Adoption factors of digitalization in cotton farming in the municipality of Banikoara in Northwestern Benin

Adoption factors of digitalization in cotton farming in the municipality of Banikoara in Northwestern Benin | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Adoption factors of digitalization in cotton farming in the municipality of Banikoara in Northwestern Benin Saddik ALIDOU, Adoté Hervé Gildas AKUESON, Afouda Jacob YABI, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3834485/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The widespread use of mobile telephony and the internet constitutes an asset for digitalization in the agricultural sectors in Africa. Cotton being the leading export crop in Benin, this study aims to understand the determinants of the adoption of digitalization (use of digital machines, membership of a social network for information exchange, use of a mobile money account) by cotton producers in the Municipality of Banikoara. In this context, a socio-economic survey was carried out with a sample of 314 producers, obtained by purposive sampling. The binary logistic regression method made it possible to identify the factors affecting the adoption of digitalization. Thus, the level of banking, the use of labor, the area of cotton sown and knowledge of agricultural information and exchange platforms had a significant impact on the use of digital machines in agriculture. Membership of a social network for information exchange between producers was influenced by the level of banking, the type of cotton grown, strengthening of relationships with others, the risk linked to the use of digitalization and the assessment of the level of security of digitization by the producer. Finally, the level of banking, the exercise of a secondary activity and the use of labor were the significant variables in the adoption of mobile money by cotton farmers in Banikoara. However, the use of more advanced technologies such as drones and sensors was not yet a reality for these producers. This information is very useful for any project to popularize these new technologies. Adoption digitalization cotton producers Benin Figures Figure 1 Figure 2 Introduction The use of information and communication technologies (ICT) has become obvious with the widespread use of mobile communication and various internet services for financing agricultural activities. It makes it possible to communicate and make available agricultural information on production, marketing and better access to State services and partners on agricultural value chains. A study conducted by the Technical Center for Agricultural Cooperation (CTA) and Dalberg Advisors noted that 33 million small producers on the African continent are registered in nearly 400 digital solutions, with an annual growth of 45% in the number of registrations since 2012 [ 1 ]. The same study, however, noted that more than 90% of the digital services market for African farmers remains untapped. The introduction of this new technology in the agricultural sector involves the use of digital technological tools such as the geolocation system, meteorological monitoring, the use of surveillance cameras in the fields, sensors to inspect and collect data (temperature, humidity, etc.), the use of drones and other automated machines. Mobile telephony and the Internet, followed by the use of mobile financial services from mobile phones by the agricultural world, constitute the basis of agricultural digitalization in developing countries. Sub-Saharan Africa is adopting and adapting the latest digital technologies massively and at increasing speed [ 2 – 3 ]. In Ivory Coast, for example, the start-up WeFly Agri provides technologies using drones to allow farm and plantation owners to monitor and manage their land remotely [ 4 ]. Remote monitoring of greenhouses allows small Kenyan producers to irrigate their crops without being present on site and improve their quality of life [ 5 ]. In Benin, the Internet is increasingly widespread [ 6 ] and its penetration has been growing in recent years. It increased from 47.79% in 2018 to 52.83% in 2019 then to 69.36% in 2020 [ 7 ]. As for the penetration rate of mobile financial services, it also increased from 23.46% in 2018 to 30.63% in 2019 then to 42.99% in 2020 [ 7 ]. Despite the wide GSM coverage over the country, family farmers in Benin are poorly connected to the Internet, i.e., 5% of social networks (WhatsApp, Facebook, Instagram), and 3% for information on agriculture [ 8 ]. For example, the use of drones by family farmers is currently absent, particularly in the Plateau department in South-East Benin [ 8 ]. In this context, for a better penetration policy of these technologies in the agricultural sector, it is important to identify the factors which contribute to its adoption. Thus, understanding the essential variables that could accelerate or hinder the adoption of these technologies has become an important concern. This study will focus on cotton production in Benin, which represents its main export product. Indeed, due to its contribution in terms of the total value of exports of agricultural products (84.8%) and the value of exports of all products combined (61.0%) in 2021 [ 9 ], the cotton sector constitutes the basis of the economy in Benin. As such, cotton occupies a special place in agricultural policy, making it the most organized of all agricultural sectors. With this support, production has experienced an exponential evolution in recent years, particularly during the 2017–2018 campaign, even reaching a growth of 122% in volume and an increase of 74% in the areas sown over the last two seasons [ 10 ]. Culture therefore has increased its added value in the transformation value chain. Thus, this study aims to understand the determinants that can influence the adoption of digital technology and the possession of a mobile account among cotton producers for a better policy of penetration of new information and communication technologies. It is based on the Unified Model of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. [ 11 ]. This model combines the factors that can influence individuals in their intentions to adopt and use technologies, in various environments [ 12 ]. It arises from the evolution of the Technological Acceptance Model (TAM) model developed by Davis et al. [ 13 ], with the consideration of new factors. Material and methods Study area The Municipality of Banikoara was selected for this study. It is in fact the first cotton production commune in Benin and its economy is mainly based on agriculture. Cereal production is also developed there like other legumes. It is located in the North-West of Benin with a Sudano-Sahelian climate which covers an area of 4,397.2 km2 including approximately 49% arable land and 50% protected areas [ 14 ]. This commune is located in the Alibori department, between 2°05’ and 2°46’ east longitude and between 11°02’ and 11°34’ north latitude. It is limited to the north by the commune of Karimama, to the south by the communes of Kérou and Gogounou, to the east by the commune of Kandi and to the west by Burkina-Faso. Its population is estimated at 246,575 inhabitants including 124,130 women (50.3%) in 2013 [ 15 ]. It has ten districts: Banikoara, Founougo, Gomparou, Goumori, Kokey, Kokiborou, Ounet, Somperoukou, Soroko, Toura. Study framework The acceptance and use of information system (IS) and information technology (IT) innovations has been a major concern for research and practice [ 16 ]. Over the last several decades, a plethora of theoretical models have been proposed and used to examine IS/IT acceptance and usage [ 16 ]. These include the Theory of Reasoned Action, the Technology Acceptance Model, the Theory of Planned Behaviour, and Model of Personal Computer Utilization [ 13 , 17 – 20 ]. Many of these theoretical models, developed to explain and predict the behavior of individuals with regard to the use of information and communication technologies, have referred to theories based on research in social psychology [ 21 ]. Based on a comprehensive review and synthesis of several theoretical models, Venkatesh et al. [ 11 ] proposed the Unified Theory of Acceptance and Use of Technology (UTAUT), which has since been used extensively by researchers in their quest to explain IS/IT acceptance and use. The choice of the UTAUT model developed by Venkatesh et al. [ 11 ] in the context of this research is justified to the extent that the latter has the advantage of being a general model of all the theoretical models that have been developed to explain the adoption behavior of humans. In addition, taking into account the moderating variables (age, gender, level of education, marital status, banking, income, variety of cotton cultivated and the main speculation practiced by the cotton farmer) further justifies this choice. Four basic variables define the Venkatesh et al. [ 11 ] model, namely, perceived ease of use, perceived usefulness, social influences and facilitating conditions. Perceived ease of use refers to the degree of ease associated with using the system. Perceived usefulness is defined as the degree to which a person believes that using the system will help them achieve gains in job performance. Social influence is the degree to which a person perceives that significant others believe that he or she should use the new system. Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system. In their model, Venkatesh et al. [ 11 ] integrated moderating variables (gender, age, experience and willingness to use) which have an influence on the explanatory variables. However, this model has evolved. Venkatesh et al. [ 22 ] proposed improvements to the first model (UTAUT 1) by adding other explanatory variables such as hedonic motivation, price value and habit. The determinants of the adoption of the unified theory of acceptance and use of technology (UTAUT 1) are summarized in Fig. 1 . As for the research analysis model, inspired by UTAUT, it is presented in Fig. 2 . Source : Venkatesh et al. , [ 11 ] Unified Theory of Technology Acceptance and Use Model Structural analysis model used Data collection The research combined a quantitative and qualitative data set. Data collection from producers was done using a questionnaire administered to each participant during February and March 2023. The questions take into account the model used (Fig. 2 ). The choice of producers was made through purposive sampling taking into account, among others, conventional cotton producers, organic cotton producers, those combining the two types of production, gender, level of education, ethnicity and other criteria (Table 1 ). Thus, a total of three hundred and fourteen (314) cotton producers distributed in the ten districts of the Municipality of Banikoara were selected (Table 2 ). In the sample, women represent 19% and producers practicing only conventional cotton constitute 94%. The numbers in each category in the sample took into account the proportions obtained for each category during the 2021–2022 census of cotton farmers. Table 1 Variables used and their modality Category Variables Modalities Variables to explain Use of digital machines in agriculture Yes ; No Membership of a social network for information exchange between producers Yes ; No Use of a Mobile Money account Yes ; No Explanatory variables Age Digital Gender Male; Women Religion Islam; Christianity; Endogenous; Atheist Marital status Single; Monogamous; Polygamous; Divorce; free Union Educational level None; Literate; Primary; Secondary; University; Unconventional Secondary activity Yes ; No Number of dependents: Digital Type of cotton grown Organic; Conventional; Both Total available