ICT-Based Versus Human-Based Climate Information: Implications for Agronomic Decisions Among Smallholder Farmers in the Eastern Cape, South Africa

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Open Access Article

by
Jabulile Zamokuhle Manyike

* and
Yanga-Inkosi Nocezo

Department of Agricultural Economics, Extension, and Agri-Business, Faculty of Science and Agriculture, University of Fort Hare, 1 King William’s Town Road, Alice 5700, South Africa

Author to whom correspondence should be addressed.

Submission received: 3 February 2026 / Revised: 7 March 2026 / Accepted: 8 March 2026 / Published: 9 August 2026

Abstract

Smallholder farmers in South Africa face increasing climate variability, yet their agronomic decision-making depends on timely and reliable climate information. Although digital ICT platforms are expanding, limited evidence exists on how their effectiveness compares to human-based advisory systems. The study addresses this gap by examining the determinants of ICT-based climate information adoption using logistic regression and assessing its influence on agronomic decisions through propensity score matching (PSM). A cross-sectional survey and multistage sampling were used to collect data from 217 smallholder crop farmers in the Eastern Cape. The results indicate that 65% of farmers accessed climate information through ICT platforms, while 35% relied on human-based sources. The adoption of ICT-based information is driven by education, digital literacy, mobile network reliability, the perceived value of real-time updates, and smartphone ownership, whereas habitual dependence on traditional channels hinders digital uptake. PSM estimates show that ICT-based climate information is 10–15% less effective than human-based sources in shaping planting decisions, input use, and the timing of farm operations, likely due to digital literacy and infrastructure constraints. The study demonstrates that access to digital tools does not automatically translate into effective use and recommends a hybrid information model integrating digital platforms with trusted human intermediaries to strengthen climate resilience and agronomic decision-making.

1. Introduction

Agriculture remains central to rural livelihoods in South Africa, particularly in provinces such as the Eastern Cape, where smallholder farmers form the majority of agricultural producers [1,2,3]. Despite its importance, this sector is highly vulnerable to climate-related risks, as smallholder farmers often operate with limited resources, weak extension support, and poor access to climate adaptation tools [4,5,6,7]. Increasingly frequent droughts, delayed rainfall, rising temperatures, and shifting pest and disease pressures threaten food security and economic stability in the region [8,9,10]. Timely, reliable climate information is therefore critical for guiding agronomic decisions such as planting dates, crop choices, water management, and input application [11,12,13,14,15]. Access to such information enhances farmers’ preparedness for climate extremes and strengthens their adaptive capacity [15,16,17,18]. Climate information services typically include rainfall onset and cessation, seasonal duration, temperature trends, soil moisture, wind patterns, and early warnings for droughts or floods [18,19,20,21,22]. Traditionally, this information has been disseminated through human-based channels—extension officers, farmer groups, NGOs, and community networks [14,22,23,24]. These sources are valued for their contextual relevance and interpersonal trust [21]. However, in South Africa, high extension-to-farmer ratios, inconsistent service delivery, communication gaps, and sociocultural barriers, including gendered access to advisory support, limit the effectiveness of these channels [25,26,27,28,29].

At the same time, the expansion of digital infrastructure and mobile technologies has enabled Information and Communication Technologies (ICTs) such as mobile weather alerts, agricultural apps, WhatsApp groups, and voice-based advisory platforms to play an increasingly prominent role in climate information delivery [23,30,31,32,33,34,35,36]. ICT-based tools have the potential to provide rapid, localized, and scalable climate information [37]. However, their adoption remains uneven due to persistent digital barriers, including poor network coverage, low digital literacy, high data costs, and language mismatches [33,38,39,40]. Moreover, farmers often continue to trust interpersonal sources more than digital platforms, creating a dual system where ICT-based and human-based information coexist but are not equally utilized or valued [30,39]. These dynamics raise important questions regarding why some farmers adopt ICT-based climate information while others rely on traditional channels, and whether ICT-based information is as effective as human-based sources in influencing agronomic decisions. Addressing these questions is essential for designing climate information systems that are both accessible and impactful.

