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Efficiency Measure (Technical and Profit) in Cucumber (Cucumis sativus L.) Production among Small Holder Farmers in South-South Nigeria

O
O.I. Ettah1,*
0000-0002-7116-1798
E
E.E. Uwah1
E
E.O. Edet1
B
B.A. Fakuta1
G
G.I. Ettah2
1Department of Agricultural Economics, Faculty of Agriculture, University of Calabar Nigeria.
2Department of Public Administration, Faculty of Management Sciences, University of Calabar Nigeria.

ABSTRACT

Background: Efficiency measure (technical and profit) in cucumber (Cucumis sativus L.) production among small holder farmers in South-South Nigeria was conducted. Technical and profit efficiencies are indicators of overall efficient production. Cucumber can contribute to economic empowerment if efficiently produced due to the high relative unit price of the crop.

Methods: Multi-stage random sampling techniques (three-stage) was adopted in the selection of respondents for the study. The data collected for the study were analyzed using stochastic frontier production function model for objectives one and two and stochastic frontier profit function model to realize objective three.

Result: Result of analysis showed that technical efficiency of sampled cucumber farmers was less than 1, implying that the farmers were producing below the maximum frontier output. The least technically efficient cucumber farmers had a technical efficiency of 0.32 and the most technically efficient ones had 0.94. Cucumber seedlings, labour and fertilizers were positive and significant at 5%, to affect technical efficiency of cucumber farmers and agrochemical at 1%. For technical inefficiency of cucumber farmers, age of farmers and farm size were negative and significant at 5% level, while household size, educational qualification, non-farm income and farming experience were positive and significant at 5% level and type of cropping was positive and significant at 10% level. Determinants of profit efficiency of cucumber farmers showed that age (0.37) was positive and significant at 1% level. A direct and significant relationship was found between education (0.67) and profit inefficiency of the farmers. Experience (-0.21) was negative and significant, household size (0.58) was positive and significant at 10% level. The following were recommended: farmers should invest in cucumber production for its efficiency, profitability and economic value.

INTRODUCTION

Cucumber (Cucumis sativus L.) is a widely-cultivated creeping vine plant in the family cucurbitaceae that bears cylindrical fruits, which are used as culinary vegetables. The crop roots in the ground and grows up on supported frames, wrapping around supports with thin, spiraling tendrils. Qamar et al. (2016) noted that the plant may also root in a soilless medium, whereby it will sprawl along the ground instead of a supporting structure. The vine has large leaves that form a canopy over the fruits, which consist of 95% water and is rich in nutrients, low in calories but high in many important vitamins and minerals. Cucumber contains antioxidants, promotes hydration and may aid in weight loss and lowers blood sugar among other health benefits (Zheng et al., 2019). It is real versatile vegetable because of variety in their use from salad to pickles as well as from digestive aids to beauty products. It was found useful against human constipation and improvement in digestion aa well as used as a cooling food in summer. A fresh cucumber provides vitamin C, niacin, iron, calcium, thiamine, fibers and phosphorus. Cucumber is an essential part of agriculture in the area because it is considered a source of livelihood and income. This is why the plant has made significant incursion into the diets of small holder farmers of the study area and hence underscores the need for this study. The fruit of typical cultivars of cucumber is roughly cylindrical, but elongated with tapered ends and may be as large as 62 centimeters (24 in) long and 10 centimeters (4 in) in diameter (Fig 1).

Fig 1: Cucumber fruit.


       
Technical and profit efficiencies are indicators of overall efficient production, with attendants benefits to the farmer and society (Abdulai and Eberlin, 2001 and Meena, 2024). The efficiency, with which farmers use available resources and improved technologies, is important in agricultural production Agbachom et al. (2022). According to Ettah et al. (2020) the efficient use of farm resources is an important part of agricultural sustainability and a prerequisite for optimum farm production. The authors went on to assert that inefficiency in resource use can distort food availability and security. Efficiency measurement as it is being studied here is important because it leads to substantial resource savings and good profit margins. Ettah and Ani (2018) noted that technically efficient production is defined as the maximum quantity of output attainable by a given input. But Cechura et al. (2014) and Jinghan (2009) noted that technical efficiency is the ability of a firm to maximize output for a given set of resource inputs. Profit efficiency on the other hand overcomes the traditional measures of a farm’s performance such as return on investment (ROI) and provides an innovative way of performance measure (Jondrow et al., 1982). This efficiency measures the farmer’s ability or that of the farm to manage its resources and produce outputs with greater economic value and hence more income to the farm, this is why it is considered the better predictor for evaluating the overall performance of farm production (Adenuga et al., 2013 and Kumar and Kumar, 2023).
       
Cucumber can contribute to economic empowerment if efficiently produced due to the high relative unit price of the commodity compared to local fruit vegetables. Inefficiency in the use of available scarce resources for the crops’ production has been the bane of increased production of the crop in the area. Crop productivity depends on the efficient use of resources used in its production process. Empirical studies on the technical and profit efficiencies of vegetables in various regions of Nigeria focused on tomato, pumpkin and watermelon. None of the studies examined the economics and determinants of the technical and profit efficiencies of cucumber production in the study area. It is against this backdrop that this study was conducted and sought to realize the following specific objectives:
i. Estimate the technical efficiency of cucumber farmers.
ii. Determine the factors affecting technical efficiency.
iii. Examine the determinants of profit efficiency of cucumber farmers.

MATERIALS AND METHODS

This research study was conducted between 2022 to 2025 and the analysis carried out in the Department of Agricultural Economics Econometric centre of the University of Calabar, Nigeria. The rationale of the study being to estimate the overall efficiency of cucumber production in South-South Nigeria (SSN).
 
Study area
 
The study area is (SSN) in West Africa and purposively chosen for its large-scale contribution to Nigeria’s cucumber production and the peculiarity of efficiency related problems in the region. The region is one of the six geopolitical divisions of Nigeria, a country on the Gulf of Guinea and regarded as the most populous in Africa and the sixth in the world (NEEDS, 2022). It comprises six states as follows: Akwa Ibom, Bayelsa, Cross River, Edo and Rivers.
       
The region stretches along the Atlantic seaboard from the Bight of Benin coast in the west to the Bight of Bonny coast in the east. It encloses much of the Niger Delta, which is instrumental in the environment and economic development of the region. Although the South-South represents only 5% of Nigerian territory, it contributes greatly to the Nigerian economy due to extensive agricultural, oil and natural gas reserves. The region has a population of about 26 million people, around 12% of the total population of the country (NPC, 2011). The study area has a tropical climate with variable rainy and dry seasons, with mean annual rainfall between 1,300 mm to 3,000 mm and temperature range of 23°C and 37°C. According to Central Bank of Nigeria (CBN) (2012) the vegetation of South-South region parades four distinct features: Mangrove Swamp (wetland), rainforest, derived savannah and parkland. The type of soil found in the area is deep laterite fertile and dark clayey basalt suitable for agricultural production.
 