area Digital Area sown for cotton Digital Use of Labor Yes ; No Possession of a bank or DFS account Yes ; No Share of cotton cultivation in income Digital Apprehensions of digitalization Not essential, Not very essential; Very essential, No idea Usefulness of the Internet and digitalization for production Useless; Not very useful; Very useful ; No idea Strengthening social relationships Yes; No; No idea Profitability of using Mobile money Little, Moderately, Profitable; Very profitable; No idea Satisfaction with Mobile Money Yes services; No; No idea Advantages of Mobile Money Accessibility; Speed, Security; No idea Security of digitalization Poor; average ; Good ; Very good ; No idea Risks linked to the use of digitalization No risk; Weak ; Average ; High ; No idea Perception of digitalization Low, Good; Very good ; No idea Reason for using digitalization By interest; By mimicry; By snobbery; No idea Sharing of personal data Yes ; No Table 2 Composition of the sample according to the districts No. Name of district Number of respondents Type of production Men Women Total Conventional cotton Organic cotton Both Total 1. Banikoara-centre 24 11 35 24 11 0 35 2. Founougo 28 7 35 35 0 0 35 3. Gomparou 27 4 31 31 0 0 31 4. Goumori 29 5 34 34 0 0 34 5. Kokey 26 4 30 26 0 4 30 6. Kokiborou 26 4 30 30 0 0 30 7. Ounet 24 6 30 30 0 0 30 8. Sompérékou 24 5 29 27 2 0 29 9. Soroko 22 6 28 23 5 0 28 10. Toura 24 8 32 32 0 0 32 Total 254 60 314 294 18 4 314 Data analysis The data collected was analyzed using R software. A synthesis of the data was carried out using descriptive statistics methods. To identify the explanatory factors for the adoption of digitalization or a mobile account by cotton producers in the Municipality of Banikoara, the procedure of generalized linear models (GLM) with the binomial distribution and the logit function as a link was used to perform a binary logistic regression model (logit model). A backward selection of variables starting from the full model was also carried out in order to identify the explanatory factors of adoption having a significant effect on the adoption of digitalization. The selection of variables was made according to the Akaike Information Criterion or AIC [ 23 ]. The goodness of fit of each model was assessed by residual deviance and AIC. In case of overdispersion, the quasibinomial distribution was replaced by the binomial distribution for parameter estimation. Binary logistic regression is a technique used to analyze the relationship between a dependence variable y, qualitative, nominal with two modalities (coded y = 0 and y = 1 for example), and one or more explanatory variables \({X}_{i}\) ( i = 1, ..., k ) quantitative and / or qualitative ordinal or nominal, assumed to be perfectly known. In this case, it is a question of modeling the probability \(\pi\) of adoption of digitalization or possession of a mobile account by a cotton producer according to different factors. To achieve this, a transformation of the success probabilities is carried out by the link functions, denoted by \(g\) . There are several link functions, but the most commonly used is the logit function (Eq. 1): \(g=logit\left(\pi \right)=\text{l}\text{n}\left[\pi /(1-\pi )\right]\) = \({\varvec{x}}_{\varvec{i}}^{{\prime }}\varvec{\beta }\) \((i=1, \cdots ,n)\) ,(1) where \({\varvec{x}}_{\varvec{i}}^{{\prime }}=(1,{x}_{i1},\cdots ,{x}_{ik})\) is the 1×(k + 1) vector corresponding to the k covariates associated with a producer i and \(\varvec{\beta }=({\beta }_{0},{\beta }_{1},\cdots ,{\beta }_{k}){\prime }\) is the (k + 1)×1 vector of associated coefficients. \({\varvec{\beta }}_{}\) are parameters to be estimated, most often by the maximum likelihood method. The inverse transformation makes it possible to find the estimated probabilities as a function of the \(\varvec{x}\) (Eq. 2 ): $$\pi =\text{exp}\left(g\right)/\left[1+\text{e}\text{x}\text{p}\left(g\right)\right]$$ 2 , For a given producer i, with characteristics \({\varvec{x}}_{\varvec{i}}^{}\) , the ratio between the probability \(\pi\) of adopting digitalization (or of having a mobile account) and the probability \((1-\pi )\) of not adopting it (or of not have a mobile account) represents the odds i.e. a ratio of chances. For example, if an individual has an odds of 3, this means that there is three times more chance of adopting digitalization (or having a mobile account). Results Factors influencing the use of digital machines in agriculture The full model showed a significant link between a number of factors and the adoption of digital technology by cotton producers in the commune of Banikoara. The search for the best model using the backward selection method (Table 3 ) led to the selection of two variables in the category of variables linked to production activity (use of labor and the area sown for cotton). In the category of variables linked to income, only the holding of a bank account was selected. Knowledge of the information and exchange platform at the agricultural sector level in Benin is the only variable that was selected in the category of variables linked to social influence. Examination of the odds ratios showed that cotton producers using labor for production have a 3.48 greater chance of adopting digital technology in cotton production. Having an account in a commercial bank or in a decentralized financial system (microfinance) by a producer gives the latter 8.24 more chances of adopting digital technologies in cotton production than those who do not have one. The coefficient − 0.15 of the quantitative variable "area sown with cotton" indicates that, all things being equal, the logarithm of the probability π/(1-π) decreases by 0.15 for each additional unit of area. Thus, increasing the surface area of a unit implies that the probability π that a producer adopts digital technology is exp(-0.15) = 0.86 times the probability (1-π) that a producer does not adopt it not. Otherwise, the more the surface area increases, the more the risk of adoption decreases (because the sign of the coefficient is negative, and therefore the odds ratio is less than 1). Likewise, knowledge of the information and exchange platform at the level of the agricultural sector in Benin gives the producer less chance of adopting digital technology, i.e., 0.55 times the probability of a producer who does not have any knowledge of the platform, because the sign of the coefficient is negative (-0.55). Table 3 Estimated coefficients and Odds Ratios of the model of use of digital machines in agriculture by cotton producers. Variables Coefficient p Odds Intercept 1.29 0.00 3.63 Bank account and/or SFD [T.Yes] 2.11 0.00 8.24 Knowledge of the platform [T.No] -0.6 0.04 0.55 Use of labor [T.Yes] 1.25 0.00 3. 48 Cotton area -0.15 0.00 0.86 Residual deviance 302.30 on .302 degrees of freedom AIC 312.3 Factors influencing membership of a social network for information exchange between producers The backward search procedure from the complete model made it possible to select the following variables: holding a bank account, strengthening relationships with others, risk linked to the use of digitalization, security linked to the use of digitalization and type of cotton used. Holding an account in a banking institution and when both types of cotton were practiced, presented coefficients with negative signs. It followed that these variables were less likely to influence adoption as indicated by their odds ratios, which are all less than one. Examining the odds ratios of variables whose coefficients have positive signs, showed that these variables were more chance to adopt digitalization. Thus, to the question related to the influence of digitalization on social relations, producers who answered yes or no are more chance to adopt digitalization than those who did not give an answer. The chance is higher among those who gave a negative answer (around 28 time’s chance while it is 2.35 times for the yes answer). Compared to the assessment of the risk linked to the use of digitalization, producers who think that the risk is medium are more chance to adopt digitalization (i.e. 234.34 times) than those who think that there is no risk. They are followed by those who think the risk is high with 49.40 times the chance of adoption. As for the security presented by digitalization, producers who think it is bad are more likely to adopt it than those who gave no answer with 8.03E + 07 times the chance of the latter. They are followed by those who think it is average and very good. Producers who practice conventional cultivation are more chance to adopt digitalization than those who practice organic cultivation or both types of cultivation with 19.83 more chances. Table 4 Estimated coefficients and Odds Ratios of the model of membership in a social network by cotton producers. Variables Agricultural information platform Coefficient p Odds (Intercept) -4.73 0.00 0.01 Bank account and/or SFD[T.Yes] -1.18 0.01 0.31 Strengthening relationships with others[T.No] 3.32 0.00 27.68 Strengthening relationships with others [T.Yes] 0.85 0.27 2.35 Digital risk[T.No idea] 0.47 0.58 1.60 Digital risk[T.High risk] 3.90 0.00 49.40 Digital risk[T.Low risk] 2.74 0.03 1.55 Digital risk[T.Average risk] 5.45 0.00 234.34 Digital security[T.Good 0.74 0.40 2.10 Digital security[T.Poor] 18.20 0.99 8.03E + 07 Digital security[T.Average] 15.73 0.99 1.68E + 06 Digital security[T.Very good 1.72 0.00 5.56 Cotton type [T.Conventional] 2.99 0.00 19.83 Cotton type[T.Both] -15.22 1.00 2.45E-07 Residual deviance 194.24 on 296 degrees of freedom AIC 230.24 Factors influencing use of a mobile money account Analysis of the results of the full model showed that no variable was significant. The backward search procedure from the complete model made it possible to select the following variables: age, holding a bank account, carrying out a secondary activity and using a hand of work (Table 5 ). These variables, all significant at the 5% threshold in the selected model, represent the factors which influence the adoption of mobile money among cotton farmers in the Municipality of Banikoara. The analysis of odds ratios showed that the increase of one unit in age implied that the probability π that a producer adopts digital technology was 1.03 times the probability (1-π) that a producer don't adopt it. That was to say practically the same probabilities. It followed that the adoption of mobile money was independent of age, because the sign of the coefficient was positive and approximately equal to 1. Holding an account in a banking or microfinance institution led to a reduction in the risk of adoption of mobile money (because the sign of the coefficient was negative so the odds were less than 1). Likewise, the exercise of a secondary activity and the use of labor led to a reduction in the risk of adoption, because the signs of the coefficients of these two variables were also negative in the selected model, and consequently their odds were less than 1. Table 5 Estimated coefficients and Odds Ratios of the model of use of mobile money by cotton producers. Variables Coefficient p Odds Intercept -0.86 0.12 0.42 Secondary activity [T.Yes] -0.81 0.00 0.45 Age 0.03 0.01 1.03 Bank account and/or SFD [T.Yes] -1.77 0.0 0.17 Use of labor [T.Yes] -1.68 0.0 0.19 Residual deviance 310.57 on 309 degrees of freedom AIC 320.57 Discussion The overview of the literature review used in this research on the adoption of digitalization focused on the evolution of online services since its emergence, and served as a fundamental basis for proposing the research model. Also, the continued growth of research based on UTAUT, as the model chosen in this study, is essentially due to the proliferation and diffusion of new