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This research focuses on the urban-rural dynamics in Motheo District in the Free State, South Africa. The study mainly focuses on the movement patterns of people, goods and services in Motheo and it’s significance for rural-urban local economic development planning. The data was collected through observations and interviews and surveys coupled with...
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... The study was conducted in the Free State Province, South Africa ( Figure 1). Free State is in the central plateau of South Africa (Mucina & Rutherford, 2006) and is the third-largest province comprising 10.6% of South Africa's land area (Davis et al., 2006). The vegetation of Free State Province is characterized predominantly by the Grassland biome with a small portion of Savanna and Nama-karoo biomes ( Figure 1; Mucina & Rutherford, 2006). ...
Olea europaea subsp. africana (Mill.) P.S. Green (medium-sized tree species known as "African wild olive"), provides important ecological goods and services for sustaining frugivores in the grassland biome in South Africa. We speculate that O. europaea subsp. africana's population has been declining due to habitat loss and exploitation for domestic benefits suggesting an unrecognized conservation threat. Therefore, the study aimed to investigate the anthropogenic conservation threats for O. europaea subsp. africana in the Free State, South Africa and to determine the potential importance of seed dispersal effectiveness in the restoration of the species in the study area. Overall, the results showed that 39% of the natural habitat range has been transformed by human-mediated activities. Agricultural activities accounted for 27%, while mining activities and human settlement accounted for 12%, of natural habitat loss. In support of the study predictions, seeds of O. europaea subsp. africana had significantly higher germination and germinated faster after passing through the mammal gut (i.e., 28% and 1.49 per week), compared to other seed treatments (i.e., over 39 weeks). However, there were no statistically significant differences between seed germination of the bird-ingested seeds, with intact fruits as the experimental control, although both were significantly greater than the de-pulped seeds. Potential seed dispersal distances by birds were relatively larger, ranging from 9.4 km to 53 km, than those of mammals (1.5 km-4.5 km). We propose that the O. europaea subsp. africana's habitat range may have been declining, and since it is a keystone plant species, we recommend that the complementary seed dispersal services by birds and mammals could be important for its recruitment and restoration in the degraded habitat.
... It is located between a latitude of 26.6°S and 30.7°S and a of longitude 24.3°E and 29.8°E. With a total surface land area of approximately 129,825 km 2 , the province occupies an estimated 10.6% of the country's land area thereby making it the secondlargest province in terms of land size (Davis, Tavasci, and Marais 2006;Nyam 2017). Crop and mixed livestock production forms the major agricultural activities in the province and is key to the economies of Thaba Nchu and Botshabelo (Nyam, Matthews, and Bahta 2020). ...
Livestock production contributes significantly to the agricultural sector in South African partly because it is a source of cash income especially for smallholder farmers who depend on agriculture for their livelihood. This article analyzes the competitiveness and profitability of smallholder livestock farmers and its impacts on their livelihood in South Africa. We applied the stochastic profit frontier approach to account for the factors influencing the productivity of sheep production and the profit efficiency of the farmers. The results of our analysis show that education and household size increase the profitability of the farmers while gender (female) and sheep loss negatively influence the profitability of the farmers. The average profitability (profit efficiency) score was estimated at 65.5% meaning that an estimated 34.9% of the profitability is lost due to the combination of technical and allocative efficiencies in production. This implies that the smallholder farmers are not producing at full capacity and thus have the ability to increase production, profitability, and their competitiveness in the farmers. Education and training and technological innovation in production can increase the profitability of the farmers.
... This study focuses on the Free State Province of South Africa whose location is shown in Fig. 1. The province covers a total area of about 129,825 km 2 and lies between latitudes 26.6°S and 30.7°S and between longitudes 24.3°E and 29.8°E (Davis & Tavasci, 2006). The eastern Free State region is characterised by rugged terrain and is associated with livestock farming rather than crop production. ...
The study analysed the temperature variability in the Free State Province, South Africa between 1960 and 2013. The three parameters considered were minimum temperature (Tmin), maximum temperature (Tmax) and diurnal temperature range (DTR) during the summer agricultural season spanning from October to March. Spatial interpolation of temperature characteristics was done using ArcMap V.10.2. Results show that the late
summer subseason (January–March) generally experiences warmer temperatures than the early summer subseason (October–December). A significant shift towards warmer temperatures was detected for Tmax during the October–December subseason around 2003 and around 1983 for the January–March subseason for Tmin. The OND Tmax shift coincides with that in cloud cover, suggesting that the reduced cloud cover could have contributed to the Tmax shift. It is found that the significance of temperature change is stronger towards the north and northwestern regions of the province.
