CHAPTER 4: RESULTS AND DISCUSSION
4.7 Highest education level of farmers
4.7.1 Relationship between agricultural skills and knowledge, and level of education . 48
Citing Sewpaul (2008), Trevor Manuel, the then Minister of Finance in South Africa, noted that 15% of rural women have school leaving certificate (matriculation) compared to 50% for urban women. Level of education among rural farmers affects the competence of farmers in agricultural skills. Cronje et al. (2003) cited low levels of education as a cause for the failure of some smallholders to perform basic farm managerial tasks.
This study set out to determine whether there is a relationship between agricultural skills and knowledge, and level of education. Level of education in this case was measured by the number of schooling years attained by farmers. Table 4.7 shows the results of the comparison of agricultural skills and knowledge across different education levels of irrigators. There was a significant mean difference between levels of competence in calibration and use of sprayer and determining seed depth and mean level of education attained by farmers in the irrigation scheme.
The significance (p < 0.05) in the competency levels of sprayer use and calibration implies that such equipment require some level of education. The results showed that there was a significant (p < 0.1) mean difference in the level of farmers‟ competence in determining seed depth.
Farmers who are more educated are able to plant their seeds at the desired planting depth. One can infer that more educated farmers get better germination and possibly achieve better yields.
The insignificance of most production skills to education level signifies the important role played by indigenous knowledge rather than formal education among rural farmers.
The effect of education on marketing skills was also assessed. Collett & Gale (2009) argue that smallholder farmers need marketing and business skills to better represent their smallholder businesses in markets. There was a significant (p < 0.1) difference in the level of competence in
the practice of financial management. This implies that as level of education increases, farmers become more competent in managing their farming income.
Table 4.7: Relationship between agricultural skills and knowledge, and level of education among irrigators in Msinga
Agricultural skills and knowledge Not
Competent Competent Very
Competent Significant level (ANOVA) Production skills Level of education (number of schooling years) Selecting appropriate planting methods
for various crops (n=184) 3 2 3 ns
Determining inter and intra row spacing
(n=184) 3 2 3 ns
Irrigation scheduling and frequency
(n=184) 2 2 3 ns
Application of herbicide and fungicide
(n=184) 2 2 3 ns
Planning and carrying out harvesting
appropriately for various crops (n=184) 2 2 3 ns
Determining the amount of fertilizer to
apply for various crops (n=184) 2 2 3 ns
Soil and water conservation for specific
farm lands (n=184) 3 2 2 ns
Determining seed depth (n=184) 3 2 3 *
Determining nutrient deficiency
symptoms in crops (n=184) 1 3 3 ns
Calibration and use of sprayer (n=184) 1 3 3 **
Maintenance of water pump (n=184) 2 3 2 ns
Storage of produce (n=184) 3 2 3 ns
Marketing skills
Packaging of produce (n=184) 2 2 3 ns
Knowledge of marketing contracts
(n=184) 2 2 2 ns
Price determination for your produce
(n=184) 3 2 3 ns
Knowledge of the market for your
produce (n=184) 2 2 3 ns
Business skills
Financial management (n=184) 1 2 3 *
Farm record keeping (n=184) 2 2 3 ns
** = significant at the 0.05 level, * =Significant at the 0.1 level, ns= not significant
The agricultural skills were compared across levels of education among non-irrigators. There was a significant (p < 0.1) difference in the level of competence in determining the correct amount of fertilizer to apply and this implies that farmers who are more educated are able to apply the correct amounts of fertilizer in their crops and possibly achieve better yields. Farmers‟
ability to determine seed depth was significant (p < 0.1). This implies that farmers with more schooling years are likely to get a better yield as they plant in the correct seed depth and can
achieve better germination rates. There was a significant (p < 0.1) difference in the level of farmers‟ competence in determining the price of their produce. Level of competence in financial management was also significant (p < 0.1). This implies that farmers who had more schooling years can manage their finances better.
Table 4.8: Relationship between agricultural skills and knowledge, and level of education among non-irrigators in Msinga
Agricultural skills and knowledge Not
Competent Competent Very
Competent Significant level (ANOVA) Production skills Level of education (number of schooling years)
Selecting appropriate planting methods for
various crops (n=66) 2 2 3 ns
Determining inter and intra row spacing (n=66) 2 2 4 ns
Irrigation scheduling and frequency (n=66) 2 3 4 ns
Application of herbicide and fungicide (n=66) 3 2 1 ns
Planning and carrying out harvesting
appropriately for various crops (n=66) 1 3 2 ns
Determining the amount of fertilizer to apply for
various crops (n=66) 1 3 5 *
Soil and water conservation for specific farm
lands (n=66) 3 2 0 ns
Determining seed depth (n=66) 1 3 3 *
Determining nutrients deficiency symptoms in
crops (n=66) 3 1 4 ns
Calibration and use of sprayer (n=66) 2 4 1 ns
Maintenance of water pump (n=66) 3 0 2 ns
Storage of produce (n=66) 2 3 2 ns
Marketing skills
Packaging of produce (n=66) 2 3 2 ns
Knowledge of marketing contracts(n=66) - - - ns
Price determination for your produce(n=66) 2 0 10 *
Knowledge of the market for your produce
(n=66) 2 - 0 ns
Business skills
Financial management (n=66) 2 7 8 *
Farm record keeping (n=66) 2 2 - ns
*=significant at the 0.1 level, ns=not significant 4.8 Age of farmers
Age of the sampled farmers was also assessed. Table 4.9 presents the results. The results show that the average age of irrigators was 57 years while the average age for non-irrigators was 59 years, with the youngest farmer being 25 years old (irrigator) and the oldest farmer being 90 years old for non-irrigators and 89 years for irrigators. However, there is no statistical significant difference in age between irrigators and non-irrigators.
Table 4.9: Age of farmers
Variable Minimum Maximum Mean Significant level (T-test) Age Irrigators (n=184) 25 89 56.88
Non-irrigators (n=66) 26 90 58.50 ns ns: not significant
A further investigation was carried out to determine if there was a statistical difference in the age of female and male farmers. The results (Table 4.10) show that there was no significant age difference among female and male farmers.
Table 4.10: Age and gender farmers
Variable Mean Std. Deviation Significant level (T-test)
Gender
Female (167) 57.3 14.57
Male (83) 57.4 12.51 ns
ns: not significant