information technologies [ 12 , 24 – 25 ] and the consideration of moderating variables such as gender, age, experience and willingness to use technology in this model. The factors for adoption of digitalization in cotton production were grouped within the framework of this study into factors linked to production activities, sociological aspects, income, appreciation of digitalization and social influence as shown in Fig. 3. The analysis of digitalization is carried out through the use of agricultural machines and other digital technological tools including mobile phones and tablets, the use of agricultural platforms and social networks and the possession of a mobile money account. Use of machines and other digital technological tools The adoption of machines and other digital technological tools was influenced by two factors (labor and area of cotton sown) linked to production activities, a factor linked to income (possession of a bank account) and a factor linked to social influence. The use of labor (odd ratio equal to 3.48) and the possession of an account in a bank or in a decentralized financial system (microfinance institution) with an odds ratio equal to 8.24 had a positive effect on adoption. This is explained by the fact that banked cotton producers have an easier time obtaining loans from these financial institutions for investment in the acquisition and use of agricultural machinery and other digital technological tools. Although the use of machines and other digital technological tools leads to a reduction or disappearance of the use of labor, it is nevertheless important to have a qualified workforce for their use. This explains the positive influence of the use of labor on the adoption of digitalization. Thus, bank loans are used not only in the acquisition of machinery and other digital tools but also for the maintenance or recruitment of labor, hence the positive influence of these two factors on the adoption of machines and other digital tools. Conversely, difficulties in accessing credit could therefore impact the decision to adopt new technologies [ 26 – 28 ]. In general, most farmers face liquidity constraints during non-harvest periods [ 29 ] and their access to credit would reinforce the use of certain inputs [ 30 – 31 ] and certain digital technologies for future production. According to Alene and Manyong [ 30 ], the size of the household, which also expresses the level of available family labor, could affect the decision to adopt a new technology in agriculture. Contrary to the results of Teno et al. [ 28 ] who stated that a labor-intensive technology will certainly be more within the reach of large families who, as a result, will be more favorable to its adoption; which would not be the case for smaller families, the variable number of dependents did not have a significant effect on adoption in the present study. The results also showed that a marginal increase in the planted area negatively impacts the risk of using machines and other digital technological equipment. The additional increase in the planted area requires the use of more complex and very expensive machines over very large areas (drones, robots, etc.); which is not yet a reality in the study area. Any marginal increase in the sown area therefore does not lead to the adoption of digital machines. Anderson et al. [ 32 ] also obtained a negative effect of increasing farm size on the adoption of organic farming. However, Carrer et al. [ 33 ] explained that large farms are more complex to manage and that new technologies have demonstrated their effectiveness in optimizing production and reducing costs. Similarly, Yatribi [ 34 ] showed that farm size is often associated with farm income and that large farms are likely to adopt new precision agriculture technologies thanks to their financial capacity. Also, cotton farmers in the Municipality of Banikoara who are not aware of agricultural information and exchange platforms and who therefore do not use them are less likely to adopt the use of agricultural machines and tools. Indeed, they are under-informed about the functioning and usefulness of these machines and digital tools and find, in turn, less interest in their use; which is not the case for those who know about it through agricultural exchange platforms. As part of this research, sociological variables such as age, gender, religion, marital status, level of education and the variable linked to the type of cotton produced did not have a significant influence on the use of agricultural machines and other digital tools. These results are consistent with those of Oulbaz et al. [ 35 ] who highlighted that the use of digital technologies in agricultural projects is not influenced by internal factors within the farm, whether the type of agricultural product marketed, the educational level, habits in the use of decision-making tools and the cost of digital technology. Knowler and Bradshaw [ 36 ] also did not find significant relationships between education and adoption. However, Yatribi [ 34 ] affirmed that the level of education is highlighted by many authors as a determinant of the adoption of new technologies [ 33 , 37 – 40 ]. Roussy et al. [ 41 ] also showed through their study that age has an influence on the adoption of a new technology by farmers. Use of agricultural platforms and social networks In the literature, studies have demonstrated that a person can believe that the use of a certain technology will influence their professional image and status [ 11 , 42 ]. In the present study, the use of agricultural platforms and social networks is influenced by the strengthening of relationships with others, the level of risk and security. Indeed, whatever the modality of the security level factor and that of the risk perceived by the producer, he is more likely to use platforms and social networks. The cotton farmers surveyed are therefore partly risk-adverse (37% producers opted for a medium and high level of risk). Thus, it is the producers who think about the existence of a risk who are more likely to use professional platforms. This could be explained by the fact it is quite difficult for the cotton farmer to understand the risk at the level of digital platforms and the majority of individuals interviewed (63% of the sample, or 197 individuals) have no idea about the degree of risk or think that the risk does not exist or is rather low. These results partly corroborate with those of Teno et al. [ 28 ] who believe that the degree of risk aversion would play a role in the decision to adopt a new technology. For these authors, risk-loving agricultural households will be more willing to accept the new technology unlike risk-phobic households [ 43 ]. But, Roussy et al. [ 41 ] showed that the level of risk aversion has been highlighted as a barrier to the adoption of innovations in agriculture [ 44 – 45 ]. However, risk aversion alone cannot explain the behavior of farmers adopting innovations [ 46 ]. Conventional cotton producers also have more chance to use the platforms than those combining both types of cotton (conventional and organic). This category of cotton farmers is the most numerous in the sample and the oldest in the sector. They know each other better and have an easier time exchanging experiences through a platform. Having a bank account makes it less chance to use agricultural exchange platforms. Banked cotton farmer’s exchange more with their bankers whom they prefer to contact physically instead of using a platform to communicate with the agricultural advisor. For the sale of their secondary crops (crops other than cotton) in order to repay their loans, they sometimes get help from the banker in finding customers rather than using a platform. Abid et al. [ 47 ], however, reported that farmers' resistance to digital platforms differs depending on the area targeted by this platform. According to these authors, perceived usefulness as well as perceived intrusion are the main factors of farmers' resistance to the adoption of resource platforms. Having a mobile money account Possession of a mobile money account by the cotton producer is influenced by age, possession of a bank account, secondary activity and use of labor. If age has practically no influence on having a mobile money account (odd ratio approximately equal to 1), this is not the case for having a bank account, carrying out a secondary activity and the use of labor which negatively influences the adoption of mobile money. In fact, cotton farmers with a bank account prefer to store their income in these accounts rather than using the mobile money account. To meet certain costs such as labor remuneration, cotton producers are sometimes forced to resort to banks and microfinance institutions to obtain loans, to the extent that they cannot access to this financing at the level of electronic money issuers. In addition, most cotton producers hold accounts with microfinance institutions. The latter do not offer digital solutions interfaced with “mobile money” (mobile banking) accounts unlike some traditional banks. It should also be noted that traditional banks have just set up in the study area (barely 3 years of activity for the first) and do not attract as many cotton farmers as microfinance institutions. Thus, cotton farmers prefer to carry out transactions from their bank account which also offers money transfer and other services. Furthermore, payment for cotton production is made through accounts opened by village cooperatives of cotton producers in these financial institutions, notably the “Caisses Locales de Crédits Agricoles Mutuels” (CLCAM). To avoid additional account management costs, cotton farmers do not opt to hold two accounts (bank and mobile money). This result corroborates with those of Akinyemi and Mushunje [ 48 ] who conclude, among other things, that it is unlikely that individuals who own or have access to bank accounts will adopt mobile money to send or receive payments. According to these authors, the reasons why mobile money is not adopted may be due to the fact that the services rendered by mobile money are also provided by commercial banks and the use of mobile money may result in duplication of services and costs. Fanta et al. [ 49 ] established that having a bank account, access to ATMs, mobile banking and internet banking are inversely related to having a mobile money account. These authors also found that mobile money adoption is lower among those who have a bank account as well as those who use ATMs, mobile banking and internet banking to access their bank account. Akinyemi and Mushunje [ 48 ] showed that apart from having a bank account, age, years of education, unemployment and mobile phone are the main determinants of the adoption of mobile money technology. The results obtained also go in the same direction as those of Ndiaye and Weibigue [ 50 ] who showed, among other things, that the variables: having a job, age, gender and currently attending school do not seem to play a determining role in the adoption of M-Banking in Senegal and that the marginal effects of these variables are not significant. Contrary to the results obtained in the present study, these authors also reported that belonging to a banking network, that is to say being a customer of a bank, a microfinance institution or a postal check center promotes the adoption of M-banking. Fall and Birba [ 51 ] and Mbiti and Weil [ 52 ] also go in the same direction by mentioning that M-banking is a complementary service to traditional banking services. Other factors influencing the adoption of mobile money have been mentioned in the literature. Thus, Fall and Birba [ 51 ] found that gender, level of education, employment, knowing