The interaction between farmers, agricultural scientists and extension workers is sometimes overlooked in agricultural entomology. In an attempt to respond to this reality this study examines some foundation of this interaction in relation to the pest management practices of subsistence and small-scale farmers and also highlights the problems that might arise in the implementation of IPM. Problems involving pests occurrence; language barriers; beliefs, knowledge and perception about insects, and visual literacy are examined. The thesis has a two-fold focus, firstly the study of pests on cabbages of subsistence farmers in Grahamstown and secondly a broader focus on other aspects such as cultural entomology, perception of insects and visual literacy specifically in relation to Xhosa speaking people in the Eastern Cape. The most important crop for emergent farmers in the Eastern Cape are cabbages, which have a variety of pests of which diamondback moths and are the most important. Traditional pest management practices tend to influence the development of IPM programmes adopted by these farmers. Eastern Cape farmers apply periodic cropping systems, which had an effect on the population densities of diamondback moth (DBM), other lepidopteran pests and their parasitoids. Considering the maximum population densities of DBM, which were 0.2 - 2.9 larvae/plant, there were no major pest problems. The availability of parasitoids, even in highly disturbed and patchy environments, showed good potential for biological control. Since some extension officers cannot speak the local farmers’ language, a dictionary of insect names was formulated in their language (isiXhosa) to assist communication. Response-frequency distribution analysis showed that the dictionary is essentially complete. The literal translations of some names show that isiXhosa speakers often relate insects to people, or to their habitat or classify them according to their behaviour. Farmers from eight sites in the Eastern Cape were interviewed regarding their knowledge and perception of insect pests and their control thereof. To some extent, farmers still rely on cultural control and have beliefs about insects that reflected both reality and superstition. There is no difference between the Ciskei and Transkei regions regarding insect-related beliefs. Farmers generally lack an understanding of insect ecology. There is a need for farmers to be taught about insects to assist with the implementation of IPM. Leftover pesticides from commercial farms or detergents are sometimes used to manage the pests. When training illiterate or semi-literate farmers, it is important to understand their media literacy so as to design useful graphic and object training media. Generally farmers showed that they either understand graphic or object media depending on the features of the insects being looked at. These findings are discussed with regard to the potential development of IPM training material for subsistence and small-scale farmers in a community.

Infield rainwater harvesting (IRWH) technology has been used in arid and semi-arid parts of the world and promising results have been achieved in terms of increasing yield. The main aim of this study was to identify socio-economic factors determining the adoption of IRWH technology for enhancing household food security by smallholder farmers. The specific objectives were to assess the level of adoption of IRWH technology using descriptive statistics (mean, frequency and percentages). To determine socio-economic factors influencing adoption of IRWH technology, the binary logistic regression mode l was used. To determine whether adopters of IRWH technology are more food secure than non-adopters, the Household Dietary Diversity Score (HDDS) was used as a measure for household food security. For the same objective, to determine socio-economic factors that influence household food security, the binary logistic regression model was also used and adoption of IRWH technology became an independent variable. The study was conducted in Khayalethu, Guquka and Krwakrwa villages in Nkonkobe Municipality in the Eastern Cape Province (EC). The unit of analysis was the individual smallholder farmers practicing agriculture. The availability (accidental) and snowball sampling techniques were used to select 34, 23, 63 respondents from Khayalethu, Guquka and Krwakrwa villages respectively. Since they are non-random, these sampling methods are problematic because of sampling errors. Overall, a sample size of 120 smallholder farmers was targeted for the interviews. Primary and secondary data collected was coded and analysed using statistical package for social sciences (SPSS) version 21. Results were presented using graphs, pie charts and tables (including cross-tables). The descriptive results showed that adoption status of IRWH technology was low in these areas, with 79% not adopting the technology. Food insecurity was high amongst the non-adopters with 86%. On the basis of descriptive analysis it can be concluded that any change in each one of the significant variables can significantly influence the probability of adopting IRWH technology and household food security. The results from the logistic regression model for the incidence of adoption revealed that 6 out of 16 variables were significant, three at 1% (access to extension services, access to information and farmers’ perception towards the IRWH technology); one at 5% (access to market) and two at 10% (access to hired labour and farm income). For the incidence of household food security, out of 17 variables, 6 were significant, three at 1% (adoption of the IRWH technology, access to extension services and farmers’ perception towards the IRWH technology); two at 5% (access to hired labour and household income) and one at 10% (household size). The empirical findings of this study indicate that there are socio-economic factors influencing adoption of IRWH technology and household food security amongst smallholder farmers. This study recommends that the government should provide extension officers and research stations with the capacity, support and physical means to expose smallholder farmers to the IRWH technology through demonstrations and trainings. The government can also introduce agricultural finance institutions in rural areas to assist the rural smallholder farmers to increase their access to credit. Further, it is recommended that smallholder farmers can expand to the communal croplands in order to gain more land size and work as a co-operative or as an association to ease labour constraints.