... Geographical Location of the Free State ProvinceThe Free State province is located in the central part of South Africa and is one of nine administrative provinces of South Africa. The Free State province is situated between latitude 26.6° S and 30.7° S and between longitude 24.3° E and 29.8° E. The province has a total land surface area of approximately 129 825 km 2 , accounting for an estimated 10.6% of the country's land area(Davis et al., 2006). It is the second-largest province in South Africa alongside Western Cape and shares borders with Gauteng, Mpumalanga, North West, KwaZulu-Natal and the Kingdom of Lesotho.Figure 3.1 shows a map of South Africa indicating the physical location of the Free State and the other eight administrative provinces. ...
In South Africa, sheep enterprises play an important role as a source of livelihood for many
farmers, especially smallholder farmers. The productivity of sheep farmers in South Africa is
very low. The lack of analytical evidence on efficiency levels of smallholder sheep farmers in the different sheep production systems limits policy-making on optimal allocation of resources. In addition, these smallholder farmers are faced with numerous constraints regarding production, which is considered to be one of the many factors impeding their productivity and livelihood. Very little is known empirically about the constraints faced by these farmers and how they can be overcome.
This study analysed the factors that influence the productivity of sheep production to enhance the livelihoods of smallholder sheep producers in the N8 development corridor and to identify and rank the constraints faced by smallholder sheep farmers along the N8
development corridor. Data for this study was collected with the use of structured
questionnaires. A sample size of 217 smallholder sheep farmers comprising 157 from Thaba Nchu and 60 from Botshabelo was used. The stochastic metafrontier model was used to estimate technical efficiency and technology gaps across the different farms in the study
areas. The Kendall’s coefficient of concordance was used to identify and rank the constraints faced by smallholder farmers.
The empirical results of the study revealed that farmers in both Thaba Nchu and Botshabelo
are technically inefficient. The empirical results show that herd size and feed cost had
significant positive effects on sheep output in Thaba Nchu municipal district, indicating that these variables are vital for enhancing sheep production in Thaba Nchu. However, land size and sheep loss were found to have a significant negative effect on sheep output in Thaba Nchu. The negative effect of land size on sheep output was completely unexpected. It is assumed that these farmers have relatively small herds, and increasing land size will only
add to the cost of managing the land. On the other hand, land and transport costs had
significant positive effects on sheep output Botshabelo, indicating that these inputs are vital to enhancing sheep production in this district municipality. Sheep loss had the expected
significant negative effect on sheep production in Botshabelo. In the pooled sample, herd size, feed cost and labour were found to have significant positive effects on sheep production in the study areas. However, land size and sheep loss were found to have a significant negative effect on sheep output in the pooled sample.
The gamma value of 0.679 means that about 67.9% of the variation in sheep output in Thaba Nchu is explained by technical inefficiency, while 32.10% of the variation is due to random shocks and statistical noise. For Botshabelo, the gamma value (0.779) was relatively higher than in Thaba Nchu, indicating that the effects of inefficiency on variation of the sheep output is far larger than that of random shocks. The pooled sample had a gamma value of 0.799. This means that 79.9% of the variation in sheep production in the study areas is due to inefficiency and 11.1% is due to random shocks. The variation in sheep production for the study areas is generally due to technical inefficiency on the part of the sheep farmers. The stochastic production frontier analysis showed that the average technical efficiency of Thaba Nchu farmers was 67.3% and 65.7% for farmers in Botshabelo. This result indicates that there is 32.7% potential for Thaba Nchu farmers to expand their production by operating at full technical efficiency level, while the scope for Botshabelo to increase the level of efficiency using available farm resources and technologies is about 34.3%. The variables that influence the technical efficiency level of Thaba Nchu farmers are indigenous sheep breed, education level, veterinary services and market distance. Indigenous sheep and market distance had a significant negative effect on technical efficiency in Botshabelo while farm experience and crossbreeding method had significant positive effects on technical inefficiency. Theft, lack of capital, diseases and parasite were found to be the most severe constraints facing the sheep farmers.
