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 American Journal of  
Food Science and Technology (AJFST)

Understanding Farmers’ Perceptions and Factors Determining the Adoption of  Good 
Agricultural Practices: Evidence from the Cashew Nut Production in Côte d’Ivoire

N. Z. Silué1*, C. C. Adassé1, R. Aboudou2, A. Assemien1

Volume 4 Issue 1, Year 2025
ISSN: 2834-0086 (Online)

DOI: https://doi.org/10.54536/ajfst.v4i1.4173
https://journals.e-palli.com/home/index.php/ajfst

Article Information ABSTRACT

Received: December 03, 2024

Accepted: January 08, 2025

Published: March 19, 2025

Côte d’Ivoire, the world’s leading cashew nut producer with 1,200,000 tons in 2022, faces 
low productivity of  raw cashew nuts (350-500 kg/ha) due to limited adoption of  Good 
Agricultural Practices (GAP). The objectives of  this study are to examine the relationship 
between producers’ perceptions and the decision to adopt good agricultural practices, and to 
determine the determinants of  the intensity of  adoption of  good agricultural practices. Probit 
and Poisson regression models were applied to data collected from 845 cashew producers in 
Cote d’Ivoire. The results show that training and access to extension services influences the 
adoption of  good agricultural practices. The average adoption scores showed that the most 
widely adopted good agricultural practices were plot preparation, direct seeding, formation 
pruning, firebreak strips, thinning, and pruning. Estimates from the probit model show 
that producer training on cashew technical itineraries, producer supervision by extension 
services, and producer perceptions are the main factors that determine the adoption of  good 
agricultural practices. The estimates of  the negative binomial model show that gender, level 
of  education, social origin, training, and access to extension services favor the adoption of  
several good agricultural practices. Policies should prioritize expanding training programs 
and strengthening extension services to enhance the adoption of  Good Agricultural 
Practices among cashew-nut producers and improve cashew production yield.

Keywords

Adoption, Cashew Nuts, Good 
Agricultural Practices, Perception

1 Institute National Polytechnique Houphouët Boigny, BP 1093 Yamoussoukro, Côte d’Ivoire
2  Africa Rice Center (AfricaRice), 01 BP 2551, Bouake 01, Côte d’Ivoire
* Corresponding author’s e-mail: nonlourouzie@yahoo.com

INTRODUCTION 
The global agricultural sector is facing increasing pressure 
to enhance productivity while ensuring environmental 
sustainability and resilience to climate change. In this 
context, the adoption of  good agricultural practices 
(GAPs) plays a crucial role in optimizing agricultural 
output and mitigating adverse environmental impacts. 
GAPs encompass various techniques and strategies aimed 
at improving soil health, water management, pest control, 
and farm management.
In contemporary agricultural discourse, the adoption of  
Good Agricultural Practices (GAPs) represents a pivotal 
strategy for enhancing productivity, sustainability, and 
socio-economic outcomes within the global agricultural 
sector. GAPs encompass a spectrum of  techniques, 
technologies, and management practices designed to 
optimize crop yields, minimize environmental impacts, 
and improve farmers’ livelihoods (FAO, 2019). The 
successful uptake of  GAPs among farmers is not only 
crucial for achieving food security goals but also for 
mitigating the challenges posed by climate change and 
fluctuating market demands (Ferraro et al., 2020).
Among various agricultural commodities, cashew nut 
(Anacardium occidentale L.) production has strategic 
significance for many developing countries, including 
Côte d’Ivoire, where it plays a pivotal role in national 
economic development and rural livelihoods (WFP, 
2018). As one of  the world’s largest producers of  cashew 
nuts, Côte d’Ivoire faces significant challenges such as 
fluctuating market prices, climate variability, and evolving 

consumer preferences. In this context, the adoption of  
GAPs in cashew nut production has emerged as a critical 
factor influencing both the economic viability of  farmers 
and the sustainable management of  natural resources 
(PNUD, 2021).
In recent years, global cashew nut production has 
witnessed substantial growth, positioning Côte d’Ivoire as 
its leading producer. The first cashew plantations in Côte 
d’Ivoire were established in the early 1959-1960s by the 
Société d’Assistance Technique pour la Modernization de 
l’Agriculture en Côte d’Ivoire (SATMACI) and Société 
de Développement des Forêts (SODEFOR) as part 
of  a program to protect the environment and combat 
erosion and deforestation (Ducroquet et al., 2017). A 
decade later, the embellishment of  raw cashew nut prices 
led to a craze among producers for the production of  
raw cashew nuts to the detriment of  forestry (Gouma, 
2003), with the creation of  the first plantations since 
1972 (Conseil Coton Anacarde, 2017). The cashew nut 
production zone will gradually expand from the savannah 
zone (Kone, 2014) southward around the 2000s, in 
cocoa-growing areas of  forest-savannah contact, cocoa 
loops (M’Bahiakro & Bouaflé), and around 2010 in some 
cocoa-growing areas (Bayota, Gagnoa) (Ruf  et al., 2019). 
However, massive adoption of  this crop has been carried 
out by the producers themselves, without any substantial 
technical or financial support from the state (Ruf  et al., 
2019).
Understanding farmers’ perceptions of  GAPs and the 
determinants influencing their adoption are essential 



