Empirical results

The empirical results presented in this section give a summary of the descriptive results and the logistic regression results.

3.5.1 Descriptive results

The descriptive results for the demographic characteristics show that from all the interviewed respondents, there were a larger proportion of male respondents (58%) as opposed to females. However, there were greater proportions of females (63%) in vegetable farming, but in both cattle and citrus farming, there were greater proportions of males. A large number of females in vegetable farming can be explained by day-to-day vegetable supervision by females where men move to cities/town in search of jobs. The majority (72%) of the smallholder and emerging farmers in the Kat River Valley are above 49 years of age. The educational level among the sampled farmers is generally low, where 18% of the household heads never attended school and 39% have gone up to primary level. Household size ranges from a minimum of two people to a maximum of 18, with a mean of 7.1 in each household.

According to Randela (2005), a larger household size has a negative effect in produce marketing because the household needs to supply household consumption before it decides to sell. Evidence from the research confirms this line of argument because larger households in this research sold less produce as compared to smaller households.

The land that is available to smallholder farmers in the Kat River Valley is usually shared between residential and farming purposes. This situation leaves less arable land for farming purposes. In addition, most smallholder farmers do not own the land they farm on, even though they have rights to use it. The sampled farmers had access to small pieces of land where 77% of the households had less than 2 hectares. Comparing land accessibility to different farming types, cattle farmers had the least land sizes where all of them had less than 2 hectares. Smaller areas of arable that is used by cattle farmers can be explained by use of communal grazing land for animal feeding. In such cases, it is difficult to measure the amount of communal grazing land that is available to each household.

Household incomes of the respondents are received from five main sources, which are farming, wages, pensions, social grants, and other small household business activities.

Of importance, is the fact that in the absence of pension and social grants, 74% of the households view farming as their main source of income. When selling produce, households had different reasons for choosing the marketing channels they use. A summary of the main market outlets used by smallholder and emerging farmer are presented in Table 3.1.

The markets outlets shown on Table 3.1 were divided into formal and informal markets, where informal markets embrace unofficial transactions between farmers and traders, and farmers and consumers whereas formal markets are guided by formal rules and regulations.

As shown in Table 3.1, more vegetable and cattle farmers make use of informal markets than formal markets. The marketing channels used by citrus farmers present a slightly different trend compared to that of vegetable and cattle farmers. Even though all citrus farmers mainly use export markets, between 20 and 40% of their produce is sold in local informal markets.

In an effort to find the reasons why the sampled farmers preferred the markets they use, they

Table 3.1. Main market outlets used by sampled households.

Type of farming Main market outlets Market type

Vegetables (n=43) farm gate (46.5%) informal

Fort Beaufort (32.5%) formal and informal around the village (14.0%) informal

roadside (7.0) informal

Citrus (n=14) export markets (100%) formal

Cattle (n=43) private sales (39%) informal

speculators (26%) informal

auctions (23%) formal

abattoirs (9%) formal

butcheries (3%) formal

were interviewed on the prevailing marketing problems they faced during selling and those that constrained them from manoeuvring into more rewarding marketing arrangements.

The most frequently mentioned marketing challenges are shown in Table 3.2.

Farmers were asked to clarify the challenges listed in Table 3.2. For example, farmers were asked what they meant by market information. The most mentioned answers were related to information on the prevailing prices, type of goods required in the markets and alternative markets. Farmers explained that the information to which they had access was unreliable because they usually got it from either other people in the village who are involved in selling or from the rural traders. They pointed out that they rarely trusted such information but they had no option because those are the only sources accessible to them. Alternatively, the farmers explained that they would just take chances and go to the market place without any prior information to receive the same price as other people selling at that selling point.

Taking a closer look at the marketing challenges that were cited by households, they could all be resolved through technological and institutional innovation. For instance, low prices for produce which can be related to poor produce quality, inability to reach other markets, being uninformed and abundance of the same produce in the markets. Poor produce quality can be resolved by availability of information on grades and standards and an improvement in technology for storage and transportation. Farmers may be able to reach other markets if they participate in groups because they share information and broaden social capital within the groups. In addition, when farmers market in groups, they eliminate competition and may diversify into producing other crops, reducing market pressure.

Table 3.2. Marketing challenges among sampled households.

Rank Marketing challenges Households affected (%)

1 Lack of capital 80

2 Bad roads 79

3 Low prices for produce 67

4 No reliable markets 65

5 No market information 64

6 Lack transport/high transportation costs 59

7 No exposure to other markets 53

8 Lack of storage facilities 51

9 High competition 46

10 No convenient place to sell from 38 11 No transparency in marketing channels 18

3.5.2 Variable correlations

A correlation analysis was run to determine the significant relationships between the explanatory variables that were used in the model. The variables correlations were tested using the Pearson correlation, at the 5% significance level.