Sampling procedure and data collection
 
Multi-stage random sampling techniques (three-stage) was adopted in the selection of respondents for the study. The six States of the region which reflected the demarcation structure of the area were covered. In the first stage, three States were selected randomly from the six States of the region, the criteria for the selection were the States with preponderance of cucumber production. The second stage involved the random selection of three Local Government Areas (LGA’s) in the three states earlier selected making a total of nine local government areas in the sample. The third stage involved a random selection of ten cucumber farmers from each of the nine LGA’s previously selected giving a total of 120 respondents for the study.
       
Data required for this study were generated from primary sources. The primary data were collected using a set of pre-tested structured questionnaires. The questionnaires were administered by well-trained enumerators (farmers), who were conversant with the selected locality. Primary data were also obtained through personal contact, oral interviews, etc. A pilot study was conducted as adopted by Agbachom et al. (2023) where enumerators were used for pre-testing of the questionnaire. This was to avoid inconsistency and incomplete response and also ensure clear understanding of the instrument as well as possession of both face and content validity. In other to check the consistency of the measuring instrument over time, reliability test was conducted using the test-retest method. The same questionnaires were given to the same respondent at two points in time (an interval of seven days) and the scores were compared.
 
Data analysis
 
The data collected for the study were analyzed using stochastic frontier production function model for objectives one and two and stochastic frontier profit function model to realize objective three. The models were specified as follows:
 
Stochastic frontier production function model for technical efficiency in cucumber farmers
 
The production technology of the cucumber farmers was assumed to be specified by the Cobb-Douglas frontier production function. Following Ettah et al., (2020), as cited in Bravo-Ureta and Rieger (1991), the Cobb-Douglas stochastic frontier production function model used was explicitly specified as:
 
lnY = βo + β1ln x1 + β2ln x2 + l3Lnx3 + β4 lnx4 + β5 lnx5 +β6lnx6+ - U1                                        
 
 
Where,
ln = Logarithm to base.
Y= Output of cucumber (Kg).
x1= Cucumber seeds (kg).
x2= Hired labour (N/man day).
x3= Fertilizer (kg).
x4= Agrochemicals  (N).
x5= Farm size (Hectare).      
x6= Capital  inputs (N).
βo = Constant term.
β0-β1............. β6 = Regression coefficients.
Ui= Random errors which were assumed to be independent and identically distributed having N (0, δ2)
Ui= Non-negative random variables associated with technical inefficiency.
       
It is assumed that the technical efficiency effects are independently distributed and arise by truncation at (zero) of the normal distribution with mean U1 and variances δ2, where U1 is specified thus;
Ui=  δ0 + δ, z1 + δ2z2iδ3iz3i + δ424i + δ5z5i + δ6z6i + δ7z7i + δ8z8i + S 
                    
 
Ui = Technical inefficiency of the ith farmer.
Z1= Age of farmer (years).
Z2= Level of education (No of years spent in school).
Z3= Farming experience (years).
Z4= Household size (number).
Z5= Extension contact (number).
Z6= Credit status (dummy variable, 1 for access, zero otherwise).
Z7= Membership of cooperative (1 for membership, zero otherwise).
Z8= Sex (binary variable, male = 1, female = 0).
S = Error term.
δ0 - δ8= Parameters.
       
The above model was incorporated in the frontier model in determining the technical inefficiency of cucumber farmers. This was done with the belief that the variables have direct influence on the level of efficiency (Ettah and Ani, 2018) as cited in Battesse and Cora (1993) and Kalisajan and Shad (1994).
 
Stochastic frontier profit function model
 
The stochastic frontier profit function was used to achieve objective three as earlier stated using Cobb-Douglas functional form which was specified as follows:
Cobb-Douglas
 
lnπ  = βoY* + β1ln x1 + β2ln x2 + l3Lnx3 + β4 lnx4 + β5 lnx5 +β6lnx6 +V - U1.                                                
 
ln= Logarithm to base.
π = Profit of cucumber (N).
X1= Land rent per ha.
X2= Cost of hired labour used in cucumber production per ha.
X3= Price of cucumber seedlings (N).
X4= Price of Agrochemical per litre.
X5= Price of fertilizer per kg.
X6= Price of capital inputs (N).
U1= Error term.
βo= Constant term.
β1-β2 ............... β6 = Regression coefficients.
Vi = Random variables which are assumed to be independent of Ui and normally distributed with zero mean and constant variance Vi-N (0, δ2) which are non-negative random variables and are assumed to account for technical inefficiency in production and are often assumed to be independent of Vi, such that U is the non-negative truncated (at Zero) U of half normal distribution with { N (0, δ2)}.
The determinant of profit inefficiency is defined by:
 
Ui=  δ0 + δ, z1 + δ2z2i + δ3iz3i + δ4z4i + δ5z5i + δ6z6i + δ7z7i  +
 
 
Where,
Ui= Cucumber profit inefficiency.
Z1= Farmers’ age (years).
Z2= Farming experience (years).
Z3= Education (years).
Z4= Training (1 if received training, 0 otherwise).
Z5= Membership of farmers’ association (1, yes, 0, no).
Z6= Household size (no. of persons).
Z7= Sex (1, Male, 0, Female).
δ0 - δ7= Parameters.

RESULTS AND DISCUSSION

Estimation of technical efficiency of cucumber farmers
 
A very important characteristic of production frontier model is its ability to estimate technical efficiency of production. The result is as shown in Table 1 indicated that, technical efficiency of sampled farmers is less than 1. This implies that cucumber farmers are producing below the maximum frontier output. The range of technical efficiency showed that 1.7% of the farmers had technical efficiency between the range of 0.31-0.40 and 0.41- 0.50 respectively, 5.0% had technical efficiency of 0.51-0.60, 12.5% had technical efficiency of 0.61-0.70, 61.7% had technical efficiency of 0.71-0.80, 15.8% had technical efficiency of 0.81- 0.90 and again 1.7% had technical efficiency of 0.91-1.00. The least technically efficient farmer had a technical efficiency of 0.32 which means that the farmer has to increase his level of production given his inputs and technology to at least 68% for him to operate at the production frontier while the most technically efficient farmer had technical efficiency of 0.94 which means that he has to increase his production at least by 6% for him to operate on the production frontier and be fully efficient. The mean technical efficiency was 0.72; this implies that all sampled farmers will need to increase production at least by 28% given the available mix of inputs at a given technology for it to operate on the production frontier. The mean of the worst 10 technically efficient farmer was 0.52 while the mean of the best 10 technically efficient farmer had a technical efficiency of 0.9. The result is in line with that of Adeyemo and Kuhlmann (2009) and Ayinde et al.  (2011) in their study of resource use efficiency in urban agriculture in Southwestern Nigeria and resource use efficiency and profitability of fluted pumpkin production under tropical conditions respectively, but contravenes that of Ettah et al. (2022) and Meena (2024) in their study resource use in sweet potato production in Delta State, Nigeria.  

Table 1: Technical efficiency distribution of cucumber farmer.


