ARTICLES

 

Farmer's Perception and Adoption of Digital Technologies as Information Sources for Farming Activities in the City of Tshwane, Gauteng

 

 

Mogashane C.^I; Loki O.^II

^IStudent: Department of Agricultural Economics, Extension and Rural Development, University of Pretoria, Pretoria, South Africa. chantellemogashane2@gmail.com, ORCID ID 0009-0005-8834-0304
^IILecturer: Department of Agricultural Economics, Extension and Rural Development, University of Pretoria, Pretoria, South Africa. o.loki@up.ac.za, ORCID ID 0000-0003-4187-3345

Correspondence

 

 

---

ABSTRACT

Smallholder farmers are challenged by limited resources, finances, and access to complex production technologies, which hinder the implementation of good production practices such as good seed selection, knowing when to plant and harvest, pest and disease control, and access to lucrative markets. Using quantitative research methods, this paper explored smallholder farmers' perceptions, adoptions, and differences in agricultural incomes between adopting and non-adopting farmers. This study reveals that smallholder farmers perceive access to real-time information as important; however, the adoption of digital technologies as information sources remains low. A binary regression analysis further revealed that the access to extension services variable positively correlated with the adoption of the internet (web pages), YouTube, and the Farmers Weekly website as information sources. Digital technologies were generally perceived to be reliable, time-effective, and easy to use; however, adopting these technologies had no significant impact on the farmer's agricultural income. The overall perception of digital technologies was that they are easy to use, reliable, easily accessible, and cost-effective. This paper concludes that digital technology adoption remains considerably low; however, more and more farmers are not only open to adopting this technology, but those who have adopted it also prefer incorporating it among the sources they use to acquire farming information. Using digital technologies did not cause differences in agricultural incomes for these farmers. This study recommends public-private partnerships and community engagement through cooperatives to further drive technology adoption, thereby fostering market access and improving the livelihoods of smallholder farmers.

Keywords: Smallholder Farmer, Perception, Adoption, Digital Technology.

---

 

 

1. BACKGROUND

Smallholder farmers are challenged by limited resources, finances, and access to complex production technologies, which hinder the implementation of good production practices such as good seed selection, planting and harvesting timing, pest and disease control, as well as access to markets (Nwafor et al., 2020; Autio et al., 2021). Accessing markets provides farmers with various opportunities, such as crop diversification, reasonable input costs, increased profits, and the ability to contribute to food security. Phiri et al. (2019) argue that accessing information that is accurate and reliable can assist in overcoming some of the above challenges. Studies by Mutero et al. (2016) and Abdulai and Fraser (2023) suggest that smallholder farmers in Africa continue to have limited access to digital technologies. There are further reports by Lwoga et al. (2011) and Ndilowe (2013) indicating that many farmers in developing countries still rely on traditional information sources, such as extension visits. Their primary digital sources include print media, television, and radio. Afful and Lategan (2014), Ghosh (2012), Hlatshwayo and Worth (2016), and World Bank (2010) report that in South Africa, the reduced government capital investment in extension services has negatively impacted service delivery, aggravated increases in the extension officer to farmer ratio, and limited the supply of inputs and relevant agricultural information. These factors directly contribute to the overall performance of smallholder farmers who rely significantly on these services. Akintude and Oladele (2019) further report that the problem with the extension services system is its failure to keep pace with new developments and technologies to acquire and disseminate information to farmers before it becomes outdated, leaving the farmers significantly disadvantaged.

There's progress in research (Food and Agriculture Organisation, 2017; Muema et al., 2018) presenting opportunities for using digital technologies in agricultural production and marketing, as well as several studies that disseminate how smallholder farmers rely on agricultural information for reliable quality food production and access to lucrative markets however, smallholder farmers are still unable to make technical and informed marketing and production decisions, they are still challenged with accessing markets and the promising impacts of integrating digital technologies in smallholder farming development have not materialised (Deichmann et al, 2016; Okello et al., 2020). According to Mushi et al. (2022), smallholder farmers still miss out on development and commercialising opportunities by not acquiring food production-related information and market information that plays a significant role in accessing competitive markets, scaling up, and being an active catalyst in ensuring food security. This is despite an increase in smallholder farmers owning ICT tools such as mobile phones and computers, and most smallholder farmers being aware of various digital technologies that can be used as sources of information from these tools (Phiri et al., 2019; Nwafor et al., 2020). Abdulai (2023) further reported that the adoption of these technologies is exponentially low among these farmers, especially in developing countries. The objectives of this paper are to:

a) Assess smallholder perceptions of digital technologies as sources of information for farming activities.

b) Determine the factors influencing the adoption of digital technologies as sources of information for farming activities.

c) Assess differences in agricultural incomes between digital technology adopting and non-adopting farmers.

