Beef Farmers in Kenya Nokia Cell Phone
1. Introduction
Lack of access to information and cognition transfer can hamper agricultural production in rural farming communities in sub-Saharan Africa (SSA). Agricultural, market, and conditions information is critical to agricultural productivity, especially for reducing uncertainty and risk associated with farthermost weather events and affliction (Baumüller, 2013). The dissemination of agro-meteorological data can amend livelihoods by reducing uncertainty and enable improved inputs and technology adoption (Hansen et al., 2007). Admission to information through mobile phones and mobile internet can also help agriculturalists manage risk and reduce vulnerabilities to a changing climate (Baumüller, 2013).
Data communication technologies (ICTs) such equally mobile phones are touted every bit digital platforms with transformative potential to reach many farmers at once across rural settings (Santosham & Lindsey, 2015; Globe Depository financial institution Group, 2018). Large-scale investment in ICT infrastructure has led to growth in telecommunications connectivity of unprecedented calibration across Africa (World Bank Grouping, 2018). Every bit the cost of mobile phones have fallen and connectivity has spread, phone ownership and net access take become possible for populations in the continent's everyman-income areas (Wyche & Olson, 2018). With this uptake of mobile phones, users can subscribe to receive mobile phone-enabled services or 'm-services' to access agro-meteorological (Baumüller, 2013) and market information (Wyche & Steinfield, 2016).
M-services deliver electronic media content through mobile technologies and is an umbrella term that includes grand-agri, m-commerce, yard-banking or thousand-payments. Thou-services come up in varied forms, including Short Message Service (SMS), Unstructured Supplementary Service Information (USSD), mobile applications (apps) and helplines. The difference between SMS and USSD protocol is that SMS is a text messaging service, whereas USSD protocol are in the form of 'Quick Codes'. Depending on the electronic media thousand-services contain, they tin be accessed by phones with and without net access. G-services tin be used to connect buyers to sellers, disseminate full general information almost farming and livestock (such as market data on prices), and send alerts on pest and affliction threats (Baumüller, 2018). Some 1000-services are gratuitous to employ or may crave a cost to use advanced features, while others are entirely proprietary. For example, Ujuzi Kilimo in Kenya offers actionable recommendations to farmers through subscription-based SMS and USSD services. 1
Whether m-services can ameliorate agricultural livelihoods is a question facing scholars and development programmes focused on addressing rural livelihood vulnerability. Qiang et al. (2012) showed that increased admission to climate, crop disease, and market place information via thou-services improved farmers' production and profitability in Kenya. However, wealthier, educated, and typically urban populations have greater admission and therefore do good from m-services in comparing to rural, poorer populations, especially rural women (David et al., 2005; Porter et al., 2012; Wyche & Olson, 2018; Wyche et al., 2019). Every bit a event of this limited access to information for some populations, scholars take questioned whether mobile-based market place data tin improve circulation of market prices and reduce information asymmetries between farmers and buyers. Srinivasan and Burrell (2013) and Wyche and Steinfield (2016) have detailed the underlying barriers to using mobile phones for accessing Market Data Systems (MIS). These barriers include, but are non limited to, toll of airtime, challenges with charging faulty and depression-quality batteries, language, and literacy (Srinivasan & Burrell, 2013; Wyche & Steinfield, 2016).
However, in the literature on ICT use in SSA, far less attention has been placed on understanding the different types of data communicated via k-services, how farmers access that information, and possible factors affecting the likelihood of m-service apply. Our written report addresses these cognition gaps by drawing on a sample of more than than 500 smallholder farmers in rural key Kenya. We first identify who uses thousand-services, the types of m-services that survey respondents use, and how the 1000-services are used. We then assess the factors that affect the likelihood of g-service employ by modelling the associations between individual-level characteristics and three classes of agronomical data bachelor via k-services: farming data, buying and selling farming products, and alerts on farming activities. The latter helps united states of america sympathize the underlying factors impeding k-services adoption. We then discuss the interactions between education, income, and gender with smartphone ownership; the important role that farmer organizations play in yard-services adoption; and how developers of m-services tin utilise this data to target unreached individuals.
2. Literature review
ii.1. ICTs, adaptive capacity, and vulnerability
ICTs are often bandage every bit technologies that tin increase access to data and resources and connect individuals. Information disseminated via m-services are therefore seen as important tools for helping farmers adapt and to address vulnerability (Eakin et al., 2017), where vulnerability, per the IPCC (2007) is 'the degree to which an environmental or social organisation is susceptible to, and unable to cope with, adverse effects of climatic change, including climate variability and extremes' (p. 883). Vulnerability is often conceptualized every bit including three interlinking elements: exposure, sensitivity, and adaptive capacity (Adger, 2006). While exposure relates to the caste and type of the perturbation, adaptive capacity relates to the chapters of individuals or groups to manage and influence their resources and risks in the face of a perturbation (Waters & Adger, 2017). Both the degree of exposure and adaptive chapters shape a arrangement'due south sensitivity to that perturbation. Many determinants of household-level adaptive capacity have been identified, which can chronicle to access to avails and resource (Moser, 1998). At the local level, one such determinant has been admission to data resources and the power of decision-makers to marshall the information (Fawcett et al., 2017; Smit & Wandel, 2006). Within this context, ICTs provide an efficient means to accomplish a growing user base and build adaptive capacity through enabling access to disquisitional information and facilitating a process of learning (Eakin et al., 2017).
