The Determinants of Smallholder Farmers on the Functionality of Plant Health Clinics in the Vhembe District, South Africa

Abstract

A plant health clinic is a system in which under-resourced and smallholder farmers can access basic services of plant healthcare from a plant clinic in relation to infected or symptomatic plants and plant products for pest diagnostic and identification purposes. The factors that influence smallholder farmers’ awareness and accessibility to plant health clinics in the Vhembe District Municipality of Limpopo Province, South Africa were examined in this study. The study also identified the constraints of the plant clinics that prevent their effective utilization. Three-hundred and twenty smallholder farmers (n = 320) from the population of 1600 were interviewed through a semi-structured questionnaire. A multistage random sampling technique was employed to select the 320 smallholder farmers from 8 irrigation schemes in the study area. To ensure representation, all categories of smallholder farmers were included in the study. Descriptive statistics such as frequency and percentages and Logit regression model were employed for the data analyses. The results revealed that there is a statistically significant and positive relation between communication channels and plant clinics awareness and accessibility among smallholder farmers. The distance to plant clinics has a negative influence on the accessibility of plant clinics among the farmers; however, the accessibility of plant clinic improves when visits by extension officers or plant doctors to farmers increase. The study pointed out some challenges affecting the functionality of plant clinics, which could be resolved through an efficient and effective plant clinic framework that involves major role players. An efficient and effective plant clinic framework was therefore developed by the study to improve its usefulness for diagnosing plant pest symptoms and diseases. The study concluded that plant clinic framework was important and should be considered and adopted by policy makers within the government, research institutes, and other NGOs.

1. Introduction

About 40–60% of crop losses is caused by plant pests and diseases affecting farmers [1]. Plant clinics or plant health clinics were found to contribute toward yield increase, food production and security [2]. A plant health clinic is an integral part of the plant health system which provides pest detection, early pest diagnosis and advisory services to the farmers [3]. Plant health clinics are found to be the primary source of information that may help farmers in their agricultural field of practice [4] and are more critical during the value chain of plant and crop production to maintain healthy plants and plant products [5].

Plant clinics provide a basic primary plant health service to farmers through advisory services and recommendations for pests and diseases management [6]. Primary plant healthcare is demand-driven by the farmers and is part of extension support to the smallholder farmers [7]. Effective plant clinic awareness among farmers is an important factor for the successful functioning and utilization of plant clinics [8]. Extensive promotion is an important tool to extend farmers’ outreach within their communities to improve awareness of services rendered by plant clinics [5].

Farmer determinants of participation and awareness of plant health clinics include age, gender, marital status, literacy level, land ownership, radio ownership and phone ownership [9]. Agricultural groups and associations are also found to be very useful for farmers’ interaction and information exchanging on matters relating to plant health challenges [10]. According to the study conducted by Etwire et al. [11], access to extension support, training and education were found to contribute significantly to the improved level of knowledge and awareness of smallholder farmers regarding services offered by plant clinics. Jowi [12] and Adetumbi et al. [13] reported that more farmers preferred using mass media as an effective tool for advocacy and awareness of plant clinics. This was also supported by the study conducted by Cameron et al. [14] who cited that the use of combined means of communication is vital to reach the farming community about plant clinics. Musebe et al. [15] reported that most farmers consider cooperatives or farmers’ groups to have significant influence on the awareness of plant clinics. With regard to distance to the plant clinic, Kansiime et al. [8] in the study conducted in Kenya cited that distance had a negative impact on plant clinic awareness and farmers’ participation.

Most farmers are unlikely to know of the existence of plant clinics due to the lack of effective awareness programs [15]. Boa et al. [16] cited that most farmers are not aware of service provisions and the role of plant clinics due to the farmers’ lack of awareness, education and training. The study conducted in Kenya reported that male farmers were found to be more aware of plant clinics than female farmers. Furthermore, a lack of interest in farming by young people often leads to limited awareness among the young farmers about the plant clinics. Plant clinic awareness and promotion are important approaches to encourage participation of farmers in the plant health clinics’ activities, regardless of their gender and age [15]. Plant health clinics are useful for addressing plant health problems and farmers’ challenges at the important production level [17]. Most farmers are dependent on the information from the extension officers [18]. Worldwide, most smallholder farmers find information about plant health clinics helpful [14,17]. However, in some cases, awareness of plant pests and diseases do not reach targeted farmers due to a lack of awareness [16]. Many farmers in rural community areas find it difficult to control and manage plant pests and diseases [15]. According to Danielsen et al. [17], most plant health doctors or extensionists require regular training to assist farmers with diseases’ symptoms, pest diagnosis and identification, and pest control and management.

In the Republic of South Africa (RSA), the existing plant health clinic was established by the Agricultural Research Council (ARC) with the collaboration of the Centre for Agriculture and Bioscience International (CABI) and the Provincial Department of Agriculture (PDA) in Limpopo Province [19]. The plant health clinic in the Vhembe District of Limpopo Province was established in 2014. The type of plant clinics established provides extension support to plant clinics at local agricultural offices (near the irrigation schemes), led by trained extension officers or “plant doctors”, whereas during farmers’ day events, the ARC often provides its Mobile Plant Clinic [20,21]. The extension officers of the Limpopo Department of Agriculture in the Vhembe District were trained about the plant clinic. Extension officers were trained and offered a course on “field diagnosis and setting up a plant clinic” and “giving practical recommendations to farmers” [19].

Much literature h indicated the importance of awareness and accessibility and revealed that age of the farmers, distance to plant clinics, farm visit by extension officers and communication channels are important factors influencing farmers’ awareness of and accessibility to plant clinics. Serote et al. [22], in the study conducted in Limpopo Province, South Africa, found that farmers’ awareness is dependent on the information from the extension officers, and this resulted in more farmers being aware of the adoption of innovation and technologies. The results of this study were compared with the previous study and allowed us to come up with sound conclusions and recommendations for improving the functionality of plant clinics through a plant clinic framework to benefit farmers in the study area.