 

2. SOCIOECONOMIC FACTORS INFLUENCING THE USE OF DIGITAL TECHNOLOGIES

There are varied factors that contribute to how an individual perceives an object or subject. These include the nature of the object, the subject, the environment, and the situation where the perceiver makes the perception. For the perceiver, the characteristics could be personal interests, expectations, self-concept, and attitudes. Rogers (2004) posited that five stages influence the adoption of new technologies in the theory of innovations. These stages are: economic profitability, compatibility, trialability, complexity, and observability. For the adoption of new technology by farmers, Bayih et al. (2022) note that factors such as cultural acceptability, the beneficial attributes of using the technology, farmers' perception of the technology after observation, the effectiveness of the technology after trial, and the economic status of the farmer influence technological adoption.

Singh et al. (2021) argued and concluded that infrastructural development, geography, type, and agro-climatic state of the land influence farmers' adoption of new technologies.

Furthermore, he stated that the kind of farming systems farmers use also influences adoption. The conclusion was derived from surveys of 200 rural farmers in four different agro-climates in India. Aker et al. (2005) analysed the adoption of computers for farming practices using a Tobit model to establish factors influencing the rate of technology adoption among 449 farmers in the United States. The analyses depicted that the educational levels of farmers and the age group of 30-40 years influenced the rate at which digital agricultural technologies are adopted. In corroboration, Mdoda (2017) denotes that farmers' ability to process information from varied information sources relies greatly on their level of education. Several studies (Lawal et al., 2017; Mtega et al., 2016) also denote that the age of farmers has a paramount influence on farmers accessing agricultural information and depict that male farmers over 35 years have greater access to agricultural information when compared to younger farmers. When the adoption of digital technology by smallholder maize farmers was studied in Nigeria, it was shown that other factors, such as attributes of the technology to be adopted and the attributes of the farm, directly influence the adoption decision (Mwangi & Kariuki, 2015; Sennuga et al., 2020).

 

3. METHODS AND PROCEDURES

3.1. Description of Study Area

This study took place in the City of Tshwane district municipality in Pretoria, which is situated in the North of the Gauteng province of South Africa. This municipality consists of 107 wards and covers an area of 6 298 km². Compared to all the other municipalities in the province, this municipality accounts for the most smallholder farmers in the province. This is because this municipality is attributed by high agricultural areas in Region 7 (Bronkhorstspruit) and Region 5 (Cullinan), which exhibit the best soil qualities for thriving agricultural productions (Department of Agriculture, Land Reform and Rural Development, 2021; Department of Cooperative Governance and Traditional Affairs, 2020).

3.2. Sampling Procedure and Sample Size

Smallholder farmers in the City of Tshwane municipality were targeted as the population of this study to achieve the intended objectives of this study effectively. A quantitative approach was used to collect and analyse data. Semi-structured interviews and questionnaires were employed to collect data. A multi-sampling method, consisting of systematic sampling and snowball sampling, was used with a sample size of 117, which was determined using Slovin's formula.

n= N/ [1 + N x e²]

Where:

n= represents the sample size

N= represents the total population size (462 smallholder farmers)

e= According to Altares et al. (2003), e signifies a probability acceptable for making a mistake

in selecting a sample size. This study will use a probability of 80%

n= N/ [1 + N xe²]

n= 462/ [1+ 462 x 0.08²]

n= 117

Because this study used a systematic sampling technique, a sampling interval of 4 was used.