In that location is, still, contradictory evidence about how useful ICTs are or can be for addressing vulnerability, with some studies finding positive effects and others no impact. In a review of climate modify accommodation and ICTs in the Caribbean and Latin America, Eakin et al. (2017) suggest that ICTs back up adaptation through increasing social capital, improving access to disquisitional information for controlling, and coordinating actors. Marenya and Barrett (2007) constitute that lack of access to information, land, and credit, constrained natural resource management efforts and thus rendered smallholder farmers in western Republic of kenya more vulnerable to climate variability. Similarly, researchers contended that in Laikipia county, Kenya, limited admission to agro-meteorological data hampers adaptive capacity (Ogalleh et al., 2012; Wiesmann, 1998). Bryan et al. (2009) came to like conclusions that lack of access to data is a main barrier to adaptation among Ethiopian farmers. Specific to the bear upon of DrumNet, a phone-based MIS, Ogutu et al. (2014) establish a positive influence on labor productivity, seeds, fertilizers and country in three Kenyan provinces.
A growing body of literature criticizes ICTs for development programmes and provides evidence for why they do not work. In the domain of distributing market price information through MIS, Camacho and Conover (2011) and Fafchamps and Minten (2012) showed poor adoption of MIS and their lack of bear upon on agricultural outcomes. Following this work, Burrell and Oreglia (2015) sought to better understand Ugandan and Chinese agriculturalists' decision-making processes and need for market price information. They suggested that using mobile phones to collect and distribute market information through MIS is of express relevance since information about toll is only one of several factors that aids decision-making (Burrell & Oreglia, 2015). Similarly, Srinivasan and Burrell (2013) suggest that mobile phones should not be given an over-privileged role in seeking marketplace cost information. Finally, although there are many apps for agriculture in Kenya and SSA, information technology is still unclear how many people actually apply them after initially subscribing and/or downloading them.
2.2. Adoption and utilization of Thousand-services in Kenya
Adoption of mobile phones and thousand-services is attributed to several factors related to the local context. In Kenya, agriculture and livestock are key economic sectors and contribute to more than one-third of full gdp (Gross domestic product) (Kenya Bureau of Statistics Economic Survey, 2017). The Communications Dominance of Kenya (2018) reports that equally of March 2018, 95.one % of the adult population had a mobile telephone subscription and 42.ix% percent had access to broadband internet. The growing affordability of net access coincides with an impressive number of m-services available for Kenyan farmers to utilise. For instance, Twiga Foods connects horticultural farmers to buyers using a mobile-based buyer-to-buyer platform, 2 and Mkulima Online, M-Subcontract, provides admission to crop prices and connects buyers and sellers (Baumüller, 2013, 2015). The high adoption rates of mobile phones in Republic of kenya compared to other sub-Saharan African countries, see Figure one, may be one factor for the multitude of m-services available.
Smallholder farmers' employ of mobile phone services in central Kenya
Published online:
09 April 2020
Figure 1. Growth rate of cellular usage as divers by mobile telephone subscriptions per 100 people between 1990–2017. Mobile phone subscriptions in Kenya appear college than mobile phone subscriptions of to the lowest degree developed countries according to United nations Classification and Sub-Saharan African countries excluding high income. Data accessed on eighteen June 2019. Data source: Globe Bank. Source: International Telecommunication Union, Earth Telecommunication/ICT Development Report and database (2018).
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tcld20/2021/tcld20.v013.i03/17565529.2020.1748847/20210421/images/medium/tcld_a_1748847_f0001_oc.jpg"})
Given the ubiquity of mobile phone buying in Kenya, ICTs may go along to have increased importance in the sphere of agronomical extension. The aim of agriculture extension is to provide services and advice to rural farmers and their families then they may maximize the resources made available to them (Katz & Barandun, 2002). Private extension services exist in Kenya in part due to the inefficiency of public extension, which was identified as a gene that impedes agricultural development and perchance an explanation for low yields (Muyanga & Jayne, 2008). M-services tin can serve as a gap-filling mechanism for the agriculture extension system. While governments may provide their own m-services through websites and/or SMS or USSD services, partnerships with other sectors such equally private companies, NGOs, and enquiry institutions can help bolster farmer productivity and better the limited capacity of government programmes (Caine et al., 2015; Donovan, 2017).
