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Agricultural Science Research Journal Vol 1(4) pp. 84  91 June 2011 Available online http://www.resjournals.com/arj ISSNL:20266073 ©2011 International Research Journals
Full Length Research Paper
Technical Efficiency of tomato production in Oyo State Nigeria.
OGUNNIYI, L.T and OLADEJO, J.A
Department of Agricultural Economics Ladoke Akintola University of Technology Ogbomoso Oyo State Nigeria Corresponding Author Email: [email protected]; [email protected] :[email protected]
Abstract
This study estimated the technical efficiency index and further examined the factors influencing technical efficiency for the sampled tomato farmers in Oyo State of Nigeria. The study made use of a crosssectional data to obtain information from 150 tomato farmers in the four agricultural zones of Oyo State. The technical efficiency index ranged from 0.031 to 1.000 under both Constant Return to Scale (CRS) and Variable Return to Scale (VRS) specification. The mean technical efficiencies were 0.423 and 0.548 under CRS and VRS specification respectively. The scale efficiency range between 0.175 and 1.000 with a mean of 0.826. Of the 150 tomato farms, 26 show constant return to scale and 94 show increasing return to scale while 30 shows decreasing return to scale. This result shows that there is small scale inefficiency in the study area. There are excess use for all inputs especially for fertilizer, family and hired labour. The determinants of technical efficiency are education, experience, diversification, marital status and gender. Keywords: tomato; DEA; CRS; VRS; technical efficiency; INTRODUCTION Tomato is regarded as a fruit in some quarters and as vegetable in others but which ever way anyone look at it, tomato is a highly nutritious food ingredient used in the preparation of many foods. Tomato is virtually used by every tribe in Nigeria. Tomato can be grown anywhere in southern Nigeria, but the best area is the Savannah zone because some diseases of tomatoes are less common in the Savannah. In agriculture, horticultural crops including vegetables have a significant place. These crops not only contribute to the share of agriculture in national economy, but possess a great potential and comparative advantage to compete in the liberalized economy. Vegetables are not only important as protective food and highly beneficial for the maintenance of health and prevention of disease, but these are also a source of livelihood for small farmers and foreign exchange earner for the national economy. Vegetables are a source of income support as well as important for food security of the people. As an important source of minerals, vitamins and health acids, tomato (Lycopersicon esculentum Mill) is one of the most important vegetable crops of solanaceae grown universally with the production of 124.75 million tonnes (FAO, 2007) Onion, tomato and chilies are most common and important kitchen items cooked as vegetables, used as condiments and salad. The consumption of tomato and onion has high income elasticity of demand. Thus, there will be more demand for these vegetables with population growth, economic growth, and urbanization (Fateh, 2009). Tomato production requires a high level of management, large labor and capital inputs and close attention to detail. Tomato production is subject to the variations that occur in weather, which may result in severe crop damage and losses. Labor requirements for
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production, harvesting, grading, packaging and transporting are very intense. Tomato production is labour intensive and bulk of production is mostly supported by small family farm (Erdogan, 2007). The economics of tomato production in the wet and dry seasons were examined by Agbabiaje and Bodunde (2002) with special reference to the efficiency of input use and varietal suitability for production seasons. Two varieties of tomato, TI563 and TI570 were used and gross margin analysis was employed to compare the performance of the two varieties. There was fruit yield difference between the two seasons in both varieties. Total production cost was higher in the dry season as a result labour required for irrigation. There was a negative returnstoscale for labour on land preparation, water and insecticide applications in the dry season. All economic indices considered indicated that the profitability of varieties TI563 and TI570 was higher in the wet and dry seasons respectively. Popescus (2002) in his study entitled economic efficiency in tomatoes production in greenhouse found out that The reduction of the tomatoes cultivated area was compensated by the increase of intensification grade using new high performance hybrids and modern technologies. Thus, the scientific production management has been looking for maintaining the total production at the same level from a year to another by an increased average tomatoes yield by 53.33 % . The continuous increase of farm input price has doubled the cost per surface unit and increased the cost per tomatoes kilogram by 33 %. The increase of tomatoes demand and of market price by 31 % has had a positive influence on the farm incomes which has doubled during the last three years. Afolami and Ayinde (2002) examined the economics of tomato production in Yewa North Local Government Area of Ogun State, Nigeria using the production function analysis to show the equilibrium or disequilibrium of resource use from the optimum. Their study revealed that