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for designing effective agricultural policies, extension 
services, and capacity-building initiatives tailored to 
local contexts (Gómez-Limón et al., 2022). Despite 
the recognized benefits of  GAPs, adoption rates vary 
widely across regions and farming communities and are 
influenced by a complex interplay of  socioeconomic, 
institutional, and environmental factors.
This study sought to provide empirical insights into the 
factors shaping farmers’ adoption of  GAPs in cashew 
nut production in Côte d’Ivoire. By exploring farmers’ 
perceptions, motivations, barriers, and enabling factors 
related to GAP adoption, this study aimed to inform 
strategies that promote sustainable agricultural practices, 
enhance farmers’ resilience, and foster inclusive economic 
growth in the region.
The literature on Good Agricultural Practices (GAPs) 
often discusses their benefits for enhancing agricultural 
productivity and sustainability, yet there remains a notable 
gap regarding the specific determinants influencing 
farmers’ adoption of  GAPs in cashew nut production, 
particularly in the context of  Côte d’Ivoire. Existing studies 
have predominantly focused on staple crops or broader 
agricultural contexts, overlooking the unique challenges 
and opportunities within cashew nut farming, a crucial 
sector of  the Ivorian economy. This study aims to fill this 
gap by empirically examining the factors influencing GAP 
adoption among cashew nut farmers in Côte d’Ivoire, 
thereby contributing to a nuanced understanding of  how 
socioeconomic, institutional, and environmental factors 
interact in shaping adoption decisions. By elucidating 
farmers’ perceptions, motivations, and barriers related 
to GAP adoption, this study seeks to provide actionable 
insights for policymakers and development practitioners 
aiming to promote sustainable agricultural practices and 
enhance the resilience of  cashew nut farmers in the 
region.
Using a mixed-methods approach that combines 
qualitative interviews and quantitative surveys, this study 
endeavors to provide a nuanced understanding of  how 
GAP adoption can be effectively promoted among 
cashew nut farmers in Côte d’Ivoire. By identifying 
the key determinants and stakeholders involved in the 
adoption process, this study aims to offer actionable 
recommendations for policymakers, development 
practitioners, and agricultural stakeholders striving to 
enhance the sustainability and competitiveness of  cashew 
nut production in the global market.
There is abundant literature on the factors that determine 
producers’ adoption of  agricultural innovations. Several 
studies based on economic theories indicate that the 
determinants of  technology and innovation adoption 
are of  various kinds: socio-economic (education, 
income, age, attitude to risk, experience in agriculture), 
demographic (number of  people in the household, 
number of  assets), institutional (information, training, 
membership of  a producer organization), and technical, 
economic, and environmental (Adégbola & Gardebroek, 
2007). Moreover, among the most widely recognized 

determinants of  the adoption of  agricultural innovations, 
farmers’ perception of  the characteristics of  innovation 
proposed by extension services is of  prime importance 
(Adesina & Baidu-Forson, 1995). Indeed, several 
studies have demonstrated the influence of  producers’ 
perceptions of  the attributes of  proposed technologies 
(nutritional quality, yield, price, availability of  inputs, etc.) 
on their decision to adopt them (Adégbola & Gardebroek, 
2007).
The objectives of  this study were to examine the relative 
effects of  producers’ perceptions on the decision to adopt 
good agricultural practices and to determine the main 
factors in the adoption and intensity of  the adoption of  
good agricultural practices. 

LITERATURE REVIEW
Concept of  good agricultural practices
Coulibaly et al. (2019) stated that Good Agricultural 
Practices are an expression used by various organizations 
linked to agriculture, and that this term refers to a set 
of  rules to be respected (good practices) for establishing 
and developing crops to optimize agricultural production 
while reducing as far as possible the risks associated 
with such practices, both with regard to man and the 
environment. In its run-up to the World Summit on 
Sustainable Development, the UN (2002) stressed the 
importance of  sustainable agriculture for food security 
and resource management. FAO (2002) defines good 
agricultural practices as a set of  sustainable agricultural 
production systems that are socially viable, economically 
profitable, and productive, while protecting human and 
animal health and welfare and the environment. In this 
context, Good Agricultural Practices can be seen as those 
that contribute to sustainable agriculture. 
According to the FAO, Good Agricultural Practices are 
based on 11 guiding principles. These principles apply to 
both the entire production and value chain of  a plant or 
animal product on a given farm, and to the various sub-
components of  agriculture.
In Côte d’Ivoire, several agricultural innovations, 
including good farming practices for various crops (e.g., 
cocoa, cashew nuts), are being disseminated to producers 
by several extension organizations. As far as cashew nuts 
are concerned, twelve (12) themes (Table 1) that bring 
together all environmentally-friendly agricultural practices 
and ethical values in cashew nut production zones have 
been retained as part of  the framework agreement (2014-
2017) between the “Conseil du Coton et de l’anacarde” 
and ANADER. These practices involve both good 
production and post-harvest practices. The adoption and 
application of  these practices aims to improve cashew 
nut productivity and quality.
These themes were disseminated by ANADER through 
an agricultural advisory service dedicated to cashew 
nut growers, which provided information and training 
in villages, school fields, demonstration units, and plot 
visits. The dissemination of  these themes involves an 
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Table 1: Good production practices in cashew nut 
cultivation
N° Themes
1 Plot preparation 
2 Plant material acquisition when creating a new 

orchard 
3 Staking when creating a new orchard 
4 Digging and filling in when creating a new orchard 
5 Direct seeding when creating a new orchard 
6 Planting when creating a new orchard 
7 Fertilization during the planting stage 
8 Creation of  firebreaks around your orchard 
9 Replanting when creating a new orchard 
10 Shaping pruning 
11 Thinning 
12 Pruning 
Source: ANADER