2.iii. Factors that influence adoption and use of M-services
Factors that influence technology adoption and use are typically related to education level, age, and gender (Meso et al., 2005); however, toll may too pose a barrier to usage. Ogutu et al. (2014) constitute a significant difference in average age betwixt DrumNet participants and nonparticipants; still no difference in gender. Wyche and Steinfield (2016) investigated factors that impede adoption of the m-service K-Farm in western Republic of kenya. The majority of interviewees endemic characteristic phones, and while participants could theoretically admission K-Farm with those phones, they did not. Wyche and Steinfield (2016) note a diverseness of barriers including limited amounts of phone credit which stifles SMS use, phone charging limits, phones with considerable clothing and tear that prohibited use, every bit well as the perception that mobile phones are for phonation communication rather than SMS-interaction.
Afterward initial adoption of thousand-services, appropriate utilize of information requires having credible information and trusting in that information. Mittal et al. (2010) establish that farmers apply mobile-enabled agricultural data when the information is timely, of practiced quality, and when they trust the information. For SMS information specifically, trust needs to be established: the recipient is unlikely to take SMS data coming from an unknown sender in the slurry of other spam SMS (Cheney, 2018; Crandall, 2012).
Enquiry as well suggests that individuals' perceptions are of import determinants of individual technology utilise. Thiga and Ndungu (2015), for example, identify lack of sensation as the primary reason why agriculture extension officers do not employ ICTs in Republic of kenya. Mobile applications in particular were the least utilized form of ICT amongst respondents (Thiga & Ndungu, 2015). Since agronomics extension officers provide outreach to farmer organizations such as agricultural and livestock cooperatives they are positioned to be agents of change and advancement within a community. Nonetheless, in cases where the extension agents are non exhibiting high adoption or promotion of these useful services, farmers may do good from information transfer through other routes such as mobile devices and their non-extension social networks.
three. Research methodology
iii.one. Report surface area
Laikipia, Meru and Nyeri counties come across at the northern and western slopes of Mount Kenya in the semi-barren highlands of Kenya. We focus on 35 smallholder farming communities within the post-obit sub-counties: Laikipia N and Laikipia E (Laikipia), Buuri, North and Central (Meru), and Kieni East (Nyeri). Many of the households are part of farmer organizations, which can exist classified as farmer cooperatives, farmer groups, or local water resources governance groups called Community H2o Projects (CWPs). In this written report, we characterize farmer cooperatives and CWPs equally formal organizations in comparison to farmer groups, which are informal organizations.
Farmer cooperatives are official, government-registered forms of collective action that are typically composed of smallholder farmers working together beyond a large area on agricultural product, sale of products, and opportunities to enter higher-value markets (Markelova et al., 2009; Narrod et al., 2009). They follow a formal construction of organization with written rules. One of the main benefits of farmer cooperative memberships is the reduction of transaction costs among smallholder agriculture producers who are often located in remote areas and have express economic capacity to enter production systems (Markelova et al., 2009). In contrast to farmer cooperatives, farmer groups are breezy and may not be officially registered with the regime. Farmers either cocky-organize or are organized into groups past agricultural extension agents to facilitate trainings and noesis exchange. Members in these informal organizations typically share data nigh best practices in agriculture. They may besides collectively pool savings to assistance beget agricultural inputs or assist group members in times of need. Community Water Projects (CWPs) are a third type of farmer system. CWPs utilize formal institutions where members officially meet and collaborate with each other on a weekly to monthly basis to attend meetings, maintain irrigation infrastructures, and access irrigation water resources. To become a CWP fellow member, farmers typically pay a joining fee to connect to irrigation h2o via piped networks from rivers off Mountain Kenya, as well as monthly maintenance fees.
3.2. Information collection
A team of eight Kenyan enumerators conducted the household survey using Qualtrics software between June and July 2018 (Qualtrics, 2019). The multilingual squad of six women and ii men conducted the survey in Kiswahili, Kikuyu, or Kimeru, depending on the respondent's background. We interviewed 605 respondents; all the same 577 responses were used for the written report. We removed respondents who either did not betoken farming as their primary occupation, were flagged by enumerators every bit giving incomplete answers, or refused to reply or did not know their level of instruction attained. Respondents were non compensated for their participation in the study. Run into Supplementary Info (Southward.I.) for more than information on survey methodology.
The households selected for the report are a representative sample of households that receive water from CWPs. Additionally, our sample is not representative of all Kenyan smallholders. These households were selected equally part of a 5-year multi-institutional enquiry project conducted in the region (Lopus et al., 2017; McCord et al., 2017). The five-year written report assesses agronomic controlling of irrigated (CWPs) and not-irrigated households in Laikipia, Meru and Nyeri counties. At the first of the project, we used a randomized sampling approach of farmers within CWPs. As the project expanded, we gathered longitudinal data too as data from non-CWP members considering we could non get in contact with everyone who had previously been sampled. Thus, we augmented our dataset with the assistance of local guides and selected neighbouring households within the communities. Respondents were either the head of household or spouse of the household head.