    
Factors affecting technical efficiency of cucumber farmers
 
Result of factors affecting technical efficiency are shown in Table 2 shows the result of the maximum likelihood estimates (MLEs) of the stochastic frontier production function for cucumber farmers. The estimates of sigma squared (σ2) for cucumber farmers were 4.3672. This was significant at 5% probability level, indicating that it is   significantly different from zero. It assures us of the goodness-of-fit as well as the correctness of the composite error term. The value of the gamma (γ) 0.7619 showed that the unexplained variation in output of cucumber farmers is the major source of random errors. It also indicates that about 76 per cent of the variation in output of cucumber is caused by inefficiency of the producers. This result confirms the presence of one-sided error components in the model and hence makes the use of Ordinary Least Square (OLS) inadequate in estimating the production function.

Table 2: Factors affecting technical efficiency.


       
The estimates of the parameters of the production function are; cucumber seedings, labour and fertilizers which were positive and significant at 5%, this implies that an increase or decrease in seedlings, labour and fertilizer will result to a corresponding increase or decrease in output of cucumber farmers respectively. Agrochemical was found to be positive and significant at 1% level of significance while capital input was positive and not significant. Factors affecting technical inefficiency of cucumber farmers are shown in the lower part of Table 2. Age of farmers and farm size were negative and significant at 0.05 levels of probability, while household size, educational qualification and farming experience were all positive and significant at 5% levels of significance and type of cropping was positive and significant at 10% level of significance. Non-farm income was positive and significant at 5% level of probability. This means that unit increase in these variables would increase technical inefficiency of the farmers and hence decreasing their technical efficiency. The larger the household size the more resources are diverted to non - farm activities like medical care, education, welfare, etc. This development causes farmers to be inefficient in the use of these resources for cucumber production. The result agrees with findings of Cechura et al., (2014) that family demands affect the efficient use of resources to farm production.         
       
The more farmers are educated, the more they turn away from cucumber farming, to other businesses perceived to bring quicker returns on investment. The less educated ones who cannot efficiently utilize resources dominate the farm industry. Highly experienced farmers tend to stick to their old methods of production which may be inefficient Ettah et al. (2022). Cucumber farmers do not invest their non-farm income in their farm, they rather channel the income to high returning investments. This result agrees with that of Sagar et al. (2023) who found out that farmers give priority to investments outside agriculture because they are relatively fast in return to capital.
 
Determinants profit efficiency of cucumber farmers
 
The parameter estimates of the determinants of profit efficiency of cucumber farmers are presented in the upper and lower section of Table 3 respectively.

Table 3: Determinants of profit efficiency and inefficiency cucumber farmers.


       
The analysis of profit inefficiency effect showed a significant gamma (γ = 0.83). This implies that 83% deviation from maximum profit obtainable was as a result of inefficiency of the cucumber farmers rather than random error or variability. The signs and significance of the estimated coefficients in the inefficiency model have important implications on profit efficiency of the farmers. The estimated coefficient for age (0.37) was positive and significant at 1% level. The positive relationship implies that as age of cucumber farmers increases, the level of profit inefficiency tends to increase thereby decreasing profit efficiency. This could be that as the cucumber farmers get older, the less efficient his labour, managerial abilities and supervision. This finding is in line with the work of Cechura et al. (2014) and Tanko and Adeniyi (2012) where age positively contributed to profit inefficiency. A direct and significant relationship was found between education (0.67) and profit inefficiency of cucumber farmers. This implied that an increase in the level of education increased the level of profit inefficiency (i.e. decrease profit efficiency). The positive value obtained is unexpected as cucumber farmers may go in search of white-collar jobs thereby neglecting cucumber farming or paying little or no attention to it. This finding disagrees with the work of (Ettah and Ani 2018 and Dey et al., 2000) that education decreases profit inefficiency in rice farming. The findings agree with that of Adeyemo and Kuhlmann (2009) and Ayinde et al. (2011) that education increases profit inefficiency in crop farming. The estimated coefficient for farming experience (-0.21) was negative and significant implying that, increase in farming experience tends to decrease the level of profit inefficiency (i.e. increase profit efficiency). This finding is in consonance with the findings of Qamar et al. (2019) who found that increase in farming experience decrease profit inefficiency of farmers in their study: analysis of off-season cucumber production efficiency in Punjab, India.
       
Household size (0.58) had positive and significant relationship with profit inefficiency at 10% probability level. This implies that, increase in household size increases profit inefficiency (i.e. decrease profit efficiency) of cucumber farmers. This result is in congruence with findings of (Etim et al. (2005) who observed a positive relationship between household size and profit efficiency in urban farms in Uyo Metropolis of Akwa Ibom State, Nigeria. This is however contrary to the findings of Adenuga et al. (2013) who found household size to increase profit efficiency among dry season tomato producers in selected areas in Kwara State, Nigeria. The result further showed a negative and significant relationship between membership of association and profit inefficiency. Membership of association decreases profit inefficiency and rather increases profit efficiency. This is expected as cucumber farmers’ membership of association could afford them the opportunity of interacting with other farmers thereby exchanging information on improved technology in arable crop farming. Although the result disagrees with the findings of Kuye and Ettah (2016) and Goni et al. (2013). In conclusion, age, education and household size had positive impact on profit inefficiency and this is contrary to apriori expectation regarding the roles of these factors.

CONCLUSION

Technical efficiency of sampled cucumber farmers was less than 1, implying that the farmers were producing below the maximum frontier output. The least technically efficient cucumber farmers had a technical efficiency of 0.32 which meant that the farmers have to increase their level of production given the available inputs and technology to at least 68% for them to operate at the production frontier. The most technically efficient cucumber farmers had technical efficiency of 0.9. Cucumber seedlings, labour and fertilizers were positive and significant at 5%, to affect technical efficiency of cucumber farmers and agrochemical at 1% level of significance.  For technical inefficiency of cucumber farmers, age of farmers and farm size were negative and significant at 5% level of significance, while household size, educational qualification, non-farm income and farming experience were all positive and significant at 5% levels of significance and type of cropping was positive and significant at 10% level of significance. Estimates of the determinants of profit efficiency of cucumber farmers showed that age (0.37) was positive and significant at 1% level. A direct and significant relationship was found between education (0.67) and profit inefficiency of cucumber farmers. Experience (-0.21) was negative and significant. Household size (0.58) had positive and significant at 10% level. The result also showed a negative and significant relationship between membership of association and profit inefficiency. Based on the results of the study, the following are recommended: farmers should be encouraged to invest in cucumber production for its technical efficiency, profitability and economic value, inputs should be made available and at affordable price especially new varieties of cucumber seedlings and cucumber farmers should be guided on the type of cropping to undertake so as to increase their technical efficiency.

ACKNOWLEDGEMENT

The authors would like to thank all the authorities of the Department of Agricultural Economics, University of Calabar, Nigeria, for providing their academic support and resources.
 
Ethical approval
 
Authors declare that this manuscript does not include any studies using animal and human beings.
 