how to read and write and being banked positively influence the probability of adoption of mobile banking. Amegnanglo and Zounmenou [ 53 ] also showed that age, gender, turnover and education are the determinants of the use of electronic money account services by artisans in southern Benin. For Bidiasse and Mvogo [ 54 ], generally speaking, the advantages offered, the information available on how mobile money works and the proximity of the service are the variables for adoption of this service. Conclusion The purpose of this article is to analyze the determinants of adoption of digitalization among cotton farmers in the Municipality of Banikoara, a cotton basin in Benin. As part of this study, digitalization was subdivided into three (3) components, namely the use of machines and other digital tools (including mobile phones and tablets), the use of agricultural platforms and social networks, and possession of a mobile money account. The adoption of digitalization in these three (3) forms is influenced by holding an account in a bank or in a decentralized financial system (microfinance institution). This variable positively influences the use of machines and other digital tools then negatively the use of platforms and the possession of a mobile money account. In addition to this factor, the use of labor, the assessment of risk and the level of safety, the exercise of a secondary activity, knowledge of agricultural information and exchange platforms and the area sown also have a significant influence either on the use of machines and digital tools, or on the use of platforms or the possession of a mobile money account. Contrary to the results of several previous studies, sociological factors, namely gender, age, level of education and marital status, did not have significant effects on the adoption of digitalization by cotton farmers in the Municipality of Banikoara through the sample. All in all, starting from the structured analysis model, the factors linked to production activity (surface area, workforce), income (possession of a bank account or in a decentralized financial system), the assessment of digitalization (perception of risk and security level) and social influence (knowledge of agricultural information and exchange platforms) have a significant impact on the adoption of digitalization in cotton farming in the Municipality of Banikoara. Thus, any action in favor of promoting digitalization among this social layer essentially amounts to promoting their banking use while integrating and generalizing mobile banking services at the level of microfinance institutions and commercial banks, which are in contact with the producers. However, the adoption decision may also depend on exogenous factors (regulatory constraints for example) which were not taken into account in the study. The method of selection of cotton farmers (reasoned choice) can also influence the results obtained. Therefore, further research work is necessary to deepen and supplement the results obtained. Declarations Declaration of interest’s statement The authors declare no competing interests. Funding Not applicable. Author Contribution 1. SA: Conceptualization, Data curation, Investigation, Methodology, Resources, Visualization, Formal analysis, Software, Writing – original draft preparation. 2. AHGA: Formal analysis, Software, Visualization. 3. AJY: Conceptualization, Methodology, Review. 4. AYJA: Conceptualization, Data curation, Methodology, Resources, Visualization, Formal analysis, Software, Writing – review and editing. Acknowledgements Not applicable. Data availability statement Data will be made available on reasonable request. References Boloh Y, Cartmell-Thorp S. CTA report, African agricultural digitalization deciphered. Spore. 2019; 194(3). https://www.inter-reseaux.org/wp-content/uploads/sp194_pdf_f.pdf . Accessed 29 Nov 2023. Kiyindou A, Anaté K, Capo Chichi A. When Africa reinvents mobile telephony. Paris, eds. L’Harmattan, coll. Études africaines. 2015. https://doi.org/10.4000/questionsdecommunication.10608 . Adeleye N, Eboagu C. Evaluation of ICT development and economic growth in Africa. Netnomics: Econ Res Electron Network. 2019; 20(1): 31–53. https://doi.org/10.1007/s11066-019-09131-6 . Maduka, E. Drone in Ivory Coast, plantations administered from the sky. 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Rev Econ Ind. 2019; 165: 85–115. https://doi.org/10.4000/rei.7845 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviews received at journal 05 May, 2024 Reviewers agreed at journal 07 Feb, 2024 Reviewers agreed at journal 06 Feb, 2024 Reviewers invited by journal 04 Feb, 2024 Editor assigned by journal 04 Jan, 2024 Submission checks completed at journal 04 Jan, 2024 First submitted to journal 04 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3834485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265322041,"identity":"9d109e63-921c-41aa-bb9e-f58d6de01076","order_by":0,"name":"Saddik ALIDOU","email":"","orcid":"","institution":"Université de Parakou","correspondingAuthor":false,"prefix":"","firstName":"Saddik","middleName":"","lastName":"ALIDOU","suffix":""},{"id":265322042,"identity":"e49d17a9-c8a0-472b-8629-2de87c37b031","order_by":1,"name":"Adoté Hervé Gildas AKUESON","email":"","orcid":"","institution":"Université de Parakou","correspondingAuthor":false,"prefix":"","firstName":"Adoté","middleName":"Hervé Gildas","lastName":"AKUESON","suffix":""},{"id":265322043,"identity":"26d62627-a477-41d5-9501-39fabdacbdeb","order_by":2,"name":"Afouda Jacob YABI","email":"","orcid":"","institution":"Université de Parakou","correspondingAuthor":false,"prefix":"","firstName":"Afouda","middleName":"Jacob","lastName":"YABI","suffix":""},{"id":265322044,"identity":"411091f9-5641-47da-8295-3990dea7283f","order_by":3,"name":"Arcadius Yves Justin AKOSSOU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYLACHgMQyQbh8DMfANMJjA3EapFsSyBGCwOSFoNjUC24VPPPPvzww5uCw/YM7McSH/74Y5O4+RjzA2beHQx5zDhskTiXZiw5x+BwYgNP2mFj3ra0xG3H2AyYec8wFON02BkeBmkeg8NAZ6S3STM2HE7cdr/B/DdvG0NiIw4t8md4mH8Dtdgz8D9vk/zx53Di5jb2D8z4tBic4WED2cLYIJF2TIKH7XDiBjYeA7xaDM+wmVnOMUhPbJN4lgzyi/GMYzwFjHPPSOD0i9wZ5sc33vyxtufnTzMEhZhsfxv7Boa3O2zyDHF5HwbYUHhAdzIQ1IIKQI6SJ0nHKBgFo2AUDGMAAKXfVypWN9INAAAAAElFTkSuQmCC","orcid":"","institution":"Université de Parakou","correspondingAuthor":true,"prefix":"","firstName":"Arcadius","middleName":"Yves Justin","lastName":"AKOSSOU","suffix":""}],"badges":[],"createdAt":"2024-01-04 11:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3834485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3834485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49306246,"identity":"191aadfc-90e9-4fc1-ad31-85f5ac22660d","added_by":"auto","created_at":"2024-01-08 11:42:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":323041,"visible":true,"origin":"","legend":"\u003cp\u003eUnified Theory of Technology Acceptance and Use Model\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3834485/v1/7962a0c98e718f766bb08ec7.jpeg"},{"id":49306245,"identity":"5cde021d-abd8-4188-8bbf-274aa50a6cb2","added_by":"auto","created_at":"2024-01-08 11:42:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":595031,"visible":true,"origin":"","legend":"\u003cp\u003eStructural analysis model used\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3834485/v1/e44a546844a718be47de73f2.jpeg"},{"id":49306670,"identity":"8597f72d-20bb-41e3-bdd6-cfd6887977e4","added_by":"auto","created_at":"2024-01-08 11:50:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":620317,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3834485/v1/8876acf6-c757-44a4-b095-f64a657375ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adoption factors of digitalization in cotton farming in the municipality of Banikoara in Northwestern Benin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe use of information and communication technologies (ICT) has become obvious with the widespread use of mobile communication and various internet services for financing agricultural activities. It makes it possible to communicate and make available agricultural information on production, marketing and better access to State services and partners on agricultural value chains. A study conducted by the Technical Center for Agricultural Cooperation (CTA) and Dalberg Advisors noted that 33\u0026nbsp;million small producers on the African continent are registered in nearly 400 digital solutions, with an annual growth of 45% in the number of registrations since 2012 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The same study, however, noted that more than 90% of the digital services market for African farmers remains untapped.\u003c/p\u003e \u003cp\u003eThe introduction of this new technology in the agricultural sector involves the use of digital technological tools such as the geolocation system, meteorological monitoring, the use of surveillance cameras in the fields, sensors to inspect and collect data (temperature, humidity, etc.), the use of drones and other automated machines. Mobile telephony and the Internet, followed by the use of mobile financial services from mobile phones by the agricultural world, constitute the basis of agricultural digitalization in developing countries.\u003c/p\u003e \u003cp\u003eSub-Saharan Africa is adopting and adapting the latest digital technologies massively and at increasing speed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In Ivory Coast, for example, the start-up WeFly Agri provides technologies using drones to allow farm and plantation owners to monitor and manage their land remotely [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Remote monitoring of greenhouses allows small Kenyan producers to irrigate their crops without being present on site and improve their quality of life [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Benin, the Internet is increasingly widespread [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and its penetration has been growing in recent years. It increased from 47.79% in 2018 to 52.83% in 2019 then to 69.36% in 2020 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As for the penetration rate of mobile financial services, it also increased from 23.46% in 2018 to 30.63% in 2019 then to 42.99% in 2020 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite the wide GSM coverage over the country, family farmers in Benin are poorly connected to the Internet, i.e., 5% of social networks (WhatsApp, Facebook, Instagram), and 3% for information on agriculture [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, the use of drones by family farmers is currently absent, particularly in the Plateau department in South-East Benin [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, for a better penetration policy of these technologies in the agricultural sector, it is important to identify the factors which contribute to its adoption. Thus, understanding the essential variables that could accelerate or hinder the adoption of these technologies has become an important concern. This study will focus on cotton production in Benin, which represents its main export product. Indeed, due to its contribution in terms of the total value of exports of agricultural products (84.8%) and the value of exports of all products combined (61.0%) in 2021 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the cotton sector constitutes the basis of the economy in Benin. As such, cotton occupies a special place in agricultural policy, making it the most organized of all agricultural sectors. With this support, production has experienced an exponential evolution in recent years, particularly during the 2017\u0026ndash;2018 campaign, even reaching a growth of 122% in volume and an increase of 74% in the areas sown over the last two seasons [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Culture therefore has increased its added value in the transformation value chain.\u003c/p\u003e \u003cp\u003eThus, this study aims to understand the determinants that can influence the adoption of digital technology and the possession of a mobile account among cotton producers for a better policy of penetration of new information and communication technologies. It is based on the Unified Model of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This model combines the factors that can influence individuals in their intentions to adopt and use technologies, in various environments [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. It arises from the evolution of the Technological Acceptance Model (TAM) model developed by Davis et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], with the consideration of new factors.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe Municipality of Banikoara was selected for this study. It is in fact the first cotton production commune in Benin and its economy is mainly based on agriculture. Cereal production is also developed there like other legumes. It is located in the North-West of Benin with a Sudano-Sahelian climate which covers an area of 4,397.2 km2 including approximately 49% arable land and 50% protected areas [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This commune is located in the Alibori department, between 2\u0026deg;05\u0026rsquo; and 2\u0026deg;46\u0026rsquo; east longitude and between 11\u0026deg;02\u0026rsquo; and 11\u0026deg;34\u0026rsquo; north latitude. It is limited to the north by the commune of Karimama, to the south by the communes of K\u0026eacute;rou and Gogounou, to the east by the commune of Kandi and to the west by Burkina-Faso. Its population is estimated at 246,575 inhabitants including 124,130 women (50.3%) in 2013 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It has ten districts: Banikoara, Founougo, Gomparou, Goumori, Kokey, Kokiborou, Ounet, Somperoukou, Soroko, Toura.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy framework\u003c/h2\u003e \u003cp\u003eThe acceptance and use of information system (IS) and information technology (IT) innovations has been a major concern for research and practice [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Over the last several decades, a plethora of theoretical models have been proposed and used to examine IS/IT acceptance and usage [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These include the Theory of Reasoned Action, the Technology Acceptance Model, the Theory of Planned Behaviour, and Model of Personal Computer Utilization [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Many of these theoretical models, developed to explain and predict the behavior of individuals with regard to the use of information and communication technologies, have referred to theories based on research in social psychology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Based on a comprehensive review and synthesis of several theoretical models, Venkatesh et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] proposed the Unified Theory of Acceptance and Use of Technology (UTAUT), which has since been used extensively by researchers in their quest to explain IS/IT acceptance and use.\u003c/p\u003e \u003cp\u003eThe choice of the UTAUT model developed by Venkatesh et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] in the context of this research is justified to the extent that the latter has the advantage of being a general model of all the theoretical models that have been developed to explain the adoption behavior of humans. In addition, taking into account the moderating variables (age, gender, level of education, marital status, banking, income, variety of cotton cultivated and the main speculation practiced by the cotton farmer) further justifies this choice.\u003c/p\u003e \u003cp\u003eFour basic variables define the Venkatesh et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] model, namely, perceived ease of use, perceived usefulness, social influences and facilitating conditions. Perceived ease of use refers to the degree of ease associated with using the system. Perceived usefulness is defined as the degree to which a person believes that using the system will help them achieve gains in job performance. Social influence is the degree to which a person perceives that significant others believe that he or she should use the new system. Facilitating conditions are defined as the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system.\u003c/p\u003e \u003cp\u003eIn their model, Venkatesh et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] integrated moderating variables (gender, age, experience and willingness to use) which have an influence on the explanatory variables. However, this model has evolved. Venkatesh et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] proposed improvements to the first model (UTAUT 1) by adding other explanatory variables such as hedonic motivation, price value and habit.\u003c/p\u003e \u003cp\u003eThe determinants of the adoption of the unified theory of acceptance and use of technology (UTAUT 1) are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As for the research analysis model, inspired by UTAUT, it is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSource\u003c/span\u003e: \u003cem\u003eVenkatesh et al.\u003c/em\u003e, [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUnified Theory of Technology Acceptance and Use Model\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStructural analysis model used\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe research combined a quantitative and qualitative data set. Data collection from producers was done using a questionnaire administered to each participant during February and March 2023. The questions take into account the model used (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The choice of producers was made through purposive sampling taking into account, among others, conventional cotton producers, organic cotton producers, those combining the two types of production, gender, level of education, ethnicity and other criteria (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thus, a total of three hundred and fourteen (314) cotton producers distributed in the ten districts of the Municipality of Banikoara were selected (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the sample, women represent 19% and producers practicing only conventional cotton constitute 94%. The numbers in each category in the sample took into account the proportions obtained for each category during the 2021\u0026ndash;2022 census of cotton farmers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables used and their modality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModalities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eVariables to\u003c/p\u003e \u003cp\u003eexplain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse of digital machines in agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026nbsp;; No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembership of a social network for information exchange between producers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026nbsp;; No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse of a Mobile Money account\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026nbsp;; No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e \u003cp\u003eExplanatory variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale; Women\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIslam; Christianity; Endogenous; Atheist\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle; Monogamous; Polygamous; Divorce; free Union\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone; Literate; Primary; Secondary; University; Unconventional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026nbsp;; No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of dependents:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of cotton grown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrganic; Conventional; Both\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal available area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea sown for cotton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse of Labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026nbsp;; No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePossession of a bank or DFS account\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026nbsp;; No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShare of cotton cultivation in income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApprehensions of digitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot essential, Not very essential; Very essential, No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsefulness of the Internet and digitalization for production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUseless; Not very useful; Very useful ; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrengthening social relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes; No; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfitability of using Mobile money\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLittle, Moderately, Profitable; Very profitable; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfaction with Mobile Money\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes services; No; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvantages of Mobile Money\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccessibility; Speed, Security; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecurity of digitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor; average ; Good ; Very good ; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisks linked to the use of digitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo risk; Weak ; Average ; High ; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerception of digitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow, Good; Very good ; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReason for using digitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBy interest; By mimicry; By snobbery; No idea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSharing of personal data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u0026nbsp;; No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposition of the sample according to the districts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eName of district\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eNumber of respondents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eType of production\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConventional cotton\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOrganic cotton\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBoth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanikoara-centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFounougo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGomparou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoumori\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKokey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKokiborou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOunet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSomp\u0026eacute;r\u0026eacute;kou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoroko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToura\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe data collected was analyzed using R software. A synthesis of the data was carried out using descriptive statistics methods. To identify the explanatory factors for the adoption of digitalization or a mobile account by cotton producers in the Municipality of Banikoara, the procedure of generalized linear models (GLM) with the binomial distribution and the logit function as a link was used to perform a binary logistic regression model (logit model). A backward selection of variables starting from the full model was also carried out in order to identify the explanatory factors of adoption having a significant effect on the adoption of digitalization. The selection of variables was made according to the Akaike Information Criterion or AIC [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The goodness of fit of each model was assessed by residual deviance and AIC. In case of overdispersion, the quasibinomial distribution was replaced by the binomial distribution for parameter estimation.