The survey took 90 min on boilerplate and covered a range of topics including agricultural management, perceptions of rainfall and climate alter, use of atmospheric condition and climate services, use and barriers to use of ICTs, migration, and household socio-demographics. The ICT module was administered about halfway through the survey and took approximately 20 min. The ICT module followed the pattern of the U.Due south. Agency for International Development's (USAID) toolkit on gender and ICT (Highet et al., 2017) and comprised of three parts: demographics, access and use of ICTs, and mobile farming use.
The USAID applied toolkit offers quantitative and qualitative methods for data collection on access, usage, barriers and perceptions of ICTs such every bit mobile phones, radios and other internet-enabled devices. We selected and modified questions from the toolkit and nosotros pre-tested the questions in three airplane pilot interviews. Additionally, we used a four-day enumerator training session to refine the questionnaire; notwithstanding, no major corrections were made. Our analysis focuses on use and not-use of 3 1000-services, which was determined past the responses to the question: 'During the last growing season, did you use your mobile phone to access any of the following services for your agronomical/livestock direction? (e.g. includes Facebook, Mkulima Bora, WhatsApp, M-Subcontract through SMS, apps such as Mkulima Bora)'. Respondents then answered 'yes' or 'no' to the following 1000-services: Accessing farming (either livestock or agriculture) information, buying and selling agricultural or livestock products, and receiving important data or alerts on agriculture/livestock activities. We also asked respondents to provide the names of agronomical, livestock, or weather services apps accessed through their phones during the concluding growing season and included these summarized open-ended responses in the Due south.I.
four. Information analysis
4.one. Clarification of variables
Nosotros selected three yard-services as our dependent variables: farming and livestock information, buying and selling products, and alerts on agricultural or livestock activities. For brevity we refer to these as farming, ownership and selling, and alerts, respectively. The following explanatory variables were taken from the household survey and grouped into the following categories (Table 1). We note the theorized consequence of the explanatory variables on the dependent variables in Table 2.
Table 1. Summary statistics of outcome variables and demographic characteristics of respondents.
Tabular array ii. Hypothesized relationships with m-services adoption.
Personal Smartphone: We asked the farmer whether their mobile telephone had admission to the internet and the power to download apps. If the farmer responded 'yes' to both of those attributes, we classified them as owning a 'smartphone' (i.e. a handset able to access the internet and download apps). Nosotros compared these to owners of non-smartphone phones which include basic and feature phones. 'Basic' phones (mulika mwizi in Kiswahili) cannot download apps or admission the internet. 'Characteristic' phones are able to admission the internet considering they come up pre-loaded with applications such equally Facebook or Twitter just they do not have the ability to download apps. An example of a basic phone available for purchase in Kenya is Nokia 1110, whereas a characteristic phone pre-loaded with Facebook is Tecno T351. If respondents had more than than ane phone, we asked about their primary handset, i.e. the i used nearly often.
Membership in Farmer Organizations: We recorded membership in three types of farmer organizations (agricultural cooperatives, farmer groups, and Customs Water Projects), and combined the responses to reverberate the total number of farmer organization types (0–3) for which a farmer is a member.
Farm Size: Respondent provided the areas of their country under production and fallow, in acres, during the March–April–May 2018 growing season. We combined these two areas to go total farm size.
Livestock Assets: We asked the respondent to count the number of livestock owned by the household from a listing of common animals including cattle, goats and sheep. We subsequently used a weighted formula to convert this livestock count to Tropical Livestock Units (TLU) (Jahnke, 1982).
Income: We asked the respondent to select household monthly income from a selection of ranges (none, 100–2000 ksh, 2001–6000 ksh, 6001–eighteen,000 ksh, eighteen,001–36,000 ksh, 36,001–54,000 ksh, 54,001–72,000 ksh, or more than than 72,000 ksh) from the post-obit sources: casual labor, regular salary, minor business, charcoal sales, horticulture, sale of forest products, livestock, remittances, rental income, pension, and savings group. Nosotros computed the income variables in multiple ways which produced similar results (see S.I.) and ultimately selected the median value from those ranges. We summed the median values of the diverse incomes to judge monthly income in Republic of kenya shillings. Nosotros reduced the number of income categories past computing quartile incomes:
, fifty% 55,000 ksh,
.
Avails: We used a simple alphabetize reflecting household ownership of a boob tube, car, motorcycle, and/or computer (including tablets). We added the buying values together and treated the sum equally a continuous variable (0–4). However, considering just four households owned all four assets, we combined those households with the households who owned three of the four assets (0–3).
Age: We asked for the respondent'south year of nascence and and so calculated their age relative to the year 2018.
Educational activity: Kenya follows the 8–four–4 educational system with 8 years in primary school, iv years in secondary, and iv years of university or vocational preparation. We asked for the highest level of education completed by the respondent. The options were coded every bit follows: No formal education or some primary (level
; reference), Completed primary or some secondary (2), Completed secondary or some mail service-secondary (3), Completed post-secondary or vocational training (four).
