International Journal of Horticulture, 2018, Vol.8, No. 2, 8-15
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Table 4 Maximum likelihood estimates of the stochastic frontier analysis for vegetable production system
Production factor
Parameter
Coefficient
Standard error
t- value
Constant term
a
0
4.5719916
0.0940988
48.5871590
Farm size (Acre)
a
1
0.1899576
0.0967272
1.963850***
Quantity of seed (Kg) a
2
0.0679005
0.0167576
4.0519197**
Farmlabour (Man/days) a
3
0.1143884
0.0262899
4.0519197**
Fertilizer (Kg)
a
4
0.0346752
0.0073193
4.7374901**
Pesticides (Litres)
a
5
0.0764614
0.0227672
2.508855***
σ
2
= 0.0026
γ = 0.99
Note: Data Analysis 2016; ***5% level of significance, ** 1% level of significance
Results in Table 5 show the elasticities of factors of production (factor substitution) and the return to scale. The
returns to scale (RTS) computed as the sum of the elasticities was found to be 0.4826. This was less than one but
greater than zero (Stage II), suggesting positive decreasing returns to scale (rational zone of production). Thus 1%
joint increased in inputs decreases the output by 0.48%.
Table 5 Elasticity of production and returns to scale
Variables Elasticity
(X
i
)
Farm size
0.1891
Seed
0.0679
Farm labour
0.1144
Fertilizer
0.0347
Pesticides
0.0765
RTS
0.4826
Note: Data analysis, 2016
2.5 Maximum likelihood estimates of the determinants of efficiency in vegetable production
Most of the coefficients were not significant except for age and years of education which are significant at 10%
level of significance (Table 6). This implies that older farmers were more efficient as against the findings of
Idiong (2007). Also, farmers with higher years of education are more technically efficient, as corroborated by
Obwona (2000) in his findings. The other coefficients were not significant, although years of experience was
positive, which implies that the years of experience of a farmer and number of visit by an extension agent does not
determine efficiency among farmers sampled. However, extension awareness of the farmers had a negative
relationship with the economic efficiency. This result is in line with the findings of Alam (2012) and Vanisaveth
(2012), that extension contact received by farmers negatively affect technical inefficiency. This implies that there
is a negative relationship between extension contact and inefficiency among the respondents.
Table 6 Maximum likelihood estimates of the determinants of efficiency in vegetable production
Variable
Parameter
Coefficient
Standard Error
T-Statistics
Intercept
Z
0
0.0764814
0.1526244
0.5009770
Age
Z1
0.0000117
0.0000064
1.8425537**
Years of experience
Z2
0.0000014
0.0000124
0.1089560
Years of education
Z3
0.0001542
0.0000905
1.7039319**
Extension awareness
Z4
-0.0052474
0.0108799
0.4822974
Log likelihood function: 212.47
Note: Data analysis 2016; ** Significant at 10% level
2.6 Conclusion and recommendations
The paper focuses on the estimates of stochastic frontier production for vegetable farmers in Oyo State. The age
and education are the major factors contributing to the efficient production of vegetable in the study areas. Other
variables such as seed (kg), farm labour (man-days), fertilizer (kg) and pesticides (litres) also showed positive
effect on the production of vegetable. It is therefore concluded that farmers with higher age and increase plot of
