Socio-demographic characteristics of the respondents

The socio-demographic information of the respondents collected in the study was racial affiliation, gender, age, educational background and farm/plot size. The results of socio-demographic characteristics of the respondents are presented in Table 1. The results showed that largest proportion of the respondents were black Africans. Thus, the recipients of public extension and advisory services in the study area were black African farmers of which majority (51.8%) were females. The findings of educational level indicated that more than two-thirds (72.8%) of the participants had basic education (primary, secondary education and ABET), less than one-fifth (13.8%) had no formal education and 13.4% had acquired tertiary qualifications (diploma, bachelor’s degree, honours degree/BTech, master’s and doctoral degrees). It implied that most farmers could read and write, because they had formal education (tertiary and basic education). The results of farm/plot size showed that on average, the respondents occupied farming land of 4.6 ha with a minimum of less than one hectare (< 1 ha) and maximum of more than seventy hectares (> 70 ha). Therefore, the recipients of government extension and advisory services in Gauteng province were both large and small-scale farmers.

Table 1 Socio-demographic characteristics of the participants (n = 442)

Effectiveness of public extension and advisory services

The perceived effectiveness of public extension and advisory services were determined using different variables derived from the South African norms and standards for extension and advisory services in agriculture. The results of the farmers’ perceived effectiveness of public extension and advisory services in the study area are presented in Table 2. The results showed that, of the 16 variables measured in the study, public extension and advisory services were perceived as effective in five variables. This is shown by more than half (> 50%) of the respondents who agreed that public extension services were effective and very effective. A median of five (5) also support the notion that public extension services were perceived to be effective in all five variables. Moreover, all five variables had IQR between 3.2 and 3.6 for 95% CI lower bound and upper bound, respectively. Most importantly, public extension and advisory services were perceived by 55.0% as effective in complying with the principles of Batho Pele (rendering good quality services and goods) when dealing with people and planning activities; followed by promoting equity through subsistence small-scale farmers, women farmers, disabled farmers and commercial farmers with 54% of the respondents. About 53% of the respondents perceived public extension services as being effective in providing and facilitating advice on skills development in agriculture. Furthermore, 52% and 51% of them held the opinion that public extension services were effective in providing and facilitating access to agricultural information for improved planning and decision-making, and using extension approaches that are relevant to the beneficiaries, respectively. Finally, 50.4% of them were of the opinion that the government was effective in rendering high quality extension and advisory services. In general, public extension and advisory services in the Gauteng province, were perceived as ineffective, because 49% of the respondents indicated that the services rendered were average. The median score of 3.3 is also in support of the above explanation. In support, extension services were perceived to be ineffective in most of the variables, with a median of ≤ 3.5 and < 50% of the respondents who perceived the services as effective.

Table 2 Perceived effectiveness of public extension and advisory services in the Gauteng province (n = 442)

Factors influencing effectiveness of public extension and advisory services

The overall effectiveness of public extension services was measured using the average score of all 16 variables which measured the perceived effectiveness of public extension and advisory services. The descriptive statistic results showed that, in general, about 43.7%, 33.5%, 10.2%, 7.2% and 5.4% of the respondents perceived public extension services as effective, average, ineffective, very ineffective and effective, respectively. It implied that a minority (49.1%) of the respondents’ perceived public extension services as effective, as shown by the proportions of very effective and effective combined. A median value of 3 and IQR (3.2–3.4) indicates and supports the notion that public extension services were perceived as ineffective. Moreover, 33.5% of the respondents held the opinion that public extension and advisory services were moderately effective, while 17.4% indicated that the services were ineffective. The results of the OLR model fitting, achieved a chi-square value of 37.994 with a degrees of freedom (df) of four (4). Moreover, the model was statistically significant at 1% interval level (p < 0.01). It implied that the model could significantly predict the threshold [p < 0.00; χ2(4) = 37.99]; therefore, the model is suitable for the data. Again, the chi-square outputs of Pearson and Deviance achieved for goodness-of-fit were 1489.20 and 925.44, respectively. The degrees of freedom (df) for both chi-square outputs (Pearson & Deviance) was 1252. However, Pearson chi-square was statistically significant (p = 0.00), while Deviance was insignificant (p = 1.00). According to [43], non-significant results of Pearson and Deviance chi-square implied that the data fit the model well. However, they do not always have to be similar. Therefore, the model fit the data, because Pearson chi-square was not statistically significant. The values of Pseudo R-Square were 0.082, 0.089 and 0.033 for Cox and Snell, Nagelkerke, and McFadden, respectively. Unlike in Multiple Regression Models, the Pseudo R-Squares measures have limitations in evaluating the overall model fit [44]. As a result, the values are accepted as they are, without further interpretation.