This study, therefore, seeks to bridge this gap by (i) identifying the factors that influence smallholder farmers’ adoption of ICT-based climate information over human-based sources, and (ii) evaluating the impact of ICT-based climate information on agronomic decision-making compared to human-based sources. Focusing on the Eastern Cape Province, where both ICT infrastructure and extension systems are active but unevenly distributed, the study provides timely insights into how information flows influence climate adaptation at the farm level. By shedding light on these dynamics, the study contributes to policy discussions on how to design integrated climate information services that are inclusive, accessible, and actionable. It also offers practical recommendations for strengthening both digital and interpersonal communication channels, ensuring that no farmer is left behind in the shift toward climate-resilient agriculture.

2. Materials and Methods

2.1. Description of the Study Area

The study was conducted in Elundini Local Municipality, located within the Joe Gqabi District of the Eastern Cape. The Municipality is made up of three towns, Mount Fletcher in the north, Maclear in the centre, and Ugie in the South. Figure 1 illustrates their locations along with the distribution of sampled households across the study area. The municipality is located near Mthatha [41], the third largest city within the province [42]. Elundini’s economy is driven by agriculture, social services, and retail trade, with the agricultural sector comprising commercial, emerging, and subsistence farming [43]. Farmers commonly grow maize, potatoes, and cabbages, and keep both large and small ruminants [41]. The area receives an annual average rainfall of approximately 1200 mm, mostly received in the summer planting season [44], but experiences significant climate variability, including erratic rainfall and periodic droughts that heighten production risks [43]. Furthermore, Elundini has many natural resources and a good climate that makes it possible for households to engage in agriculture [45]. However, its agricultural performance remains below its potential, constrained by limited market access, infrastructure, and technology uptake—conditions that contrast sharply with highly mechanized and well-serviced agricultural zones [46]. In the area, government support is available through extension services and farmer assistance programmes such as the provision of seedlings, livestock facilities, and fencing materials [41,46]. Nonetheless, the extent of their reach is limited due to high farmer-to-officer ratios and communication constraints [25,26,27,28,29].

Figure 1. Map of study area. Source: Department of GIS, University of Fort Hare. Note: Numbers 1–17 show the municipal wards; red dots mark the locations of the sampled households.

ICT infrastructure is provided through public libraries equipped with computers and internet access [46], though network reliability and digital literacy remain barriers for many community members. Given the combination of high agricultural potential, exposure to climate-related risks, uneven technological advancement, and mixed advisory and ICT service environments, Elundini provides an ideal setting for analyzing differences in the adoption and effectiveness of ICT-based versus human-based climate information sources.

2.2. Research Design

This study adopted a quantitative cross-sectional research design to investigate the adoption and impact of ICT-based climate information among smallholder crop farmers in the Eastern Cape Province of South Africa. A cross-sectional approach was deemed appropriate for capturing data at a single point in time, enabling an assessment of current patterns of ICT use, climate information access, and related agronomic decisions.

2.3. Sampling Procedure and Sample Size

A multistage sampling technique was employed to ensure representativeness and practicality in data collection. In the first stage, Elundini municipality was purposively selected. Within the municipality, eight wards were randomly selected based on their agricultural relevance and reported farming activity levels. Within each ward, one or two villages were randomly selected, leading to a total of twelve participating villages. Finally, within each village, a random sampling technique was used to select 217 smallholder farmers. Table 1 presents the distribution of sampled farmers across the three towns. From the total sample, 167 farmers indicated that they had access to some form of climate information, either through ICT-based platforms or human-based sources, while 50 had no access, and were excluded from the impact assessment component of the analysis. Additionally, among the 167 farmers included, 69 were from Maclear, and 65 from Mount Fletcher, with Ugie represented by 33 farmers.

Table 1. Sampling size per location.