tomato production is a profitable venture, but the levels of resource use with respect to fertilizer, land and seed were below optimum. The problems militating against tomato production were identified to be high cost of fertilizer, pest and disease problems, and inefficient transportation network resulting in spoilage of output and inadequate credit facilities. Sreenivasa Murthy et al., (2009) estimated the technical and scale efficiencies of tomatoproducing farms in Karnataka, considering different production levels using DEA and has identified the determining factors of their technical efficiency. Their study has indicated that most of the farms irrespective of size of holding have shown technical inefficiency problems. According to Robinson and Kolavalli (2010), depending on the region the key drivers of production costs in tomato production are labor, irrigation and fertilizer. Technical efficiency studies the conversion of physical
inputs such as land inputs, labor inputs, and other raw materials and semi finished goods, into outputs. Technical efficiency can be output, reflecting the maximum output that can be achieved from each input, or alternatively representing the minimum input used to produce a given level of output. It describes the current state of technology in any particular industry [Hassan, (2004)]. The concept of technical efficiency including price efficiency and production efficiency was initially used by Farrell (1957). This method has been continued by Hassan (2004), Shah et al. (1994), and Ali et al(1994). This study demonstrates an approach to determining the farm efficiency using DEA technique. The estimate of resourceuse efficiency obtained will be useful in providing insights to assess the potential for and sources of improvements in fish farms production. DEA is a nonparametric technique that measures the efficiency of DecisionMaking Units (DMU) relative to production possibility or input requirement set. It was further described by Seiford and Thrall (1990) in terms of floating piecewise linear surface to rest on top of the observations. Specifically, the key constructs of a DEA model are the envelopment surface and the efficient projection path to the envelopment surface (Charnes et al., 1985). The envelopment surface and the efficient projection path depend on the scale assumption that underlined the model and the optimisation assumption respectively. The optimisation production process could be output or inputoriented model. The inputoriented model shows how much the input could be proportionally reduced without changing the quantity of the output produced while the outputoriented shows how much the output quantity could be proportionally expanded without altering the input quantity. Outputoriented model gives credence to neoclassical production function defined as the maximum output given input quantity (Fare et al., 1994). In this study, the outputoriented model approach was used to estimate various efficiency indices. METHODOLOGY This study was carried out in Oyo States of Nigeria. Oyo 0 1 0 1 State is located between latitudes 2 38 and 4 35 east of the Greenwich meridian. Oyo State covers an area of 28, 454 square kilometres. According to NPC [2006], Oyo State had a population of 5,591,585 people. The state has two distinct ecological zones the western rain forest to the south and the intermediate savannah to the north. A multistage random sampling technique was employed in selecting the sample. The four agricultural zones were taken as the sampling units as a first stage of sampling. At the second stage, two local government areas were randomly selected to represent the zone making a total of eight Local Government Areas. The last stage involved random selection of 150 tomato farmers
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from the selected Local Government Areas. DEA is non parametric approach method which involves the use of linear programming to construct a piecewise linear envelopment frontier over the data points such that all observed points lie on or below the production frontier. Let X be a K * N matrix of inputs, which is constructed by placing the input vectors xi, of all N firms side by side and Y denotes the M * N output matrix which is formed in analogous manner. The output oriented VRS DEA frontier is defined by the solution to N linear programs of the form Min , T Subject to  yi / + YT > 0 xi + XT > 0 N /T = 1 T>0 Where NI is an N x I vector of Is, T is an N * I vector of weights and is the output distance measure. We have to note that 0 < < 1 and that 1/ is the proportional expansion in outputs that could be achieved by the i+e firm, with input quantities held constant. In a similar manner, the input oriented VRS DEA frontier is defined by the solution to N linear programs of the farm. Min , T Subject to  yi + YT > 0 xi /  XT > 0 N /T = 1 T>0 Where is the input distance measure. Also note that 1 < < and that 1/ is the proportional reduction in inputs that could be achieved by the i+e firm, write output quantities held constant. The technical efficiency measure under CRS, also called the "overall" technical efficiency measure, is obtained by solving N linear programs of the form. Min CRS i Subject to  YT + Yi > 0 CRS i xi  XT > 0 T>0 Where i is a technical efficiency measure of the ite CRS firm under CRS and 0 i 1. The output and input oriented models will estimate exactly the same frontier surface and therefore, by definition, identify the same set of firms as being efficient.
