Unlocking the potential: challenges and factors influencing the use of ICTs by smallholder maize farmers in Zimbabwe

Nyakudya, Stella; Jambo, Newettie; Madududu, Pamela; Manyise, Timothy Article Unlocking the potential: challenges and factors influencing the use of ICTs by smallholder maize farmers in Zimbabwe Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Nyakudya, Stella; Jambo, Newettie; Madududu, Pamela; Manyise, Timothy (2024) : Unlocking the potential: challenges and factors influencing the use of ICTs by smallholder maize farmers in Zimbabwe, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-16, https://doi.org/10.1080/23322039.2024.2330431 This Version is available at: https://hdl.handle.net/10419/321455 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Unlocking the potential: challenges and factors influencing the use of ICTs by smallholder maize farmers in Zimbabwe Stella Nyakudya, Newettie Jambo, Pamela Madududu & Timothy Manyise To cite this article: Stella Nyakudya, Newettie Jambo, Pamela Madududu & Timothy Manyise (2024) Unlocking the potential: challenges and factors influencing the use of ICTs by smallholder maize farmers in Zimbabwe, Cogent Economics & Finance, 12:1, 2330431, DOI: 10.1080/23322039.2024.2330431 To link to this article: https://doi.org/10.1080/23322039.2024.2330431 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 21 Mar 2024. Submit your article to this journal Article views: 1394 View related articles View Crossmark data Citing articles: 4 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Unlocking the potential: challenges and factors influencing the use of ICTs by smallholder maize farmers in Zimbabwe Stella Nyakudya a , Newettie Jambo a , Pamela Madududu b and Timothy Manyise c a Department of Agricultural Economics and Development, Manicaland State University of Applied Sciences, Mutare, Manicaland, Zimbabwe; b Agricultural Economics, Extension and Rural Development, University of Pretoria, Hatfield, South Africa; c WorldFish, Penang, Malaysia ABSTRACT The study aims to investigate the challenges faced by smallholder maize farmers and identify the pivotal factors influencing the adoption of ICTs in agriculture. A blend of descriptive and probit regression analytical techniques is applied by analyzing crosssectional survey data from a selected multistage random sample of 155 maize farmers in Marondera Rural District, Zimbabwe. The study findings revealed that the foremost obstacles hampering ICT adoption include electricity shortages attributable to loadshedding and persistent communication network challenges. Additionally, it was observed that the utilization of mobile phones for agricultural purposes remains moderately low, while the use of computers in agriculture is strikingly minimal. The probit regression model results revealed that age, gender, access to credit, and extension contact are significant determinants for computer use in agriculture. Furthermore, critical influencers of mobile phone adoption for agricultural activities that were identified include farming experience, engagement in non-farm activities, credit access, remittances, and extension visits. The study recommends fostering an enabling environment to encourage farmers to embrace ICTs for agricultural purposes. To support this endeavor, the study advocates an improved agricultural training and extension system, with particular attention to less experienced and elderly farmers who may exhibit resistance to technological advancements. ARTICLE HISTORY Received 5 January 2024 Revised 7 March 2024 Accepted 10 March 2024 KEYWORDS Smallholder farmers; probit regression model; challenges; ICTs; Zimbabwe REVIEWING EDITOR Goodness Aye, University of Agriculture, Makurdi Benue State, Nigeria SUBJECTS Agricultural Economics; Development Economics; Rural Development; Econometrics; Economics JEL CLASSIFICATIONS Q16; Q28; O10 1. Introduction 1.1. Background Agricultural development is crucial for emerging countries to foster economic growth and support their growing populations (Pawlak & Kołodziejczak, 2020; Sassi, 2023). Agriculture reduces health-related costs by providing crops that prevent undernutrition, obesity, and diet-related diseases (Adenle et al., 2019). However, in many developing countries, including Zimbabwe, smallholder farmers, who contribute approximately 80% of the agricultural output, continue to grapple with numerous challenges (Kamara et al., 2019). These challenges encompass limited access to credit and markets, volatile market prices, pest and disease pressures, and changing environmental conditions (Hlatshwayo et al., 2021; Paudel et al., 2023). Therefore, the smallholder farmers’capacity to adapt, cope with, and navigate the increasingly dynamic and complex markets is hindered (Abate et al., 2023). Research suggests that many of these challenges can be overcome if smallholder farmers adopt technologies that provide access to up-to-date information, facilitating prompt and well-informed decisionmaking in their agricultural operations (Abate et al., 2023; Campo et al., 2017; Nwafor et al., 2020). Considering the dynamic and complex markets, characterized by an increasing demand for adaptable CONTACT Newettie Jambo [email protected] Agricultural Economics and Development, Manicaland State University of Applied Sciences, Mutare, Manicaland 7001, Zimbabwe ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2330431 https://doi.org/10.1080/23322039.2024.2330431 decision-making, adopting Information and Communication Technologies (ICTs) has become imperative (Ali & Kumar, 2011; Ayim et al., 2022). ICTs encompass a spectrum of technologies, tools, and systems for collecting, processing, storing, transmitting, and disseminating information electronically (Mdoda & Mdiya, 2022). This adoption has particularly become crucial for accessing and disseminating agricultural information in developing countries (Kante et al., 2019; Mayoyo et al., 2023). Existing studies have extensively debated the benefits of ICT use in agriculture. For instance, evidence suggests that the use of ICTs equips farmers with reliable market information, enabling them to refine their marketing strategies, thereby maximizing profitability and improving their standard of living (Adegbidi et al., 2012; Magesa et al., 2020; Mayoyo et al., 2023). By furnishing farmers with comprehensive market information, ICTs promote active market participation (Chancellor, 2023; Makaula, 2021; Mishra et al., 2020). Additionally, ICTs facilitate the exchange of essential information for enhancing agricultural production, ensuring broader access to various products, services, and initiatives (Musungwini et al., 2023). This empowerment enables farmers to make informed decisions and optimize resource utilization in agricultural production (Abebe & Cherinet, 2019; Otene et al., 2018; Spielman et al., 2021). Ultimately, this drives agricultural transformation (Ajani, 2014; Obeng et al., 2019), fosters growth in the agricultural sector, and contributes to economic sustainability and self-reliance. Similar to other developing countries, agriculture in Zimbabwe is evolving into a high-tech industry as farmers increasingly adopt ICT technologies to monitor their crops and livestock and enhance their production methods. This transformation is reinforced by government policy to develop e-agriculture (Masuka et al., 2016), facilitating collaborative knowledge exchange between farmers and agricultural extension service providers (Musungwini et al., 2023). Despite the government efforts and rich literature on the pivotal role ICTs play in accessing agricultural information and the related benefits, only a few studies have investigated the use of ICTs among smallholder farmers in Zimbabwe, and these studies observed the low usage of ICTs for agricultural purposes (Ifeoma & Mthitwa, 2015; Musungwini et al., 2023). Consequently, an understanding of ICT usage among smallholder farmers in rural Zimbabwe remains limited, including the challenges they encounter and the primary factors driving its adoption. A more comprehensive grasp of the barriers to ICT utilization and the critical factors influencing its use can provide valuable insights into how to effectively reach and support smallholder farmers in the context of the ICT revolution (Fawole & Olajide, 2012). 1.2. Conceptual framework The conceptual framework (Figure 1) guiding the study analysis draws from literature on the challenges and factors affecting the use of ICTs in agriculture. This literature suggests that these factors can be grouped into social, economic and institutional factors (Ali & Kumar, 2011; Mdoda & Mdiya, 2022; Yaseen et al., 2016). The institutional factors in the conceptual framework reflect some of the challenges being faced by smallholder farmers concerning the use of ICT, which is the first objective of the study. The utilization of computers and mobile phones for agricultural purposes can be faced with several challenges, including illiteracy, electricity and network issues, insufficient means of transportation for internet access, a lack of repair facilities, affordability constraints regarding ICT devices, and a deficit in knowledge and available time (Ahsan et al., 2022; Misaki et al., 2018; Mutambara & Munodawafa, 2014; Okello et al., 2012; Tata & McNamara, 2016). Regarding the second objective of the study on the factors influencing ICT use, studies in other countries suggest that socioeconomic and demographic factors encompass age, marital status, household size, level of education, land size, farming experience, engagement in non-farm activities, credit acquisition, and ICT experience (Ali, 2012; Awuor & Rambim, 2022; Derso et al., 2014; Khan et al., 2022; Rajkhowa & Qaim, 2022; Wawire et al., 2017). 1.3. Objectives As an empirical window to understand the challenges and factors influencing ICT usage, this study examined the case of smallholder farmers in rural Zimbabwe, with a specific focus on smallholder maize farmers in Marondera District, Zimbabwe. Hence, the study’s overarching objectives are two-fold: (i) to examine the challenges faced by smallholder maize farmers when using ICTs in agriculture, and (ii) to 2 S. NYAKUDYA ET AL. investigate the factors determining smallholder farmers’use of ICTs, including mobile phones and computers, for farming purposes. 