The results of this study will contribute to a better understanding of the importance of the efficient and effective functionality of plant clinics among farmers through implementation of a plant health clinic framework. Improved plant clinic accessibility and awareness will increase the clinics’ utilization among farmers for plant pest and disease advice and diagnosis. This will further encourage most farmers to report any occurrence or suspicion of occurrence of any pests and diseases to the relevant district office, plant clinics, and authority. The study provides for an effective plant clinic framework for efficient and effective utilization to benefit farmers for the appropriate control and management of plant pests and diseases to optimize food production and security. The aim of this study was to analyze the determinants of smallholder farmers’ awareness of and accessibility to plant health clinics and to determine the limitations of the plant clinics militating against its utilization in the Vhembe District Municipality of Limpopo Province, South Africa.

2. Materials and Methods

2.1. Description of the Study Area

The study was conducted in the local municipalities of Musina, Thulamela and Makhado in the Vhembe District, Limpopo Province, South Africa (Figure 1). The district is in the Limpopo Province, which shares borders with the following neighboring countries: Botswana, Zimbabwe and Mozambique [23]. The Vhembe District Municipality (VDM) covers about 21,407 square km of land with an estimated 1.3 million people [24,25,26]. The VDM has about 249,757 hectares of arable land, with only 74,927 hectares owned by the smallholder farmers (30%) [26]. However, Nefale [24] stated that there are about 70 community-based agricultural cooperatives focusing on vegetables and field crops in the VDM. The dominating farming practice is subsistence farming activities for grain, vegetables and fruit production [23]. In terms of number of households in agricultural activities, there are about 2,329,043 [25]. The VDM is a subtropical region which produces and exports high volumes of banana, avocado, litchi and mango fruits [27].

2.2. Data Collection and Sampling Technique

Data was collected from the Khumbe Irrigation Scheme, Murara Irrigation Scheme, Dzindi Irrigation Scheme, Tshiombo Irrigation Scheme, Malavuwe Irrigation Scheme, Ha-Mphaila Irrigation Scheme, Dopeni Irrigation Scheme and Nwanedi Irrigation Scheme located within the VDM in 2022. In the VDM, the schemes were selected because they are predominantly operated by smallholder farmers whose farming activities mainly involve vegetable and grain production. A semi-structured questionnaire informed by the specific objectives of the study was utilized for data collection (

Table A1. Demographic and socio-economic information of the smallholder farmers.

NO	Questions	Expected Response
1.	Age
2.	Gender status	Male	Female
3.	Marital status	Married	Single	Divorced
4.	Level of education	Primary school
		Secondary School
		University
		College
		No formal education
5.	Years of experience in farming
6.	Employment status	Employed	Unemployed
7.	Occupation
8.	Farming activity in your farm	Vegetables
		Field crops
		Fruits
		Grain production
9.	Who is owning the production farm	Cooperatives
		Individual (Yourself)
		Farm groups
		Private companyTrust
		Other, specify:
10.	Communication channel	Radio
		Television
		Local news paper
		Meetings
		Other, specify:
11.	Distance to the plant clinic or extension officer	Short	Medium	Long
12.	Access to finances	Yes	No
13.	Government support program	Yes	No
14.	Own transport	Yes	No

,

Table A2. Awareness about plant health clinics.

NO	Questions	Response
1.	Plant health clinic was launched in 2014 in your district?	Yes	No	Not aware
2.	Are you aware of the existence of plant health clinic or diagnostic services in Vhembe District?	Yes	No
3.	ARC or Provincial Department of Agriculture is responsible for running the plant health clinic?	Yes	No
4.	Is there any plant health clinic forum discussing matters relating to pests affecting crops?	Yes	No
5.	Plant healthcare practitioner comes to your area?	Yes	No
6.	Are you aware that plant disease can spread from farm to farm?	Yes	No
7.	Are you aware of any treatment to control plant pest and disease?	Yes	No
8.	Are you aware of any Department of agriculture in your area?	Yes	No

,

Table A3. Accessibility to plant health clinics.

NO	Questions	Response
1.	Do you own your own transport	Yes	No
2.	Are you able to access the plant healthcare service from plant health clinic or extension officers	Yes	No
3.	Do you have transport money to access the extension officer or plant healthcare practitioner?	Yes	No
4.	Extension officer visits your farm from time to time?	Yes	No
5.	Have you ever reported any to the relevant center of official for identification?	Yes	No
6.	Is the agriculture district office accessible or closer to you for pest advice?	Yes	No
7.	Do you report any pest and disease occurrences in you farm?	Yes	No

and

Table A4. Limitations of basic plant health clinic services.

NO	Questions	Response
1.	Plant clinic or extension officer are not accessible?	Yes	No
2.	Recommendations made require more expensive inputs?	Yes	No
3.	Visits by extension officer are not usually taking place?	Yes	No
4.	I do fail to identify pest problems?	Yes	No
5.	I don’t have a tool to identify plant pests and diseases	Yes	No
6.	It is a distance to go and receive plant health clinic advisory services	Yes	No
7.	Plant health clinic activities do not take place anymore?	Yes	No
8.	Information received on plant pest and disease?	Yes	No
9.	Lack of awareness of pests and disease affecting pests?	Yes	No
10.	Your crops are attacked by plant pests without getting assistance from the plant health clinics.	Yes	No
11.	I do receive information from cooperatives or extension officer?	Yes	No
12.	There is lack of pest/disease identification guide?	Yes	No
13.	Do you have any proposed solution to the challenges	Yes	No
14.	If you answer is yes in item 13, please specify:

). From the population of 1600 smallholder farmers which was obtained from the data base of the VDM office, the sample size of n = 320 was determined using Slovin’s formula. The population of the study included smallholder farmers under vegetable and other production irrigation schemes in the study area. The sample size was deemed appropriate to ensure validity and fairness. A multistage random sampling technique was employed to select 320 smallholder farmers (from a population of 1600) from 8 irrigation schemes in the study area. The random sampling technique gives the farmers an equal chance to be selected to participate in the study. All the selected 320 participants were available and participated in the study. To ensure representation, all categories of smallholder farmers were included in the study. There was no missing data. The questionnaire was structured to gather information on the plant health clinic in the study area from participants in accordance with the required data and information of the study. Both closed and open-ended questions were included in the questionnaire. The questionnaire was validated by 2 agricultural economists to ensure that the questions fully cover what is required by the set objectives for the study. Two trained enumerators from the VDM municipality assisted in terms of the administration of the questionnaire. Permission to collect data from the irrigation schemes was granted by the Limpopo Department of Agriculture and Rural Development (LDARD) and respective managers and extension officers at the Vhembe District Municipality (Ethic No.REC-170616-051).