Canton: Farmers were located in 1 of three counties: Laikipia, Meru, or Nyeri. Nosotros created dummy variables for Meru and Nyeri residents with Laikipia every bit the reference category.
Gender: Men were the reference category (coded every bit 0).
four.2. Logistic regression analysis to identify drivers of K-service adoption
We used a binary logistic Generalized Linear Model (GLM) to test the likelihood that a respondent adopts diverse thousand-services. Nosotros estimated the odds ratio (OR) for each dichotomous dependent variable: employ or non-employ of weather data, data well-nigh agriculture and livestock, or important alerts on agriculture and livestock activities.
Because several farmers were function of the aforementioned water governance groups or farmer organizations, household-level data were not fully independent. Therefore, nosotros also ran the regressions using a Generalized Linear Mixed Effects Model (GLMM) to account for possible overdispersion and clustering by requiring a group gene. Random effects modelled the correlation between the groups using customs subsets called H2o Resources User Associations (WRUAs) every bit the group factor. For more than information regarding the treatment of WRUAs and grouping variables in the GLMM, see our Methodological Appendix (S.I.). To test for the difference between the models with and without random furnishings, we used a nested ANOVA model comparison. We determined to go along in using the GLM without random effects because the Aikake information benchmark (AIC) values were lower for the GLMs compared to the GLMMs.
We cleaned information and developed the model specification using Python programming linguistic communication (Python Software Foundation, 2019). The logistic regression analysis was completed using the lmer function in the lme4 library in R (Bates et al., 2015).
5. Results
v.1. Descriptive statistics
Nosotros begin by compiling the rates of telephone ownership (smartphone, characteristic phone, basic phone) by gender to identify the extent to which lack of access to internet-enabled phones could underlie gender-based differences in the use of g-services in Table 3. Approximately 34 % of respondents own a smartphone: a greater proportion of men own smartphones compared to women. Basic phones are endemic past 56% of women compared to 48% of men. Considering that access to a smartphone is meaningful for m-services access, nosotros investigated differences in age, pedagogy, and membership in farmer organizations betwixt smartphone owners and non-owners, as shown in Tabular array 4. The average instruction and number of farmer organizations is greater for smartphone owners compared to non-owners. The variance of those statistics is similar betwixt smartphone owners and non-owners.
Table 3. Types of handset owned by respondent.
Table 4. Descriptive statistics of smartphone non-owners and owners membership in farmer organizations, age, and didactics.
To help discern whether education, age, and membership in farmer organizations influences admission to g-services we compare differences in these socioeconomic factors between men and women m-services users and non-users. Every bit shown in Table 5, the boilerplate age and education level is lower for female m-services users than male. Similarly, women are on average members of fewer farmer organizations. The variances of those statistics are generally like between men and women. Nosotros employ these differences between men and women to contextualize the importance of these socioeconomic factors in governing m-service use, despite them non always being meaning in the logistic regression models.
Table v. Summary statistics of one thousand-services users and non-users by gender.
5.two. Logistic regression results
five.two.ane. Smartphone ownership
Personal smartphone ownership, defined as owning a handset with access to net and power to download apps, increases the likelihood of m-services use in all three models every bit shown in Tabular array vi. Smartphone owners are between 1.83 and 2.72 times every bit likely equally non-smartphone owners to apply 1000-services (p < .05 for buying and selling and p < .001 for farming, and alerts).
Tabular array half-dozen. Predictors of utilize of subcontract/livestock data, to buy or sell agricultural produce or livestock, and alerts m-services.
5.2.2. Farmer organizations
Nosotros notice that membership in farmer organizations positively influenced one thousand-service apply. Every bit shown in Table 6, respondents in farmer organizations are 1.64–ii.06 times more likely than non-members to apply 1000-services across the iii types of farmer organizations, which include farmer cooperatives, informal farmer groups, and Community Water Projects (
). When disaggregating by farmer organization blazon in Table seven our results bear witness that members of breezy farmer groups are betwixt 1.82 and 2.87 times more probable to use farming (p < .01), buying and selling (p < .01), and alerts (p < .001) m-services. However members of agricultural cooperatives are only more likely to use yard-services for obtaining farming data (p < .01). Members of CWPs are just more than probable to use m-services for accessing information nearly buying and selling (p < .001).