3.3. Data Analysis

This section of the paper gives insights into the quantitative tools used to analyse the collected data. Firstly, descriptive statistics in frequencies, percentages and mean values were used to characterise smallholder farmers demographically and socioeconomically. Descriptive statistics were also used to explore perceptions of digital technologies. The predictive modelling tool of a binary regression was used to model the relationship between the set of independent variables, which are the socioeconomic factors of smallholder farmers and the dependent variable which is diverse and is modelled as a logit of p that represents a probability of the dependant variable which are perceptions and adoption taking a value of 1 (Harrell & Harrell, 2015). An exploratory analysis was conducted, and a statistical model was used, followed by a likelihood test and an analysis using R Studio to test the hypotheses of this study. The following equation was used for the regression model:

Where ρ represents the probability that y = 1 given x, the y represents the dependent variable, which is the various digital technologies. x₁, x₂,...x_k represent independent variables which are the farmers' characteristics, such as age, gender, level of education, farming years, type of farm, access to extension services, and government support. b₀,b₁,...b_k are the parameters of the model.

 

4. RESULTS AND DISCUSSION

This section reports findings and explains demographic and socioeconomic characteristics of smallholder farmers in the City of Tshwane municipality regions and empirical results that address this study's objectives.

4.1. Characteristics of Smallholder Farmers

This section explains characteristics such as farmer age, gender, educational level, marital status, household size, and farming purpose, represented as frequencies, percentages, means, and standard deviation in Table 1.

Smallholder farming in these regions is accounted for by females with a dispersion of 53.8% to 46% of female to male ratio; this finding corroborates various socioeconomic-centric surveys, such as the General Household Survey (2016) (DAFF, 2016; StatsSA, 2017), which delineate that the South African smallholder farming sector is predominantly female-dominated. This sector is dominated by middle-aged farmers, with the 35-45 age group accounting for the largest proportion (36%) of smallholders. The majority (95%) of these smallholder farmers are considered literate, as they have some level of education. This suggests that farmers can comprehend information related to their farming activities, which aligns with the sentiments of Oyewole and Sennunga (2020) that education is a crucial factor in adopting innovative farming techniques. The average household size is six members, which also serves as the source of labour on their farms (Yusuf, 2018). The smallholder farming sector in these regions is predominantly composed of crop farmers (57%), with only 35% of livestock farmers and 8% of mixed farmers. This evidently supports characterising statements that the regions in the municipality of Tshwane display exceptional soil qualities that are excellent for crop production (DALRRD, 2021; Department of Cooperative Governance and Traditional Affairs, 2020). These farmers also stated that they do not have any income outside of their agricultural income. This contradicts the WRC report by Manona et al. (2023), which states that smallholder farmers rely on other income streams independent of their farming activities. When asked about the challenges encountered in their farming activities, 33% of the smallholder farmers state that finances pose the biggest constraints in their farming activities. These findings are consistent with Loki et al. (2023), who state that financial support was among the biggest challenges encountered by smallholder farmers.

4.2. Extension Services and Technology Use

This section reports on the exploration of technology use, government support, and extension services in the City of Tshwane regions, focusing on their availability, utilisation, and general perceptions of them by smallholder farmers in these regions.

Table 2 shows that 28% of smallholder farmers in this region receive support from the government in their farming activities, which is in line with the report by Aliber and Hall (2012), which displayed limited government support for smallholder farmers. Nyaga et al. (2021) and FAO (2022) state that the lack of government support contributes to the uneven access and adoption of digital technologies. As shown in Table 2, 60% of the farmers are aware of the extension services to be utilised for their farming needs. Awareness positions these farmers at a better chance of accessing extension services. Mgbenka et al. (2015) report a general lack of awareness about extension services among smallholder farmers, which stems from the illiterate nature of the farmers and causes a lag in the adoption of technologies. Despite the raging awareness, only 38% of the smallholder farmers have access to these services. This contradicts Loki and Mdoda's (2023) survey, which showed that 78% of smallholder farmers in the Eastern Cape had access to extension services. Extension services delivery was negatively impacted in South Africa during and after the COVID-19 outbreak due to its face-to-face communication, which compromised smallholder farmers' access to information (Karubanga et al., 2016; Yusuf et al., 2022). When it comes to sharing farming information, 91% of smallholder farmers found extension officers to be helpful, which concurs with the findings of Loki and Mdoda (2023) that extension officers are very helpful in disseminating information relevant to their farming activities, thereby keeping them informed.