2.3. Model Selection

The logit regression model was used to analyze the determinants of the smallholder farmers’ awareness of and accessibility to the plant health clinic in the study area [28]. The model was specified as follows: The logit model is appropriate since the dependent variable (response) is dichotomous. Farmers who were aware of the plant health clinic in the study area were recorded as 1, otherwise 0; accessibility to the plant clinic was recorded as 1, otherwise = 0.

Given that the dependent variable is dichotomous, the logit model is applicable. The independent variables were the demographic and socio-economic characteristics of the farmers, as shown in

Table 1. Explanation of independent variables determining smallholder farmers’ awareness and accessibility in the logit regression model.

Independent Variable	Variable Description	Measurement	Expected Outcome	Explanation
X1	Age of the farmers	Years	+	Positive
X2	Gender of the farmers	1 = male, 0 = female	-	Negative
X3	Experience in farming	Years	+	Positive
X4	Farm ownership	Hectares	+	Positive
X5	Communication channels	Number of channels	+	Positive
X6	Distance to plant clinic	km	-	Negative
X7	Government support	1 = yes, 0 = no	-	Negative
X8	Extension service provisions	1 = yes, 0 = no	+	Positive
X9	Farm visits by extension officers	1 = yes, 0 = no	+	Positive
X10	Distance to obtain advice	1 = yes, 0 = no	_	Negative
X11	Level of education	Years	_	Negative
X12	Size of farm	Hectares	+	Positive
X13	Access to finance	1 = yes, 0 = no	+	Positive
X14	Farming activities	Types of crops planted	+	Positive
X15	Plant clinic advisory services	1 = yes, 0 = no	_	Negative

, which represent the determinants of the proportion of smallholder farmers’ awareness of and accessibility to the plant health clinic in the study area. Using the Logit Model, the log-odds of the outcome were modeled as a linear combination of the predictor variables. The logit function is specified as the inverse of the sigmoidal used in mathematics, particularly in statistics. When the function’s parameter represents a probability p, the logit function gives the log-odds, or the logarithm of the odds p/(1 − p).

(i)

The logit of a number p between 0 and 1 is given by the formula:

$$\mathrm{logit}\left(p\right)=\log \left(\frac{p}{1-p}\right)=\log \left(p\right)-\log (1-p)=-\log \left(\frac{1}{p}-1\right)$$

(ii)

The “logistic” function of any number α is given by the inverse-logit:

$${\mathrm{logit}}^{-1}(\propto )=\frac{1}{1+\exp \left(-\propto \right)+1}=\frac{\exp (\propto )}{\exp (\propto )+1}$$

(iii)

If p is a probability, then p/(1 − p) is the corresponding odds; the logit of the probability is the logarithm of the odds. Similarly, the difference between the logit of two probabilities is the logarithm of the odds ratio (R), thus providing a shorthand for the correct combination of odds ratios simply by adding and subtracting:

$$\log \left(R\right)=\log \left(\frac{p_1/\left(1-p_1\right)}{p_2/\left(1-p_2\right)}\right)=\log \left(\frac{p_1}{1-p_1}\right)-\log \left(\frac{p_{21}}{1-p_2}\right)=\mathrm{logit}\left(p_1\right)-\mathrm{logit}\left(p_2\right)$$

(iv)

So, putting all this together, the key equation (usually termed the “multivariate logistic regression equation” or “multivariate logistic regression model”) to which one fits the data is:

$$\log \left(\frac{p_i}{1-p_i)}\right)=\alpha +{\beta }_1{{\rm X}}_{i1}+{\beta }_2{{\rm X}}_{i2}+\dots {\beta }_p{{\rm X}}_{ip}$$

where Pi is the probability and Yi is 1.

The value of Pi/(1 − Pi) is called the “odds”. In the analysis, the function is estimated with the maximum likelihood method: when the smallholder farmers are aware of the plant health clinic, Y = 1; when a smallholder farmer is not aware of the plant health clinic, Y = 0. Similarly, when the smallholder farmers indicate that plant health clinics are accessible, Y = 1; when a smallholder farmer does not have accessibility to the plant health clinic, Y = 0.

The independent variables and expected outcomes for smallholder farmers’ awareness of and accessibility to plant clinics are presented in Table 1. The independent variables below are considered based on their influence on farmers’ awareness of and accessibility to the plant clinic. Although not all variables are explained, a summary of some explanatory variables is provided as follows:

Age of the farmers is expected to influence plant clinic awareness and accessibility among farmers. Older farmers tend to gain more experience in farming, and they tend to be more aware of and access plant clinics as they have been in farming for more years in the area [15]. Communication channels are also expected to increase farmers’ awareness of and accessibility to plant clinics. As the farmers’ reliance on a combination of communication channels increases, the farmers’ awareness of and accessibility to plant health also increases, as opposed to the use of one channel [12,13]. Distance to plant clinics is expected to negatively influence awareness and accessibility, indicating that long distance (Km) may hinder farmers’ accessibility to and information about plant clinics [8]. Regular farm visits by extension officers are expected to have a positive relation with farmers’ awareness of and accessibility to plant clinics. This is because most farmers are reliant on information from the extension officers, which they tend to pay more attention to as they interact with extension officers [22]. The variable of farming activities is expected to have a positive association with farmers’ awareness of and accessibility to plant clinics. It is expected that when there is an increase in farming activities or a shift to polyculture, farmers tend to take the importance of plant clinics more seriously to enable them to respond to any plant health problem (pests and diseases) at the farm level.