dissemination, and a producer as the “advised agent” 
advised agent. Local radio stations are also used to 
broadcast information in the local languages.
Determinants of  the adoption of  agricultural innovations
Among the definitions proposed by several authors 
on innovation adoption, Rogers (1983) appears most 
frequently. He defines innovation as “an idea, practice, or 
object perceived as new by a person or unit of  adoption”. 
For him, one of  the characteristics of  technological 
innovations is that they are not automatically adopted 
once they appear, even if  they have superior qualities 
compared with older technologies. Rogers (2003) 
considered that the degree of  adoption is linked to the level 
of  diffusion, which is the process by which an innovation 
is communicated to members of  a social system through 
certain channels over time. Thus, adopting agricultural 
innovation means that it offers something more than the 
current practices.
Roussy et al. (2015) reveal that many works on innovation 
adoption behavior are based on the hypothesis that the 
overall perceived utility of  an innovation corresponds to 
the sum of  the utilities of  the characteristics making up 
that innovation, referring in this to the work of  Lancaster 
(1966). 
In addition, several theoretical models have been developed 
for technology adoption and choice since the work of  
Rogers (1983) to explain the behavior of  actors. These 
include i) the diffusion of  innovation model (Rogers, 
1983), ii) the technology acceptance model (Davis, 1989), 
iii) the theory of  interpersonal behavior (Triandis, 1980), 
and iv) the neoclassical theory of  rational expectations 
(Muth, 1961). Studies based on these theories indicate 
that the determinants of  technology and innovation 
adoption are of  various types: social, technical, economic 
and environmental.
Recent empirical studies have examined the adoption of  
good agricultural practices for cashew cultivation in Côte 

d’Ivoire. Ouattara (2017) identified the determinants of  
the adoption of  good pre-harvest cultivation practices for 
cashews. Using an unordered multinomial logit model, 
she shows that social variables, such as location and level 
of  education, favor the adoption of  good pre-harvest 
cashew production practices. Conversely, variables such 
as the age of  the farm manager and household size had a 
negative impact on the adoption of  certain good cashew-
growing practices. 
Coulibaly et al. (2019) examine the financial profitability 
of  adopting good production practices. They used the 
score method and budget. Analyses show that 54.7% of  
growers apply good production practices, which positively 
impact yield and income. However, their application is 
financially less profitable because it incurs higher costs.

Perceptions and adoption of  agricultural innovations
Since the seminal work of  Kivlin and Fliegel (1966a, 
b, 1967), many studies in agricultural economics have 
been conducted on the factors that influence producers’ 
perceptions and adoption of  new agricultural technologies. 
Perceptions are psychological factors that can influence an 
individual’s behavior. Sheth and Mittal (2004) stated that 
this is a process by which individuals select, organize, and 
interpret information. Their perceptions were subjective. 
Economists (Jones, 1989; Lin & Milon, 1993) who have 
worked on consumer demand have corroborated that 
consumers have subjective preferences for product 
characteristics and that their demand for products is 
significantly affected by their perception of  product 
attributes. In the context of  agriculture, the key attributes 
described in the literature include: i) compatibility, ii) 
complexity, iii) observability, transferability, iv) cost, 
profitability, v) risk and uncertainty, vi) trialability, and vii) 
relative advantage (Barroga, 2019).
Empirically, according to Rogers’ model (2003), the 
particular characteristics of  the technology and the way 
potential users perceive its value can explain between 
50% and 90% of  the variation in adoption rates. Similarly, 
Adesina and Baidu-Forson (1995) show that technology 
adoption by farmers reflects rational decision making 
based on farmers’ perception of  the relevance of  the 
characteristics of  the technologies studied through their 
work on adoption decisions of  improved rice varieties 
by Sierra Leonean farmers. Indeed, they showed that 
farmers’ adoption decisions depended mainly on their 
perceptions of  these new varieties. Specifically, factors 
such as cooking, yield, ease of  dehulling, and milling play 
decisive roles in the decision to adopt these rice varieties.

MATERIALS AND METHODS
Study area and sampling
The present study was conducted from October to 
November 2017 in the cashew nut production basin in 
Côte d’Ivoire, which covers 19 regions in the northern 
half  of  the country (Figure 1).
This methodological approach was based on a survey of  
raw cashew nut producers in various zones. The sample 



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targeted 1020 raw cashew nut producers in the cashew 
production zone, representing a sampling rate of  15% 
compared to the parent population of  149,600 producers. 
The final database covered 845 cashew nut growers 
throughout the production zone, regardless of  whether 
they were included in the training and monitoring project 

for good agricultural practices.
Data were collected using a questionnaire intended for 
cashew-nut growers. They covered sociodemographic 
information on growers, plot characteristics, growers’ 
perceptions of  good agricultural practices, and economic 
data.

Data analysis
Data were processed using STATA software and focused 
on the socioeconomic and demographic characteristics 
of  producers, technical characteristics of  cashew nut 
plots, producers’ perceptions, and institutional factors. 
Data analysis was performed using descriptive statistics 
(mean, standard deviation, percentile, etc.), correlation 
coefficients, score method, and two regression models. 
Two regression models were used: probit and Poisson.

The score method
The score method was used to assess attitudes towards 
adopting good farming practices. This method was 
implemented as follows: For each good practice, the 
farmer was asked to choose the level of  appreciation 
or application. Four modalities (not at all, little, 
moderately, and strongly) were submitted according to the 
manufacturer’s judgment.  The ratings were as follows: not 
at all, 1; somewhat = 2, moderately = 3, and strongly, 4.
The average score for each good practice was obtained 
by averaging the scores assigned to the theme by each 
producer. The average score was calculated as follows:
means score =  (∑scorei)/N                                        .....(1)
where i is the responding producer and N is the total 
number of  responding producers.
The average score for good practice was between 1 and 3. 
The more a theme was adopted, the higher the adoption 
score, which tended to be 4. A theme with a score of  one 
implies that it has not been adopted.