Based on the results, it was noticed that some of the variables were correlated. Extension contact was significantly related to market information, organisational support services, expertise on grades, ability to add value and availability of an extensive social capital. The other variables that were correlated included contractual agreements and group participation, storage facilities and expertise on grades, availability of an extensive social capital and group participation, and market information and expertise on grades. Since the variables were highly correlated (where r>0.5 in most cases), it raised suspicion of the multicollinearity problem. The problem may have been caused by the use of dummy variables as Rao and Rao (1998) stated that multicollinearity is common when using dummy variables. However, Hill et al. (2001) explained that multicollinearity does not influence the reliability of the model as a whole but affects the individual predictor variables.

In order to rectify the problem and improve the results, one of the explanatory variables was dropped. The extension service variable was chosen because most variables were correlated to it. It seemed reasonable to drop the variable because not much information would be lost after dropping it. The other variables would be used to capture its importance because most of the knowledge related to market information, grades, adding value and importance of group support was imparted through extension workers. After dropping the extension service variable there was minimal correlation of variables, with only two variables being significantly correlated. A reduction in the correlation of explanatory variables indicates that the multicollinearity problem was corrected. The remaining explanatory variables were then used in the model.

3.5.3 Significant variables in the model

The hypotheses generated in Section 3.5 were tested in a multinomial logistic regression model. The results of the parameter estimation in the model are presented in Table 3.3.

The table shows the estimated coefficients (β values), standard error, significance values and odds ratios of variables in the model. According to Gujarati (1992), the coefficient values measure the expected change in the logit for a unit change in the corresponding independent variable, other independent variables being equal. The sign of the coefficient shows the direction of influence of the variable on the logit. It follows that a positive value indicates an increase in the likelihood that a household will change to the alternative option from the baseline group. On the other hand, a negative value shows that it is less likely that a household will consider the other alternative (Gujarati, 1992; Pundo and Fraser,

2006). Therefore, a positive value implies an increase in the likelihood of changing from not participating in marketing to either informal or formal market participation choice.

The variables were tested at the 5% significance level. Thus, if the significance value is greater than 0.05, then it shows that there is insufficient evidence to support that the independent variable influences a change away from the baseline group and vice versa. The odds ratio indicates the extent of effect on the dependent variable caused by the predictor variables.

Its value is obtained by calculating the anti-logarithm of each slope coefficient of predictor

Table 3.3. Multinomial logistic results for informal and formal market choices as compared to non-marketing choice (n=100).

Variable Informal market choice Formal market choice

Coefficient Std.

Error

Significance^bOdds ratio

Coefficient Std.

Error

Significance^bOdds ratio

MKTINFO 2.686 1.050 0.011* 14.673 4.217 1.385 0.006* 67.83

GRDS -1.623 0.905 0.073 0.197 3.830 0.848 0.016* 46.06

ORGMEM 0.788 0.786 0.316 2.199 1.324 1.384 0.330 3.758

FMNGTYP1 -0.248 0.754 0.742 0.780 -1.164 1.854 0.530 0.312

FMNGTYP2 1.798 0.854 0.028* 6.038 -1.543 1.017 0.033* 0.214

FMNGTYP1^a -0.523 0.675 0.065 0.593 0.762 0.545 0.048* 2.143

RDINFR 0.862 0.841 0.305 2.368 2.992 2.171 0.168 19.92

SOCIAILK 0.222 0.948 0.050* 1.248 1.180 2.863 0.031* 3.254

ADDVAL 1.352 1.079 0.210 3.865 0.392 0.218 0.860 1.479

MKTINFR 2.557 1.030 0.013* 12.897 -0.687 0.026 0.735 0.503

STOR 0.584 0.777 0.453 1.793 0.259 1.873 0.890 1.296

CONTRCT 0.844 0.755 0.263 2.326 2.803 1.912 0.047* 16.49

TRANS 0.843 0.774 0.276 2.323 0.449 1.644 0.785 1.567

PART 1.899 0.854 0.026* 6.679 1.997 1.418 0.039* 7.367

TRAD 2.477 1.441 0.031* 11.905 -2.144 2.296 0.007* 0.117

INTERCEPT -4.934 2.860 0.048 - -17.069 4.466 0.044

-Goodness-of-fit

Chi² df Significance Pearson 111.372 168 0.150 Deviance 84.301 168 0.988

a Variable tested for 86 respondents (43 vegetable farmers and 43 cattle farmers).

b * Statistically significant at 5% significance level.

variables. A value greater than one implies greater probability of variable influence on the logit and a value less than one indicates that the variable is less likely to influence the logit.