Table 7. Model results which separates farmer organizations into farmer groups, agricultural cooperatives, and Community H2o Projects (CWPs).
five.two.iii. Wealth, gender and socioeconomic factors
Household-level characteristics, such as household assets and livestock assets, and private-level characteristics such equally didactics are also associated with yard-service utilise. Subsequently bookkeeping for whether a respondent owns a smartphone, we practice not find age or subcontract size to be significantly related with m-service employ any model iterations (p>.i, Tables 6 and seven). Income is significantly related to one thousand-service use in Tables six and 7 in the highest income quartile only. Other measures of wealth are livestock assets (TLU), household avails, and the interaction between avails and TLU. Household and livestock assets are both significantly associated with likelihood of using k-services. Household avails are significantly associated with alerts m-services (p < .01) while livestock assets are significantly associated with all three models in Table 7 and ownership and selling and alerts m-services in Table 6. Although the coefficients on livestock assets and household assets were less than one, the interaction term was greater than one, pointing to a complex relationship between various forms of wealth and the adoption of m-services. As described in our Methodological Appendix (S.I.), the results presented here are largely consistent with results of other iterations of the models, which controlled for interactions between wealth variables, including smartphone ownership. Additionally, in one iteration of the model, nosotros replaced farm size as a proxy for income. We did not detect whatsoever statistical significance for farm size or income.
We observe some levels of educational attainment to exist significantly correlated with chiliad-service employ in Table 6. Compared with the reference educational level, respondents who completed primary school or some secondary schoolhouse were iii.16 and 4.55 times every bit likely to use farming and alerts yard-services (p < .01). Similarly, respondents who completed secondary or some post-secondary schoolhouse were 3.46 and 3.53 times more probable than the reference grouping to utilise farming and alerts thousand-services (p < .01 and p < .05 , respectively). Respondents who completed post-secondary school or vocational training were two.58 times more likely than the reference group to employ farming m-services (p < .05). The results in Table vii are similar for those education levels.
The coefficient on being a male person respondent is positive in all iterations of the models (Tables half dozen, seven). In the models that do not distinguish between farmer organization types, Table half-dozen, the association betwixt gender and m-service utilize is significant for ownership and selling g-services (p < .01) and alerts (p < .001). Men are i.xx and 1.35 times more likely to employ grand-services for buying and selling and alerts.
Nosotros asked respondents who did not select utilize of m-services for accessing farming and livestock information to respond to a list that describes barriers preventing their use of yard-services as shown in Figure 2. Compared to men, a greater proportion of women perceive barriers to mobile agronomics/livestock services beyond all of the categories. Still, irrespective of gender, the foremost barriers limiting use of m-services are lack of sensation, lack of availability, and lack of understanding about how m-services piece of work. Of these three categories, women are less enlightened and knowledgeable about how to use m-services compared to men.
Smallholder farmers' use of mobile phone services in central Kenya
Published online:
09 April 2020
Figure 2. Bar plot showing barriers to grand-service utilise past gender. We elicited specific reasons for farmers' not-utilize of mobile service in response to the question: 'What are the reasons why yous don't access data about farming (either livestock or agriculture): I'm non aware of these services; These types of services are not available in my surface area/on my network; I don't know how to use these types of services; I get my information from other sources (e.yard. my community); My phone has no internet; I practice not have an ID or required documents; The content is not in a language I understand; The content isn't relevant to me; I take trouble reading the content; I have trouble understanding the content; They are too expensive; My family unit doesn't allow information technology; I've used information technology in the past only did not find it useful and/or did non like using it; I've used it in the by only it is no longer bachelor.
![]({"type":"image","src":"/na101/home/literatum/publisher/tandf/journals/content/tcld20/2021/tcld20.v013.i03/17565529.2020.1748847/20210421/images/medium/tcld_a_1748847_f0002_oc.jpg"})
6. Discussion
half dozen.1. The office of smartphones in Thou-services use
Nosotros observe smartphone buying to be a significant factor in one thousand-services utilize. Withal, smartphone ownership beyond our study site is far from ubiquitous with 31% ownership for women and 38% ownership for men (Tabular array 3). GSMA (2016) estimates 226 one thousand thousand smartphone connections be in Africa, approximately a quarter of all connections, and is reflected most strongly in established mobile markets including Kenya, Egypt, Nigeria, and South Africa. Thus, compared to the rates of smartphone adoption in e Africa (17% of the population in 2015) (GSMA, 2016), smartphone buying is relatively high amid survey respondents. The prevalence of smartphone ownership as a predictor of thousand-service utilize may be due to the nature of how m-services tend to exist designed: technology developers mostly design for smartphones rather than basic or characteristic phones (Cheney, 2018; Wyche & Murphy, 2012). Our results indicate that one thousand-services, created by developers and designers focused on smartphone applications, accept indeed reached the segments of the population who own smartphones. Because that the bulk of respondents own basic or feature phones (as shown in Table 3), yard-services designed for smartphone-based applications may exist declining low-income and basic phone-owning subscribers.
As suggested by several previous studies (e.g. Wyche & Murphy, 2012; Wyche et al., 2019; Wyche & Steinfield, 2016), designing chiliad-services with the needs of basic phone users in listen is a clear way to expand accessibility of m-services beyond smartphone users. Developers need not only to design for basic phones simply also the constraints usually experienced in developing countries. Later relating barriers of basic telephone use to lack of airtime credit, exhausted batteries, difficulty charging and lack of capital to upgrade to a smartphone, Wyche and Irish potato (2012) provide an culling pattern vision given the challenges faced by rural and often unconnected agriculturalists in Kenya. They suggest that mobile phone designers and developers assume off-filigree use with unreliable electricity sources for agriculturalists in rural and peri-urban Kenya (Wyche & White potato, 2012). Lastly, another consideration is to focus on USSD protocol and vocalization call services aimed for widely spread simple and cheap phones rather than power-intensive functionalities of smartphones which are not ubiquitously used (Wyche & White potato, 2012). These intersections need to be addressed past ICT for development programmes – such as those described in the USAID toolkit used for survey drove – for proper information dissemination.