CRS CRS
The efficiency measures may, however, differ between the input and output orientations. Under the assumption of CRS, the estimated frontier and the efficiency measures remain unaffected by the choice of orientation (Coelli and Perelman, 1999). One output and five inputs were used in the models. The only output is the tomato yield. The inputs are farm size, seed, family labour, hired labour and fertilizer. Determinants of Efficiency Model A second step regression model was applied to determine the farm specific attributes in explaining efficiency in this study. Alternatively, the factors can be incorporated directly into the model. This study applied 2 second step approach by using a Tobit regression. The model assume Where y is a DEA efficiency model and used as a dependent variable. Z1 = education (years) Z2 = experience (years) Z3 =household size (number) Z4 = diversification (1 for non farm employment and zero otherwise) Z5 = gender (1 for male and zero otherwise) Z6 = marital status (1 for married and zero otherwise) is the unknown parameter vector associated with the farm specific attributes and e is an independently distributed error term assumed to be normally distributed 2 with zero mean and constant variance, . Therefore, the model assumed that there is underlying, stochastic index equal to (Z + e) which is observed only when it is less than 100 and qualified as an unobserved latent variable. The dependent variable, that is efficiency invest cannot be normally distributed but censored distribution because it has a value between 0 and 1. OLS will yield an inconsistent estimate, hence this study used Tobit regression model using maximum likelihood estimate (MLE) approach (Tobin, 1958). The expected value becomes
E ( y / z ) = 1  (b) x 100 + (b) Z  (b)
where b =
(100  z )




( 2)
RESULTS AND DISCUSSION Summary Statistics The summary statistics of variables for the production frontier estimation is presented in Table 1. The table
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Table 1. Summary Statistics for Variables Used. Input/output variable Output(basket) Farm size(ha) F Labour(mandays) Hired labour(mandays) Seed(kg) Fertilizer(kg) Source: Field survey, 2010 Mean 137.97 2.25 62.38 57.63 11.68 28.86 Standard Deviation 128.91 1.54 18.74 23.94 8.85 12.73 Min 8.00 0.40 15.00 11.00 2.00 6.5 Max 995.00 6.07 104.60 119.00 60 57.00
revealed that the output of tomato is 137.97baskets with a standard deviation of 128.91baskets. The small variability by the standard deviation implies that the farmer operated at the same levels of farm size which does not affect their output levels. The mean farm size was 2.25 ha with a standard deviation of 1.54 ha. The variability is due to changes in hectares of tomato under the production seasons. As it is seen from table 1, small variations exist in all of the inputs. The smallest variation is in farm size. Efficiency Scores Out of the 150 tomato farms studied, 16 farms under CRS and 26 farms under VRS are fully efficient. 42 farms under CRS and 29 farms under VRS show a performance below 0.2. The greatest efficiency score was found to be 0.548. The average overall technical efficiencies are 0.423 and 0.548 for CRS and VRS respectively. Substantial inefficiencies occurred in the farming operation of the sampled farms in the state. Under the prevailing conditions, about 11% and 26% 0f farms were identified as fully technically efficient under CRS and VRS specification respectively. The observed difference between CRS and VRS measures further indicated that some of the farmers did not operate at an efficient scale and improvement in the overall efficiencies could be achieved if the farmers adjusted their scales of operation. In this study the mean technical efficiency score vary between 0.423 and 0.548. These results indicate that technical efficiencies can be increased by at least 45% through better use of available resources, given the current state of technology. The lowest technical efficiency score falls within the 0.8 0.89 group under the CRS specification while the lowest TE score falls within the 0.9 0.99 group under the VRS specification. This shows that TE scores under the VRS were higher than those obtain under the CRS specification. This study is in line with the earlier findings by Alemdar and Oren (2006). Spearman Correlation Spearman correlation coefficients between the technical
