Impact of export promotion and market development on social welfare in South Africa: Evidence from the agricultural sector

South Africa’s industries in the agricultural sector spend some of the statutory levy income on export promotion and market development (EPMD) activities. Some industries argue that statutory levy expenditure on EPMD activities generates satisfactory returns on investment but empirical evidence is yet to be presented to support the argument. Hence, this study filled this gap by building a unique data set based on statutory levy expenditure on EPMD for four industries (citrus, deciduous fruits, table grapes and wine) and used econometric analysis to assess the impact of EPMD on social welfare over a 10-year period (2006–2015). Furthermore, we estimated the returns generated on social welfare per rand of statutory levy expenditure. In the analysis, we controlled for unobserved heterogeneity, multicollinearity and reverse causality. The results suggest that statutory levy expenditure on EPMD has a statistically significant positive impact on social welfare across the four industries. On average, a unit increase in statutory levy expenditure on EPMD leads to an improvement in social welfare ranging between 0.2% and 0.4% depending on the industry. In addition, the results suggest that 1 rand spent on EPMD for the four industries in question, on average, generates a US$26 worth of improvement in social welfare. Conclusively, statutory levy expenditure on EPMD played a key role in enhancing social welfare improvement. Therefore, there is a need to mobilise more resources to facilitate the EPMD initiative into new markets and products for the industries

Stakeholder-driven transformative adaptation is needed for climate-smart nutrition security in sub-Saharan Africa.

Improving nutrition security in sub-Saharan Africa under increasing climate risks and population growth requires a strong and contextualized evidence base. Yet, to date, few studies have assessed climate-smart agriculture and nutrition security simultaneously. Here we use an integrated assessment framework (iFEED) to explore stakeholder-driven scenarios of food system transformation towards climate-smart nutrition security in Malawi, South Africa, Tanzania and Zambia. iFEED translates climate-food-emissions modelling into policy-relevant information using model output implication statements. Results show that diversifying agricultural production towards more micronutrient-rich foods is necessary to achieve an adequate population-level nutrient supply by mid-century. Agricultural areas must expand unless unprecedented rapid yield improvements are achieved. While these transformations are challenging to accomplish and often associated with increased greenhouse gas emissions, the alternative for a nutrition-secure future is to rely increasingly on imports, which would outsource emissions and be economically and politically challenging given the large import increases required. [Abstract copyright: © 2024. The Author(s).

A new integrated assessment framework for climate-smart nutrition security in sub-Saharan Africa : the integrated Future Estimator for Emissions and Diets (iFEED)

Funding statement This work was supported by the Biotechnology and Biological Sciences Research Council through UK Research and Innovation as part of the Global Challenges Research Fund, AFRICAP programme, grant number BB/P027784/1.Peer reviewedPublisher PD

Determinants of smallholder farmers' participation in cattle markets in Ngaka Modiri Molema district of the North West Province, South Africa

Thesis (M.Sc.(Agric Economics) North-West University, Mafikeng Campus, 2013The study was designed to identify and get a better understanding of the determinants of Smallholder farmers' participation in cattle markets in the Ngaka Modiri Molema District of North West Province, A hundred and nine smallholder cattle farmers were randomly selected using the simple random sample method. The list of smallholder cattle farmers was obtained from the North West Department of Agriculture and Rural Development (Ngaka Modiri Molema District). Data was collected through the use of structured questionnaire that consisted of demographic characteristics market-related constraints encountered by smallholder cattle farmers, availability of infrastructure, access to market information, cattle production, cattle nutrition, cattle health, cattle husbandry, and reasons far keeping cattle and markets participated in by smallholder cattle farmers. The data was coded, captured and analysed using the statistical package for social science (SPSS) for frequencies, percentage and profit regression analysis. The results of the study show that the majority of the respondents in this research were male (72%): married (68%); Christians (74%); not formally educated 45°o; and having less than 10 years in farming (61%). The results highlighted that majority of the smallholder cattle farmers used informal markets to market their cattle (83%); mainly used auctions (58%) as a formal marketing channel and were mostly familiar with informal (62%) marketing channels. The farmers received higher prices (65%) from the cattle markets they regularly use and were nearer. The majority of smallholder farmers (55%) do not participate in the most rewarding channels. Majority of the smallholder cattle farmers were affected by, lack of support from government (96%); limited market information (95%); financial constraints (79%); (distance to mainstream markets (60%); and bureaucracy (62%). The results of probit regression model show that out of 15 independent variables considered, the coefficients for 5 variables' were statistical/v significant, These were the number of heifers (Z= 2, 742: P<P0. 05), smallholder cattle farmers keeping of farm records (Z=2. 611: P<0. 05), the number of years in farming (Z=2,45] P<0.01), level of education Z=-1. 745,' P<0.01) and smallholder farmers slaughtering of cattle and selling as carcass (Z- 1,899: P<0, 01).Master