The standard error measures the standard deviation of the error in the value of a given variable (Gujarati, 1992; Hill et al., 2001).

As indicated in Table 3.3, some predictor variables influence market participation choices significantly. Of the 14 independent variables used in the model, five and six variables in informal and formal market choices respectively, are statistically significant at the 5%

significance level. In all but one of the cases, the signs of the estimated coefficients are consistent with the a priori expectations.

Access to market information has a positive sign for both formal and informal market choices, which is consistent with the a priori expectations. The significance values of 0.011 for the informal market choice and 0.006 for the formal market choice imply that there is enough evidence to support that an increase in the availability of market information results in an increase in both informal and formal market participation. The larger values in odds ratios show that households are most likely to increase participation in both informal and formal markets with the availability of market information. As shown by the coefficients, the increase in formal marketing, resulting from market information availability is about twice the increase in informal marketing.

Expertise on grades and standards is significant for the formal market choice with a significance value of 0.016. A positive sign on its coefficient indicates that an improvement in expertise on grades and standards results in an increase in the formal market participation choice by households. When households acquire expertise in grades and standards, they prefer selling their produce in the better paying formal markets, in order to cover costs associated with acquiring the expertise (Reardon and Barrett, 2000).

A positive and significant (0.047) relationship was found between formal market participation and the availability of contractual agreements. The relationship implies that households tend to increase formal market participation with the availability of contractual agreements. This relationship is most likely due to the influence of the citrus farmers. The value of the odds ratio (16.49) supports the higher probability of the variable influence on the formal market choice.

The variable existence of extensive social capital is significant for both informal (0.050) and formal (0.031) market choices. The positive relationship in both formal and informal market participation choices explains that an increase in social capital results in households shifting from non-participation to formal and informal market participation. The odds ratios for both formal and informal market choices suggest a higher probability of shifting to formal and informal marketing with an increase in social capital. Therefore, it can be concluded

that social networks are important in produce marketing, regardless of the choice of market being used.

It was expected that the availability of good market infrastructure could have a positive influence on alternative market participation choices, away from not participating in marketing. However, the a priori expectations hold true for the informal market choice only. There is sufficient evidence (significance value of 0.013) to support that the availability of good market infrastructure is likely to encourage households to market their produce through informal channels. Unlike formal channels where market infrastructure is not important for farmers, as they supply their produce in bulk once harvested to the higher level of the marketing channel (Takavarasha and Jayne, 2004).

Group participation in marketing was expected to have a positive influence on the dependent variable. The results shown in Table 3.3 for this variable are consistent with the a priori expectations. For both formal and informal market choices, there is enough evidence to support that when households market their produce in groups, there is a higher chance of participating in either formal or informal markets. Thus, group participation encourages market penetration among smallholder farmers who find it difficult individually to gain market access.

A positive and significant (0.031) relationship was found between informal marketing and guidance from traditions and beliefs. These results are contradiction to the a priori expectations, where guidance from traditions and beliefs was expected to influence the dependent variable negatively. The positive relationship between the variables may possibly be explained by traditional wisdom and skills passed on in families and creation of marketing links through traditions and beliefs. For instance, some households may prefer to sell their produce (especially in cattle marketing) to people they are familiar with. On the other hand, there is a negative and significant (0.007) relationship between formal marketing and guidance from traditions and beliefs. The explanation to this relationship may be that the marketing environment is ever changing (Kherallah and Kirsten, 2001); therefore, if farmers are to be part of the formal markets, they have to be receptive to changes.

The variable FMNGTYP2 is positively related to informal marketing but negatively related to formal marketing. The relationships imply that there is a difference between farming types and marketing in both formal and informal markets. The results show that vegetable farmers, when compared to a combination of citrus and cattle farmers, are more likely to use informal marketing channels than formal marketing channels. When the farming type variable was tested for 86 respondents (43 cattle and 43 vegetable farmers), a positive relationship was noted for the formal marketing choice. These results explain that cattle farmers are most likely to use the formal marketing channels as compared to vegetable growers in the Kat River Valley.

The goodness-of-fit test for a logistic regression model measures the suitability of the model to a given data set. An adequate fit corresponds to a finding of non-significance for the tests (Hill et al., 2001). The results for the goodness-of-fit test shown in Table 3.3 indicate that the model fits the data well. Thus, the results for both Pearson and Deviance chi-squared methods show that the multinomial logistic regression model is well suited to predict the influence of independent variables on the dependent variable.

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