID-19, 
intelligence émotionnelle et santé psychologique des 

soignants. European Journal of  Trauma & Dissociation, 
5(2), 100212. French. htpps://doi.org/10.1016/j.
ejtd.2021.100212. 

WHO (World Health Organization). (2020). Coronavirus 
Disease (COVID-19) Dashboard. https://covid19.who.
int/.

Zeufack, A. G., Calderon, C., Kambou, G., Djiofack, C. 
Z., Kubota, M., Korman, V., & Cantu Canales, C. 
(2020). Africa’s pulse, no. 21, spring 2020 : An analysis 
of  issues shaping africa’s economic future. World Bank, 
Washington, DC.

Zhang, Y., & Ma, Z. F. (2020). Impact of  the COVID-19 
pandemic on mental health and quality of  life among 
local residents in Liaoning Province, China : A cross-
sectional study. Int. J. Environ. Res. Public Health, 17, 
2381.