6.2. Membership in farmer organizations increases likelihood of M-service use
Membership in informal farmer groups increased the likelihood of m-services use across all types of 1000-service use in comparing to formal farmer organizations (e.k. farmer cooperatives and CWPs). Unlike formal farmer cooperatives or the CWPs, informal farmer organizations are free to bring together. Yet, one benefit of paying to join an agronomical cooperative or CWP is access to information services and networks provided past these organizations. In other words, these types of formal organizations provide a 'guild good' version of agricultural information (i.e. data exclusively available to members of an arrangement) (McNutt, 1999). Members gain exclusive access to and benefit from agronomical data at the price of paying a monthly membership fee to vest to these organizations.
Contrary to formal agricultural information, members of informal farming groups may make greater use of publicly-available agronomical information, which is a 'public practiced' (i.due east. where an individual's use of information available to the public does not diminish others' utilise of the same information) (McNutt, 1999). Virtually m-services platforms exercise not require an explicit joining fee to admission information and are therefore offer agronomics information as a publicly available good. Thus, we would await that farmers in informal farming groups would have a greater incentive to apply and seek out yard-services agriculture data that they tin can freely access without having to pay the types of membership fees required of formal farmer organizations.
Farmers with membership in agricultural cooperatives or CWPs were significantly more likely to utilize only 1 type of m-service. Farmers in cooperatives already benefit from sectional data and alerts that are embedded in their membership to a cooperative and the facilitated access to extension services. Thus, we would not expect these farmers to make substantive apply of freely available k-services information. Rather than using the buying and selling function of m-services, farmers in formal cooperatives are likely already benefiting from their farmer cooperative membership with greater admission to opportunities for marketing and buying and selling their products in higher-value markets (Markelova & Mwangi, 2010; Narrod et al., 2009). Similarly, farmers with CWP membership are likely to admission agricultural information every bit an additional benefit to the primary do good of obtaining h2o. In Kenyan CWPs, members interact on a weekly to monthly basis through labour activities for maintaining irrigation infrastructures and to attend meetings regarding irrigated h2o resource direction. These activities would likely offer both informal and formal opportunities to obtain agronomical information. As with agricultural cooperatives, CWP membership probable provides access to sectional agricultural information, and thereby reduces the incentive to access additional agricultural information via m-services.
By framing agricultural information access in the context of club and public goods, nosotros can explain why farmers in informal farmer organizations are more likely to use m-services across all use types in comparison to those farmers belonging to agricultural cooperatives or CWPs. We conclude that membership in breezy farmer groups is a strong predictor of k-services use due to their greater incentive to use publicly-available grand-services compared to their agricultural cooperative and CWP counterparts, who inherently already have access to these types of services via cooperative or CWP membership. We discuss endogeneity challenges related to farmer organizations and related explanatatory variables in the S.I.
half-dozen.three. Wealth indicators and teaching
The non-significant results for income and farm size, forth with the ubiquity of mobile phones among respondents, point to the maturity of telephone buying in the report area relative to income. In earlier phases of mobile telephone adoptions, college income levels were associated with telephone ownership, but over time, mobile phones take become attainable to even low-income households, as indicated by the 98% buying of mobile phones among survey participants. Withal this ubiquity in phone ownership of all types does not stand for a ubiquity of smartphone ownership, which is associated with higher incomes.
Although mobile phone ownership and use have been expanding across communities and throughout Kenya irrespective of wealth, smartphone buying is the cistron that separates the wealthy from the poor in 1000-service use. Moreover, smartphones open access to potentially the almost comprehensive and/or useful 1000-services which are applications and/or cyberspace-based platforms. Since Republic of kenya is on the forefront of ICT use and mobile phone ownership compared to other countries in the region (see Effigy 1), our results tin can serve as a model for future trajectories of development regarding utilize of mobile phones for climate and agronomics information. While these wealth indicators are non-significant across our models, we recognize that cost is still a major barrier to farmers using phones every bit much as they would similar (meet Due south.I.). We cannot conclude that access to smartphones means that farmers are using the net. Notwithstanding given that purchasing airtime credit and/or data bundles is the primary reason why respondents exercise not use their phones (of any type) every bit much every bit they would like, we can infer that across all telephone types, cost is notwithstanding the greatest inhibitor of mobile phones and m-services use.