Probit model
Let Uij be the utility that producer i hopes to obtain 
using technique j, and let i = {1, 2,..., n}. The producer’s 
decision involves two mutually exclusive alternatives. The 
ith producer will use technique j if  Ui1 >Ui0 (1 for good 
farming practices and 0 for peasant practices). Anticipated 
profit (U*i) is an unobserved latent variable that depends 
on alternative choices and the farmer’s socioeconomic, 
demographic, and institutional characteristics (Xij). 
According to the probit model, if  the producer considers 
good farming practices to be more profitable, Ui*>0; 
otherwise, he continues with the technique he was 
using and Ui*≤ 0. With Ui* the unobservable latent 
variable associated with the adoption decision; Where 
Xij constitute a vector of  explanatory variables, 𝛽𝑖 the 
parameter vector and Ɛi the error term.
Ui=∑n

j=1 βi Xij+ εi; i=1,…,n; j=1,…,n                             .....(2)
  Ui=1 adoption of  good agricultural practice
  Ui=0  non-adoption of  good agricultural practice
The probability of  adopting a good practice is then equal 
to:
Pi = prob (ai=1) = ∑n

j=1 βi Xij+ εi>0                           .....(3)
Pi= F(∑n

j=1 βi Xij; i=1,…,n; j=1,…,n                             .....(4)
Where F is the normal distribution function.
F(x)=∫x

-∞ (1/(√2π)) e-t2/2) dt                                     .....(5)
The distribution function F follows a normal distribution, 
and The Z statistic is used to estimate various coefficients 
and parameters of  the equation.

Figure 1: Study survey areas
Source: Author based on survey data



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Poisson model
Poisson regression is estimated by considering the total 
number of  Good Agricultural Practices that can be adopted 
by a producer. Poisson regression is usually employed 
when the dependent variable is a count variable, which 
in this case is the sum of  the good agricultural practices 
adopted. The Poisson probability distribution is more 
appropriate than the normal distribution used in the probit 
model, or the logistic distribution used in the logit model. 
The probability density function is expressed as follows:
F(yi/xi) = P(Yi/yi) = (e-λλy)/y!, y= 0,1,2,3,4,5,...    .....(6)
Where yi is the total number of  good agricultural practices 
adopted by the farmer and xi  is the variables influencing 
the adoption process. The expected mean parameter (λ) 
of  the probability function is defined as 
E(yi/xi) = λi = exp(x’iβ) = exp(β0+β1x1i+β2 x2i+.....+βk xk

i+εi)                                                                                     .....(7)
This equation can be estimated using the maximum 
likelihood approach.
 
RESULTS AND DISCUSSION
Socio-demographic characteristics of  cashew nut 
growers and farm techniques
The descriptive statistics for the continuous and discrete 
categorical variables used in this study are presented in 
Table 2. The results showed that cashew-nut growers 
were mainly men (95%), with a marginal proportion of  
women (5%). The average age of  producers was 47 years, 
with a standard deviation of  11 years. The predominance 
of  men over women in cashew nut cultivation can be 
explained by the difficult access of  women to land, as 
noted by Koné (2011), who affirmed that traditionally, 
women rarely receive or inherit valuable land definitively 

with exclusivity and are excluded from the management 
rights of  lineage land heritage. Indeed, given its 
occupation of  space over several years and its economic 
interest, the cultivation of  cashew trees constitutes a cash 
crop owned mainly by men.
The average size of  cashew-growing households was 10 
members who could participate in the fieldwork. With 
regard to the education level of  cashew nut growers, 
the results showed that more than a majority of  growers 
(63%) were illiterate. In terms of  social status, the results 
showed that the majority of  cashew nut growers were 
natives (82%), with a small proportion being non-natives 
(8%) and allochtones (10%). The most likely explanation 
is that non-native and non-native faces, in the same way 
as women, have difficult access to land capital. Indeed, 
the majority of  non-natives and non-natives had access 
to land for the establishment of  cashew plots through 
purchase or rental. In terms of  marital status, the 
majority of  cashew growers lived as couples (64%), either 
cohabiting or being married. The results of  the study 
showed that the majority of  cashew growers acquired 
their plots by inheritance (54%), compared with 21% of  
growers who created their own farms and owned their 
plots. On average, producers own around 1.5 plots per 
farming household. The average size of  cashew plots 
was 4.2 ha. These plots were 17 years old on average; 
therefore, cashew orchards tended to age. These plots 
were located approximately 4 km from producers’ homes. 
Within the framework of  the training program dedicated 
to producers, the study revealed that 79% of  producers 
have been trained, sensitized, or informed about good 
cashew-growing practices, compared to 56% who have 
received advisory support from supervisory services.

Table 2: Producer socio-economic characteristics and cashew nut farm techniques
Variables  Obs  Mean  Std. Dev.
Socio-demographics
Sex (Female=0; Male=1) 845 0.948 0.222
Producer age (year) 845 46.807 11.182
Household size 845 10.372 5.499
Literate 845 0.370 0.483
Social status (0=native; 1=native;) 845 0.820 0.384
Marital status (1=in couple; 0=not in couple) 845 0.645 0.479
Cashew nut plot
Inheritance mode (1=inheritance; 0=other) 845 0.544 0.498
Ownership mode (1=owner; 0=other) 845 0.214 0.411
Purchase mode 845 0.044 0.205
Rental of  plot 845 0.031 0.173
Number of  plots 845 1.498 0.667
Plot age (year) 845 16.843 6.805
Production area (ha) 845 4.172 3.205
Plot distance (km) 845 3.507 2.597
Institutional



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GAP training 845 0.796 0.403
Contact with extension agent 845 0.563 0.496
Source: Author based on survey data

Cashew nut growers’ perception of  good agricultural 
practices
The mean rank of  growers’ perceptions of  each good 
agricultural practice was calculated and the rank was 
determined using Kendall’s coefficient of  agreement 
(Table 3). Among the various good agricultural practices, 

Table 3: Kendal’s w test on farmers’ perception of  good agricultural practices
Good agricultural practices Mean rank Rank
Pruning 5,40 1
Thinning 5,57 2
Making firebreaks around your orchard 5,89 3
Plot preparation 6,15 4
Acquiring plant material when creating a new orchard 6,58 5
Training pruning 6,59 6
Direct seeding when creating a new orchard 6,81 7
Staking when creating a new orchard 6,81 8
Planting when creating a new orchard 6,93 9
Fertilization during the planting stage 6,95 10
Digging and filling when creating a new orchard 7,03 11
Regarnishing when creating a new orchard 7,29 12
W de Kendalla 0,076 ***
Khi-deux 705,706
ddl 11
Source: Author based on survey data