Impact of agricultural infrastructure on productivity of smallholder farmers in the North West Province, South Africa

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, one hundred and fifty 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 only occasionally and did not engage in non-farming activities. Most of the smallholder farmers had less than 10 years of farming experience, had household sizes 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 farmer organisation. Generally, the farmers were involved in dry land farming. Farmers who irrigated their farms, did so on approximately 15 and 45 hectares of land. Farmers also 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 and majority did not have to repay for the inputs. Majority of farmers indicated that infrastructure impacted on their farming enterprises through increases in both productivity and sizes of 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 farmers without agricultural infrastructure available, factors influencing agricultural income were: physical infrastructure availability index (Coef = 0.74; P<0.01); social infrastructure availability index (Coef = 0.77: P<0.01); institutional infrastructure availability index (Coef = 0.61: P<0.01); level of education (Coef = 0.89: P<0.01); access to extension services (Coef=1.24: P<0.01); age of farmers (Coef = 0.06: P<0.01) and assistance of household members in farming enterprises (Coef=0.33: P<0.01). 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). With regards to smallholder farmers without accessible agricultural infrastructure, factors influencing agricultural income were: Physical infrastructure accessibility index (Coef=0.92, P<0.01); Equipment accessibility index (Coef=0.43, P<0.05); Level of education (Coef=1.25: P<0.01P); access to extension services (Coef = 1.63; P<0.01); membership of farming organisations (Coef = 0.86; p<0.01); age of farmers (Coef= 0.07; P<0.01) and assistance of household members in farming enterprises (Coef = 0.34; P<0.05). 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). With regards to farmers not satisfied with agricultural infrastructure, factors influencing agricultural income were: institutional infrastructure satisfaction index (Coef = 0.54: P< 0.05); level of education (Coef=1.25: P<0.01); access to extension services (Coef = 1.77: P<0.01); age of farmers (Coef = 0.06: P<0.01) and assistance of household members in farming enterprises (Coef = 0.34: P<0.01). 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 HealthD. Litt et Phil. (Agriculture

Impact of agricultural infrastructure on productivity of smallholder farmers in the North West Province, South Africa

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 HealthPh. D. (Agriculture

How has consumer education influenced pork consumption in South Africa? Instrumental variable regression analysis

We evaluate the impact of consumer education on pork consumption in South Africa by using time series data on levy expenditure on consumer education over a ten-year period. Furthermore, we introduce quantitative measures of two non-economic factors (health and nutrition, and tastes and preferences) based on previous research which indicates that they are important drivers of meat consumption. We employ an instrumental variable regression analytical framework. To account for the dynamic response of consumer education effects on consumption patterns, a lagged variable for expenditure on consumer education is included in the specified model. The positive estimate (0.045) on consumer education is highly significant, implying that consumer education is associated with a 4.5% increase in pork consumption. In concurrence with previous studies, findings show that peoples’ tastes and preferences for processed pork products positively impact on pork consumption while health and nutrition (severe malnutrition) exhibits a negative effect on pork consumption. As recommendation, consumer education should focus more on the low-income earners since this segment of the population accounts for a relatively small proportion (10%) of total pork consumed

Identifying Potential Markets for African Leafy Vegetables: Case Study of Farming Households in Limpopo Province, South Africa