2.4. Data Analysis

The quantitative data was analysed using the Statistical Package for the Social Science (SPSS) Version 28. Descriptive analysis (frequency and percentages) was used for the socio-economic characteristics and challenges that smallholder farmers in the study area faced when using plant health clinics, while a logit regression model was used to identify factors influencing farmers’ awareness of and access to the plant clinic. The data were tested for multicollinearity and auto-correlation using the VIF and Durbin–Watson statistic. All the VIF values were between 2.2221 and 1.048, less than 2.5 indicating that there was no multicollinearity present. The Durbin–Watson statistic was also 1.72, which is between 1 and 2, showing that there was no autocorrelation.

3. Results and Discussion

3.1. Demographic Characteristics of Smallholder Farmers: Descriptive Statistics

The results of the demographic characteristics of the respondents are presented in

Table 2. Demographic characteristics of smallholder farmers (n = 320).

Variable	Frequency	Percent
Age (years)
20–30	34	10.6
31–40	50	15.6
41–50	47	14.7
51–60	73	73
61–70	57	17.8
71 and above	59	18.4
Total	320	100.0
Gender
Female	150	46.9
Male	170	53.1
Total	320	100.0
Experience in farming (years)
1–5	64	20.0
6–10	78	24.4
11–15	34	10.6
16–20	49	15.3
21 and above	95	29.7
Total	320	100.0

. The results show that the majority (22.8%) of the farmers are older, between the age of 51 and 60 years; 18.4% and 17.8% were within the age range of 71 and above and 16–70 years, respectively. Amongst the respondents, the majority (53.1%) were males whereas 46.9% were females. Approximately 60.3% of respondents were married while 37.5% were single. The majority (29.7%) of the respondents had 21 and above years of farming experience, whereas 24.4% had between 6 and 10 years of farming experience. The results of the study further revealed that 20% of the farmers had between 1 and 5 years of farming experience. Approximately 91.3% of the respondents own between 0.5 and 5 hectares (ha) for grain and vegetables production. According to Mwangi [29], age and gender play a significant role in the value chain of agriculture production. In the VDM, most smallholder farmers are between 50–60 years. This is consistent with the result of the study conducted in Vhembe District [30]. Regarding the participation of youth in farming, the results of this study were consistent with the finding of the study conducted by Masikhwa [23] in Vhembe District. In terms of gender, the finding of the study is consistent with the study conducted in VDM which revealed that the majority of smallholder farmers are males as compared to females [31]. According to Adhikari et al. [32], farmers’ demographic characteristics, including gender, age and the size of the land, influence their participation in plant health clinics. The majority of males are heads of the household and consider farming activity as significant for their livelihood to sustain their family [33]. Globally, males are found to be more involved in terms of making decisions in farming and participating in plant clinics [32]. In Uganda, plant clinic users were predominantly males in agricultural and plant clinic activities [5].

3.2. Socio-Economic Characteristics of Smallholder Farmers

The results in

Table 3. Socio-economic characteristics of the smallholder farmers (n = 320).

Variable	Frequency	Percent
Size of farm/ha.
0–5	292	91.3
6–10	18	5.6
11–15	5	1.6
16–20	3	0.9
21 and above	2	0.6
Total	320	100.0
Farming activities
Vegetables	88	27.5
Grains	65	20.3
Both	34	10.6
Total	320	100.0
Access to finance
Yes	36	11.3
No	284	88.8
Total	320	100.0
Farm owner
Cooperatives	52	16.3
Individual	233	72.8
Family groups	29	9.1
Private company	2	0.6
Other	4	1.3
Total	320	100.0

of the socio-economic characteristics revealed that 91.3% of the respondents own between 0.5–5 hectares (ha) for grain and vegetables production. Many smallholder farmers have small farm sizes in terms of hectares; small sizes are also often easy to manage and require less agricultural input for high productivity [2]. Approximately 52% of the respondents planted both vegetables and grain, whereas 27.5% and 20.3% of the farmers were also producing vegetables and grain, respectively, for their livelihood. This may be attributed to the fact that farmers want to fully utilize the size of the land for both vegetable and grain production, which generate more income. The results indicated that 88.8% of the farmers had no access to finance. The majority (65.9%) of smallholder farmers did not own transport, whereas only 34% had their own transport to access plant clinic advisory services. The study conducted in Rwanda showed that the majority of smallholder farmers have access to finance [2]. This is contrary to the results of this study which indicated that the majority of smallholder farmers have no access to finance. Consistent with the results of this study, Mulaudzi et al. [30] found that most of the smallholder farmers in the VDM do not have access to finance. In the study conducted in Zimbabwe, Rubhara et al. [34] also found that most of the smallholder farmers lacked access to finance. The result of this study aligned with the previous studies of other authors. Chisasa and Makina [35] suggested that smallholder farmers should be fully supported with financial resources to enable them to enhance their productivity.

4. Logit Regression Results

4.1. Factors Influencing Smallholder Farmers’ Awareness of Plant Clinics

4.1.1. Age of the Farmers

In

Table 4. Parameter estimates of logit regression model of factors influencing smallholder farmers’ awareness of plant health clinic advisory services (n = 320).