The average technical efficiency scores estimated relative to the metafrontier (TEm) for Thaba Nchu was 0.495 while for Botshabelo was 0.442. The results indicate further that a regional production frontier is necessary to advise farmers in each district on ways to improve the productivity and efficiency of sheep production. It can be concluded from the results of the study that farmers in the study area are producing well below the production frontier. This means that farmers have the potential to increase their productivity and efficiency in order to produce at full capacity.
The policy recommendation arising from this study is that farmers should be trained on proper farm management techniques and that proper market channels should be developed for farmers to sell their products. Building new fences and improving old ones will help prevent theft and will increase sheep outputs.
Key Words: Technical efficiency, determinants of technical inefficiency, South Africa, N8 development corridor, metafrontier, productivity, smallholder sheep production, technology gap ratio, stochastic metafrontier model.
... Free State is the third largest province in South Africa in terms of land area, with approximately 129,825 km 2 . This represents a 10.6% of the country's land area [35]. The province is situated between latitudes 26.6 • S and 30.7 • S and between longitudes 24.3 • E and 29.8 • E. It borders Lesotho and six other provinces, namely: Northern Cape, North West, Gauteng, Mpumalanga, Eastern Cape and KwaZulu-Natal. ...
The Free State (FS) and North West (NW) Provinces are often hard hit by droughts with impacts on water availability, farm production and livestock holdings. The South African government declared the two Provinces drought disaster areas in the 2015/2016 hydrological year. This is a major drawback, since both the Provinces play an important role to South African economy as they are a haven to agricultural production and have major water reservoirs in South Africa. This study was undertaken to investigate the historical evolution of drought within the FS and NW Provinces over the past 30 years. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) calculated based on monthly meteorological data from 14 weather/climate stations within the FS and NW Provinces were used to explore and characterize variation in drought intensity, duration, frequency and severity in FS and NW Provinces during 1985-2015. Results indicate that there exist localized positive and negative trends with spatial dependence across the selected stations. In particular, about 60% of the weather stations exhibiting a decreasing trend are located in FS Province, suggesting that FS has being experiencing increasing drought during the analyzed period compared to NW Province. Results from the analysis of drought evaluation indicators (DEIs) calculated from SPEI suggest that drought severity and frequency was more pronounced in FS while the intensity of the drought was more in NW Province during 1985-2015. In addition, based on SPEI calculations, moderate drought occurrences increased during 1985-1994 and 1995-2004 periods and decreased thereafter (2005-2015) in both Provinces. Drought classification based on parameters derived from SPEI produced similar results for mild drought occurrences during the same time scales.
... However, this remains the biggest challenge for most municipalities around the country. Rucker & Trah (2007) and Davis, Tavasci & Marais (2006) indicate that government in particular local government is arguably the key role player in the LED process and misinterpretation of its role and mandate may severely affect the success of such a process. In most cases, local government is seen as neither best-equipped nor does it have the necessary capacity to drive LED (Rucker & Trah, 2007). ...
The purpose of this paper is to assess the linkage between the theory of Local Economic Development (LED) and its application in South Africa during the 20 years of democratic dispensation. The responsibilities of local government in South Africa include the promotion of social, economic, cultural and political transformation of all the communities. The concept of LED became acceptable as a locally-based planning instrument in response to poverty, inequality and unemployment challenges that are currently crippling local municipalities and communities in South Africa. Local municipalities are therefore required through various legislative frameworks to develop and implement LED as a strategic tool that could be used to address social and economic challenges within their areas of jurisdiction. These frameworks are intended to create conducive environment for effective preparation and implementation of LED strategies. Theoretically, LED is viewed as an outcome based on local initiatives driven by local stakeholders that involve identifying and using local resources, skills and ideas as well as economic opportunities to promote economic development. However, the recent practice in terms of LED in the majority of municipalities in the post-apartheid South Africa appears to reflect a disjuncture between LED theory and its operation. The paper argues that if such disparities exist, the realisation of LED objectives in municipalities would be impaired.
DOI: 10.5901/mjss.2014.v5n20p218
... The Free State Province is situated between latitudes 26.6° S and 30.7° S and between longitudes 24.3° E and 29.8° E. It is South Africa's third-largest province with an area of around 129 825 km 2 , 10.6% of the country's land area (FSP, 2005; Davis et al., 2006). The province is administratively divided into 5 municipal districts (Fig. 1) (Davis et al., 2006): Fezile Dabi, Lejweleputswa, Motheo, Thabo Mofutsanyane and Xhariep. ...