Table 4: Level of  adoption of  good farming practices by producers
Good agricultural practices Not at all Little Moderately Very much Mean score
Plot preparation 411 78 114 242 2,221
Acquiring plant material when creating a new 
orchard

517 102 122 104 1,779

Staking when creating a new orchard 559 48 100 138 1,783
Digging and filling in when creating a new orchard 578 59 90 118 1,702
Direct seeding when creating a new orchard 473 57 127 188 2,036
Planting when creating a new orchard 567 62 103 113 1,718
Fertilization during the planting stage 621 55 64 105 1,589
Laying firebreaks around your orchard 398 42 79 326 2,394

Kendall’s test revealed that pruning is the first and 
most important in terms of  growers’ perceptions, 
which promotes improved yield and nut quality. This 
was followed by thinning and fire banding around the 
orchards. Practices such as fertilization, digging, and 
relining are in the last position.

Frequency of  adoption of  good agricultural practices 
by producers
The level of  adoption of  each good agricultural practice, 
based on the average score, is presented in Table 5. 
This study revealed that growers preferentially adopted 
tree branch pruning, thinning, and firebreak creation 
to protect their orchards, soil preparation, semi-direct 
seeding, and formation pruning, for which the level 
of  adoption is high. However, they had a low level of  

adoption for the acquisition of  planting materials, staking, 
digging, planting, fertilization, and replanting.
Although descriptive, these analyses have the advantage 
of  being easy to understand. Moreover, they constitute 
a first step towards a more in-depth study of  the 
implications of  agricultural practices and socio-economic 
characteristics on the perception of  the adoption of  good 
cashew production practices.



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Replanting when creating a new orchard 532 91 94 128 1,785
Shaping pruning 422 104 129 190 2,103
Thinning 326 99 135 285 2,449
Pruning 325 81 130 309 2,501
Note: Average weight = 2.005

Multi-collinearity test results
Multicollinearity tests were performed for all econometric 
models. For each model, the partial correlation test 
indicated a correlation between explanatory and dependent 
variables. Similarly, a correlation exists between the 
model’s explanatory variables. This led to multicollinearity. 
To solve this problem (Nana & Thiombiano, 2018) cite 
several authors such as Asfaw et al. (2012); Cahuzac 
and Bontemps (2008), who recommend calculating the 
variance inflation factor (VIF). According to Chatterjee et 
al. (2000), a multicollinearity problem is identified when 
the VIF has a value greater than or equal to 10 and/or 
when the mean of  the VIFs is greater than or equal to 2. 
Conversely, some authors (Allison) indicated VIF values 
of  4, 5, and 10 before raising concerns. On the other 
hand, if  none of  these two values is reached, the impact 
of  multicollinearity is not, according to these authors, 
cause for concern, and all the explanatory variables 
can therefore be retained for the analysis, the latter not 
being redhibitory “distorted” by the existing level of  
multicollinearity. The VIF calculation in this study shows 
that multicollinearity for each model is not a problem 
in the estimation. In fact, all VIFs are less than five, the 
inverse of  the VIFs of  each variable is less than one, and 
the mean of  the VIFs is less than two. Consequently, 
we conclude that there is no multicollinearity between 
explanatory variables.

Presentation and interpretation of  probit estimation 
results
The results of  the maximum likelihood and marginal 
effects probit estimations are presented in Tables 6, 7, 
8, and 9. The critical probabilities associated with the 
various likelihood ratio statistics are 0.000 significant at 
the 1% level. The various models are, therefore, highly 
significant overall, and the variables considered as a 
whole are also significant. Relationships exist between the 
explanatory variables and the variables that explain the 
adoption of  each good farming practice. In dichotomous 
models, coefficients cannot be interpreted directly. We 
can only suggest that a positive coefficient increases the 
probability, and vice versa for a negative coefficient.

Statistical interpretation of  estimated parameters
Tables 5 on the estimated parameters of  the probit 
model for each good agricultural practice show that all 
(independent) explanatory variables sometimes show 
statistically significant differences for some good practices 
and not for others. Similarly, the coefficients of  the same 
variable have positive signs for some good farming 
practices, and negative signs for others. However, certain 
variables, such as farmers’ training in good agricultural 
practices, farmers’ access to extension services, and all 
the variables relating to farmers’ perceptions of  good 
agricultural practices, showed statistically significant 
and positive differences in the adoption of  the 12 good 
agricultural practices.

Table 5: Estimation of  probit model parameters on factors determining adoption of  each good production practice
Variables Plot preparation Plant material Staking Digging

 & filling
Direct seeding Planting

Sex 0.264 0.268 0.798*** 0.689** 0.251 1.042***
(0.232) (0.254) (0.297) (0.297) (0.240) (0.321)

Producer age 0.009* 0.005 0.013** 0.011** 0.007 0.015***
(0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

Household size -0.017* -0.005 0.005 0.006 -0.004 -0.002
(0.010) (0.011) (0.010) (0.010) (0.010) (0.010)

Literate 0.029 0.181 0.113 0.021 0.151 0.111
(0.107) (0.110) (0.110) (0.110) (0.108) (0.108)

Social status 0.001 0.013 0.130 0.042 0.153 0.064
(0.146) (0.152) (0.149) (0.152) (0.147) (0.150)

Marital status -0.004 -0.132 0.119 0.024 -0.039 -0.141
(0.108) (0.115) (0.116) (0.116) (0.113) (0.114)

Inheritance 
mode

-0.112 -0.178 -0.438*** -0.540*** -0.204 -0.375***
(0.145) (0.146) (0.145) (0.142) (0.145) (0.143)



