Total number of hectares were found to be significant at 1% level and were positively related to the yield of table grapes. Expected sign for farm size had a positive relation to output, thus, it concurred with findings from this study. This implied that as more land is cultivated, there will be an increase in production output. This concurred with a study that was done by Belete et al., (2016). This was furthermore, supported by a study that was done by Conradie et al. (2007), which stated that efficiency is dependent on farm size.
Cost of establishment
This variable was found to have a negative relationship to the production of grapes.
However, it was significant at 10% level. Findings from this study showed that there was a negative relationship between cost of establishment and output, which was in contrast with the expected sign. This suggested that, as there was a decrease in the cost of establishment, there would be an increase in production. This concurred with a study that was done by Lwelamira et al. (2015b), which stated that small farm areas tend to produce more output as their cost of establishments were small.
Equipment costs
The expected sign for equipment costs was negative and this was validated by the findings from this study. Costs for the equipment used in the production of table grapes were positively related to the production of table grapes. However, it was not significant, this meant, for every additional equipment, there would be an increase in equipment costs of 0.03%. This concurred, with a study that was done by Kopeva and Noev (2001), which found that table grape equipments had a negative significant impact for
31 | P a g e producers, however, for cereal and vegetable producers, it had a positive significant impact on farm efficiency.
Pesticide used
For every output increase, the use of pesticides increased by an additional 0.65%.
Pesticides were significant at 5% confidence level. The expected sign of pesticide costs was positive, likewise, this was validated by the findings from this study. This implied that when productivity of table grapes increased, cost of pesticides also increased being expensive for the farmers to purchase. According to a study that was done by Koçtürk and Engindeniz (2016), it was concluded that a decrease in the cost of pesticides resulted in an increase in table grape production. Therefore, this enabled an increase in exportation of table grapes.
Cost of water
The cost of water was found to be negatively related to the production of table grapes and it was significant at 1 % level. Expected sign for the cost of water was negative and as such this concurred with the findings of this study. This implies that when yield increases, cost of water decreases by 0,80%. Similarly, in a study that was done by Deng et al. (2016), it was found that water and electricity were the lowest input cost for farmers at 7%, while labour costs were found to be the second important input cost (38%).
Grape Prices
This study found that prices for table grapes were statistically significant at 5% level.
Grapes prices were found to be negatively related to the production of table grapes.
This simply implied that the cost of producing additional units of table grapes increases as more was produced. Thus, in a study that was conducted by Conradie et al. (2007), showed that table grape farmers produced more and showed more variance on their farm productions due to higher prices of table grapes.
Number of labourers
The work force was found to be negatively related to the production of table grapes and was significant at 1% level. The expected sign of the quantity of labourers at the grape
32 | P a g e farms was negative and concurred with the findings from this study. This implied that increased labour, results in a decrease in production of table grapes. This concurred with a study that was done by Townsend et al. (1998); Tasevska (2012), which found that grape production in South Africa had a negative influence on labour use with regards to efficiency and as variable cost of labour increases it decreases efficiency of the farm.
Household income
Farmers’ household income was found to be positively related to the production of table grapes; however, it is not statistically significant. The expected sign of the household income was to be positively related to efficiency. Thus, the findings from the study validated the expectations. Consequently, this was validated by a study that was done by Lwelamira et al. (2015a), which found that if productivity of table grapes were to be improved this could potentially reduce poverty as household income would be increased. Furthermore, it was stated that grape production highly contributes to household income despite its low productivity and low grape pricing.