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBinary logistic regression is a technique used to analyze the relationship between a dependence variable y, qualitative, nominal with two modalities (coded y\u0026thinsp;=\u0026thinsp;0 and y\u0026thinsp;=\u0026thinsp;1 for example), and one or more explanatory variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{i}\\)\u003c/span\u003e\u003c/span\u003e (\u003cem\u003ei\u0026thinsp;=\u0026thinsp;1, ..., k\u003c/em\u003e) quantitative and / or qualitative ordinal or nominal, assumed to be perfectly known. In this case, it is a question of modeling the probability \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pi\\)\u003c/span\u003e\u003c/span\u003e of adoption of digitalization or possession of a mobile account by a cotton producer according to different factors. To achieve this, a transformation of the success probabilities is carried out by the link functions, denoted by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(g\\)\u003c/span\u003e\u003c/span\u003e. There are several link functions, but the most commonly used is the logit function (Eq.\u0026nbsp;1):\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(g=logit\\left(\\pi \\right)=\\text{l}\\text{n}\\left[\\pi /(1-\\pi )\\right]\\)\u003c/span\u003e \u003c/span\u003e= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{x}}_{\\varvec{i}}^{{\\prime }}\\varvec{\\beta }\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((i=1, \\cdots ,n)\\)\u003c/span\u003e\u003c/span\u003e ,(1)\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{x}}_{\\varvec{i}}^{{\\prime }}=(1,{x}_{i1},\\cdots ,{x}_{ik})\\)\u003c/span\u003e\u003c/span\u003e is the 1\u0026times;(k\u0026thinsp;+\u0026thinsp;1) vector corresponding to the k covariates associated with a producer i and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{\\beta }=({\\beta }_{0},{\\beta }_{1},\\cdots ,{\\beta }_{k}){\\prime }\\)\u003c/span\u003e\u003c/span\u003e is the (k\u0026thinsp;+\u0026thinsp;1)\u0026times;1 vector of associated coefficients. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{\\beta }}_{}\\)\u003c/span\u003e\u003c/span\u003e are parameters to be estimated, most often by the maximum likelihood method. The inverse transformation makes it possible to find the estimated probabilities as a function of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{x}\\)\u003c/span\u003e\u003c/span\u003e (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\pi =\\text{exp}\\left(g\\right)/\\left[1+\\text{e}\\text{x}\\text{p}\\left(g\\right)\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003eFor a given producer i, with characteristics \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{x}}_{\\varvec{i}}^{}\\)\u003c/span\u003e\u003c/span\u003e, the ratio between the probability \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pi\\)\u003c/span\u003e\u003c/span\u003e of adopting digitalization (or of having a mobile account) and the probability \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((1-\\pi )\\)\u003c/span\u003e\u003c/span\u003e of not adopting it (or of not have a mobile account) represents the odds i.e. a ratio of chances. For example, if an individual has an odds of 3, this means that there is three times more chance of adopting digitalization (or having a mobile account).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFactors influencing the use of digital machines in agriculture\u003c/h2\u003e \u003cp\u003eThe full model showed a significant link between a number of factors and the adoption of digital technology by cotton producers in the commune of Banikoara. The search for the best model using the backward selection method (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) led to the selection of two variables in the category of variables linked to production activity (use of labor and the area sown for cotton). In the category of variables linked to income, only the holding of a bank account was selected. Knowledge of the information and exchange platform at the agricultural sector level in Benin is the only variable that was selected in the category of variables linked to social influence. Examination of the odds ratios showed that cotton producers using labor for production have a 3.48 greater chance of adopting digital technology in cotton production. Having an account in a commercial bank or in a decentralized financial system (microfinance) by a producer gives the latter 8.24 more chances of adopting digital technologies in cotton production than those who do not have one. The coefficient \u0026minus;\u0026thinsp;0.15 of the quantitative variable \"area sown with cotton\" indicates that, all things being equal, the logarithm of the probability π/(1-π) decreases by 0.15 for each additional unit of area. Thus, increasing the surface area of a unit implies that the probability π that a producer adopts digital technology is exp(-0.15)\u0026thinsp;=\u0026thinsp;0.86 times the probability (1-π) that a producer does not adopt it not. Otherwise, the more the surface area increases, the more the risk of adoption decreases (because the sign of the coefficient is negative, and therefore the odds ratio is less than 1). Likewise, knowledge of the information and exchange platform at the level of the agricultural sector in Benin gives the producer less chance of adopting digital technology, i.e., 0.55 times the probability of a producer who does not have any knowledge of the platform, because the sign of the coefficient is negative (-0.55).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated coefficients and Odds Ratios of the model of use of digital machines in agriculture by cotton producers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOdds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBank account and/or SFD [T.Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eKnowledge of the platform [T.No]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUse of labor [T.Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3. 48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCotton area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual deviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e302.30 on .302 degrees of freedom\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e312.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFactors influencing membership of a social network for information exchange between producers\u003c/h2\u003e \u003cp\u003eThe backward search procedure from the complete model made it possible to select the following variables: holding a bank account, strengthening relationships with others, risk linked to the use of digitalization, security linked to the use of digitalization and type of cotton used. Holding an account in a banking institution and when both types of cotton were practiced, presented coefficients with negative signs. It followed that these variables were less likely to influence adoption as indicated by their odds ratios, which are all less than one. Examining the odds ratios of variables whose coefficients have positive signs, showed that these variables were more chance to adopt digitalization. Thus, to the question related to the influence of digitalization on social relations, producers who answered yes or no are more chance to adopt digitalization than those who did not give an answer. The chance is higher among those who gave a negative answer (around 28 time\u0026rsquo;s chance while it is 2.35 times for the yes answer). Compared to the assessment of the risk linked to the use of digitalization, producers who think that the risk is medium are more chance to adopt digitalization (i.e. 234.34 times) than those who think that there is no risk. They are followed by those who think the risk is high with 49.40 times the chance of adoption. As for the security presented by digitalization, producers who think it is bad are more likely to adopt it than those who gave no answer with 8.03E\u0026thinsp;+\u0026thinsp;07 times the chance of the latter. They are followed by those who think it is average and very good. Producers who practice conventional cultivation are more chance to adopt digitalization than those who practice organic cultivation or both types of cultivation with 19.83 more chances.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated coefficients and Odds Ratios of the model of membership in a social network by cotton producers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAgricultural information platform\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank account and/or SFD[T.Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrengthening relationships with others[T.No]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrengthening relationships with others [T.Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital risk[T.No idea]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital risk[T.High risk]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital risk[T.Low risk]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital risk[T.Average risk]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital security[T.Good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital security[T.Poor]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.03E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital security[T.Average]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital security[T.Very good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCotton type [T.Conventional]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCotton type[T.Both]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-15.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.45E-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual deviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e194.24 on 296 degrees of freedom\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e230.