2.4. Data Collection

Data was collected in May 2022 using a structured questionnaire, administered through face-to-face interviews to ensure comprehension and minimize non-response. The questionnaire consisted of questions on farmer’s socio-demographic characteristics (age, education, income, household size, and gender), sources of climate information (e.g., extension agents, radio, mobile apps, WhatsApp groups, local farmer associations), ICT access and use (mobile phone ownership, digital literacy, access to internet or network), and agronomic decision-making (e.g., planting time, crop choices, irrigation practices, input application). The instrument was pre-tested with a small group of farmers outside the sample area to ensure clarity and relevance, and necessary adjustments were made before final data collection. During the data collection phase, authors ensured that verbal consent was obtained from farmers, they were treated with respect and allowed to refuse to be part of the research or withdraw from it at any given time, and that their information was kept confidential and used for academic purposes only. All of these requirements were guided by the ethical approval letter [MAN011SNOC01], which was obtained from the department of agricultural economics and extension, faculty of Science and Agriculture at the University of Fort Hare.

2.5. Data Analysis

Data was first analyzed using descriptive statistics to summarize the characteristics of the sample, including age distribution, education levels, climate information sources, and ICT use. To identify the factors influencing the adoption of ICT-based climate information, a binary logistic regression model was employed. Stata/SE 15.1 (Stata Corp LLC) was used for the analysis.

2.5.1. Logistic Regression Model

The dependent variable was a binary indicator of whether a farmer accessed climate information via ICT (1) or through human-based sources (0). The probability (

$P_{i}$
) that farmers receive climate information via ICT platforms is represented as

$$
Y_{i } = \beta_{0} + \sum_{i = 1}^{n} \beta_{i} X_{i ,}
$$

(1)

The equation represents a binary choice, which involves the estimation of the probability of receiving climate information via ICT platforms (Y) as a function of independent variables (X).

$\beta_{0 }$
is constant and

$Y_{i }$
is equal to one 1 when farmers receive ICT-based climate information and 0 if through human-based sources. The logit model uses a logistic cumulative distributive function to estimate, P given by

$$
P = \left(\right. Y = \frac{1}{X} \left.\right) = \frac{e^{y}}{1 + e^{y}}
$$

(2)

$$
P = \left(\right. Y = \frac{0}{X} \left.\right) = 1 - \frac{e^{y}}{1 + e^{y}}
$$

(3)

$$
Y = \mathsf{\beta}{1} X{1} + \mathsf{\beta}{2} X{2} + \ldots + \mathsf{\beta}{k} X = \sum{i = 1}^{k} \mathsf{\beta}{i} X{i ,}
$$

(4)

where k represents the number of explanatory predictors that are to be included in the analysis. The model is as follows:

$$
Y = \mathit{Ln} \left(\right. \frac{P}{1 - P} \left.\right) = \beta_{0} + \beta_{k} + \epsilon ,
$$

(5)

where Y = farmer accessed climate information via ICT; Ln

$\left(\right. \frac{P}{1 - P} \left.\right)$
the ratio of probability of accessing climate information via ICT (p) to receiving it through human-based sources (1 − P); β = slope of coefficient;

$X_{k}$
= vector of the independent variables presented in Table 2; ε = error term.

Table 2. Independent variables and expected signs in ICT adoption model.

2.5.2. Measuring Agronomic Decision

The Agronomic Decision Index (ADI) was employed to assess the extent of agronomic decisions made by smallholder farmers. This index serves as a tool to quantify the degree to which each household adopts key agronomic practices. While the existing literature commonly measures agronomic decision-making by simply noting whether a farmer made a decision or not [17], this approach does not capture the depth of adoption. In contrast, ADI provides a more comprehensive measure. Each agronomic decision was coded as 1 if undertaken by the farmer and 0 otherwise. The index for each farmer was then calculated as shown in Equation (6):

$$
A D I = \frac{D_{1} + D_{2} + D_{3} + D_{4} + D_{5} + D_{6} + D_{7}}{7}
$$

(6)

where D1 to D7 include decisions on land preparation, planting date decis