2. Materials and methods 2.1. Study area The study was conducted in Marondera Rural District, located in the Mashonaland East Province of Zimbabwe. It is bounded by Murewa district to the north, Makoni district in the province of Manicaland to the east, Wedza and Chikomba Districts to the south, as well as Manyame and Goromonzi Districts to the west (Marondera RDC, 2023). Marondera Rural District is one of the nine districts in the Mashonaland East Province, covering a total area of 3,414 square kilometers and accommodating a population of 136,173 (ZIMSTAT, 2022). This district falls within agricultural, natural region 2, with an average annual rainfall of 500 mm to 1000 mm and relatively constant temperatures year-round (Mafuse et al., 2021). In addition, the district is reported to have the most fertile lands, making it well-suited for agriculture (Mafuse et al., 2021). Farmers in this district primarily engage in two major agricultural activities, maize and beef production, although it grapples with recurring dry spells, necessitating irrigation systems (Musoma, 2016). Administratively, the district is divided into 23 wards (Marondera RDC, 2023), which can be classified into three distinct regions: urban, peri-urban, and rural communities constituting resettlement areas. The resettlement areas are home to small-scale (A1) and large-scale (A2) commercial farmers. Notably, this district holds the highest number of A1 farmers in the province (Mafuse et al., 2021). Previous studies have underscored the significance of farming in these resettlement areas as the primary source of employment and income for many households. This study focused on A1 farmers, with small farm sizes averaging approximately 6 hectares of arable land (Musoma, 2016). Figure 2 depicts the study’s geographical location. 2.2. Research design A blend of descriptive and causal research designs was used in this study. The study employed a descriptive research design to explain the current circumstances including the challenges on the use of ICT by smallholder maize farmers in the Marondera District. A causal research design is then employed Figure 1. Conceptual Framework on the use of mobiles and computers in agriculture. (Source: Authors’conceptualization). COGENT ECONOMICS & FINANCE 3 to assess the factors influencing the decision by smallholder maize farmers to use ICT in computers and mobile phones in their farming operations. The quantitative approach used in this study helps to understand the farmers’characteristics, their farming activities and the challenges they are facing in the use of ICT for agricultural purposes. 2.3. Sampling The study’s target population comprised smallholder farmers who grew maize during the 2022–2023 cultivation season. The study employed a multistage random sampling method, targeting all smallholder farmers in the study area. The sampling strategy commenced with a purposive selection of the Marondera Rural District based on its proximity, followed by the selection of wards with the highest concentration of maize farmers as maize production is the primary agricultural activity. A comprehensive list of all farmers was obtained from extension officers in the district. Given the uneven distribution of maize farmers across wards, the study conducted a proportionate-to-size selection of maize farming villages within the chosen wards to ensure a representative sample of smallholder maize farmers, encompassing both those who use and those who do not use ICTs, including mobile phones and computers, for agricultural purposes. Questionnaires containing inaccuracies were disregarded, therefore, a sample of 155 smallholder maize producers was employed for the study. 2.4. Data collection The primary data used in this study was collected using questionnaires that were administered by trained individuals. The designed questionnaire captured information on maize production, ICT usage by farmers as well as the demographic and socio-economic characteristics of the households. Before collecting data, a mobilization exercise was conducted to inform local authorities and community leaders in the study area about the survey. This proactive approach proved instrumental in maximizing cooperation among the respondents (Lemay & Durand, 2002). The data collection team comprised four Figure 2. Map for Marondera, Zimbabwe. (Source: Dzwairo et al., 2006). 