Consent to publication
 
All authors read and approved the final manuscript.

CONFLICT OF INTEREST

On behalf of all authors, I declare that we have no conflict of interests of any sort.

REFERENCES


  1. Abdulai, A. and Eberlin, R. (2001). Technical efficiency during economic reform in Nicaragua: Evidence from farm household survey data. Economic Systems. 25(2): 113- 125.

  2. Adenuga, A.H. Muhammad-Lawal, A. and Rotimi, O.A. (2013). Economics and technical efficiency of dry season tomato production in selected areas in Kwara state, Nigeria; Agris on-line papers in economics and informatics. Czech University of Life Sciences Prague, Faculty of Economics and Management. 5(1): 11-19. 

  3. Adeyemo, R. and Kuhlmann, F. (2009). Resource use efficiency in urban agriculture in Southwestern Nigeria. Tropicultura. 27(1): 49-53. 

  4. Agbachom, E., Ikutal, A., Alawa, D.A., Maria, O., Out, E. and Anoh, R.A.  (2022). impact of agricultural policy regimes on the output of food crops in Nigeria (1980-2015). Global Journal of Agricultural Science. 21(1): 7-11.

  5. Agbachom, E.E., Maria, B.O., Victor, A., Regina, A., Ikutal, A., Ettah, O.I., Ubi, G.M. (2023). Influence of poverty gap and severity index on production among cassava based farmers in Southern Nigeria. Bhartiya Krishi Anusandhan Patrika. 37(5): 1-6. doi: 10.18805/BKAP595.   

  6. Ayinde, I.A, Akerele, D. and Ojeniyi, O.T. (2011). Resource use efficiency and profitability of fluted pumplin production under tropical conditons. International Journal of Vegetable Science. 17(1): 75-82.

  7. Battase, G.E. and Cora, G.S. (1993). Estimation of a production frontier model with application to the pastoral zone of eastern Australia. Australian Journal of Agricultural Economics. 21(7): 145-161.

  8. Bravo-Ureta, B.E. and Rieger, L. (1991). Dairy farm efficiency measurement using stochastic frontier and neoclassic duality. American Journal of Agricultural Economics. 73: 421-428.

  9. Cechura, L.H., Zdenka, M.H. and Michal, M. (2014). Productivity and efficiency differences between czech and slovak milk producers. Review of Agricultural and Applied Economics. 17(2): 17-21.

  10. Central Bank of Nigeria (CBN) (2012). Performance of the agricultural sub sector of the Nigeria; economy. CBN Publication, Abuja, Nigeria.

  11. Dey, M.M., Paraguas, F.J, Bimbao, G.B. and Regaspi, B. (2000). Technical efficiency of tilapia growout pond operations in the Philippines. Aquacultural Economic Management. 4(1-2): 33-47.

  12. Etim, N.A., Udoh, E.J. and Awoyemi, T.T (2005). Measuring Technical efficiency of urban farms in uyo metropolis. Global Journal of Agricultural Science. 4: 19-95.

  13. Ettah, O.I. and Stella, O.A.  (2018). Allocative and technical efficiencies among small holder cocoyam farmers in cross river state central agricultural zone, Nigeria. Journal of Agriculture and Crops. 4(6): 68-72.

  14. Ettah, I.O., Emmanuel, E., Ikutal, A.A and Ubi, G.M. (2020). Determinants of poverty status among crop/fish farmers in southern Nigeria. Annual Research and Review in Biology. 33(5): 1-12.

  15. Ettah, O.I., Bassey, O.U., Victor, B.A. and Francis, A.B. (2022). Resource use in sweet potato production in Delta State, Nigeria: A technical efficiency approach. Global Journal of Agricultural Sciences. 21(1): 1-6.

  16. Goni, M., Umar, A.S.S. and Usman, S. (2013). Analysis of resource- use efficiency in dry season vegetable production in jere, Borno State, Nigeria. Journal of Biology, Agriculture and Healthcare. 3(19): 18-23. 

  17. Jinghan, M.C. (2009). Advanced Economics Theory 13th Edition. Vrinda Publication Ltd Delhi, India.

  18. Jondrow, J.C., Lovell, A.K., Materov, S. and Schmidt, P. (1982). The estimation of technical inefficiency in the stochastic frontier production function model. Journal of  Econometrics. 19(12): 2333-238.

  19. Kalisajan, K.P. and Shand, R.T. (1994). Modeling and measuring economic efficiency under risk. Indian Journal of Agricultural Economics. 49(4): 579-590.

  20. Kumari, P. and Kumar, R. (2023). Resource conservation technologies in era of climate change: A review.  Agricultural Reviews. 44(3): 336-342. doi: 10.18805/ag.R-2365.

  21. Kuye, O.O. and Ettah, O.I. (2016). Contribution of urban mixed cropping in household food security in Nigeria and around the globe. International Journal of Environment, Agriculture and Biotechnology. 1(2): 284-301.

  22. Meena, L.R., Kochewad, S.A., Kumar, D., Suresh, M., Meena, S.R., Anjali. (2024). Development of sustainable integrated farming systems for small and marginal farmers and ecosystem ser1vices- A comprehensive review. Agricultural Science Digest. 44(3): 391-397. doi: 10.18805/ag.D-5961.

  23. National Economic Empowerment and Development Strategy (NEEDS), (2022). Economic Status of Nigeria: Agricultural perspective. National Planning Commission Abuja, Nigeria. 9(4): 10-15.

  24. National Population Commission, (2011). Census Enumeration Survey N. P. C. Abuja, Nigeria. 26-8. Nigeria.

  25. Sagar, L., Sagar, M., Sultan, S., Masina, S. (2023). Influence of precision nutrie2nt management on dry matter accumulation and partitioning of rice in Southern Odisha. Agricultural Science Digest. 43(6): 767-775. doi: 10.18805/ag.D- 5822.

  26. Tanko, L. Ajani, R. and Adeniyi, (2012). Relative technical efficiency of credit and non-credit users in rice-based enterprises in Lavun LGA of Niger State, Nigeria. International Journal of Agricultural Economics and Rural Development. 5(4): 2012.  

  27. Qamar, A., Ashfaq, M. and Khan, M.T.I. (2016). Analysis of off- season cucumber production efficiency in Punjab: A DEA. Journal of Experimental Biology and Agricultural Sciences. doi: http://dx.doi.org/10.18006/2016.4(Issue6) .653.661.

  28. Zhang, T., Li, X., Yang, Y., Guo, X., Feng, Q., Dong, X., Chen, S. (2019). Genetic analysis and QTL mapping of fruit length and diameter in a cucumber (Cucumber sativus L.) recombinant inbred line (RIL) population. Scientia 

  29.   Horticulturae. 250: 214-222. doi: 10.1016/j.scienta.2019. 01.062. S2CID 92837522.
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    Full Research Article

    Efficiency Measure (Technical and Profit) in Cucumber (Cucumis sativus L.) Production among Small Holder Farmers in South-South Nigeria

    O
    O.I. Ettah1,*
    0000-0002-7116-1798
    E
    E.E. Uwah1
    E
    E.O. Edet1
    B
    B.A. Fakuta1
    G
    G.I. Ettah2
    1Department of Agricultural Economics, Faculty of Agriculture, University of Calabar Nigeria.
    2Department of Public Administration, Faculty of Management Sciences, University of Calabar Nigeria.