Regarding technology use, Table 4 shows that most smallholder farmers (76%) do not own computers, and 54% do not have access to one. However, 73% of smallholder farmers own a smartphone, and 72% of these farmers have access to a smartphone. This finding evidently supports the results of Abdulai (2023), which indicate that these types of farmers have access to digital devices that are generally considered simple, such as mobile phones, serving as a bridge to accessing digital resources. The results of this study show that 44% of the farmers prefer to acquire information from other farmers. These farmers stated that this preference stems from the people they know and trust. In line with this finding, a European study by Kernecker et al. (2020) shows that peer-to-peer communication is the preferred source of information for smallholder farmers. 22% prefer digital technologies due to their readily available and time-effective characteristics, while 21% of the farmers prefer acquiring information from extension officers because they are experienced, trained, and qualified to disseminate agricultural information effectively.

 

Table 3

 

Regarding technology use, Table 4 shows that most smallholder farmers (76%) do not own computers, and 54% do not have access to one. However, 73% of smallholder farmers own a smartphone, and 72% of these farmers have access to a smartphone. This finding evidently supports the results of Abdulai (2023), which indicate that these types of farmers have access to digital devices that are generally considered simple, such as mobile phones, serving as a bridge to accessing digital resources. The results of this study show that 44% of the farmers prefer to acquire information from other farmers. These farmers stated that this preference stems from the people they know and trust. In line with this finding, a European study by Kernecker et al. (2020) shows that peer-to-peer communication is the preferred source of information for smallholder farmers. 22% prefer digital technologies due to their readily available and time-effective characteristics, while 21% of the farmers prefer acquiring information from extension officers because they are experienced, trained, and qualified to disseminate agricultural information effectively. Lastly, 41% of the smallholder farmers state that they prefer all of the above-mentioned information sources. This means that farmers who rely on other farmers and extension officers for information also consult digital technologies, which represents a positive change in the use of these technologies for information acquisition. This finding is consistent with the views of Mavhunduse and Holmner (2019), who suggest that adopting digital technologies can enhance traditional methods of disseminating agricultural information. 96% of smallholder farmers state that it is important to access realtime farming information, which is a positive perception for smallholder farmers, as their development relies greatly on access to real-time agricultural information.

4.3. Perceptions of Digital Technologies as Information Sources

Smallholder farmers were asked to state their perceptions of digital technologies as information users using a Likert-scale questionnaire. The tool ranged from 1 being "Strongly agree" to 5 being "Strongly disagree".

The results in Table 4 show that the common and persistent perception rated on the scale by the farmers is "Neutral," which could indicate that these farmers have limited knowledge of these technologies, nor have they adopted them. Consistent with this finding, Kernecker et al. (2020) state that farmers' perceptions of technologies are primarily informed by their personal experiences with using those technologies. 15% of the farmers strongly agree that digital technologies are complicated to use, 33% were neutral, while 27% strongly disagree. Regarding the economic aspect, 24% of the farmers state that digital technologies are costly, while 17% disagree. This result is consistent with that of Hoang and Tran (2023), which depicts that most smallholder farmers in their study perceived the acquisition and utilisation of digital technologies to be costly. Similarly, a study in the Eastern Cape by Bontsa (2023) reported that most of the studied population perceived the adoption of digital technologies to be expensive compared to other technologies.

Table 4 shows that 29% of the farmers strongly agree that digital technologies are easily accessible, while 37% also strongly agree that these technologies are time-effective. Pishnyak and Khalina (2021) demonstrate that farmers' perceived effectiveness of digital technologies positions them better to adopt these technologies. Digital technologies are considered easy to use by 32% who strongly agree, and a reliable source of information by 38% of farmers. Caffaro et al. (2020) highlight that farmers who perceive digital technologies as helpful, reliable, and easy to use are more likely to adopt them. The information acquired from digital technologies is perceived to be up-to-date and helpful by 37% and 32% of smallholder farmers who strongly agree. When comparing digital technologies as information sources to traditional sources, 20% of the farmers strongly agree that there is a better option, a majority (50%) were neutral, and only 7% strongly disagree. Most farmers (38%) strongly agree that digital technologies are easy to use, with 20% stating that they agree with this phenomenon. Hoang and Tran (2023) found that most smallholder farmers in their study perceived digital technologies as difficult to use, largely due to a perceived lack of training on their use. These farmers' perceptions were generally positive, indicating a potential openness and willingness to adopt digital technologies as information sources. This finding contradicts that of Banga et al. (2020), which states that smallholder farmers in Africa perceive digital technologies as risky, resulting in hesitation and unwillingness to adopt. Because of this, exploring farmers' perceptions to establish their impact on adoption patterns is crucial.