4 S. NYAKUDYA ET AL. enumerators and one supervisor, all well-qualified and with prior experience collecting farm-level data in the same region. Additionally, they underwent a one-day training session to familiarise themselves with the survey’s objectives and methodology and comprehensively understand each survey question. The survey was conducted in November 2022, with all selected participants agreeing to participate. A quality check mechanism was established to ensure the collection of high-quality data. A WhatsApp group was also created to facilitate real-time communication among all enumerators, supervisors, and the research team. This platform was instrumental for seeking clarification on questions, sharing challenges, and discussing experiences. At the end of each workday, each enumerator submited a daily report to their supervisor for review and feedback. 2.5. Data analysis Data analysis in this study predominantly employed descriptive statistics and a probit regression model, with the results presented in tabular format. The data obtained from the questionnaire surveys was first coded using Microsoft Excel, before being analyzed using Stata software. First, the study used Pearson’s chi-square test for independence to understand the relationships between categorical variables. Utilizing a 5-point Likert Scale, the challenges faced by smallholder farmers in their use of ICT were systematically ranked to identify the most prevalent issues among this group of farmers. The challenges were ranked from strongly disagree to strongly agree. When using the Likert Scale, the responses for strongly disagree were assigned a value of −2, whilst disagree, neutral, agree and strongly agree responses were assigned values of −1, 0, þ1 and þ2, respectively. The frequencies for each challenge were then multiplied by the respective value assigned for each response and then added to get the ranking score for each challenge. The challenge with the highest score was ranked first whilst the challenge with the lowest score was ranked the last. Subsequently, the study employed the probit regression model to gain deeper insights into the relationship between ICT use in agriculture and various socio-economic factors. These factors encompassed age, gender, marital status, educational attainment, farming experience, household size, access to credit, and several other pertinent variables. This comprehensive approach unraveled the multifaceted dynamics surrounding the utilization of ICT among smallholder farmers. 2.5.1. Analytical model This study employed a probit regression model to elucidate the determinants of ICT usage in agriculture. As recommended by Wooldridge (2012), the probit model was chosen for its adherence to a standard normal distribution, which effectively addresses various specification issues. The dependent variable in this analysis is binary, representing either the utilization of ICT (coded as 1) or non-utilization (coded as 0) (Gujarati, 2004). Specifically, the study centered on mobile phones and computers, the primary ICT tools employed by farmers for accessing agricultural information (Muhammad et al., 2019; Mdoda & Mdiya, 2022). According to Wooldridge (2012), the probit model can be derived from a latent variable model that satisfies the assumptions of a classical linear model. The latent variable (y) is determined using the following equation 1: y¼b0þx,bþe(1) Where: y ¼1[y>0], b¼coefficients and E¼random errors, assumed to be normally distributed, with a mof 0 and a r 2 of 1. In this study, y is specified as 1 for ICT users and 0 for non-users, while x represents the vector of independent variables. The response probability of y was derived using Equation (2): Py¼1jx ðÞ ¼Ub 0þb1x1þ...þbkxk ðÞ ¼Gðb0þx,bÞ(2) where P(y ¼1jx) represents the conditional probability of ICT usage, Uis the cumulative distribution function of a standard normal distribution, which ensures that the probability lies between 0 and 1 (Habyarimana, 2015; Wooldridge, 2012). The parameters to be estimated are denoted as b 0 ,b 1 ,…,b k . The variables x1,x2…,xkrepresent a set of predictor variables, which are socio-economic factors like age, gender, remittance, credit, marital status, level of education, farm size, and farming experience. COGENT ECONOMICS & FINANCE 5 Equation (3) depicts the specified probit regression model to understand the relationship between ICT usage in agriculture and various socio-economic factors. P ICT User ¼1jx ðÞ ¼Uðb0þb1age þb2gender þb3maritalstatus þb4householdsize þb5farmingexperience þb6farmsize þb7remittance þb8creditaccess þb9educationlevelÞ (3) Since the primary interest of the study lies in estimating how each variable (x) influences the probability of using ICT for agricultural purposes, the magnitudes of individual coefficients (b) obtained from the probit model results may not provide a clear understanding (Wooldridge, 2012). Therefore, this study incorporated average marginal effects for dummy and continuous variables. Marginal effects enable one to quantify the impact of a one-unit change in an explanatory variable on the likelihood of achieving a favorable outcome (Nyakatonje and Jambo, 2023; Habyarimana, 2015; Wooldridge, 2012). Following the estimation of the probit model in Equation (3), the study derived the average marginal effects (AME) of the various explanatory variables on the probability of using ICT for agricultural purposes as outlined in Equation (4): oPy i¼1jxi ðÞ =oxi¼oEyjxi ðÞ =oxi¼Uðb0þxibÞb(4) 2.5.2. Description of variables used in the model Table 1 describes the explanatory variables considered in this study and their respective expected regression outcomes. 