Variables		Estimate	Std. Error	Z	Sig.	95% Confidence Interval
						Lower Bound	Upper Bound
LOGIT a	Age of the farmers	0.027	0.013	2.142	0.032	0.002	0.052
	Gender of the farmers	−0.034	0.029	−1.158	0.247	−0.091	0.023
	Experience in farming	0.017	0.012	1.385	0.166	−0.007	0.042
	Farm owner	−0.043	0.023	−1.842	0.065	−0.088	0.003
	Communication channel	0.019	0.004	4.514	0.000	0.011	0.027
	Distance to plant clinic	−0.059	0.020	−3.019	0.003	−0.098	−0.021
	Government Support	−0.112	0.033	−3.371	0.001	−0.178	−0.047
	Extension service provisions	0.017	0.017	1.013	0.311	−0.016	0.049
	Farm visits are by extension officers	0.173	0.032	5.383	0.000	0.110	0.236
	Distance to obtain advice	−0.240	0.038	−6.276	0.000	−0.314	−0.165
	Education	0.004	0.015	0.243	0.808	−0.026	0.034
	Size of farm	−0.018	0.025	−0.722	0.470	−0.067	0.031
	Access to finance	−0.020	0.047	−0.436	0.663	−0.112	0.071
	Farming activities	0.028	0.018	1.564	0.118	−0.007	0.064
	Plant clinic advisory services	0.020	0.015	1.293	0.196	−0.010	0.049
	Intercept	−1.867	0.165	−11.329	0.000	−2.032	−1.702

^a. LOGIT model: LOG(p/(1-p)) = Intercept + BX.

, the Logit coefficient estimate associated with the age of the smallholder farmers and awareness of plant health clinics was statistically significant at the 5% level and positive (sig 0.032, coef = 0.027). This indicates that an increase in the age of the farmers results in an increase in farmers’ awareness of the plant clinics, other factors remaining equal. This may be because as farmers’ age increases, they tend to gain more experience in farming, and they tend to be more aware of plant clinics as they have been in farming for more years in the area. It means that older farmers are more aware than younger farmers. This finding corroborates the study conducted by Zhu et al. [36] who indicate that age (factor) of the farmers plays a significant role in farmers’ participation in farming activities. According to Musebe et al. [15], the interest of young people in agricultural activities is minimal. Karubanga et al. [5] stated that extensive promotion and awareness are important tools to reach more farmers within the community and suggested that this would reach more farmers to be more aware of plant clinics.

4.1.2. Farm Ownership

The Logit coefficient estimate associated with farm ownership and awareness of plant clinics was statistically significant at the 10% level and negative (sig 0.065, coef = −0.043). As farm ownership changes from cooperatives, family groups, to individuals, awareness of plant clinics among the farmers decreases. This may be attributed to the fact that ownership for individuals and family groups do not operate as a cooperative and they may not strive for information. Moreover, this finding is consistent with that of Musebe et al. [15], who reported that most farmers consider farm cooperatives on the basis that information of plant clinics is shared among farmers for awareness purposes. The finding of this study suggests that lack of information sharing may lead to lack of awareness among farmers.

4.1.3. The Use of Communication Channels among Farmers on Plant Clinics

The association of communication channel and awareness of plant health clinics among the farmers was statistically significant at the 1% level and positive (sig 0.000, coef = 0.019). This implies that as farmers use a combination of communication channels, awareness of plant health clinics among farmers improves, with other factors held constant. This may be explained by the fact that farmers who rely on various communication media channels frequently learn more about plant clinics and increase their awareness. These include the use of local radio, television programs, meetings and newspapers or magazines. Jowi [12] and Adetumbi et al. [13] reported that more farmers preferred using mass media as an effective tool for advocacy and awareness of plant clinics. The results of the study also aligned with those of the study conducted by Cameron et al. [14], who recommended the use of combined means of communication channels as vital to reach the farming community about plant clinics. However, the results contradict the findings of the study conducted by Serote et al. [22] in Limpopo, South Africa, who found that farmers who increase their awareness depend more on the information from the extension officer compared with other types of communication channels.

4.1.4. Distance to Plant Clinic among the Farmers

The results in Table 4 revealed that the coefficient estimate of distance to the plant clinic and awareness of the plant clinic was statistically significant at the 1% level and negative (sig 0.003, coef = −0.059). This implies that as distance among the farmers to plant clinics increases, the awareness of plant clinics among the farmers reduces. This can be because access and involvement of farmers in plant clinic activities will be negatively impacted by distance. It could also mean that most farmers in the study area do not have money for transport expenses. This finding is consistent with the study conducted by Kansiime et al. [8] in Kenya stating that distance has a negative impact on plant clinic awareness and participation.

4.1.5. Government Support to Farmers for Plant Clinic Awareness

From Table 4, the association of government support and awareness of the plant clinic among the farmers was statistically significant at the 1% level and had negative influence on plant clinic awareness (sig 0.001, coef = −0.112). This might be because farmers often overlook the value of plant clinics when they receive government assistance in the form of grants and pesticides. This implies that farmers tend be reliant on government support which lead farmers not to be aware of the importance of plant clinics. These results confirmed that farmers do get government support but are not aware of plant clinics. MoANR [37] reported that many extension approaches tend to provide farmers with other means of support and provide less support in plant health problems and solutions. This implies that farmers’ awareness in the study area is limited regarding plant health problems, and this could result in less awareness of plant pest diagnosis and identification.

4.2. Factors Influencing Smallholder Farmers’ Accessibility to Plant Health Clinics

4.2.1. Age of the Farmers

The Logit coefficient estimate associated with the age of the farmer and accessibility of the smallholder farmers to the plant health clinic’s advisory services in

Table 5. Parameter estimates of logit regression model of factors influencing smallholder farmers’ accessibility to plant health clinic advisory services (n = 320).