South Africa and the rest of developing countries are faced with poverty and poor rural development. Rural participation in agricultural activities is one of the components that can be used to address the poverty challenge facing the people residing in rural areas. The broad objective of this research is to determine factors affecting participation rate in farming in the rural areas of Amathole District Municipality of Eastern Cape. In this study stratified random sampling method was applied in order to choose a sample out of 30 households that were interviewed 13 people belonged to Participants and 17 people belonged to non-Participants. The results from this study show that women participate a lot in farming activities. The multiple regression model was used to test the participation rates of the people in Amathole region specifically Phumlani area. A number of variables were considered in this study to assess the impact of different variables on participation in farming activities. The results showed that about 57% of the respondents are not participating in farming while 43% of the respondents participate. The farming participants that were interviewed all claim that there is a lack in farming support in the area. When there is no support of any kind, rural people would not be motivated to start development projects on their own. Consequently, this lack of farming support in the Phumlani area may have an influence on the number of farming participants. Therefore, the lack of support in the area may serve as a motivation for non-participants not to be influenced to farm. Rural farming needs to be promoted amongst the youth so as to protect and sustain agricultural growth in rural areas. The study has discovered that the youth of Phumlani is not actively involved in farming activities. Government can provide community members with farming resources so as to promote farming in the area. It would be wiser for the government to provide physical farming resources and implements rather than cash grants.

No description available.

No description available.

Using data drawn from a sample of 80 farmers in the Kat River valley, this thesis presents the results of an assessment of the technical constraints affecting smallholder development and their implications for market access. A review of the relevant literature on the smallholder farm sector, technical change and technical constraints affecting smallholder farmers along with an overview of the agricultural marketing environment in South Africa has been presented. A critical review of the theoretical framework for consideration of technical change in agricultural development, with particular attention to the induced innovation model was undertaken. General information on the institutional set up was obtained by open-ended interviews of community leaders and focus groups. These interviews supplemented information obtained through literature study and document analysis. The other method of data collection employed was the single-visit household survey using structured questionnaires. The demographic and socio-economic characteristics of the surveyed farmers are described in this study. The selection process of the variables influencing market access was done by employing correlation and logistic regression analyses. Correlation analysis was conducted to ascertain the relationship among variables to find out the extent to which they mirror theory or intuition regarding their causation to constraints influencing market access. The logistic model was employed in the step-wise manner using each of key production inputs as response variables sequentially. On the basis of a binary logistic model, it can be concluded that the farmers still operate under a number of technical constraints. The most influential constraints are information, asset ownership, value of agricultural production and extension assistance. The study reflects the previous findings in South Africa that the legacy of apartheid continues to negatively impact on its agricultural economy. Key words: Technical Constraints, Technical Change,Market Access, Smallholder Farmers, Agricultural Development, Induced Innovation Model, Kat River Valley, Correlation analysis, Theoretical framework and Binary Logistic Model

Please read the abstract in the section 00front of this document / Dissertation (M Inst Agrar (Sustainable Insect Management))--University of Pretoria, 2005. / Zoology and Entomology / unrestricted