Table 4.9: Efficiency factors
Production quantity Coefficient Std. Err. Z P>z
Farm size 8719.79 1314.80 6.62 0.000***
Cost of establishment -.0433 .006759 -5.38 0.1*
Equipment costs .03 .0259063 1.18 0.241
Pesticides used .65801 .1122 4.98 0.05**
Cost of water -.8005408 .1393405 -5.75 0.000***
Grape prices -6742.50 186.111 4.58 -0.15**
Quantity of labourers 2328.493 -371.5989 -6.28 0.000***
Household income 18.14016 27.90677 0.67 0.521
_cons -382992 165973.6 -2.31 0.021
Source: Author’s computation from data, coefficient significant @ 1%, 5% and 10% (***,
** and *)
The age of farmer (1%), household size (1%), fertilisers used (5%) and extension services (10%) were positively related to economic efficiency of table grape production and was significant at 1%, 5% and 10% respectively. This concurred with a study done by Lwelamira et al. (2015a), that stated that fertilisers used was significant at a 5% level.
The educational level of farmers was significant at 1%, this was similar to a study that
33 | P a g e was done by Oluwatayo and Adedeji (2019), which found that years of formal education played an important positive significant impact on the efficiency of production.
Table 4.10: Inefficiency factors
Coefficient Std. Err. Z P> z
Gender -0.568 0.143 2.173 2.45
Age 0.223 1.506 1.593 0.000***
Educational level 0.5208 -0.258 -1.241 0.000***
Credit -4532.50 4351.56 3.78 5.69
Marital status -3458,54 8956.400 -3.25 -0.45 Extension services 719.601 317.4 -2.265 0.33*
Fertilisers 0.1956 -3.800 -0.418 1.66**
Household size 0.5208 -0.258 -1.241 0.000***
_cons -0.8888 286.50 2.501 0.02
Source: Author’s computation from data, coefficient significant @ 1%, 5% and 10% (***,
** and *)
Summary of Efficiency Scores for Waterberg and Sekhukhune District Table Grape Farmers
Results showed that AE scores of table grape farmers had a mean of 0.6841, with a minimum of 0.473 and a maximum of 1,000. It was evident that farmers were not utilising inputs given the input price and average costs. Technical efficiency score ranged from 0.80 to 1,000 with a mean of 0.8925. This implied that 89% of the farmers were technically efficient and could produce over 80% of the maximum feasible output. This was similar to a study that was done by Tasevska (2012), that found technical score ranges between 0.80 and 1, 000. Economic efficiency scores on average were found to be 0.7256, with a minimum of 0.563 and a maximum of 1. This clearly implied that table grape farmers were economically efficient, and the cost of table grape production could be increased on average by approximately 56%.
Table 4.11: Efficiency scores for table grape farmers
Variable Mean Standard deviation Minimum Maximum
AE 0.6841 0.1432 0.473 1
TE 0.8925 0.078545 0.80 1
EE 0.7256 0. 16532 0.563 1
Source: Author’s computation from data
34 | P a g e Constraints Faced by Table Grape Farmers in the Study Area
A number of constraints were faced by the table grape farmers such as diseases, financial instability, theft, quality of water which affects sales, instabilities surrounding land policies, labour and electrical costs, marginalization of groups, maladministration and lastly corruption. The constraints that topped the rankings were labour costs and high electricity bills, instabilities surrounding land policies, thus, this poses a serious threat to the growth of their businesses, especially for the export market. Diseases, lack of rainfall, financial instabilities and theft ranked second when it comes to the constraints that they were faced with.
On the other hand, marginalization of groups, maladministration and corruption were ranked third, thus, this can be seen from farms that were provided through the land restitution programme. The land is owned in groups and profits are shared amongst themselves, however, proper monitoring and evaluation of the farms were not adhered to. Lastly, the quality of water which affected the sales of table grapes ranked fourth.
This clearly showed that constraints that farmers faced, were the ones that hindered their progress in terms of growth in the production of grapes.
Table 4.12: Constraints ranking by table grape farmers
Constraints Rankings
Instabilities surrounding land policies, labour costs, electrical costs
1 Diseases, rainfall, financial instability and theft
2 Marginalization of groups, maladministration and corruption
3 Quality of water which affects sales 4 Source: Author’s computation from data
35 | P a g e CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS