La competencia lectora

The characteristics of the respondents for the study is one of the basic requirements for understanding issues on the effects of two group-based extension approaches on the knowledge and production of dry season vegetable farmers in south-west Nigeria. Also, the characteristics provide the demographic elements that define the appropriateness of the participants for the study.

This result reveals that 10.0 % of the participants awee single, 68.0 % werre married, 15.0% were widows while .03% is divorcees. Farmers need their wives and children to assist them on their farms in order to reduce the cost of labour.

25

165

36 7

0 20 40 60 80 100 120 140 160 180

SINGLE MARRIED WIDOW DIVORCED

Frequency

Fig 4.1: Distribution of Participants by Marital Status

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Fig4. 2: Distribution of Participants by Number of Wife (ives)

Fig 4.2 shows that 11.6% of the respondents were not married, 66.0% married only one wife, 17.5% married two wives, 0.05% married three wives, 0.017% married four wives while 4.3% of married five wives. Wives often give helping hands to their husbands on the farm, especially during harvesting, transportation and grading of dry season crops.

0 0

27 1

151

2 41

3 11 4 2 10 1 0

20 40 60 80 100 120 140 160

Frequency

0 1 2 3 4 5

Figure 4.2: Number of wife or wives.

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Fig 4.3: Distribution of Participants by Number of House(s)

The result shows that 48.9% participants had no house of their own, 47.2% had built one house each, 0.03% had built two houses each, while 0.02% had three houses each. This reveals that farmers too have houses of their own contrary to general belief that farmers are poor.

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Fig 4.4: Distribution of Participants by Number of Cars

Figure 4.4 shows that 33.0% of the participants had cars, while 67.0% did not have.

These cars are being used for the transportation of the farmer, the labourers, farm inputs as well as farm products to and fro the farm. It also reveals that dry season farmers are not poor and that some of them cold afford to buy cars which are not a luxury but a necessity.

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Fig 4.5 : Distribution of Participants by Number of Motorcycles

This result shows that 57.5% of the participants do not have motorcycles, 38.6% of them had one each, 0.03 had two each, while 0.06% had more than two. Dry season farmers make use of these motor cycles as a means of transportation to and fro the farm. The motorcycles are also used for the transportation of farm inputs and produce.

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126 Fig 4.6 Distribution of Participants by Age

Fgure 4.6 shows that 8.0% of the participants were young or less than 25 years of age while 92.0% of the participants were adult or more than 25 years of age. This reveals that most farmers were old and that young people are not taking to farming . This result is in line with the findings of Bawja (2010) who reports that, 36.4% respondents are 41 years and above.

8%

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127 Fig 4.7: Distribution of Participants by sex

Figure4.7 shows that 15.0% of the participants were females, while 85.0% were male.

This reveals that majority of dry season farmers were male while only few of them were female. The reason for this could be the that when dry season farming is not done mechanically it is laborious and requires a lot of physical energy which most females may not be able to with provide.

85%

15%

MALE FEMALE

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Fig 4.8 Distribution of Participants by Educational Level

Level of education and efficiency of farmers were positively correlated, the higher the level of education, the more will be the efficiency of the farmers. Education motivates and creates awareness amongst farmers to adopt new agricultural technologies. In their studies on the adoption of improved agroforestry technologies among contact farmers in Imo State, Nigeria Orisakwe l. and Agomuo, (2011) reported that farmers‘ educational level has a positive relationship with adoption rate of agroforestry technologies implying that the more educated farmers adopted agroforestry technologies more than the less educated farmers. This shows that 53.0% of the participants had primary and adult education certificates, 39.0% had secondary school education while 8.0% had tertiary education. This reveals that most dry season farmers could read and write in their local and English languages. This makes it easier for them to read and interpret instructions on agro-chemicals, inputs as well as research findings on leaflets, posters and other print media. This result supports Bawja (2010)‘s findings that only 14.67% of respondents were illiterate.

On the relationship between age and adoption, Caswel et. al (2001) note that increasing age reduces the probability of adopting technologies. Older farmers, perhaps because of

53%

39%

8%

LOW MEDIUM HIGH

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investing several years in a particular practice, may not want to jeopardise it by trying out a completely new method. In addition, farmers‘ perception that technology development and the subsequent benefits require a lot of time to realise, can reduce their interest in the new technology because of farmers‘ advanced age, and the possibility of not living long enough to enjoy it (Caswell et al., 2001; Khanna, 2001). Further, elderly farmers often have different goals other than profit maximisation, in which case, they will not be expected to adopt an income –enhancing technology. As a matter of fact, it is expected that the old that adopt technology do so at a slow pace because of their tendency to adapt less swiftly to a new phenomenon (Tjornhom, 1995). On the other hand, young farmers tend to have more education and are often hypothesised to be more willing to innovate (Ejembi, Omoregbee & Ejembi, 2006).

These findings seem to agree with previous findings from an Integrated Household Survey Report (GoM, 2005) in which the North registered higher literacy levels (90%) compared with the Southern and Central regions which registered 71% and 75%

respectively. That majority of farmers are literate means these farmers would be more receptive to information pertaining to new improved farming practices. Education has been found (Caswel et. al., 2001) to create a favourable mental attitude for the acceptance of new practices especially of information-intensive and management-intensive practices on adoption. Similarly, Adesina and Zinnah (1992) have also echoed that education contributes to general awareness and thus, favours adoption. If the amount of complexity perceived in a technology is reduced, the likelihood of a technology‘s adoption

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Fig 4.9 Distribution of Participants by Status of Farming

Figure 4.9 shows that 83.0 % of the dry season farmers covered in the study were full-time farmers, while 17.0% were not full-full-time farmers but have other jobs so they were doing dry season farming on part-time basis. This reveals that dry season farming can be practiced on full time and part-time bases. It also reveals that full-time farmers engage in dry season farming to keep them busy throughout the year and to earn additional income.

83%

17%

FULL-TIME PART-TIME

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Fig 4.10: Distribution of Participants by Scale of Operation (farm size)

Figure 4.10 that 16.0% of the participating farmers can be regarded as low scale farmers because they cultivate less than one hectare or 2.5 acres of dry season land, 49.0% are ranked medium scale farmers because they cultivate between 1and 2 hectares or 2.5and5 acres of dry season land while 35.0% were regarded as large scale farmers for having cultivated more than 2 hectares or 5 acres of land.

Part B Analysis of Hypotheses

Part B contains the analysis of the result obtained from the pre-test and post-test questions as answered by the participating farmers. Each hypothesis is analysed using the mean and standard deviation scores, graphical representation of the mean plots, ANCOVA and Scheffe Pairwise Multiple comparison Tables. The results obtained were compared with previous results from similar studies.

Hypothesis 1: There is no significant main effect of the two group-based extension