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Ownership 
mode

-0.767*** -0.744*** -0.744*** -0.779*** -0.967*** -0.745***
(0.168) (0.175) (0.173) (0.171) (0.173) (0.171)

Purchase mode -0.385 -0.097 -0.460 -0.608** -0.514* -0.395
(0.289) (0.302) (0.305) (0.303) (0.289) (0.292)

Rental of  plot -0.305 -0.994** -1.022** -1.015** -0.995** -0.915**
(0.364) (0.479) (0.478) (0.465) (0.478) (0.465)

Number of  
plots

-0.070 -0.028 -0.067 0.011 0.002 -0.099
(0.082) (0.084) (0.085) (0.083) (0.083) (0.083)

Plot age -0.011 -0.020** -0.039*** -0.028*** -0.018** -0.024***
(0.008) (0.009) (0.009) (0.008) (0.008) (0.008)

Production area 0.020 0.042** 0.042** 0.022 0.025 0.005
(0.018) (0.019) (0.018) (0.018) (0.018) (0.018)

Plot distance -0.020 -0.046** -0.016 -0.013 -0.030 -0.039*
(0.020) (0.022) (0.021) (0.021) (0.021) (0.021)

GAP training 0.708*** 0.479** 0.508** 0.445** 0.758*** 0.683***
(0.160) (0.187) (0.199) (0.198) (0.176) (0.198)

Contact with 
extension agent

0.679*** 0.890*** 0.892*** 0.840*** 0.582*** 0.686***
(0.116) (0.123) (0.126) (0.129) (0.118) (0.122)

Perception of  
plot preparation

1.126*** NA NA NA NA NA
(0.114)

Perception of  
plant material

NA 1.292*** NA NA NA NA
(0.120)

Perception of  
staking

NA NA 1.093*** NA NA NA
(0.122)

Perception of  
digging 

NA NA NA 1.042*** NA NA
(0.120)

Perception of  
direct sowing

NA NA NA NA 1.239*** NA
(0.114)

Perception of  
planting

NA NA NA NA NA 0.991***
(0.116)

Constant -1.642*** -1.883*** -2.747*** -2.475*** -1.936*** -2.743***
(0.402) (0.433) (0.472) (0.467) (0.416) (0.487)

NA: Not applicable
Source: Author, based on survey data.
*** significant at 1%; ** significant at 5%; *significant at 10%.

Regarding the determinants affecting the adoption of  all 
GAPs, the results of  the probit model showed that the 
variables training, coaching, and producers’ perceptions 
had positive and significant relationships with the 
adoption of  all (12) GAPs, with marginal effects ranging 
from 11.3% to 33.6%. With regard to the training and 
coaching of  producers on good agricultural practices, 
the results show that the majority of  producers (80%) 
were informed, sensitized, or trained on good production 
practices, and 56% were coached by agricultural advisory 
services.
As part of  the implementation of  the agricultural 
advisory program dedicated to cashew nut growers, 
the many information dissemination channels used 
(supervisory structures, NGOs, CCA, radio, etc.) have 

made it possible to inform many growers on cashew nut-
related topics. Similarly, ANADER’s training courses on 
good production practices through farmers’ field schools 
and visits to plots by agricultural advisors have enhanced 
growers’ knowledge and interest in these practices. These 
results are similar to those of  Roussy et al. (2015), who 
argue in their work that the informational context, both 
formal (advisor visits) and informal (producer networks), 
affects the adoption of  innovations. These results suggest 
that information campaigns and producers’ technical 
and cognitive capacities must be further strengthened 
to better support them in the process of  adopting and 
appropriating good production practices. Regarding 
producers’ perceptions, the results suggest that the more 
cashew producers believe that good agricultural practices 



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improve tree yield and nut quality, the more willing they 
are to adopt these practices. 
Indeed, Djaha et al. (2012) noted that high plantation 
densities are among the constraints at the root of  low 
yields observed in cashew production zones. Indeed, all 
good practices have been indicated to sustainably increase 
yields. This result confirms our basic hypothesis that a 
producer’s perception of  a good production practice 
improves its probability of  adoption. Our results concur 
with those of  Adesina and Zinnah (1993), who showed 
that Sierra Leonean farmers’ decisions to adopt improved 
rice varieties largely depend on their perceptions of  new 
varieties. The assumption that farmers’ perceptions of  
their own capacity to adapt and change are a determining 
factor in the choice of  agricultural practices (Hyland et al. 
2016; Talanow et al., 2021; Topp et al., 2023). Consequently, 
to facilitate the adoption of  good production practices, 
emphasis should be placed on the benefits. To this end, 
practical demonstration sessions in farmers’ school fields 
between demonstration plots and farmers’ plots can be 
organized to assess the yield and quality of  raw cashew 
nuts. This study is also in line with the findings of  Niambe 
et al. (2024), who provide insights into the socioeconomic 
factors affecting the adoption of  sustainable practices in 
Nigeria.

Factors determining the intensity of  adoption of  
good farming practices
Frequency of  intensity of  adoption of  Good 
Agricultural Practices
Among the 12 GAP, cashew nut growers can adopt 
different sums of  good agricultural practices, referred to 
here as adoption intensity, ranging from 0 to 12 (Table 
10). This study revealed that producers adopt an average 
of  five GAP at a time. In terms of  proportion, the study 
shows that 28% of  cashew growers have not adopted 
any of  the 12 Good Agricultural Practices, preferring 
traditional farming practices. On the other hand, 
approximately 19% of  growers have adopted all twelve 
(12) recommended agricultural practices for the better 
management of  cashew plots. Between these extremes, 
the proportion of  growers with adoption intensities 
ranging from 1 to 11 fluctuated between 3.2% and 6.86%. 
The results also show that more than half  of  the growers 
(53%) did not adopt the entire technological package of  
good agricultural practices, but rather made partial or 
sequential choices of  the package of  good agricultural 
practices they applied to the plots. Similar results were 
reported by Roussy et al. (2015). Indeed, several factors 
can explain producers’ sequential adoption behavior 
of  technological packages, including fixed costs, credit 
constraints, risk, and uncertainty (Aldana et al., 2011).