3. Results & discussion 3.1. The socio-economic characteristics of the respondents related to ICT use The study categorized the respondents into those who used ICT for agriculture and those who did not. Table 2 shows that only 73 farmers used mobile phones for agricultural purposes, while 82 respondents did not use mobile phones for agricultural purposes. The level of mobile use in agricultural activities in the study area is low, as highlighted by the 47.1% in Table 2. The results also indicate that, out of the 155 respondents, only 34 farmers utilized a computer for agricultural tasks, with the majority not employing computers for any agricultural-related activities. This underscores the deficiency in computer usage for agricultural purposes (21.94%) in the Marondera Rural District. This low ICT use can be Table 1. Definitions and descriptions of variables. Variable Description Measurement Expected results Age The age of the farmer. Years Positive/ negative Maritalstatus Marital status of the farmer. 0 ¼single, 1 ¼married, 2 ¼divorced, 3 ¼widow Positive/negative Household size Household size Number of people living together in one dwelling Positive Farming experience Number of years in farming Years Positive Gender Male or female. 1 ¼male, 0 ¼female Positive/negative Education level Number of years in formal education. Years in formal education. Positive Farm size Total hectares of land owned by a farmer. Hectares positive Non-farm activities Having non-farm activity. 1 ¼yes, 0 ¼no positive Access to credit Having received credit 1 ¼yes, 0 ¼no positive Extension visit Having frequent extension visits 1 ¼yes, 0 ¼no positive Remittances Having received remittance 1 ¼yes, 0 ¼no positive Table 2. Mobile and computer use for agriculture. Mobile use for agriculture Freq Percentage n¼155 Yes 73 47.10 No 82 52.90 Computer use for agriculture Yes 34 21.94 No 121 78.06 6 S. NYAKUDYA ET AL. attributed to the prolonged communication network problems and electricity challenges caused by load shedding. These results are similar to the findings of previous studies which associated the low ICT adoption in agriculture with several challenges such as lack of awareness and confidence, unreliable internet, lack of technological skills and lack of access to internet gadgets (Lawal-Adebowale, 2015; Muktar et al., 2022). Table 3 compares mobile ownership to mobile use for agricultural purposes. The study results indicate that, out of the 137 farmers who owned mobile phones, only 73 used mobile phones for any activities related to their farming work. This implies that a large number of farmers who own mobile phones do not use them for agricultural purposes, but rather for social reasons. Mousavi et al. (2018) pointed out that there is low use of ICT for agricultural purposes mainly because the majority of people do not use ICT gadgets such as mobile phones for agricultural benefit but rather for social chatting and messaging. Table 4 also compares the level of computer use for agriculture against the ownership of computers by the respondents. Of the 66 farmers who indicated they own computers, 34 individuals used them for agricultural-related activities. As indicated by Lawal-Adebowale (2015), even though farmers may have ICT gadgets such as mobile phones and computers, the majority of them lack the knowledge and skills needed during the use and maintenance of technology equipment situation. Table 5 reveals a significant association between the use of mobile phones in agriculture and several demographic factors, including gender, marital status, access to remittances, and access to credit, as indicated by their respective p-values. The results indicate that most male farmers did not utilize mobile phones for agricultural purposes, whereas many female farmers incorporated mobile phones into their agricultural activities. This may be because male farmers mostly have non-farming activities which leaves them without enough time to attend trainings being brought to farmers on how to use ICTs for agricultural purposes. Lwoga and Chigona (2020) mention that smallholder farmers lack proper training and knowledge concerning the use of ICTs like computers for agricultural purposes. Farmers who were Table 3. Comparison between mobile ownership and mobile use for agricultural purposes. Mobile ownership Mobile use for agriculture TotalYes No Yes 73 64 137 No 0 18 18 Total 73 82 155 Pearson chi2(1) ¼18.1298; Pr ¼0.000 Table 4. Comparison between computer ownership and computer use for agricultural purposes. Computer ownership Computer use for agriculture TotalYes No Yes 34 32 66 No 0 89 89 Total 34 121 155 Pearson chi2(1) ¼58.7315; Pr ¼0.000 Table 5. Chi-square tests of association regarding mobile use in agriculture. 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