    ABSTRACT

    Background: Efficiency measure (technical and profit) in cucumber (Cucumis sativus L.) production among small holder farmers in South-South Nigeria was conducted. Technical and profit efficiencies are indicators of overall efficient production. Cucumber can contribute to economic empowerment if efficiently produced due to the high relative unit price of the crop.

    Methods: Multi-stage random sampling techniques (three-stage) was adopted in the selection of respondents for the study. The data collected for the study were analyzed using stochastic frontier production function model for objectives one and two and stochastic frontier profit function model to realize objective three.

    Result: Result of analysis showed that technical efficiency of sampled cucumber farmers was less than 1, implying that the farmers were producing below the maximum frontier output. The least technically efficient cucumber farmers had a technical efficiency of 0.32 and the most technically efficient ones had 0.94. Cucumber seedlings, labour and fertilizers were positive and significant at 5%, to affect technical efficiency of cucumber farmers and agrochemical at 1%. For technical inefficiency of cucumber farmers, age of farmers and farm size were negative and significant at 5% level, while household size, educational qualification, non-farm income and farming experience were positive and significant at 5% level and type of cropping was positive and significant at 10% level. Determinants of profit efficiency of cucumber farmers showed that age (0.37) was positive and significant at 1% level. A direct and significant relationship was found between education (0.67) and profit inefficiency of the farmers. Experience (-0.21) was negative and significant, household size (0.58) was positive and significant at 10% level. The following were recommended: farmers should invest in cucumber production for its efficiency, profitability and economic value.

    INTRODUCTION

    Cucumber (Cucumis sativus L.) is a widely-cultivated creeping vine plant in the family cucurbitaceae that bears cylindrical fruits, which are used as culinary vegetables. The crop roots in the ground and grows up on supported frames, wrapping around supports with thin, spiraling tendrils. Qamar et al. (2016) noted that the plant may also root in a soilless medium, whereby it will sprawl along the ground instead of a supporting structure. The vine has large leaves that form a canopy over the fruits, which consist of 95% water and is rich in nutrients, low in calories but high in many important vitamins and minerals. Cucumber contains antioxidants, promotes hydration and may aid in weight loss and lowers blood sugar among other health benefits (Zheng et al., 2019). It is real versatile vegetable because of variety in their use from salad to pickles as well as from digestive aids to beauty products. It was found useful against human constipation and improvement in digestion aa well as used as a cooling food in summer. A fresh cucumber provides vitamin C, niacin, iron, calcium, thiamine, fibers and phosphorus. Cucumber is an essential part of agriculture in the area because it is considered a source of livelihood and income. This is why the plant has made significant incursion into the diets of small holder farmers of the study area and hence underscores the need for this study. The fruit of typical cultivars of cucumber is roughly cylindrical, but elongated with tapered ends and may be as large as 62 centimeters (24 in) long and 10 centimeters (4 in) in diameter (Fig 1).

    Fig 1: Cucumber fruit.


           
    Technical and profit efficiencies are indicators of overall efficient production, with attendants benefits to the farmer and society (Abdulai and Eberlin, 2001 and Meena, 2024). The efficiency, with which farmers use available resources and improved technologies, is important in agricultural production Agbachom et al. (2022). According to Ettah et al. (2020) the efficient use of farm resources is an important part of agricultural sustainability and a prerequisite for optimum farm production. The authors went on to assert that inefficiency in resource use can distort food availability and security. Efficiency measurement as it is being studied here is important because it leads to substantial resource savings and good profit margins. Ettah and Ani (2018) noted that technically efficient production is defined as the maximum quantity of output attainable by a given input. But Cechura et al. (2014) and Jinghan (2009) noted that technical efficiency is the ability of a firm to maximize output for a given set of resource inputs. Profit efficiency on the other hand overcomes the traditional measures of a farm’s performance such as return on investment (ROI) and provides an innovative way of performance measure (Jondrow et al., 1982). This efficiency measures the farmer’s ability or that of the farm to manage its resources and produce outputs with greater economic value and hence more income to the farm, this is why it is considered the better predictor for evaluating the overall performance of farm production (Adenuga et al., 2013 and Kumar and Kumar, 2023).
           
    Cucumber can contribute to economic empowerment if efficiently produced due to the high relative unit price of the commodity compared to local fruit vegetables. Inefficiency in the use of available scarce resources for the crops’ production has been the bane of increased production of the crop in the area. Crop productivity depends on the efficient use of resources used in its production process. Empirical studies on the technical and profit efficiencies of vegetables in various regions of Nigeria focused on tomato, pumpkin and watermelon. None of the studies examined the economics and determinants of the technical and profit efficiencies of cucumber production in the study area. It is against this backdrop that this study was conducted and sought to realize the following specific objectives:
    i. Estimate the technical efficiency of cucumber farmers.
    ii. Determine the factors affecting technical efficiency.
    iii. Examine the determinants of profit efficiency of cucumber farmers.

    MATERIALS AND METHODS

    This research study was conducted between 2022 to 2025 and the analysis carried out in the Department of Agricultural Economics Econometric centre of the University of Calabar, Nigeria. The rationale of the study being to estimate the overall efficiency of cucumber production in South-South Nigeria (SSN).
     
    Study area
     
    The study area is (SSN) in West Africa and purposively chosen for its large-scale contribution to Nigeria’s cucumber production and the peculiarity of efficiency related problems in the region. The region is one of the six geopolitical divisions of Nigeria, a country on the Gulf of Guinea and regarded as the most populous in Africa and the sixth in the world (NEEDS, 2022). It comprises six states as follows: Akwa Ibom, Bayelsa, Cross River, Edo and Rivers.
           
    The region stretches along the Atlantic seaboard from the Bight of Benin coast in the west to the Bight of Bonny coast in the east. It encloses much of the Niger Delta, which is instrumental in the environment and economic development of the region. Although the South-South represents only 5% of Nigerian territory, it contributes greatly to the Nigerian economy due to extensive agricultural, oil and natural gas reserves. The region has a population of about 26 million people, around 12% of the total population of the country (NPC, 2011). The study area has a tropical climate with variable rainy and dry seasons, with mean annual rainfall between 1,300 mm to 3,000 mm and temperature range of 23°C and 37°C. According to Central Bank of Nigeria (CBN) (2012) the vegetation of South-South region parades four distinct features: Mangrove Swamp (wetland), rainforest, derived savannah and parkland. The type of soil found in the area is deep laterite fertile and dark clayey basalt suitable for agricultural production.
     