Figure 2: Intensity of  adoption of  good farming practices
Source: Author based on survey data

Test for normality of  independent variable
The Skewness and kurtosis test shows that the “Intensity_
Adopt” dependent variable does not follow a normal 
distribution. The Skewness p-value was less than 0.05; 
therefore, we rejected the H0 hypothesis, and the data did 
not have a normal distribution.

Multicollinearity test between variables
Before proceeding with multivariate analysis, it is essential 
to examine the correlations of  the explanatory variables to 
detect any multicollinearity that may bias the conclusions 

of  the analysis.
Examination of  the correlation matrix shows that 
there is no strongly elevated level of  correlation that 
requires corrective actions. The correlation coefficients 
ranged from − -0.571 to a maximum of  0.509. All these 
correlation coefficients are below the 0.8 threshold 
and therefore, do not reveal the presence of  a serious 
multicollinearity problem.

Detecting over-dispersion
The fish model is the most widely used probabilistic 



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framework for the analysis of  count data; however, this 
model is only appropriate if  the mean of  the count variable 
is equal to its variance E(y) = Var (y). If  this hypothesis is 
not verified, the parameters estimated using the maximum 
likelihood method will be biased. In this case, an 
alternative counting model that considers overdispersion 
(negative binomial model) is indispensable. The ratio 
of  the variance to the mean of  the dependent variable 
estimated for the 845 producers in the sample, with a 
value of  4.24, favored an over-dispersion of  observations 
in relation to the Poisson distribution hypothesis.

Model estimation using poisson and negative 
binomial regression
The over-dispersion test performed after estimating the 
regression of  the Poisson model (Table 6) shows a highly 
significant over-dispersion (p-value < 0.05), allowing us 
to reject the null hypothesis of  equality of  the mean and 
variance of  the “Intensity Adop” variable. Therefore, it is 
necessary to use a negative binomial model.
The estimates of  the Poisson and negative binomial 
models (Table 6) confirm the superiority of  the negative 
binomial model. The likelihood ratio test (Prob ≥ chibar2 
= 0.000) led to the choice of  a negative binomial model.
The Poisson model is a nonlinear model, and Incidence 
Rate Ratios (IRR) were used to interpret the coefficients 
obtained after regression. An incidence rate ratio of  less 
than 1 (IRR < 1) means that an adopter is less likely 
to adopt several good agricultural practices, while an 
incidence rate ratio of  greater than or equal to 1 (IRR ≥ 
1) means that an adopter is more likely to adopt several 
good agricultural practices.
The results of  the Poisson regression model show that the 
gender of  the producer, level of  education, social status, 
marital status, and training and supervision of  producers 
in good agricultural practices have positive effects on the 
intensity of  adoption of  good agricultural practices within 
the population studied. On the other hand, the mode 
of  acquisition of  cashew plots (inheritance, ownership, 
rental) and the number of  cashew plots owned by the 
producer had a reducing effect on the intensity of  
adoption of  good agricultural practices. 
The marital status of  couples positively influenced the 
intensity of  the adoption of  good farming practices. This 
result is similar to that of  Sale et al. (2014), who noted 
that married farmers have children who serve as their 
labor force for various agricultural tasks. This marital 
status determines the farmer’s needs and expenses, 
and conditions his decisions to improve agricultural 
productivity.
The incidence rate ratio associated with the sex variable 
was greater than 1 (1.439 > 1) and was statistically 
significant at the 1% threshold. This means that the 
intensity of  the adoption of  good agricultural practices 
among men was 1.439 times higher than that among 
women. The incidence rate ratio associated with the 
literacy variable was greater than 1 (1.206> 1) and was 
statistically significant at the 1% threshold. This means 

that the intensity of  the adoption of  good farming 
practices among literate farmers is 1.206 times higher than 
that among non-literate farmers. This result confirms 
the importance of  producers’ level of  education in the 
adoption of  good farming practices. Male producers, 
who comprise 95% of  cashew nut growers, have a higher 
adoption intensity than female producers. This result is 
consistent with several studies that have shown a positive 
correlation between decisions to adopt innovation 
and education level in sub-Saharan Africa (Kebede et 
al., 1990). Indeed, a high level of  education influences 
the attitudes and thoughts of  producers, making them 
more open, rational, and able to analyze the benefits of  
good production practices. Education facilitates critical 
thinking and the effective use of  information received 
by producers (Dissanayake et al., 2022). Thus, literate 
producers understand their interest in adopting the entire 
technological package of  good agricultural practices, 
which impacts the yield and quality of  raw cashew nuts. 
This implies that actions to strengthen the capacities of  
current cashew producers in functional literacy will have 
to be carried out to improve their level of  education and 
to make them aware of  educating future generations of  
producers, with the aim of  improving their decision to 
adopt good production practices.
The incidence rate ratio associated with the social status 
variable was greater than 1 (1.233> 1) and statistically 
significant at the 1% threshold. This means that the 
intensity of  adoption of  good farming practices among 
indigenous producers is 1.233 times higher than that 
of  allogeneic or allochthonous producers. This result 
shows that origin has a positive influence on producers’ 
adoption behavior. This result can be explained by the 
fact that indigenous producers are more likely to take part 
in training courses (75%) on good farming practices and 
receive coaching (80%) from extension agents, as well as 
by their access to land (Nkamleu & Coulibaly, 2000). 
The incidence rate ratio associated with the training 
variable was greater than 1 (3.813> 1) and statistically 
significant at the 1% threshold. This means that the 
intensity of  the adoption of  good farming practices 
among producers who have been trained in good 
farming practices is 3.813 times higher than that among 
producers who have not been trained. The incidence rate 
ratio associated with access to advisory support from 
extension services is greater than 1 (3.813> 1) and is 
statistically significant at the 1% threshold. This means 
that the intensity of  adoption of  good farming practices 
among producers who receive support from extension 
services on good farming practices is 3.813 times higher 
than that of  producers who receive no support.
Conversely, the incidence rate ratios associated with 
the plot acquisition mode variable are less than 1 and 
statistically significant at the 1% threshold for the owner 
and lease modes, and 10% for the inheritance mode. 
These results show that these modes of  access to plots 
by producers have a reducing effect on the intensity of  
adoption of  good farming practices.