Educational attainment is an important factor in determining g-service use. The differences in boilerplate education level and age in m-service use for women, and to a bottom extent, men (Tabular array 5) bespeak at the importance of these socioeconomic factors in governing yard-service use, despite them not ever beingness significant in the models. While historic period does not significantly affect the likelihood of m-service use in our models, we do find that the average age of women using yard-services is younger than those women who do not (Table 5), and the education level of those using yard-services across both genders is higher than those who exercise not use m-services (Tabular array v).
half-dozen.4. Gendered barriers to Thou-service use
The previously described intersections between gender and wealth, smartphone ownership, and education affect m-service use in complex ways. At the household level, men's and women's roles on Kenyan farms are unlike, and therefore levels of agronomical decision-making may vary depending on gender roles when men are head of household. Braimok (2017) found in some cases that in the presence of a male head, female Kenyan dairy farmers did not perceive themselves every bit making or finalizing choices.
A variety of reasons govern these gendered barriers in use. Santosham and Lindsey (2015) concluded that cost is the greatest barrier to ownership and usage of mobile phones for women, due to their reduced fiscal independence compared to men. Although mobile phone employ is nigh ubiquitous in our written report site for both men and women, the increased costs associated with smartphone ownership and use nowadays a potential barrier to women's ownership of smartphones, which may explicate why women lag behind men in smartphone ownership (Table iii). Wyche and Olson (2018) found that mobile use and therefore one thousand-service admission among rural women remains limited due to technical literacy, mobile phone atmospheric condition, perceptions of the Internet, time required to larn how to use the Internet, and seasonal income fluctuations.
7. Implications for future Thou-services in Kenya
Membership in farmer organizations, the relative cost to ain a smartphone, and smartphone usability are important considerations of futurity access to mobile agricultural services in Republic of kenya. Although smartphone ownership is a commuter of k-service utilise and smartphones are more than expensive and hard to apply (Wyche & Steinfield, 2016), Karlsson et al. (2017) show that increasingly affordable smartphones are on the rise and commercially sound. African consumers can at present buy the affordable Mara X phone, which is designed in partnership with Google and fully manufactured in Rwanda 3 as well every bit Chinese designed smartphones that are expected to go more than prevalent amid African phone markets. four
Beyond affordability, however, and from a supply point of view, 1000-services are not typically designed for low income, rural, and less educated groups, such as women, who utilise basic phones (Santosham & Lindsey, 2015; Wyche & Olson, 2018). Our quantitative findings corroborate previous qualitative studies and bear witness that women were less likely to utilize m-services in comparison to men and that g-service users had college levels of income and educational attainment. Increasing m-service use involves addressing both smartphone affordability and designing m-services for audiences with lower technological literacies.
Our study introduces boosted novel findings that demonstrate linkages between adoption rates and the role of informal and formal farmer groups. Membership in increased numbers of farmer organisation – formal or informal – was a significant predictor of m-service utilize. However, farmers not office of formal agriculture groups, such as agricultural cooperatives and water governance groups were more than likely to use thousand-services than farmers who were involved in informal farmer groups. Understanding that not all m-service users are embedded in formal agronomical groups is important for thou-service design. Specifically, agronomical information shared amidst farmers in formal groups may be different than the m-services information accessed by farmers in breezy groups. Overall, dissemination of meaningful and useful agro-meterological information via m-services requires consideration of the types of individuals that are currently accessing thou-service information.
viii. Conclusions
Nosotros investigated factors that influence farmer use of agronomics information from mobile phones, specifically chiliad-services. While much of the literature in academic and government spheres stress the importance of historic period, location and education in defining the digital gender gap, our results highlight boosted drivers of m-services use including participation in farmer organizations, higher-levels of instruction, and smartphone buying. Each of these factors interacts with gender to highlight the disproportionate access of these services past women. Our results show that age and income are non significantly related to farmer use of mobile phone services; however smartphone ownership is a metric of individual wealth and assets that is significantly related to one thousand-service utilise.
The prevalence of smartphone ownership across our models point to the tendency that one thousand-services are increasingly designed for advanced mobile phone features. Thus, we suggest that thou-services providers design for the user, which is predominantly a basic or feature telephone owner in the case of rural Kenyan farmers. Additionally, while rates of mobile telephone buying in Kenya are high, smartphone ownership is not as widespread. Although phones take been expanding through rural communities, smartphones are the item that separates the wealthier from the poorer. Smartphones tin can requite farmers access to potentially the near useful of climate and agriculture related m-services. Mobile phones and other ICTs will continue to play important roles in managing risks and vulnerabilities associated with a changing climate (Eakin et al., 2017). Efforts in the climate accommodation policy infinite should address the general affordability of m-services and mobile phones, and better target under-served groups of users, peculiarly women and those not belonging to farmer organizations to improve efficient and targeted dissemination of agro-meteorological data services.
Source: https://www.tandfonline.com/doi/full/10.1080/17565529.2020.1748847
0 Response to "Beef Farmers in Kenya Nokia Cell Phone"
Post a Comment