efficiency scores were computed and given in Table 4 in order to examine agreement between results obtained from DEA. The correlation coefficient is positive and significant at 0.01 Level. This indicates a strong agreement between the two models. Return to Scale Properties For the inefficient farms, the causes of inefficiency may be either inappropriate scale or misallocation of resources. Inappropriate scale suggests that the farm is not taking advantage of economies of scale, while misallocation of resources refers to inefficient input combinations. In this study, scale efficiencies are relatively high. Therefore, efficiencies are mainly due to improper input use. Mean scale efficiency of the maize farm is 0.826 (Table 2). Of the 150 tomato farms, 26 show constant return to scale and 94 show increasing return to scale while 30 shows decreasing return to scale (Table5). This result shows that there is small scale inefficiency in the study area. This implies that most of the farm should be smaller than their present size in order to achieve higher production given the available factor mix. This study is contrary to the earlier findings by Abay et al., (2004); Binam et al., 2004 and Stiljn et al., 2007 whose work identified large scale inefficiencies. Haji (2006) found that scale inefficiencies were nearly absent in more traditional farming systems. As it is seen from Table 4, mean farm size and mean output are 2.63 ha and 250 baskets respectively for fully efficient farms. The mean output of optimal scale is larger than that of superoptimal scale as well as suboptimal scale in the state. The result indicates that the optimal output level overlap a substantial portion of super optimal and suboptimal output. Output Slack and Excess Input Use The output slack was found to be zero for all the farms. This result indicates that, given the present scale of operation and the available resources, the farmers could not do anything to increase their output levels beyond the
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Table 2. Distribution of Technical Efficiency Scores obtained by DEA Models. Efficiency scores <0.2 0.2  0.29 0.3  0.39 0.4  0.49 0.5  0.59 0.6  0.69 0.7  0.79 0.8  0.89 0.9  0.99 1.00 Total Mean Min Max S.D Source: Data Analysis, 2010. *Figures in parentheses are percentages CRS 42(28.0) 15(10.0) 26(17.3) 12(8.0) 21(14.0) 9(6.0) 6(4.0) 1(0.7) 2(1.3) 16(10.7) 150(100) 0.423 0.031 1.000 0.3279 VRS 29(19.3) 15(10.0) 24(16.0) 12(8.0) 8(5.3) 9(6.0) 5(3.3) 5(3.3) 4(2.7) 39(26.0) 150(100) 0.548 0.031 1.000 0.337 SE 6(4.0) 1(0.7) 4(2.7) 2(1.3) 14(9.3) 10(6.7) 16(10.7) 7(4.7) 66(44.0) 24(16.0) 150(100) 0.826 0.175 1.000 0.228
Table 3. Spearman Correlation Coefficient among alternative Efficiency Measures. TE DEA (CRS) TE DEA (CRS) TE DEA (VRS) 1.000 0.778* TE DEA (VRS) 1.000
* means coefficients are significant at the 0.01 level (2 tailed).
Table 4. Characteristics of farms with respect to returns to scale. No of Farms Sub Optimal Optimal Super  Optimal Source: Field Survey, 2010. 94 26 30 Mean of Size (ha) 2.11 2.63 2.37 Mean Output (basket) 102.395 250.58 151.83
present values irrespective of the adjustment in their input levels because of resource fixity. In Table 5, the greatest input excess is fertilizer. Family and hired labour working mandays follow this. According to these results, sample farms could fertilizer use by 17% staying at the same production level. Number of farms using excess family labour is also high (81). This analysis reports excess use for all inputs especially for fertilizer, family and hired labour.
The value of land slack was observed to be 0.147ha. This indicates that farm size could be reduced by this amount to obtain the same level of output. The fertilizer slack of 4.84kg implies that there should be reduction in the use of fertilizer by 4.84kg. In essence, the same level of output that were realized from this inputs use could still be obtained if the quantity of the various inputs were reduced by the corresponding values of slacks among the inputs.