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Table 6: Parameter estimates for Poisson and negative binomial models of  intensity of  adoption of  good farming 
practices
VARIABLES Poisson regression Negative binomial regression

IRR dydx IRR dydx
Sex 1.586*** 2.408*** 1.439** 1.923*

(0.150) (0.496) (0.251) (0.924)
Producer age 1.002 0.010 1.001 0.003

(0.001) (0.008) (0.003) (0.017)
Household size 1.004 0.023 1.004 0.019

(0.003) (0.016) (0.007) (0.036)
Literate 1.136*** 0.664*** 1.206** 0.991**

(0.036) (0.166) (0.085) (0.378)
Social status 1.198*** 0.942*** 1.233** 1.108*

(0.055) (0.241) (0.118) (0.511)
Marital status 1.062* 0.316 1.143* 0.707

(0.036) (0.177) (0.085) (0.396)
Inheritance mode 0.844*** -0.882*** 0.854* -0.833

(0.033) (0.205) (0.079) (0.494)
Ownership mode 0.647*** -2.269*** 0.697*** -1.904**

(0.032) (0.262) (0.078) (0.596)
Purchase mode 0.935 -0.352 1.029 0.149

(0.082) (0.459) (0.199) (1.019)
Rental of  plot 0.276*** -6.717*** 0.232*** -7.716***

(0.053) (1.006) (0.067) (1.579)
Number of  plots 0.96* -0.210 0.904* -0.531

(0.023) (0.127) (0.049) (0.291)
Plot age 0.991*** -0.048*** 0.995 -0.026

(0.002) (0.013) (0.005) (0.028)
Production area 1.001 0.006 0.996 -0.022

(0.005) (0.028) (0.012) (0.061)
Plot distance 0.992 -0.043 0.998 -0.013

(0.006) (0.033) (0.014) (0.073)
GAP training 3.416*** 6.412*** 3.813*** 7.069***

(0.281) (0.440) (0.443) (0.695)
Contact with extension agent 2.073*** 3.805*** 2.107*** 3.935***

(0.083) (0.217) (0.163) (0.462)
Constant 0.715** 0.69

(0.107) (0.195)
Observations
Prob > chi2       =
Chi-square        =
Pseudo R2        =

845
0.000
1593.600
0.240

845
0.000
355.338
0.077

Source: Author based on survey data

CONCLUSION
The objectives of  this study were to examine the 
relationships between producers’ perceptions and the 
decision to adopt good agricultural practices and to 
determine the main factors in the intensity of  adoption 
of  good agricultural practices in the context of  improving 

cashew nut farm productivity. To this end, a probit 
model and negative binomial regression model were 
implemented using data collected from 845 producers. 
The results showed that in terms of  growers’ perceptions, 
certain good agricultural practices, such as pruning, 
thinning, and firebreaks, were the most important for 



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improving the yield and quality of  raw cashew nuts. 
Good agricultural practices are among those with a high 
level of  adoption. This link is confirmed by the results of  
the probit model, which showed that training, coaching, 
and producers’ perceptions were the main factors 
determining the probability of  all (12) good farming 
practices. An important finding of  this study is that 
more than half  of  cashew growers adopt among some 
growers do not adopt the entire technological package 
of  good agricultural practices, but rather make partial 
or sequential choices. On the other hand, the mode of  
acquisition of  cashew nut plots (inheritance, ownership, 
rental) and the number of  cashew nut plots owned by 
the producer reduced the intensity of  the adoption of  
good agricultural practices. These results suggest that 
industry management actors should initiate programs to 
strengthen producers’ technical capacities and provide 
incentives for the adoption of  good agricultural practices 
to raise cashew nut plot productivity. In practice, this 
can be achieved through information campaigns and 
practical demonstration sessions in farmers’ school 
fields (technical and cognitive capacity-building). In-situ 
demonstration sessions will help improve cashew-nut 
growers’ perceptions.

Policy Implication
Based on the findings that highlight the significant factors 
influencing the adoption of  Good Agricultural Practices 
(GAPs) in cashew nut production in Côte d’Ivoire, several 
policy recommendations can be formulated to enhance 
adoption rates and promote sustainable agricultural 
practices. First, it is crucial to prioritize and expand 
training programs tailored to the cashew technical itinerary 
to ensure that producers have access to comprehensive 
knowledge and skills necessary for the effective 
implementation of  GAPs, such as plot preparation, direct 
seeding, and pruning techniques. Second, strengthening 
and expanding extension services, focusing on regular 
supervision and advisory support for farmers, is 
imperative. This can enhance technical assistance and 
guidance, which are pivotal in overcoming the barriers 
to adoption identified in this study, particularly for 
practices such as formation pruning and firebreak strips. 
Additionally, addressing socioeconomic factors, such as 
sex disparities, educational levels, and social backgrounds, 
through targeted interventions and incentives can further 
incentivize adoption. Policy efforts should also aim to 
improve access to the resources and inputs necessary 
for GAP implementation, thereby fostering a conducive 
environment for sustainable agricultural development in 
the cashew sector in Côte d’Ivoire.
 
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