    Sampling procedure and data collection
     
    Multi-stage random sampling techniques (three-stage) was adopted in the selection of respondents for the study. The six States of the region which reflected the demarcation structure of the area were covered. In the first stage, three States were selected randomly from the six States of the region, the criteria for the selection were the States with preponderance of cucumber production. The second stage involved the random selection of three Local Government Areas (LGA’s) in the three states earlier selected making a total of nine local government areas in the sample. The third stage involved a random selection of ten cucumber farmers from each of the nine LGA’s previously selected giving a total of 120 respondents for the study.
           
    Data required for this study were generated from primary sources. The primary data were collected using a set of pre-tested structured questionnaires. The questionnaires were administered by well-trained enumerators (farmers), who were conversant with the selected locality. Primary data were also obtained through personal contact, oral interviews, etc. A pilot study was conducted as adopted by Agbachom et al. (2023) where enumerators were used for pre-testing of the questionnaire. This was to avoid inconsistency and incomplete response and also ensure clear understanding of the instrument as well as possession of both face and content validity. In other to check the consistency of the measuring instrument over time, reliability test was conducted using the test-retest method. The same questionnaires were given to the same respondent at two points in time (an interval of seven days) and the scores were compared.
     
    Data analysis
     
    The data collected for the study were analyzed using stochastic frontier production function model for objectives one and two and stochastic frontier profit function model to realize objective three. The models were specified as follows:
     
    Stochastic frontier production function model for technical efficiency in cucumber farmers
     
    The production technology of the cucumber farmers was assumed to be specified by the Cobb-Douglas frontier production function. Following Ettah et al., (2020), as cited in Bravo-Ureta and Rieger (1991), the Cobb-Douglas stochastic frontier production function model used was explicitly specified as:
     
    lnY = βo + β1ln x1 + β2ln x2 + l3Lnx3 + β4 lnx4 + β5 lnx5 +β6lnx6+ - U1                                        
     
     
    Where,
    ln = Logarithm to base.
    Y= Output of cucumber (Kg).
    x1= Cucumber seeds (kg).
    x2= Hired labour (N/man day).
    x3= Fertilizer (kg).
    x4= Agrochemicals  (N).
    x5= Farm size (Hectare).      
    x6= Capital  inputs (N).
    βo = Constant term.
    β0-β1............. β6 = Regression coefficients.
    Ui= Random errors which were assumed to be independent and identically distributed having N (0, δ2)
    Ui= Non-negative random variables associated with technical inefficiency.
           
    It is assumed that the technical efficiency effects are independently distributed and arise by truncation at (zero) of the normal distribution with mean U1 and variances δ2, where U1 is specified thus;
    Ui=  δ0 + δ, z1 + δ2z2iδ3iz3i + δ424i + δ5z5i + δ6z6i + δ7z7i + δ8z8i + S 
                        
     
    Ui = Technical inefficiency of the ith farmer.
    Z1= Age of farmer (years).
    Z2= Level of education (No of years spent in school).
    Z3= Farming experience (years).
    Z4= Household size (number).
    Z5= Extension contact (number).
    Z6= Credit status (dummy variable, 1 for access, zero otherwise).
    Z7= Membership of cooperative (1 for membership, zero otherwise).
    Z8= Sex (binary variable, male = 1, female = 0).
    S = Error term.
    δ0 - δ8= Parameters.
           
    The above model was incorporated in the frontier model in determining the technical inefficiency of cucumber farmers. This was done with the belief that the variables have direct influence on the level of efficiency (Ettah and Ani, 2018) as cited in Battesse and Cora (1993) and Kalisajan and Shad (1994).
     
    Stochastic frontier profit function model
     
    The stochastic frontier profit function was used to achieve objective three as earlier stated using Cobb-Douglas functional form which was specified as follows:
    Cobb-Douglas
     
    lnπ  = βoY* + β1ln x1 + β2ln x2 + l3Lnx3 + β4 lnx4 + β5 lnx5 +β6lnx6 +V - U1.                                                
     
    ln= Logarithm to base.
    π = Profit of cucumber (N).
    X1= Land rent per ha.
    X2= Cost of hired labour used in cucumber production per ha.
    X3= Price of cucumber seedlings (N).
    X4= Price of Agrochemical per litre.
    X5= Price of fertilizer per kg.
    X6= Price of capital inputs (N).
    U1= Error term.
    βo= Constant term.
    β1-β2 ............... β6 = Regression coefficients.
    Vi = Random variables which are assumed to be independent of Ui and normally distributed with zero mean and constant variance Vi-N (0, δ2) which are non-negative random variables and are assumed to account for technical inefficiency in production and are often assumed to be independent of Vi, such that U is the non-negative truncated (at Zero) U of half normal distribution with { N (0, δ2)}.
    The determinant of profit inefficiency is defined by:
     
    Ui=  δ0 + δ, z1 + δ2z2i + δ3iz3i + δ4z4i + δ5z5i + δ6z6i + δ7z7i  +
     
     
    Where,
    Ui= Cucumber profit inefficiency.
    Z1= Farmers’ age (years).
    Z2= Farming experience (years).
    Z3= Education (years).
    Z4= Training (1 if received training, 0 otherwise).
    Z5= Membership of farmers’ association (1, yes, 0, no).
    Z6= Household size (no. of persons).
    Z7= Sex (1, Male, 0, Female).
    δ0 - δ7= Parameters.

    RESULTS AND DISCUSSION

    Estimation of technical efficiency of cucumber farmers
     
    A very important characteristic of production frontier model is its ability to estimate technical efficiency of production. The result is as shown in Table 1 indicated that, technical efficiency of sampled farmers is less than 1. This implies that cucumber farmers are producing below the maximum frontier output. The range of technical efficiency showed that 1.7% of the farmers had technical efficiency between the range of 0.31-0.40 and 0.41- 0.50 respectively, 5.0% had technical efficiency of 0.51-0.60, 12.5% had technical efficiency of 0.61-0.70, 61.7% had technical efficiency of 0.71-0.80, 15.8% had technical efficiency of 0.81- 0.90 and again 1.7% had technical efficiency of 0.91-1.00. The least technically efficient farmer had a technical efficiency of 0.32 which means that the farmer has to increase his level of production given his inputs and technology to at least 68% for him to operate at the production frontier while the most technically efficient farmer had technical efficiency of 0.94 which means that he has to increase his production at least by 6% for him to operate on the production frontier and be fully efficient. The mean technical efficiency was 0.72; this implies that all sampled farmers will need to increase production at least by 28% given the available mix of inputs at a given technology for it to operate on the production frontier. The mean of the worst 10 technically efficient farmer was 0.52 while the mean of the best 10 technically efficient farmer had a technical efficiency of 0.9. The result is in line with that of Adeyemo and Kuhlmann (2009) and Ayinde et al.  (2011) in their study of resource use efficiency in urban agriculture in Southwestern Nigeria and resource use efficiency and profitability of fluted pumpkin production under tropical conditions respectively, but contravenes that of Ettah et al. (2022) and Meena (2024) in their study resource use in sweet potato production in Delta State, Nigeria.  