Indigenous crops, through their high nutritional value and hardy attributes, offer potential trade opportunities for rural farmers. There is a niche market that can be explored for these indigenous crops particularly with the growing demand for high nutritional value food in the country. These crops are mostly produced by rural households or gathered from the wild by rural farmers. Thus, the purpose of this study was to identify potential markets for African leafy vegetables (ALVs) by farmers in Limpopo Province. Sixty households producing ALVs were selected with the composition of 54 women and six men, with this selection done using a purposive sampling procedure. Of the total production, 50–60% of the produce was sold in the informal market. It was evident that local rural markets constituted a greater portion of the total market at 73% and 20% allocated to hawkers in town. As a result, urban and periurban consumers present potential buyers since these areas are populated with the middle-class population which is susceptible to changing consumption trends. Because of this potential, supermarkets and township hawkers are proposed as the potential channel for ALVs targeting the identified population. Thus, it is suggested that, in order to create a synergy between economic improvement of rural farmers and trending consumer demands, the Department of Agriculture in Limpopo Province creates a conducive environment through which ALV farmers can be connected with supermarkets and township marketers

Is there justification for levy expenditure on export promotion and market development in the agricultural sector in South Africa?

South Africa’s industries in the agricultural sector spend some of the statutory levy income on export promotion and market development (EPMD) activities. Some industries argue that levy expenditure on EPMD activities generates satisfactory returns on investment but empirical evidence is yet to be presented to support the argument. Hence, this study fills this gap by building a unique dataset based on levy expenditure on EPMD for four industries (citrus, deciduous fruits, table grapes and wine) and using econometric analysis to assess the impact of EPMD on exports, net agricultural income and social welfare over a ten years’ period (2006- 2015). Furthermore, we estimate the returns generated on exports, agricultural net income and social welfare per Rand of levy expenditure on exports, net agricultural income and social welfare. In the analysis, we control for unobserved heterogeneity, multi-collinearity and reverse causality. Results suggest that levy expenditure on EPMD has a statistically significant positive impact on exports, net income and social welfare across all industries. On average, a unit increase in levy expenditure on EPMD leads to an increase in exports by 7.3 percent (table grapes and deciduous fruits), 5.6 percent (wine), 5.25 percent (citrus). For agricultural net income, a unit increase in levy expenditure on EPMD is on average associated with a 7.5 percent, 4.9 percent, 4.3 percent and 3.6 percent increase for table grapes, citrus, wine and deciduous fruits, respectively. Across all industries, the range of social welfare improvement lies between 0.2 percent and 0.4 percent per unit increase in levy expenditure on EPMD. Furthermore, results suggest that one Rand spent on EPMD for the four industries in question on average generates a R404 increase in exports, R39 of additional agricultural net income and a US$26 worth of improvement in social welfare. All in all, levy expenditure on EPMD plays a key role in fostering exports, agricultural net income and social welfare improvement. Policy wise, there is need for mobilisation of more resources to facilitate the EPMD initiative into new markets and products for the industries

Factors influencing communal livestock farmers' participation into the National Red Meat Development Programme (NRMDP) in South Africa: the case of the Eastern Cape Province

In 2005, ComMark embarked on the Eastern Cape Red Meat Development Programme (ECRMDP) as an initiative to increase formal market participation of communal farmers. With the end of support from ComMark in 2008, the National Agricultural Marketing Council (NAMC) took over. With funding from the Department of Rural Development and Land Reform (DRDLR) and partnerships with the provincial departments and the municipalities, the programme has expanded effectively within the Eastern Cape Province and it has been rolled out to other provinces as well, hence it is now known as the National Red Meat Development Programme (NRMDP). The initiative emanated from the observation that the local demand for beef outstrips production, hence resulting into importation of more beef. This was against the background that there was untapped potential in the communal farming areas where 40% of beef production takes place in South Africa, of which 3.3 million heads of cattle is found in the Eastern Cape alone. Although the programme has so far had a significant contribution towards communal farmers’ participation in formal markets as well as their understanding of the value of formal market participation, empirical evidence to support this notion is still desirable. Hence this case study was conducted to determine the factors that influence farmers’ participation in the programme, focusing on the Eastern Cape Province. A logistic regression model was used to determine factors influencing farmers’ participation in the programme, and the results indicated that distance to markets, stock size, days of fattening and the contribution of the programme (income earned from livestock sales through the programme) significantly influence farmers’ participation. This is an indication that farmers are slowly beginning to understand how they can best make use of the opportunity presented by the programme. Hence policy wise, it is commendable to encourage communal livestock farmers to participate in the programme