4.4. Adoption of Digital Technologies as Information Sources

This section presents findings that assess farmers' adoption of digital technologies as information sources and the impact of these technologies on agricultural income.

 

Table 5

 

When the adoption of digital technologies as information sources was assessed, only 26% of smallholder farmers reported having adopted some type of digital technology, such as social media, as a source of information. This finding evidently corroborates reporting by Abdulai (2023) that adoption of digital technologies by smallholder farmers in developing countries is exponentially low. Engâs et al. (2023) state that the digital divide propels the low adoption levels of digital technologies. When the technologies were analysed independently, the most commonly adopted digital technology as an information source is the internet at 38%. A similar study in Vietnam by Hoang and Tran (2023) found that the internet and wireless connectivity accounted for most of the adoption, with 81% cases. In Rwanda, McCampbell et al. (2023) reported that this type of digital technology was only adopted by 10% of the banana farmers, which makes it contradictory to this study. Nie et al. (2021) highlighted a positive effect of using the internet on the general well-being of the farmers and their households. The adoption of YouTube was the second most adopted technology at 24%, followed by 16% of Farmers' Weekly adoption. YouTube is reported to be among the social platforms that can be used to disseminate agricultural information and promote extension services and activities to a broader clientele (Kipkurgat, Onyiego, & Chemwaina, 2016; Saravanan & Suchiradipta, 2017; Barau & Afrad, 2017). The Farming Solutions app and GPS were the least adopted technologies among farmers, at 6% and 4%, respectively. Hoang and Tran (2023) corroborate this finding by revealing that mobile applications and digital technologies are the second and third-most commonly adopted digital technologies among farmers, respectively. An American study by Schimmelpfennig et al. (2020) found that GPS was the most widely adopted technology among farmers in this region, which is inconsistent with the result of this study.

4.5. Effect of Socioeconomic Factors on the Adoption of Digital Technologies

To examine the influence of socioeconomic factors, modelled as independent variables, on the adoption of digital technologies, presented as dependent variables, a regression analysis was conducted, and a chi-square test was used to assess the significance of the relationship between the independent and dependent variables. Table 6 indicates that only three of the five outcomes had a statistically significant variable relative to the choice of adopting digital technologies as information sources: access to extension services and age. Variables such as farming experience, type of farm, government support, gender, and educational level showed no statistical significance in relation to the adoption of digital technologies. The binary regression results are presented in the table below.

Table 6 shows that only one of the eight independent variables fits the binary model and has a statistical significance relative to the choice of adopting digital technologies as information sources, which is access to extension services. This variable was significant in the adoption of digital technologies, including the internet, YouTube, and the Farmers' Weekly website. Variables such as farming experience, type of farm, government support, gender, and educational level showed no statistical significance in relation to the adoption of digital technologies.

The adoption of digital technologies as information sources among smallholder farmers is significantly influenced by access to extension services, which showed a positive and statistically significant effect for the internet, YouTube, and Farmers Weekly website, with probabilities of adoption increasing by 85%, 88%, and 91%, respectively. Factors such as farming experience, age, gender, government support, and education also demonstrated positive correlations with the adoption of specific digital platforms. Conversely, ownership of farming land had a negative influence on the likelihood of internet and YouTube adoption, while variables such as education and farming years had mixed effects across the technologies.

This finding highlights the significance of extension services and individual farmer characteristics in influencing technology adoption.