    Table 1: Technical efficiency distribution of cucumber farmer.


        
    Factors affecting technical efficiency of cucumber farmers
     
    Result of factors affecting technical efficiency are shown in Table 2 shows the result of the maximum likelihood estimates (MLEs) of the stochastic frontier production function for cucumber farmers. The estimates of sigma squared (σ2) for cucumber farmers were 4.3672. This was significant at 5% probability level, indicating that it is   significantly different from zero. It assures us of the goodness-of-fit as well as the correctness of the composite error term. The value of the gamma (γ) 0.7619 showed that the unexplained variation in output of cucumber farmers is the major source of random errors. It also indicates that about 76 per cent of the variation in output of cucumber is caused by inefficiency of the producers. This result confirms the presence of one-sided error components in the model and hence makes the use of Ordinary Least Square (OLS) inadequate in estimating the production function.

    Table 2: Factors affecting technical efficiency.


           
    The estimates of the parameters of the production function are; cucumber seedings, labour and fertilizers which were positive and significant at 5%, this implies that an increase or decrease in seedlings, labour and fertilizer will result to a corresponding increase or decrease in output of cucumber farmers respectively. Agrochemical was found to be positive and significant at 1% level of significance while capital input was positive and not significant. Factors affecting technical inefficiency of cucumber farmers are shown in the lower part of Table 2. Age of farmers and farm size were negative and significant at 0.05 levels of probability, while household size, educational qualification and farming experience were all positive and significant at 5% levels of significance and type of cropping was positive and significant at 10% level of significance. Non-farm income was positive and significant at 5% level of probability. This means that unit increase in these variables would increase technical inefficiency of the farmers and hence decreasing their technical efficiency. The larger the household size the more resources are diverted to non - farm activities like medical care, education, welfare, etc. This development causes farmers to be inefficient in the use of these resources for cucumber production. The result agrees with findings of Cechura et al., (2014) that family demands affect the efficient use of resources to farm production.         
           
    The more farmers are educated, the more they turn away from cucumber farming, to other businesses perceived to bring quicker returns on investment. The less educated ones who cannot efficiently utilize resources dominate the farm industry. Highly experienced farmers tend to stick to their old methods of production which may be inefficient Ettah et al. (2022). Cucumber farmers do not invest their non-farm income in their farm, they rather channel the income to high returning investments. This result agrees with that of Sagar et al. (2023) who found out that farmers give priority to investments outside agriculture because they are relatively fast in return to capital.
     
    Determinants profit efficiency of cucumber farmers
     
    The parameter estimates of the determinants of profit efficiency of cucumber farmers are presented in the upper and lower section of Table 3 respectively.

    Table 3: Determinants of profit efficiency and inefficiency cucumber farmers.


           
    The analysis of profit inefficiency effect showed a significant gamma (γ = 0.83). This implies that 83% deviation from maximum profit obtainable was as a result of inefficiency of the cucumber farmers rather than random error or variability. The signs and significance of the estimated coefficients in the inefficiency model have important implications on profit efficiency of the farmers. The estimated coefficient for age (0.37) was positive and significant at 1% level. The positive relationship implies that as age of cucumber farmers increases, the level of profit inefficiency tends to increase thereby decreasing profit efficiency. This could be that as the cucumber farmers get older, the less efficient his labour, managerial abilities and supervision. This finding is in line with the work of Cechura et al. (2014) and Tanko and Adeniyi (2012) where age positively contributed to profit inefficiency. A direct and significant relationship was found between education (0.67) and profit inefficiency of cucumber farmers. This implied that an increase in the level of education increased the level of profit inefficiency (i.e. decrease profit efficiency). The positive value obtained is unexpected as cucumber farmers may go in search of white-collar jobs thereby neglecting cucumber farming or paying little or no attention to it. This finding disagrees with the work of (Ettah and Ani 2018 and Dey et al., 2000) that education decreases profit inefficiency in rice farming. The findings agree with that of Adeyemo and Kuhlmann (2009) and Ayinde et al. (2011) that education increases profit inefficiency in crop farming. The estimated coefficient for farming experience (-0.21) was negative and significant implying that, increase in farming experience tends to decrease the level of profit inefficiency (i.e. increase profit efficiency). This finding is in consonance with the findings of Qamar et al. (2019) who found that increase in farming experience decrease profit inefficiency of farmers in their study: analysis of off-season cucumber production efficiency in Punjab, India.
           
    Household size (0.58) had positive and significant relationship with profit inefficiency at 10% probability level. This implies that, increase in household size increases profit inefficiency (i.e. decrease profit efficiency) of cucumber farmers. This result is in congruence with findings of (Etim et al. (2005) who observed a positive relationship between household size and profit efficiency in urban farms in Uyo Metropolis of Akwa Ibom State, Nigeria. This is however contrary to the findings of Adenuga et al. (2013) who found household size to increase profit efficiency among dry season tomato producers in selected areas in Kwara State, Nigeria. The result further showed a negative and significant relationship between membership of association and profit inefficiency. Membership of association decreases profit inefficiency and rather increases profit efficiency. This is expected as cucumber farmers’ membership of association could afford them the opportunity of interacting with other farmers thereby exchanging information on improved technology in arable crop farming. Although the result disagrees with the findings of Kuye and Ettah (2016) and Goni et al. (2013). In conclusion, age, education and household size had positive impact on profit inefficiency and this is contrary to apriori expectation regarding the roles of these factors.

    CONCLUSION

    Technical efficiency of sampled cucumber farmers was less than 1, implying that the farmers were producing below the maximum frontier output. The least technically efficient cucumber farmers had a technical efficiency of 0.32 which meant that the farmers have to increase their level of production given the available inputs and technology to at least 68% for them to operate at the production frontier. The most technically efficient cucumber farmers had technical efficiency of 0.9. Cucumber seedlings, labour and fertilizers were positive and significant at 5%, to affect technical efficiency of cucumber farmers and agrochemical at 1% level of significance.  For technical inefficiency of cucumber farmers, age of farmers and farm size were negative and significant at 5% level of significance, while household size, educational qualification, non-farm income and farming experience were all positive and significant at 5% levels of significance and type of cropping was positive and significant at 10% level of significance. Estimates of the determinants of profit efficiency of cucumber farmers showed that age (0.37) was positive and significant at 1% level. A direct and significant relationship was found between education (0.67) and profit inefficiency of cucumber farmers. Experience (-0.21) was negative and significant. Household size (0.58) had positive and significant at 10% level. The result also showed a negative and significant relationship between membership of association and profit inefficiency. Based on the results of the study, the following are recommended: farmers should be encouraged to invest in cucumber production for its technical efficiency, profitability and economic value, inputs should be made available and at affordable price especially new varieties of cucumber seedlings and cucumber farmers should be guided on the type of cropping to undertake so as to increase their technical efficiency.

    ACKNOWLEDGEMENT

    The authors would like to thank all the authorities of the Department of Agricultural Economics, University of Calabar, Nigeria, for providing their academic support and resources.
     