The results of the parameter estimates of the Ordered Logistic Regression (OLR) model of the factors influencing perceptions towards the effectiveness of public extension and advisory services are presented in Table 3. The results showed that only two of the four independent variables (education level and age), fitted in the regression model, were positive, while the others were negative (gender and farm/plot size). Both positive variables (education level and age group) were statistically significant at 1% and 5% levels of significance (99% and 95% confidence interval), respectively. Education level had a positive (β = 0.35) and significant relationship (p < 0.02) with perceived effectiveness of public extension and advisory services, with all other factors being constant. Furthermore, there was a positive (β = 0.35) and significant correlation (p < 0.00) between age and perceived effectiveness of public extension services. Therefore, when farmers’ age increased, they perceived extension services as more effective.

Table 3 Parameter estimates of the OLR results of the factors influencing perceptions towards the effectiveness of public extension and advisory services (n = 442)

Nevertheless, the relationship between farm/plot size and farmers’ perceptions toward public extension and advisory services, was negative (β = − 0.04) and statistically significant (p < 0.00). It means that when farm/plot size increases, farmers perceive public extension services as less effective, with all things being equal.

Exploratory factor analysis

This section presents the results of the exploratory factor analysis which was performed using PAF. The purpose was to identify underlying factors regarding the perceived effectiveness of public extension and advisory services in the study area (Gauteng province). First, the results of the adequacy of the sample size for PAF analysis and the test of sphericity are presented, followed by the scree plot; the cumulative column explaining total variance; the exploratory factor analysis; and the factor correlation matrix. After the first analysis, three factors were extracted from the exploratory factor analysis. Furthermore, 12 variables were retained for further analysis after dropping those with loadings less than 0.50. The KMO score obtained was 0.96, which implied that the sample size was still adequate for factor analysis. Furthermore, Bartlett’s test of sphericity was statistically significant (p < 0.01), meaning the data was also appropriate for factor analysis. The Chi-square value obtained, was 5113.89 with 66 degrees of freedom (df).

Figure 3, presents the scree plot that indicates how eigenvalues were plotted against factors. The results in the scree plot showed that the elbow started to decrease at Factor 4 with an eigenvalue of 0.35. Therefore, the first three factors on the slope, before the graph started decreasing to form an elbow, were retained. A detailed explanation regarding the names of the factors that were retained is provided in Table 4.

Fig. 3
figure 3

Scree plot for factor analysis

Table 4 Cumulative column explaining total variance

The results of the cumulative column explaining total variance is presented in Table 4. The results depict that the three extracted factors contributed 81.81% of the variance. Individually, factors 1, 2 and 3 contributed 70.72%, 6.10% and 5.00% to the total variance, respectively. Factor 1 demonstrated the highest eigenvalue with 8.49, followed by Factor 2 with 0.73 and 0.60 for Factor 3. Descriptions of all the factors, loading values and their communalities are presented in Table 5.

Table 5 Results of the exploratory factor analysis of the effectiveness of public extension and advisory services (n = 442)

Table 5 presents the results of the exploratory factor analysis of the effectiveness of public extension and advisory services. The results show that the analysis extracted three factors for the effectiveness of public extension and advisory services, in the study area. Factor 1 consisted of six variables, followed by Factor 2 and Factor 3 with four and two variables, respectively. The three extracted factors are labelled as follows: Factor 1 is relevant and good quality extension and advisory services (Promoting equity when rendering relevant and good quality extension services; and using appropriate approaches that are flexible and effective in monitoring and evaluation). Factor 2 is the provision of information which improves agricultural production (Facilitating and providing access to information which improves agricultural skills; planning and decision-making; and which sustains agricultural production and strengthens institutional relationships). Factor 3 is providing technologies required by farmers (Facilitating and providing access to technology that prioritises farmers’ needs). Factor loading for a large proportion of the participants was more than 0.60; therefore, the correlation between the extracted factors and the items associated with them was high. In addition, most variation was extracted, because the communalities of all the items were between 0.63 and 0.79. The results of the communalities showed that 63–79% of the variability in the perceived effectiveness of public extension and advisory services, is explained by the three factors (1–3). Therefore, the factor analysis explains the variation in eleven of the twelve (11 out of 12) variables very well.

After extracting all the factors and their individual variables, the factor correlation matrix was generated. The results indicated that relevant and good quality extension and advisory services (Factor 1) was positively correlated with provision of information that improves agricultural production (Factor 2), r = 0.74. This implied that participants, who were of the opinion that public extension and advisory services were effective in rendering relevant and good quality extension services, perceived the provision of relevant information that improves agricultural production as an important measure of effective extension services. Factors 1 (rendering relevant and good quality extension and advisory services) and 3 (Providing technologies required by farmers) were correlated (r = 0.74). This means that farmers who perceived relevant and good quality extension and advisory services as a measure of effectiveness, held the opinion that extension services should provide technologies required by farmers to be considered effective. Finally, factors 2 (providing information that improves agricultural production) and 3 (Providing technologies required by farmers) were positively correlated (r = 0.71). Therefore, farmers who perceived public extension and advisory services as effective in providing information that improves agricultural production, held the opinion that extension services that provide technologies to the farmers are effective.

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