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFactors influencing use of a mobile money account\u003c/h2\u003e \u003cp\u003eAnalysis of the results of the full model showed that no variable was significant. The backward search procedure from the complete model made it possible to select the following variables: age, holding a bank account, carrying out a secondary activity and using a hand of work (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These variables, all significant at the 5% threshold in the selected model, represent the factors which influence the adoption of mobile money among cotton farmers in the Municipality of Banikoara. The analysis of odds ratios showed that the increase of one unit in age implied that the probability π that a producer adopts digital technology was 1.03 times the probability (1-π) that a producer don't adopt it. That was to say practically the same probabilities. It followed that the adoption of mobile money was independent of age, because the sign of the coefficient was positive and approximately equal to 1. Holding an account in a banking or microfinance institution led to a reduction in the risk of adoption of mobile money (because the sign of the coefficient was negative so the odds were less than 1). Likewise, the exercise of a secondary activity and the use of labor led to a reduction in the risk of adoption, because the signs of the coefficients of these two variables were also negative in the selected model, and consequently their odds were less than 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated coefficients and Odds Ratios of the model of use of mobile money by cotton producers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary activity [T.Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBank account and/or SFD [T.Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of labor [T.Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual deviance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e310.57 on 309 degrees of freedom\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e320.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe overview of the literature review used in this research on the adoption of digitalization focused on the evolution of online services since its emergence, and served as a fundamental basis for proposing the research model. Also, the continued growth of research based on UTAUT, as the model chosen in this study, is essentially due to the proliferation and diffusion of new information technologies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and the consideration of moderating variables such as gender, age, experience and willingness to use technology in this model. The factors for adoption of digitalization in cotton production were grouped within the framework of this study into factors linked to production activities, sociological aspects, income, appreciation of digitalization and social influence as shown in Fig.\u0026nbsp;3. The analysis of digitalization is carried out through the use of agricultural machines and other digital technological tools including mobile phones and tablets, the use of agricultural platforms and social networks and the possession of a mobile money account.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUse of machines and other digital technological tools\u003c/h2\u003e \u003cp\u003eThe adoption of machines and other digital technological tools was influenced by two factors (labor and area of cotton sown) linked to production activities, a factor linked to income (possession of a bank account) and a factor linked to social influence. The use of labor (odd ratio equal to 3.48) and the possession of an account in a bank or in a decentralized financial system (microfinance institution) with an odds ratio equal to 8.24 had a positive effect on adoption. This is explained by the fact that banked cotton producers have an easier time obtaining loans from these financial institutions for investment in the acquisition and use of agricultural machinery and other digital technological tools. Although the use of machines and other digital technological tools leads to a reduction or disappearance of the use of labor, it is nevertheless important to have a qualified workforce for their use. This explains the positive influence of the use of labor on the adoption of digitalization. Thus, bank loans are used not only in the acquisition of machinery and other digital tools but also for the maintenance or recruitment of labor, hence the positive influence of these two factors on the adoption of machines and other digital tools. Conversely, difficulties in accessing credit could therefore impact the decision to adopt new technologies [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In general, most farmers face liquidity constraints during non-harvest periods [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and their access to credit would reinforce the use of certain inputs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and certain digital technologies for future production. According to Alene and Manyong [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the size of the household, which also expresses the level of available family labor, could affect the decision to adopt a new technology in agriculture. Contrary to the results of Teno et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] who stated that a labor-intensive technology will certainly be more within the reach of large families who, as a result, will be more favorable to its adoption; which would not be the case for smaller families, the variable number of dependents did not have a significant effect on adoption in the present study.\u003c/p\u003e \u003cp\u003eThe results also showed that a marginal increase in the planted area negatively impacts the risk of using machines and other digital technological equipment. The additional increase in the planted area requires the use of more complex and very expensive machines over very large areas (drones, robots, etc.); which is not yet a reality in the study area. Any marginal increase in the sown area therefore does not lead to the adoption of digital machines. Anderson et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] also obtained a negative effect of increasing farm size on the adoption of organic farming. However, Carrer et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] explained that large farms are more complex to manage and that new technologies have demonstrated their effectiveness in optimizing production and reducing costs. Similarly, Yatribi [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] showed that farm size is often associated with farm income and that large farms are likely to adopt new precision agriculture technologies thanks to their financial capacity.\u003c/p\u003e \u003cp\u003eAlso, cotton farmers in the Municipality of Banikoara who are not aware of agricultural information and exchange platforms and who therefore do not use them are less likely to adopt the use of agricultural machines and tools. Indeed, they are under-informed about the functioning and usefulness of these machines and digital tools and find, in turn, less interest in their use; which is not the case for those who know about it through agricultural exchange platforms.\u003c/p\u003e \u003cp\u003eAs part of this research, sociological variables such as age, gender, religion, marital status, level of education and the variable linked to the type of cotton produced did not have a significant influence on the use of agricultural machines and other digital tools. These results are consistent with those of Oulbaz et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] who highlighted that the use of digital technologies in agricultural projects is not influenced by internal factors within the farm, whether the type of agricultural product marketed, the educational level, habits in the use of decision-making tools and the cost of digital technology. Knowler and Bradshaw [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] also did not find significant relationships between education and adoption. However, Yatribi [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] affirmed that the level of education is highlighted by many authors as a determinant of the adoption of new technologies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Roussy et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] also showed through their study that age has an influence on the adoption of a new technology by farmers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eUse of agricultural platforms and social networks\u003c/h2\u003e \u003cp\u003eIn the literature, studies have demonstrated that a person can believe that the use of a certain technology will influence their professional image and status [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the present study, the use of agricultural platforms and social networks is influenced by the strengthening of relationships with others, the level of risk and security. Indeed, whatever the modality of the security level factor and that of the risk perceived by the producer, he is more likely to use platforms and social networks. The cotton farmers surveyed are therefore partly risk-adverse (37% producers opted for a medium and high level of risk). Thus, it is the producers who think about the existence of a risk who are more likely to use professional platforms. This could be explained by the fact it is quite difficult for the cotton farmer to understand the risk at the level of digital platforms and the majority of individuals interviewed (63% of the sample, or 197 individuals) have no idea about the degree of risk or think that the risk does not exist or is rather low. These results partly corroborate with those of Teno et al. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] who believe that the degree of risk aversion would play a role in the decision to adopt a new technology. For these authors, risk-loving agricultural households will be more willing to accept the new technology unlike risk-phobic households [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. But, Roussy et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] showed that the level of risk aversion has been highlighted as a barrier to the adoption of innovations in agriculture [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, risk aversion alone cannot explain the behavior of farmers adopting innovations [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional cotton producers also have more chance to use the platforms than those combining both types of cotton (conventional and organic). This category of cotton farmers is the most numerous in the sample and the oldest in the sector. They know each other better and have an easier time exchanging experiences through a platform.