    Ethical approval
     
    Authors declare that this manuscript does not include any studies using animal and human beings.
     
    Consent to publication
     
    All authors read and approved the final manuscript.

    CONFLICT OF INTEREST

    On behalf of all authors, I declare that we have no conflict of interests of any sort.

    REFERENCES


    1. Abdulai, A. and Eberlin, R. (2001). Technical efficiency during economic reform in Nicaragua: Evidence from farm household survey data. Economic Systems. 25(2): 113- 125.

    2. Adenuga, A.H. Muhammad-Lawal, A. and Rotimi, O.A. (2013). Economics and technical efficiency of dry season tomato production in selected areas in Kwara state, Nigeria; Agris on-line papers in economics and informatics. Czech University of Life Sciences Prague, Faculty of Economics and Management. 5(1): 11-19. 

    3. Adeyemo, R. and Kuhlmann, F. (2009). Resource use efficiency in urban agriculture in Southwestern Nigeria. Tropicultura. 27(1): 49-53. 

    4. Agbachom, E., Ikutal, A., Alawa, D.A., Maria, O., Out, E. and Anoh, R.A.  (2022). impact of agricultural policy regimes on the output of food crops in Nigeria (1980-2015). Global Journal of Agricultural Science. 21(1): 7-11.

    5. Agbachom, E.E., Maria, B.O., Victor, A., Regina, A., Ikutal, A., Ettah, O.I., Ubi, G.M. (2023). Influence of poverty gap and severity index on production among cassava based farmers in Southern Nigeria. Bhartiya Krishi Anusandhan Patrika. 37(5): 1-6. doi: 10.18805/BKAP595.   

    6. Ayinde, I.A, Akerele, D. and Ojeniyi, O.T. (2011). Resource use efficiency and profitability of fluted pumplin production under tropical conditons. International Journal of Vegetable Science. 17(1): 75-82.

    7. Battase, G.E. and Cora, G.S. (1993). Estimation of a production frontier model with application to the pastoral zone of eastern Australia. Australian Journal of Agricultural Economics. 21(7): 145-161.

    8. Bravo-Ureta, B.E. and Rieger, L. (1991). Dairy farm efficiency measurement using stochastic frontier and neoclassic duality. American Journal of Agricultural Economics. 73: 421-428.

    9. Cechura, L.H., Zdenka, M.H. and Michal, M. (2014). Productivity and efficiency differences between czech and slovak milk producers. Review of Agricultural and Applied Economics. 17(2): 17-21.

    10. Central Bank of Nigeria (CBN) (2012). Performance of the agricultural sub sector of the Nigeria; economy. CBN Publication, Abuja, Nigeria.

    11. Dey, M.M., Paraguas, F.J, Bimbao, G.B. and Regaspi, B. (2000). Technical efficiency of tilapia growout pond operations in the Philippines. Aquacultural Economic Management. 4(1-2): 33-47.

    12. Etim, N.A., Udoh, E.J. and Awoyemi, T.T (2005). Measuring Technical efficiency of urban farms in uyo metropolis. Global Journal of Agricultural Science. 4: 19-95.

    13. Ettah, O.I. and Stella, O.A.  (2018). Allocative and technical efficiencies among small holder cocoyam farmers in cross river state central agricultural zone, Nigeria. Journal of Agriculture and Crops. 4(6): 68-72.

    14. Ettah, I.O., Emmanuel, E., Ikutal, A.A and Ubi, G.M. (2020). Determinants of poverty status among crop/fish farmers in southern Nigeria. Annual Research and Review in Biology. 33(5): 1-12.

    15. Ettah, O.I., Bassey, O.U., Victor, B.A. and Francis, A.B. (2022). Resource use in sweet potato production in Delta State, Nigeria: A technical efficiency approach. Global Journal of Agricultural Sciences. 21(1): 1-6.

    16. Goni, M., Umar, A.S.S. and Usman, S. (2013). Analysis of resource- use efficiency in dry season vegetable production in jere, Borno State, Nigeria. Journal of Biology, Agriculture and Healthcare. 3(19): 18-23. 

    17. Jinghan, M.C. (2009). Advanced Economics Theory 13th Edition. Vrinda Publication Ltd Delhi, India.

    18. Jondrow, J.C., Lovell, A.K., Materov, S. and Schmidt, P. (1982). The estimation of technical inefficiency in the stochastic frontier production function model. Journal of  Econometrics. 19(12): 2333-238.

    19. Kalisajan, K.P. and Shand, R.T. (1994). Modeling and measuring economic efficiency under risk. Indian Journal of Agricultural Economics. 49(4): 579-590.

    20. Kumari, P. and Kumar, R. (2023). Resource conservation technologies in era of climate change: A review.  Agricultural Reviews. 44(3): 336-342. doi: 10.18805/ag.R-2365.

    21. Kuye, O.O. and Ettah, O.I. (2016). Contribution of urban mixed cropping in household food security in Nigeria and around the globe. International Journal of Environment, Agriculture and Biotechnology. 1(2): 284-301.

    22. Meena, L.R., Kochewad, S.A., Kumar, D., Suresh, M., Meena, S.R., Anjali. (2024). Development of sustainable integrated farming systems for small and marginal farmers and ecosystem ser1vices- A comprehensive review. Agricultural Science Digest. 44(3): 391-397. doi: 10.18805/ag.D-5961.

    23. National Economic Empowerment and Development Strategy (NEEDS), (2022). Economic Status of Nigeria: Agricultural perspective. National Planning Commission Abuja, Nigeria. 9(4): 10-15.

    24. National Population Commission, (2011). Census Enumeration Survey N. P. C. Abuja, Nigeria. 26-8. Nigeria.

    25. Sagar, L., Sagar, M., Sultan, S., Masina, S. (2023). Influence of precision nutrie2nt management on dry matter accumulation and partitioning of rice in Southern Odisha. Agricultural Science Digest. 43(6): 767-775. doi: 10.18805/ag.D- 5822.

    26. Tanko, L. Ajani, R. and Adeniyi, (2012). Relative technical efficiency of credit and non-credit users in rice-based enterprises in Lavun LGA of Niger State, Nigeria. International Journal of Agricultural Economics and Rural Development. 5(4): 2012.  

    27. Qamar, A., Ashfaq, M. and Khan, M.T.I. (2016). Analysis of off- season cucumber production efficiency in Punjab: A DEA. Journal of Experimental Biology and Agricultural Sciences. doi: http://dx.doi.org/10.18006/2016.4(Issue6) .653.661.

    28. Zhang, T., Li, X., Yang, Y., Guo, X., Feng, Q., Dong, X., Chen, S. (2019). Genetic analysis and QTL mapping of fruit length and diameter in a cucumber (Cucumber sativus L.) recombinant inbred line (RIL) population. Scientia 

    29.   Horticulturae. 250: 214-222. doi: 10.1016/j.scienta.2019. 01.062. S2CID 92837522.
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