However, the analysis of GPS adoption revealed no statistically significant effect from the independent variables, suggesting a limited influence of factors like gender, farming years, or government support. While access to extension services, age, and land ownership showed some positive correlations, their effects varied. The study highlights that, despite positive perceptions of digital technologies' ease of use, reliability, and efficiency, their adoption remains uneven, with demographic and contextual factors playing a crucial role. This calls for targeted interventions, such as improving extension services and addressing barriers to land ownership and resource access, to enhance technology adoption in smallholder agriculture.

4.6. Differences in Agricultural Income Between Adopting and Non-Adopting Farmers

Tables 7 and 8 represent the effect of digital technologies on agricultural income using results from an independent t-test.

Table 7 displays the distribution of agricultural income of adopting and non-adopting farmers. There is a difference in the mean value between the group that adopted digital technologies, with a mean of 1.16, and the non-adopting group, with a mean of 1.22. The difference in the means is considerably smaller, with the number of adopting groups considered average compared to the non-adopting group. Despite the low adoption rates reported in various literature (Nyaga et al., 2021; Abdulai, 2023), this finding suggests that an increasing number of smallholder farmers are adopting digital technologies as a source of information. Baumüller et al. (2019) and Ndhlovu (2020) attribute this acceleration to the COVID-19 pandemic, which restricted physical interactions, as well as climate change.

The mean difference indicates a variance between the adopting and non-adopting groups. Levene's test for equality of variances showed that equal variances are assumed, with a p-value of 0.388, which is greater than the significance level of 0.05. Therefore, the null hypothesis that the means of adopters and non-adopters are equal is accepted, as there is no statistical difference between the means of the two groups.

Table 8 shows that non-adopters have a higher mean relative to the distribution of agricultural income. This finding addresses the fourth objective, which reveals no difference in the agricultural incomes of smallholder farmers between those who have adopted digital technologies as information sources and those who have not. This finding aligns with Hoang and Tran (2023), who reported that smallholder farmers perceived a lack of real-life depiction of economic benefits from digital technologies, which would be reflected in an increase in agricultural income/productivity. Essentially, this states that smallholder farmers are still determining the economic benefits derived from incorporating digital technologies into their farming activities, which could make adopting these technologies a financial liability.

 

5. CONCLUSIONS

This study investigated smallholder farmers' perceptions, adoption rates, and the impact of digital technologies as information sources for farming activities. Farmers generally found these technologies to be user-friendly, reliable, accessible, yet sometimes costly. Despite these positive perceptions, the actual adoption of digital tools among farmers remained low. Many farmers preferred obtaining information from fellow farmers, extension officers, and digital technologies. This preference suggests a growing willingness to adopt digital tools among those who have not yet done so. However, there was no significant difference in the agricultural incomes of farmers who had adopted and those who had not. The study concludes that factors beyond socioeconomic status influence technology adoption among smallholder farmers. Moreover, the adoption of digital technologies did not significantly affect agricultural income. Access to extension services was statistically significant in the adoption of digital tools, such as the internet, YouTube, and Farmers Weekly. While extension services remain crucial for disseminating information, farmers expressed dissatisfaction with the frequency of extension visits and the quality of the information shared.

 

6. RECOMMENDATIONS

This study recommends prioritising partnerships with tech companies and offering incentives to reduce costs and increase access to digital technologies for smallholder farmers. Peer-to-peer learning should be encouraged, allowing tech-savvy farmers to share knowledge with others. Extension officers must actively promote digital tools to bridge information gaps. Policy implications include creating supportive frameworks to improve digital infrastructure, enhance digital literacy, and provide financial support. Monitoring and evaluating the impact of technology on productivity and income should inform future policies. Public-private partnerships and community engagement through cooperatives can further drive technology adoption, thereby fostering market access and improving the livelihoods of smallholder farmers.