Variable		Estimate	Std. Error	Z	Sig.	95% Confidence Interval
						Lower Bound	Upper Bound
LOGIT a	Age of the farmers	0.027	0.016	1.765	0.078	−0.003	0.058
	Gender of the farmers	0.032	0.036	0.875	0.382	−0.039	0.103
	Experience in farming	0.005	0.016	0.298	0.766	−0.0026	0.035
	Farm owner	−0.075	0.029	−2.574	0.010	−0.0131	−0.018
	Communication channel	0.017	0.005	3.307	0.001	0.007	0.027
	Distance to plant clinic	−0.070	0.024	−2.911	0.004	−0.117	−0.023
	Farm visits are no longer taking place	0.079	0.040	1.955	0.051	0.000	0.158
	Distance to obtain advice	−0.205	0.047	−4.326	0.000	−0.298	−0.112
	Education	0.018	0.019	0.959	0.338	−0.019	0.055
	Size of farm	0.014	0.031	0.463	0.643	−0.046	0.075
	Access to finance	−0.073	0.057	−1.280	0.201	−0.186	0.039
	Farming activities	0.046	0.022	2.059	0.040	0.002	0.090
	Plant clinic advisory services	−0.023	0.015	−1.511	0.131	−0.053	0.007
	Intercept	−2.313	0.197	−11.742	0.000	−2.510	−2.116

^a—LOGIT model: LOG(p/(1 − p)) = Intercept + BX.

was statistically significant at the 10% level and positive (sig 0.078, coef = 0.027). This indicates that as the age of the farmers increases, farmers’ accessibility to basic plant health care increases, with other factors held constant. This may be because elderly farmers tend to be visited more frequently by extension agents to receive basic advice and care for their plants. Karubanga et al. [5] reported that plant clinics should be accessible and reach farmers regardless of their age group. Furthermore, it could also be that those farmers aged 50 years and above are passionate about farming and they consider accessing basic plant health care information and assistance important for successful farming.

4.2.2. Communication Channels among the Farmers

The results in Table 5 showed that the Logit coefficient estimate of communication channel and accessibility to plant health clinics had a positive relation and was statistically significant at the 1% level (sig 0.000, coef = 0.019). This implies that as farmers use a combination of communication channels to get more information about plant clinics, farmers’ accessibility to plant clinic advisory services improves, with other factors held constant. This may be due to the fact that smallholder farmers in the study may prefer to use a combination of communication channels to get more information about basic plant health care, including radio and meetings to improve their accessibility to the plant clinic. Most of the farmers in the study area are more dependent on local radio and meetings to receive announcements of pest and disease outbreaks. According to Kansiime et al. [8], radio, meetings and television are the most common means of communication on plant clinic issues among the farmers.

4.2.3. Distance to Plant Health Clinics among the Farmers

The Logit coefficient estimate regarding distance to plant clinic in Table 5 was negative and statistically significant at the 1% level (sig 0.003, coef = −0.059), indicating a negative relation between the distance to the plant health clinic and accessibility of the plant clinic. This may be due to the fact that as distance to the plant clinic increases, the accessibility of the plant clinic reduces and smallholder farmers may find it difficult to access the plant clinic. A long distance to the plant clinic will have a negative influence on the accessibility of the plant clinic [9,15] This finding was further supported by the study conducted by Kansiime et al. [8] in Kenya which reported that distance to plant clinic affects farmers’ accessibility to plant clinics and extension officers.

4.2.4. Visit by Extension Officer

The results in Table 5 showed that the Logit coefficient estimate of visit by extension officer and accessibility to plant clinic was positive and statistically significant at the 1% level (sig 0.000, coef = 0.173). This indicates that as regular visits by extension officers increase, the accessibility of plant clinics among farmers also improves, with other factors remaining constant. This may be due to the fact that since farmers may not afford transport costs to access the plant clinic office, extension officers tend to visit farmers in their farm operations or respective schemes to provide the plant clinic’s advisory services. The result corroborated those of Akpalu’s studies [22,38] conducted in Limpopo, South Africa, which indicate that regular farm visits by extension officers are common and effective in improving farmers’ accessibility to plant health advice.

4.2.5. Farming Activities among Farmers

In Table 5, the Logit coefficient estimate regarding farming activities of the respondents in irrigation schemes and accessibility to the plant clinic’s advisory services was positive and statistically significant at the 5% level (sig 0.040, coef = 0.046). This implies that as farmers move from monoculture to polyculture farming activities, the accessibility of plant clinics increases, with other factors held constant. It could be that farmers who use a combination of farming activities operate in large scales in terms of farm size. This could be because farmers tend to consider farming serious and their accessibility to plant clinic advisory services is important for high productivity. According to Kansiime et al. [8], farmers who own bigger hectares in terms of farm size are mostly engaged in mixed crop farming activities and consider plant clinic advisory services more significant to adequately respond to plant pests and diseases. This finding was supported by the study conducted by Muchiri [39] in Kenya who stated that plant clinic demand or accessibility is influenced by farm size. This tends to increase farmers’ accessibility to plant clinics for advice.

4.3. Challenges Encountered by Smallholder Farmers

4.3.1. Affordability of Agrochemicals

In

Table 6. Challenges confronted by smallholder farmers.

Variable	Frequency	Percent
Unaffordability of agrochemicals
Yes	309	96.6
No	11	3.4
Total	320	100.0
Inaccessibility of plant health clinics
Yes	195	60.9
No	125	39.1
Total	320	100.0
Recommendations require more expensive inputs
Yes	289	90.3
No	31	9.7
Total	320	100.0
Failure to identify pest problems
Yes	243	75.9
No	77	24.1
Total	320	100.0
Lack of farm visits by plant doctors
Yes	225	70.3
No	95	29.7
Total	320	100.0
Unavailability of tool to identify plant pests
Yes	255	79.7
No	65	20.3
Total	320	100.0
Inaccessibility to plant clinic advisory services
Yes	255	79.7
No	65	20.3
Total	320	100.0
Unavailability of plant clinic activities
Yes	241	75.3
No	79	24.7
Total	320	100.0
Lack of awareness
Yes	246	76.9
No	74	23.1
Total	320	100.0

, the results showed that the majority (96.6%) of farmers indicated that the cost of agrochemicals or pesticides is high, which farmers may not afford. Most (90.3%) farmers stated that recommendations on the use and application of pesticides require more expensive inputs in terms of agrochemicals. This suggests that most farmers could not afford pesticides to control and manage plant pests and diseases due to hikes in prices. The outbreak of the COVID-19 pandemic from 2020 to 2022 could have worsened the situation among the farmers. Due to the high price of pesticides, farmers are unable to respond rapidly to an outbreak of pests and diseases. These findings are in accordance with the study conducted by Bentley et al. [40], which revealed that some recommendations provided to smallholder farmers require expensive inputs to manage pests. Lack of finance among the smallholder farmers and government support in the study area may have also contributed to this. The study conducted by Emoghene and Futughe [41] reported that agrochemicals are expensive for smallholder farmers and suggested that alternative pest control measures should be employed.