The aim of the study was to investigate the impact of agricultural infrastructure on agricultural productivity and agricultural income of smallholder farmers in the North West Province, South Africa. Factors that contribute to the availability, accessibility and satisfaction of smallholder farmers with regards to agricultural infrastructure were also assessed in the study. Using cross sectional data from the North West Province of South Africa, 150 smallholder farmers were selected using stratified sampling to group farmers into those who had agricultural infrastructure and those who did not have. Data were collected using a structured questionnaire, divided into six sections as follows: personal socio-economic characteristics of farmers; characteristics of the land; agricultural infrastructure of smallholder farmers; agricultural production and markets; and production activities and financial support rendered to farmers. The data were coded, captured and analysed using STATA 14.0. Data were analysed through descriptive analyses, Principal Components Analysis (PCA), Stochastic Frontier Analysis, Heckman selection procedure and Tobit Regression Models. This result revealed that most of the farmers were male, aged between 41 and 60 years of age, had contact with extension services, had contact with extension services only occasionally and did not engage in non-farming activities. Smallholder farmers had less than 10 years of farming experience, a household size of less than or equal to five members, had about one household member assisting in the day-to-day farming activities. Most of the farmers did not belong to any organisation. Generally, the farmers indicated that they were involved in dry land farming. Farmers who irrigated their farms, did so on approximately 15 and 45 hectares of land. Farmers also indicated that they received agricultural support from CASP and used commercial seeds, fertilizers and animal vaccines as their production inputs. Furthermore, smallholder farmers in the study area received support for inputs while majority indicated they did not have to repay for the inputs. Majority of farmers indicated that infrastructure impacted on their farming enterprises through increases in productivity in their farming enterprises. The study found that the factors influencing agricultural income for smallholder farmers with agricultural infrastructure were: Physical infrastructure index (Coef=0.78: P=0.01); Social infrastructure availability index (Coef=0.61: P<0.01); Institutional infrastructure availability index (Coef=1.05: P<0.01); Level of education of farmers (Coef=0.96: P<0.01); Access to extension services (Coef=1.05: P<0.01); Membership of farmers’ organisations (Coef=0.59: P<0.05); Age of smallholder famers in the study area (Coef=0.05: P<0.01); and Household members assisting in farming activities (Coef=0.24: P<0.05). In terms of smallholder farmers with accessible agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure access index (Coef=1.29: P<0.01); Social infrastructure access index (Coef=0.38: P<0.1); Equipment infrastructure access index (Coef=0.62: P<0.01); Level of education for smallholder farmers (Coef=1.21: P<0.01); Access to agricultural extension services (Coef=1.64: P<0.01); Membership of Farmers’ organisations (Coef=0.77: P<0.05); Age of smallholder farmer (Coef=0.01: P<0.01); and Household members assisting in the farming enterprises (Coef=0.39: P<0.01). In terms of satisfaction of smallholder farmers with agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure satisfaction index (Coef=0.35: P<0.1); Social infrastructure satisfaction index (Coef=0.37: P<0.1); Institutional infrastructure satisfaction index (Coef=1.25: P<0.01); Equipment infrastructure satisfaction index (Coef=1.04: P<0.01); Level of education of respondents (Coef=1.24: P<0.01); Access to extension services (Coef=1.58: P<0.01); Age of smallholder farmers in the study area (Coef=0.05: P<0.01); Number of years farming (Coef = -0.57: P<0.1); and Number of household members assisting in farming (Coef=0.19: P<0.1). The results of the Heckman selection model revealed that the variables impacting on agricultural income were: agricultural infrastructure availability index (Coef=1.12: P<0.01); and access to extension services (Coef=0.62: P<0.05). Furthermore, those impacting on agricultural production were: infrastructure satisfaction index (Coef=-1.31: P<0.01); infrastructure accessibility index (Coef=-0.59: P<0.05); Level of education of smallholder farmers (Coef=0.64: P<0.01); access to extension services (Coef=1.29: P<0.01); and membership of farmers’ organisations (Coef=0.66: P<0.01). The results of the Tobit Regression Model showed that among others factors influencing availability of agricultural infrastructure, the following variables played a critical role: assistance of household members in farming enterprise (Coef=0.702: P<0.01); farm ownership (Coef=0.962: P<0.01); farm acquisition (Coef=0.323: P<0.01)farmer occupation (Coef=0.785: P<0.01); member of farmers’ organisations (Coef=2.066: P<0.01); sources of labour (Coef=1.283: P<0.01); farming experience (Coef=0.100: P<0.01); and agricultural production inputs (Coef=-0.763: P<0.05). In terms of accessibility to agricultural infrastructure, the following variables played a critical role: engagement in non-farming activities Coef=1.275: P<0.01); contact with extension services (Coef=1.205: P<0.01); farm ownership (Coef=0.403: P<0.01); farmer occupation (Coef=0.456: P<0.01); membership of farmers’ organisations (Coef=1.111: P<0.01); sources of labour (Coef=0.653: P<0.01); farming experience (Coef=0.045: P<0.05) and land tenure (Coef=0.156: P<0.01). In terms of satisfaction with agricultural infrastructure, among other factors influencing satisfaction with agricultural infrastructure, the following variables played a critical role: organisation for extension services (Coef=1.779: P<0.01); assistance of household members in farming enterprise (Coef=0.411: P<0.01); government agricultural support to farmers (Coef=0.419: P<0.01); farm ownership (Coef=0.464: P<0.01); membership of farmers’ organisations (Coef=1.011: P<0.01); age of farmer (Coef= 0.030: P<0.01); level of education (Coef= 0.483: P<0.01); marital status (Coef=0.290: P<0.01); and gender (Coef= -0.576: P<0.01). The results of the analysis were used to close the knowledge gap with regards to the impact of agricultural infrastructure, availability, accessibility and satisfaction on the productivity and agricultural income of smallholder farmers in the North West Province. In terms of recommendations, the study highlighted that agricultural industries and government should commit in assisting smallholder farmers to be productive and to participate in economic activities. This could be achieved through collaboration with industries in implementing initiatives that assist and accelerate the development of smallholder farming and also through assisting smallholder farmers access agricultural infrastructure. / Agriculture and Animal Health / Ph. D. (Agriculture)

MSAEC / Department of Agricultural Economics and Agribusiness

MSCAGR (Agricultural Economics) / Department of Agricultural Economics and Agribusiness / See the attached abstract below