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Table 5. Input slack and number of farms using excess inputs. Inputs Farm Size Family Lab Hired Lab Seed Fertilizer No of farms 15 81 56 22 51 Mean Slack 0.147 9.559 6.00 0.761 4.840 Mean input use 2.250 62.38 57.63 11.68 28.86 Excess input use% 6.53 15.32 10.41 6.52 16.77
Source: Field Survey, 2010.
Table 6. Frequency distribution of output target Target 1  100 101  200 201  300 301  400 401  500 501  600 601  700 >700 Total Source: Data analysis, 2010 Table 7. Result of CRS Tobit analysis Variable Constant EDU EXP HOUSHLD DIVERSTI GENDER M_STATUS 2 Pseudo R F test Log likelihood function Source: Data analysis, 2010 Coefficient 0.476 0.010 0.056 0.825 0.155 0.072 0.222 0.099 2.64** 13.24994 Standard Error 0.123 0.005 0.342 0.287 0.065 0.060 0.078 tvalue 3.881 2.130** 0.164 2.876*** 2.385** 1.206 2.839*** Frequency 29 24 45 32 17 07 02 03 150 Percentage 13.3 16.0 30.0 21.3 11.3 4.7 1.3 2.0 100
Peer and Output Target In this study, five farms became a peer 20 or more than 20 times for other farms. Those farms were identified as robustly efficient farms since their production practices are such that they were frequently used to construct the efficient frontier for the other farms. Table 6 gives the summary of output target. The output target refers to the amount of output the decision making units should aim at producing given the available unit of inputs. The minimum output target that some of the DMU
should aim at producing fell within the range of 1100 baskets. Only 13.3% of the total DMU in the state is applicable. The maximum output target range is 700 baskets above. 30% of the farmers should aim at producing between 201 and 300 baskets. Determinants of CRS Analysis Table 7 reports the estimates of the Tobit regression for the study area under the CRS specification. Four
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Table 8. Result of VRS Tobit analysis. Variable Constant EDU EXP HOUSHLD DIVERSTI GENDER M_STATUS Pseudo R F test Log likelihood function Source: Data analysis, 2010
2
Coefficient 0.420 0.056 0.011 0.010 0.146 0.146 0.104 0.055 1.39 44.890
Standard Error 0.152 0.058 0.004 0.012 0.081 0.074 0.096
tvalue 2.770 0.965 2.750*** 0.818 1.811* 1.972** 1.081
variables had significant effects on the efficiency of tomato farmers. These are education, household size, diversification and marital status. Education has a negative relationship with technical efficiency. This implies that the more educated the farmer is, the less likely efficient he is. This is contrary to the results of Parikh et al., 1995. Household size has a positive and significant effect on technical efficiency under CRS specification. This indicates that efficiency increases with increase in household size. Diversification has a positive and significant effect on technical efficiency. Farmers that diversified (have other source of income) tends to be technically efficient than those that depend solely on farming. Married farmers are more technically efficient than unmarried farmers. Determinants of VRS Analysis Under the VRS specification, only three variables were statistically significant. These are experience, diversification and gender (Table 8). Experience has a negative and significant effect on technical efficiency under the VRS specification. This indicates that efficiency decreases with increase in farming experience. This result was consistent with those of Onu et al., (2000) ;Ojo (2003) and Ogundele and Okoruwa (2006) whose results had a negative relationship between experience and TE. Diversification also has a positive and significant effect on TE. Gender has a positive and significant effect on TE. This implies that male farmers tend to be more technically efficient than female farmers. CONCLUSION The study concluded that there exists more potential that remained untapped in tomato production in the study
area. There is scope for increasing tomato production by about 57% and 45% for technical efficiency under CRS and VRS specification respectively with the present technology in Oyo State. The determinants of efficiency are education, household size, experience, diversification and gender. The analysis of technical efficiency revealed that tomato farmers were not presently operating on the frontier. Productivity improvements can be achieved by implementing policies such as improved farmers' access to education and technical assistance, to ensure farmers use the existing technology more efficiently. This would make farmers operate more closely to the existing frontier. Also, research efforts directed towards the generation of new technology should not be neglected because a productivity gain stemming from technological innovation remains critical importance.
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