4.3.2. Plant Clinic Accessibility

From Table 6, 60.9% of farmers indicated that their inability to reach a plant clinic was as a result of the distance. The majority (79.7%) of farmers have confirmed that it is a distance to obtain advice from the plant clinic or extension officers. Furthermore, 70.3% of farmers revealed that regular visits by extension officers are no longer taking place. This could be due to the fact that some irrigation schemes wherein vegetable and grain/maize are grown are located far from the local agriculture office. It could also be that, due to bad road conditions, visits by extension officers to farmers at their farms are largely affected. Furthermore, due to the lack of regular visits by extension officers to irrigation schemes in the study area, farmers find it difficult to implement appropriate measures for plant pest and disease management. Sometime farmers tend to rely more on agro-dealers for plant clinic advice. According to Danielsen and Matsiko [42], the distance to the plant clinic restricts farmers from accessing the plant clinic for direct plant clinic advice. This was supported by a study conducted by Tambo et al. [43] in Rwanda who stated that long distance hinders farmers from accessing plant clinics. Adhikari et al. [44] indicated that the purpose of plant clinic establishment within the community is to enhance accessibility among smallholder farmers. Furthermore, 75.3% of respondents indicated that unavailability of planned plant clinic activities is a challenge in the study area.

4.3.3. Plant Pest Identification Guide or Manual

The results in Table 6 revealed that the majority (75.9%) of farmers in the study area experienced difficulties in identifying pest symptoms, especially if the pest was a virus, pathogen or fungi. This could be attributed to the lack of training among farmers since this requires some expertise. Consistent with this finding, Flood [45] and Bhadane et al. [46] reported that most role players fail to identify pest problems due to their limited knowledge about certain pests and diseases. Lack of training from extension officers and farmers regarding pest identification remains a critical challenge confronting farmers at the farm level [47]. Furthermore, 78.8% of the respondents further stated that they do not have a tool to identify plant pests and diseases. It may be that, due to current budget cuts within the provincial government, farmers are not equipped, well-resourced and trained in order to correctly identify and report plant pests and disease outbreaks. Often, these challenges may constitute failure of farmers to know the plant health problem affecting their crops, and thus they may not be able to adequately respond to a pest outbreak. If farmers are not able to identify pest symptoms and pest problems at the farm level, incorrect pesticides may be applied. According to Sibanda et al. [48], pest identification at the farm level is important to enable farmers to employ appropriate control strategies to manage the pests. Holt [49] indicated the importance of pest detection at an early stage for better management and control.

4.3.4. Lack of Plant Health Clinic Awareness

The results in Table 6 indicated that the majority (76.9%) of farmers lacked awareness about the plant clinic in the study area. Approximately 88.1% indicated that there should be a pest identification guide booklet to assist farmers at the farmer level to distinguish and identify various pests’ symptoms. This finding suggests that the majority of farmers in the study area find that there are no activities regarding the plant clinic. This could also be attributed to the fact that pamphlets, promotional material, banners, farmers’ day and workshops on plant clinics are limited in the study area. Budget constraints in the study area could also be one of the contributing factors since awareness among farmers requires some financial resources. Tambo et al. [43], in the study conducted in Rwanda, also reported that awareness among farmers about plant clinics is a challenge and further suggested that this could hinder farmers from visiting the plant clinic for advice. According to Kansiime et al. [8], plant clinic awareness among farmers is important to enable farmers to know about the importance, roles, and availability of existing plant clinics in an area. The finding of this study also corroborates those of Smith et al. [47] who reported that awareness among farmers is minimal regarding plant clinics and requires intervention to expand to all regions. Smith et al. [47] further suggested that development of a plant clinic training manual is key to assist both the extension officers and farmers for pest identification.

4.3.5. Pest and Disease Problems Encountered

In

Table 7. List of problematic pests and diseases detected in the Vhembe district.

Pests and Diseases	Frequency	Percentage
American bollworm Aphids Bagrada hilaris (Burmeister) Birds Black beetles Butterflies Caterpillars Downy mildews Alternaria solani (Sorauer,1896) Spodoptera frugiperda (J.E. Smith) Fruit flies Fusarium oxysporum Late blight Locusta migratoria Busseola fusca Mealybugs Mites Nematodes Phytophthora spp. Phthorimaea operculella Tetranychus urticae (C.L.Koch, 1836) Rodents Rust Snails Striga Tuta absoluta (Meyrick, 1917) White flies Total	5 55 6 3 3 1 46 7 1 72 10 1 5 16 5 5 3 6 1 1 19 14 5 9 5 21 34 359	1.4 15.3 1.7 0.8 0.8 0.3 12.8 1.9 0.3 20.1 2.8 0.3 1.4 4.5 1.4 1.4 0.8 1.7 0.3 0.3 5.3 3.9 1.4 2.5 1.4 5.8 9.5 100.0

, among the plant pest and disease problems encountered, most of the farmers have recorded fall armyworm (Spodoptera frugiperda) (20.1%), aphids (15.3%), cutworms/caterpillars (12.8%), followed by white flies (9.5%). The results indicated that the top five most common pests prevalent in vegetables and grain were S. frugiperda, aphids, caterpillars and white fly. This may be due to the fact that constant high temperature and environment conditions are more favorable for pest introduction and establishment in the study area. This finding is consistent with the study conducted by Phophi et al. [50] in the Vhembe District who found that S. frugiperda, aphids, caterpillars and white flies are the most problematic pests in the Vhembe District. Pest problems may continue to affect smallholder farmers due to the lack of pesticides and appropriate pest management strategies and control measures. This finding is also consistent with the research findings presented in the study conducted in Kenya and Ethiopia which found that S. frugiperda and caterpillars, respectively, are the major problem pests for smallholder farmers [8,51]. The results of the study as presented in Table 7 further revealed other pest species that cause problems in vegetables crops; these include bagrada/painted bug (Bagrada hilaris), migratory locust (Locusta migratoria), two-spotted spider mite (Tetranychus urticae) and tomato leafmine (Tuta absoluta).