... The Free State Province is situated between latitudes 26.6° S and 30.7° S and between longitudes 24.3° E and 29.8° E. It is South Africa's third-largest province with an area of around 129 825 km 2 , 10.6% of the country's land area (FSP, 2005; Davis et al., 2006). The province is administratively divided into 5 municipal districts (Fig. 1) (Davis et al., 2006): Fezile Dabi, Lejweleputswa, Motheo, Thabo Mofutsanyane and Xhariep. The main economic activities contributing significantly towards the gross domestic product of the province are community service (24.7%), agriculture (20.1%), trade (10.7%) and mining (9.6%) (FSP, 2005). ...
The study assesses onset of rains, cessation of rains, duration of rainy season and seasonal rainfall at different probability levels. Daily rainfall data for 309 stations located in the Free State Province of South Africa were analysed from 1950 to 2008. The cumulative rainfall over 3 consecutive dekads (10-day periods) and cumulative rainfall over 1 dekad were used to determine onset of rains and cessation of rains respectively. Seasonal rainfall was determined as the accumulated rainfall from November to March. Rainbow statistical software was utilised to test for normality and determine probabilities at 20%, 50% and 80% risk levels. The other rainy season characteristics investigated were the probability of onset failure and probability of rainy season duration of less than 50, 100, 120 and 140 days. These rainy season indices were investigated in relation to maize production in the Free State. Rainfall behaviour during the growing period is one of the main limiting factors to rain-fed maize production, consequently influencing household food security. The results show that for onset of rains there is a large spatial variance over the Free State while cessation of rains shows small variance. There is also an east to west progression of onsets while the duration of the rainy season and seasonal rainfall also increased from west to east. Areas of low risk associated with rainy season characteristics are evident over the Thabo Mofutsanyane, eastern Motheo, eastern and northeastern Lejweleputswa and the Fezile Dabi districts, making these areas highly suitable for maize production. By contrast, high-risk areas are in the western and southern parts of the province and thus dryland maize production has low production potential in these areas.
... The province is situated between the latitudes 26.6°South and 30.7°South of the equator and between the longitudes 24.3°E ast and 29.8°East of the Greenwich meridian ( Fig. 1). It is the country's third-largest province making 10.6% of South Africa's land with an area of around 129,825 km 2 (Davis et al., 2006). The climate of the province is mostly semi-arid except the eastern and northeastern parts where a humid subtropical climate is experienced according to the Köppen climate classification. ...
... The province is situated between the latitudes 26.6°S and 30.7°S of the equator and between the longitudes 24.3°E and 29.8°E of the Greenwich meridian ( Fig. 1). It is the country's third largest province making 10.6% of South Africa's land with an area of around 129,825 km 2 (Davis et al. 2006). The climate of the province is mostly semiarid according to the Köppen climate classification. ...
... The main agricultural commodity produced in the province is maize accounting for over 30% of overall maize production in South Africa (Department of Agriculture, Forestry, and Fisheries DAFF 2010;de Jager et al. 1998). The province is administratively divided into five municipal districts shown in Fig. 1, namely (Davis et al. 2006): ...
Water Requirement Satisfaction Index (WRSI) at three different probability levels (20%, 50%, and 80%) was used to quantify drought affecting rain-fed maize production in the Free State Province of South Africa based on climate data from 227 weather stations. Results showed high spatial variability in the suitability of different areas: the southern and southwestern localities are unsuitable due to high drought incidences; the northern, central, and western regions are marginally suitable; the eastern, northerneastern areas and a few patches in the northwest are highly suitable with relatively low drought severity. Proper choice of maize varieties to suit conditions at different localities is crucial. The Mann–Kendall test and coefficient of variation were further used to determine trends and temporal variability, respectively, in the WRSI, seasonal rainfall, and seasonal maize water requirements. Results of this analysis revealed no significant positive trends in the WRSI, no significant negative trends in seasonal rainfall, and no significant positive trends in maize water requirements, contradicting previous findings of increased drought severity. Seasonal rainfall and the WRSI showed high interseasonal variability, while seasonal maize water requirements showed low variability. In view of these observations, it is apparent that realignment of management practices is an overdue prerequisite for sustainable maize production.