\u003c/p\u003e \u003cp\u003eHaving a bank account makes it less chance to use agricultural exchange platforms. Banked cotton farmer\u0026rsquo;s exchange more with their bankers whom they prefer to contact physically instead of using a platform to communicate with the agricultural advisor. For the sale of their secondary crops (crops other than cotton) in order to repay their loans, they sometimes get help from the banker in finding customers rather than using a platform. Abid et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], however, reported that farmers' resistance to digital platforms differs depending on the area targeted by this platform. According to these authors, perceived usefulness as well as perceived intrusion are the main factors of farmers' resistance to the adoption of resource platforms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHaving a mobile money account\u003c/h2\u003e \u003cp\u003ePossession of a mobile money account by the cotton producer is influenced by age, possession of a bank account, secondary activity and use of labor. If age has practically no influence on having a mobile money account (odd ratio approximately equal to 1), this is not the case for having a bank account, carrying out a secondary activity and the use of labor which negatively influences the adoption of mobile money. In fact, cotton farmers with a bank account prefer to store their income in these accounts rather than using the mobile money account. To meet certain costs such as labor remuneration, cotton producers are sometimes forced to resort to banks and microfinance institutions to obtain loans, to the extent that they cannot access to this financing at the level of electronic money issuers. In addition, most cotton producers hold accounts with microfinance institutions. The latter do not offer digital solutions interfaced with \u0026ldquo;mobile money\u0026rdquo; (mobile banking) accounts unlike some traditional banks. It should also be noted that traditional banks have just set up in the study area (barely 3 years of activity for the first) and do not attract as many cotton farmers as microfinance institutions. Thus, cotton farmers prefer to carry out transactions from their bank account which also offers money transfer and other services. Furthermore, payment for cotton production is made through accounts opened by village cooperatives of cotton producers in these financial institutions, notably the \u0026ldquo;Caisses Locales de Cr\u0026eacute;dits Agricoles Mutuels\u0026rdquo; (CLCAM). To avoid additional account management costs, cotton farmers do not opt to hold two accounts (bank and mobile money). This result corroborates with those of Akinyemi and Mushunje [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] who conclude, among other things, that it is unlikely that individuals who own or have access to bank accounts will adopt mobile money to send or receive payments. According to these authors, the reasons why mobile money is not adopted may be due to the fact that the services rendered by mobile money are also provided by commercial banks and the use of mobile money may result in duplication of services and costs. Fanta et al. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] established that having a bank account, access to ATMs, mobile banking and internet banking are inversely related to having a mobile money account. These authors also found that mobile money adoption is lower among those who have a bank account as well as those who use ATMs, mobile banking and internet banking to access their bank account. Akinyemi and Mushunje [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] showed that apart from having a bank account, age, years of education, unemployment and mobile phone are the main determinants of the adoption of mobile money technology.\u003c/p\u003e \u003cp\u003eThe results obtained also go in the same direction as those of Ndiaye and Weibigue [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] who showed, among other things, that the variables: having a job, age, gender and currently attending school do not seem to play a determining role in the adoption of M-Banking in Senegal and that the marginal effects of these variables are not significant. Contrary to the results obtained in the present study, these authors also reported that belonging to a banking network, that is to say being a customer of a bank, a microfinance institution or a postal check center promotes the adoption of M-banking. Fall and Birba [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and Mbiti and Weil [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] also go in the same direction by mentioning that M-banking is a complementary service to traditional banking services. Other factors influencing the adoption of mobile money have been mentioned in the literature. Thus, Fall and Birba [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] found that gender, level of education, employment, knowing how to read and write and being banked positively influence the probability of adoption of mobile banking. Amegnanglo and Zounmenou [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] also showed that age, gender, turnover and education are the determinants of the use of electronic money account services by artisans in southern Benin. For Bidiasse and Mvogo [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], generally speaking, the advantages offered, the information available on how mobile money works and the proximity of the service are the variables for adoption of this service.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe purpose of this article is to analyze the determinants of adoption of digitalization among cotton farmers in the Municipality of Banikoara, a cotton basin in Benin. As part of this study, digitalization was subdivided into three (3) components, namely the use of machines and other digital tools (including mobile phones and tablets), the use of agricultural platforms and social networks, and possession of a mobile money account. The adoption of digitalization in these three (3) forms is influenced by holding an account in a bank or in a decentralized financial system (microfinance institution). This variable positively influences the use of machines and other digital tools then negatively the use of platforms and the possession of a mobile money account. In addition to this factor, the use of labor, the assessment of risk and the level of safety, the exercise of a secondary activity, knowledge of agricultural information and exchange platforms and the area sown also have a significant influence either on the use of machines and digital tools, or on the use of platforms or the possession of a mobile money account. Contrary to the results of several previous studies, sociological factors, namely gender, age, level of education and marital status, did not have significant effects on the adoption of digitalization by cotton farmers in the Municipality of Banikoara through the sample. All in all, starting from the structured analysis model, the factors linked to production activity (surface area, workforce), income (possession of a bank account or in a decentralized financial system), the assessment of digitalization (perception of risk and security level) and social influence (knowledge of agricultural information and exchange platforms) have a significant impact on the adoption of digitalization in cotton farming in the Municipality of Banikoara. Thus, any action in favor of promoting digitalization among this social layer essentially amounts to promoting their banking use while integrating and generalizing mobile banking services at the level of microfinance institutions and commercial banks, which are in contact with the producers. However, the adoption decision may also depend on exogenous factors (regulatory constraints for example) which were not taken into account in the study. The method of selection of cotton farmers (reasoned choice) can also influence the results obtained. Therefore, further research work is necessary to deepen and supplement the results obtained.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of interest\u0026rsquo;s statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1. SA: Conceptualization, Data curation, Investigation, Methodology, Resources, Visualization, Formal analysis, Software, Writing \u0026ndash; original draft preparation. 2. AHGA: Formal analysis, Software, Visualization. 3. AJY: Conceptualization, Methodology, Review. 4. AYJA: Conceptualization, Data curation, Methodology, Resources, Visualization, Formal analysis, Software, Writing \u0026ndash; review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eData will be made available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoloh Y, Cartmell-Thorp S. CTA report, African agricultural digitalization deciphered. Spore. 2019; 194(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.inter-reseaux.org/wp-content/uploads/sp194_pdf_f.pdf\u003c/span\u003e\u003cspan address=\"https://www.inter-reseaux.org/wp-content/uploads/sp194_pdf_f.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Exploratory analysis of the effect of the emergence of electronic money account services (Mobile Money) on financial inclusion in southern Benin. Rev Econ Theo Appl. 2020; 10(2): 167\u0026ndash;186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBidiasse H, Mvogo GP. Determinants of mobile money adoption: The importance of factors specific to Cameroon. Rev Econ Ind. 2019; 165: 85\u0026ndash;115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4000/rei.7845\u003c/span\u003e\u003cspan address=\"10.4000/rei.7845\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adoption, digitalization, cotton, producers, Benin","lastPublishedDoi":"10.21203/rs.3.rs-3834485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3834485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe widespread use of mobile telephony and the internet constitutes an asset for digitalization in the agricultural sectors in Africa. Cotton being the leading export crop in Benin, this study aims to understand the determinants of the adoption of digitalization (use of digital machines, membership of a social network for information exchange, use of a mobile money account) by cotton producers in the Municipality of Banikoara. In this context, a socio-economic survey was carried out with a sample of 314 producers, obtained by purposive sampling. The binary logistic regression method made it possible to identify the factors affecting the adoption of digitalization. Thus, the level of banking, the use of labor, the area of cotton sown and knowledge of agricultural information and exchange platforms had a significant impact on the use of digital machines in agriculture. Membership of a social network for information exchange between producers was influenced by the level of banking, the type of cotton grown, strengthening of relationships with others, the risk linked to the use of digitalization and the assessment of the level of security of digitization by the producer. Finally, the level of banking, the exercise of a secondary activity and the use of labor were the significant variables in the adoption of mobile money by cotton farmers in Banikoara. However, the use of more advanced technologies such as drones and sensors was not yet a reality for these producers. This information is very useful for any project to popularize these new technologies.\u003c/p\u003e","manuscriptTitle":"Adoption factors of digitalization in cotton farming in the municipality of Banikoara in Northwestern Benin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 11:42:21","doi":"10.21203/rs.3.rs-3834485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-07-16T13:52:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220976133020729553730821413152793540114","date":"2024-07-15T06:25:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281370908193375783897153895402841968964","date":"2024-07-12T11:11:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-05T07:21:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3968a330-da7d-4f1e-be84-dae76c60f6d3","date":"2024-02-07T06:13:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61b89896-4131-47d8-8a05-d68b396a1f10","date":"2024-02-07T02:20:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-05T02:14:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-05T02:17:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-05T02:11:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Agriculture","date":"2024-01-04T11:56:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Agriculture](https://www.springer.com/journal/44279)","snPcode":"44279","submissionUrl":"https://submission.nature.com/new-submission/44279/3","title":"Discover Agriculture","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bbe96299-97e2-481a-a764-81b0a2e347a6","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-18T04:38:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 11:42:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3834485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3834485","identity":"rs-3834485","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}