Maize appeared to experience more pest problems than any other crops or vegetables, and these include S. frugiperda, caterpillars, maize stalk borer (Busseola fusca), L. migratoria and Striga spp. Among these pests, S. frugiperda is a quarantine pest for South Africa and it is regulated in terms of the Agricultural Pests Act, 1983, as amended. S. frugiperda was first detected in Limpopo in 2017 [52]. Figure 2 shows an example of S. frugiperda maize damage and a pheromone trap. Although the oriental fruit fly (Bactrocera dorsalis) (2.8%) was also reported to attack certain vegetables, it did not attack most vegetables except tomatoes and Cucurbitaceae. According to Hui [53], the fruit fly can cause severe damage to host crops and substantially cause crop losses. Furthermore, T. absoluta was also reported to cause severe damage in tomatoes in the Vhembe District. T. absoluta was first detected in South Africa in the year 2016 [54].

4.4. Plant Health Clinic Framework

Based on the identified challenges and proposed solutions by both farmers and extension officers for the plant clinics in the Vhembe District, a framework of the plant clinics was developed for effective and efficient plant clinics in the VDM (Figure 3). This framework will assist farmers in the diagnosis of plant pests and diseases. According to Rajkumar and Anabel [55], plant pest diagnosis has a significant effect on farmers in terms of pest identification and pest management which would ultimately enhance productivity at the farm level. The plant clinic framework (Figure 3) presented below indicates an effective plant clinic system. This framework differs from the one presented by CABI [56], since the framework below has been significantly improved to enhance efficiency based on the data collected from the study.

According to the plant clinic framework illustrated in Figure 3, farmers should report or take plant/leaf samples of any suspected occurrence of pests and diseases or occurrence of new pest and disease to the plant clinics for diagnosis and advice. Then, extension officers or “plant doctors” would diagnose the plant and provide appropriate recommendations for pest management and control. In cases where the plant doctors or extension officers cannot successfully diagnose and identify any pest and disease on the submitted plant samples, the samples of symptomatic plants or leaves would then be taken to the research institution (ARC) or the DALRRD national laboratory for identification and confirmation. Furthermore, universities may be involved to render assistance as well. Upon pest identification and conformation by the ARC or DALRRD, the feedback would be sent to the farmers through the plant clinics. Continuous training and awareness among farmers about plant clinics is important to optimize the clinics’ usage. The government may carry out regulatory framework interventions for the detected or identified pests for the management and control of pests and diseases. This framework allows farmers to purchase agrochemicals from agro-dealers; sometimes the government or plant clinics provide such assistance to farmers. This framework further provides for continuous plant clinic awareness and training. Figure 3 evidently indicates the effective plant clinic framework which is more practical for the benefit of the farmers.

5. Conclusions

The study was to assess the determinants of smallholder farmers’ awareness of, accessibility to and the functionality of plant health clinics in the Vhembe District, South Africa. Despite the existing challenges on the functionality of plant clinics in the study, the study suggested some solutions for improvement. This paper revealed that plant clinics are important for the benefit of smallholder farmers in optimizing food production and security. Resources mobilization, awareness, farm visits, training of extension officers as “plant doctors, institutional arrangements and policy formulations are recommended for the effective functionality of smallholder farmers’ awareness of and accessibility to plant clinics in the study area. Farmers are also encouraged to report any occurrence and suspicion of any pests and diseases to the relevant district office, plant clinics, and authority for diagnostic and advisory services. The findings of the logit regression model concluded that awareness of plant clinics among smallholder farmers was influenced by the use of communication channels. This suggests that as more farmers use a combination of communication channels, their awareness of plant health clinics improves, with other factors held constant. It can be concluded that farmers should be encouraged to rely more on a combination of communication channels in order to increase farmers’ awareness of plant clinics. In terms of accessibility to plant clinic advisory services, the findings concluded that the use of a combination of communication means had a positive influence on the accessibility of plant clinics. This implies that as farmers use a combination of communication channels to get more information about plant clinics, farmers’ accessibility to plant clinic advisory services improves, with other factors held constant. The government, through extension support, should advocate for more farm visits and awareness programs to reach farming communities since this was found to be positively influencing farmers’ awareness about plant clinics. The adoption of a farm visit mechanism by extension officers increases farmers’ awareness and could assist farmers, especially those who are older and who might be experiencing some health conditions which make it a challenge to obtain information without visiting the plant clinics. This is because distance to plant clinic was found to be a hindrance to farmers’ awareness and accessibility. This study further concluded that a long distance to plant clinics had a negative influence on the accessibility of plant clinics among the farmers, whereas the accessibility of plant clinics improves when visits by extension officer or plant doctors to farmers increase. It is recommended that extension support should be intensified to address the existing shortcomings as observed by the study.

The study further concluded that there were numerous plant clinic challenges faced by smallholder farmers; these include unaffordability of agrochemicals or pesticides, inaccessibility of plant clinics, lack of regular visits by extension officers/plant doctors, lack of a plant pest identification guide and manual, and a lack of plant clinic awareness, equipment, and tools. These identified challenges require a well-coordinated institutional arrangement, resources, awareness, private–public partnership, well-trained extension officers, and policy.

Based on the challenges identified by farmers, the study concluded with some solutions for the improvement of the functionality of plant clinics. These included the development of an efficient and effective plant clinic framework to enhance its functionality for plant pest symptoms and disease diagnosis for consideration and adoption by policymakers within Government, research institutes, and other NGOs. Furthermore, there is a need to have plant clinics across South Africa for the benefit of under-resourced smallholder farmers for pest and disease diagnosis.