 

REFERENCES

ABDULAI, A.R., BAHADUR, K.C. & FRASER, E., 2023. What factors influence the likelihood of rural farmer participation in digital agricultural services? Experience from smallholder digitalization in Northern Ghana. Ag. Outlook., 52(1): 57-66.         [ Links ]

AKER, J.C., HEIMAN, A., MCWILLIAMS, B. & ZILBERMAN, D., 2005. Marketing institutions, risk, and technology adoption. Preliminary Draft. Agricultural Issues Centre, University of California.         [ Links ]

ALIBER, M. & HALL, R., 2012. Support for smallholder farmers in South Africa: Challenges of scale and strategy. Dev. South. Afr., 29(4): 548-562.         [ Links ]

AUTIO, A., JOHANSSON, T., MOTAROKI, L., MINOIA, P. & PELLIKKA, P., 2021. Constraints for adopting climate-smart agricultural practices among smallholder farmers in Southeast Kenya. Agric. Syst., 194: 103284.         [ Links ]

BARAU, A.A. & AFRAD, M.S.I., 2017. An overview of social media use in agricultural extension service delivery. J. Agric. Inf., 8(3): 50-61.         [ Links ]

BARNES, A.P., SOTO, I., EORY, V., BECK, B., BALAFOUTIS, A., SÁNCHEZ, B., VANGEYTE, J., FOUNTAS, S., VAN DER WAL, T. & GÓMEZ-BARBERO, M., 2019. Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers. Land Use Policy., 80: 163-174.         [ Links ]

BANGA, K., RODRIGUEZ, A.R. & TE VELDE, D.W., 2020. Digitally enabled economic transformation and poverty reduction. Available from https://set.odi.org/wp-content/uploads/2020/09/DEET-and-poverty-reduction_final-2.pdf        [ Links ]

BAYIH, A.Z., MORALES, J., ASSABIE, Y. & DE BY, R.A., 2022. Utilization of internet of things and wireless sensor networks for sustainable smallholder agriculture. Sensors., 22(9): 3273.         [ Links ]

BESE, D., ZWANE, E. & CHITENI, P., 2021. Adoption of sustainable agricultural practices by smallholder farmers in Mbhashe Municipality in the Eastern Cape Province, South Africa. African J. Dev. Studies., Si(1): 11-33.         [ Links ]

BONTSA, N.V., MUSHUNJE, A., NGARAVA, S. & ZHOU, L., 2023. Utilisation of digital technologies by smallholder farmers in South Africa. S. Afr. J. Agric. Ext., 51(4): 104146.         [ Links ]

BUKCHIN, S. & KERRET, D., 2020. Character strengths and sustainable technology adoption by smallholder farmers. Heliyon., 6(8): e04694.         [ Links ]

COGGINS, S., MCCAMPBELL, M., SHARMA, A., SHARMA, R., HAEFELE, S.M., KARKI, E., HETHERINGTON, J., SMITH, J. & BROWN, B., 2022. How have smallholder farmers used digital extension tools? Developer and user voices from Sub-Saharan Africa, South Asia and Southeast Asia. Glob. Food Secur., 32: 100577.         [ Links ]

DEPARTMENT OF AGRICULTURAL, FORESTRY AND FISHERIES (DAFF)., 2016. Funds redirected towards drought relief- Ministry of Agriculture Report in Budget Vote Speech. DAFF News.         [ Links ]

DEICHMANN, U., GOYAL, A. & MISHRA, D., 2016. Will digital technologies transform agriculture in developing countries? Ag. Econ., 47(S1): 21-33.         [ Links ]

ECONOMIC REVIEW OF THE SOUTH AFRICAN AGRICULTURE., 2020. Department of Agriculture, Land Reform and Rural Development, Statistics and Economic Analysis. Available from https://www.dalrrd.gov.za/Portals/0/Statistics%20and%20        [ Links ]

EVANGELISTA, R., GUERRIERI, P. & MELICIANI, V., 2014. The economic impact of digital technologies in Europe. Econ. Innov. New Technol., 23(8): 802-824.         [ Links ]

FOOD AND AGRICULTURE ORGANISATION (FAO)., 2017. E-agriculture in action. FAO and ITU.         [ Links ]

FOOD AND AGRICULTURE ORGANISATION (FAO)., 2022. Status of Digital Agriculture in 47 sub-Saharan African Countries. FAO and ITU.         [ Links ]

FRANK, F.E. & HARRELL, J.R., 2015. Binary logistic regression. In F.E. Frank & J.R. Harrell (eds.), Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis. Cham: Springer, pp. 219-274.         [ Links ]

HOANG, H.G. & DRYSDALE, D., 2021. Factors affecting smallholder farmers' adoption of mobile phones for livestock and poultry marketing in Vietnam: Implications for extension strategies. Rural Ext Innov Syst., 17(1): 21-30.         [ Links ]

LOKI, O. & MDODA, L., 2023. Assessing the contribution and impact of access to extension services toward sustainable livelihoods and self-reliance in Eastern Cape Province, South Africa. Afr. J. Food Agric. Nutr. Dev., 23(4): 23000-23025.         [ Links ]

MAVHUNDUSE, F. & HOLMNER, M.A., 2019. Utilisation of mobile phones in accessing agricultural information by smallholder farmers in Dzindi Irrigation Scheme in South Africa. Afr. J. Libr. Arch. Inf. Sci., 29(1): 93-101.         [ Links ]

MDODA, L., 2017. Market participation and value chain integration among smallholder homestead and irrigated Crop farmers in the Eastern Cape Province of South Africa. Doctoral thesis, University of Fort Hare.         [ Links ]

MGBENKA, R.N., MBAH, E.N. & EZEANO, C.I., 2016. A review of smallholder farming in Nigeria: Need for transformation. Int. J. Agric. Ext. Rural Dev. Studies., 3(2): 43-54.         [ Links ]

MIINE, L.K., AKORSU, A.D., BOAMMPONG, O. & BUKARI, S., 2023. Drivers and intensity of adoption of digital agricultural services by smallholder farmers in Ghana. Heliyon., 9(12): e23023.         [ Links ]

MTEGA, W.P., NGOEPE, M. & DUBE, L., 2016. Factors influencing access to agricultural knowledge: The case of smallholder rice farmers in the Kilombero district of Tanzania. S. Afr. J. Inf. Manag., 18(1): 1-8.         [ Links ]

MUEMA, E., MBURU, J., COULIBALY, J. & MUTUNE, J., 2018. Determinants of access and utilisation of seasonal climate information services among smallholder farmers in Makueni County, Kenya. Heliyon., 4(11): e00889.         [ Links ]

MUSHI, G.E., DI MARZO SERUGENDO, G. & BURGI, P.Y., 2022. Digital technology and services for sustainable agriculture in Tanzania: A literature review. Sustain., 14(4): 2415.         [ Links ]

NDLAZILWANA, L.C., 2022. Perceptions, coping strategies and welfare impact of drought among small stock farmers in Amathole, Eastern Cape. Doctoral dissertation, North-West University, South Africa.         [ Links ]

OKELLO, J.J., KIRUI, O.K. & GITONGA, Z.M., 2020. Participation in ICT-based market information projects, smallholder farmers' commercialisation, and agricultural income effects: Findings from Kenya. Dev Pract., 30(8): 1043-1057.         [ Links ]

PFEIFFER, J., GABRIEL, A. & GANDORFER, M., 2021. Understanding the public attitudinal acceptance of digital farming technologies: A nationwide survey in Germany. Agric Hum Values., 38(1): 107-128.         [ Links ]

TEMBA, B.A., KAJUNA, F.K., PANGO, G.S. & BENARD, R., 2016. Accessibility and use of information and communication tools among farmers for improving chicken production in Morogoro municipality, Tanzania. Livestock Res Rural Dev., 28(1).         [ Links ]

TULU, A., KHUSHI, Y.R. & CHALLI, D.G., 2018. Supplementary value of two Lablab purpureus cultivars and concentrate mixture to natural grass hay basal diet based on feed intake, digestibility, growth performance and net return of Horro sheep. Int. J. Livest. Prod., 9(6): 140-150.         [ Links ]

WIGGINS, S. & KEATS, S., 2013. Smallholder agriculture's contribution to better nutrition. London: Overseas Development Institute.         [ Links ]

YUSUF, S.F.G., POPOOLA, O.O. & YUSUF, F.T.O., 2022. Harnessing the use of alternative media for South Africa's agricultural extension service delivery in the face of the COVID-19 global pandemic. S. Afr. J. Agric. Ext., 50(2): 137-15        [ Links ]

 

 

Correspondence:
C. Mogashane
Correspondence Email: chantellemogashane2@gmail.com