Agricultural Science Digest

  • Chief EditorArvind kumar

  • Print ISSN 0253-150X

  • Online ISSN 0976-0547

  • NAAS Rating 5.52

  • SJR 0.156

Frequency :
Bi-monthly (February, April, June, August, October and December)
Indexing Services :
BIOSIS Preview, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Submit

Full Research Article

Agricultural Science Digest, volume 43 issue 4 (august 2023) : 524-529

​Impact of Policy Change on Technical Efficiency: Evidence from Small Scale Food Crop Farmers in Kellem Wollega, Ethiopia

Melkamu Kena Terefe1,*
1Department of Economics, Madda Walabu University, P.O. Box: 247, Ethiopia.
Cite article:- Terefe Kena Melkamu (2023). ​Impact of Policy Change on Technical Efficiency: Evidence from Small Scale Food Crop Farmers in Kellem Wollega, Ethiopia . Agricultural Science Digest. 43(4): 524-529. doi: 10.18805/ag.DF-389.

ABSTRACT

Background: An effort was made by the Ethiopian government to increase the level of technical efficiency of farmers across the country.  However, due to climate change, smallholder farmers were facing challenges to increase technical efficiency in crop production. Adaptation to climate change is crucial to uphold and increase food crop productivity. This study analysis the impact of climate change adaptation and policy issues on food crop production efficiency in Kellem Wollega, Ethiopia. 

Methods: The data was gathered from 400 randomly selected food crop smallholder farmers. The Cobb-Douglas production function was used by including the climate change adaptation measures as explanatory variables in technical inefficiency. Simulation was made to adaption measures that can be influenced by the policy variables to see their impact on the level of technical efficiency.

Result: The finding show that the use of adaptive practices (multiple crop type, improved crop varieties, adjusting planting dates and irrigation) had a significant and positive effect on technical efficiency whereas land fragmentation reduces efficiency level. Regarding simulation of policy variables the result show that the mean technical efficiency would increase with rising level of improved crop varieties, adjusting planting dates and irrigation practices. The results of the simulation of land fragmentation climate change adaptation variables show that the mean technical efficiency declines as a result of land fragmentation. Empirical results reveal that with appropriate policy intervention (climate change adaptation measures) the technical efficiency level of food crop farmers can be enhanced.

INTRODUCTION

Climate change is endangering the agricultural productivity (Umunakwe et al., 2015) and due to constriants to adaptaion developing countries reamined vulnearbel to the impcat of climate change (Boko et al., 2007). But with adaptation the level of vulnerability can be moderated and/or enhance the positive impacts of climate change (Mendelsohn et al., 1994; Rosenzweig and Parry, 1994; Easterling, 1996; Mendelsohn and Dinar, 1999; Smith et al., 1999; Fankhauser, 2017).
 
Farmers especially food crop farmers can reduce the potential damage by making tactical responses to climate changes (Reilly, 1996; Smit et al., 2000; Smit and Skinner, 2002; Adger et al., 2003). The adaptation options range from management alterations by farmers themselves (changing timing of operations, adoption of conservation and diversification in production systems) to investment of funds by public agencies (in order to develop or improve irrigating schemes, modification of farm support programs and development of new plant varieties (Klein and Maciver, 1999; Adger et al., 2003; Bradshaw et al., 2004).
 
The stochastic frontier production function (Meeusen and van Den Broeck, 1977; Aigner and Chu, 1986) has been contributing to the econometric modelling of production and estimation of technical efficiency of firms. The econometric approach based on the deterministic parameter frontier (Aigner and Chu, 1986) has been used to develop stochastic frontier models. This stochastic frontier involved random components of technical inefficiency and random error  (Aigner et al., 1977; Meeusen and van Den Broeck, 1977; Oren and Alemdar, 2006; Ahmed et al., 2018).
 
In literature, Stochastic Frontier Analysis is often used in agricultural economics because of the inherent nature of agricultural production that is the uncertainty and variability associated with agricultural production due to change in climate (weather, water, rain fall, pests, diseases, etc.) (Coelli, 1996; Coelli et al., 1998).
 
In this work, we investigate whether climate adaptation measures and simulation of the policy variables increase food crop production technical efficiency in Kellem Wollega, Ethiopia. It is expected that farmers who are more aware and better adapted to climate change will be able to make more efficient use of their resources and thus cope with any adversities. This study adds valuable information for agricultural policy design, as it provides evidence of the impact of alternative adaptation strategies to climate change and its impact on the level of technical efficiency
 

MATERIALS AND METHODS

Study area and data
 
The study was conducted in Kellem Wollega Zone found in the Western part of Oromia Regional States of Ethiopia. It has ten rural districts and one administrative town. The zone is endowed with diverse socio-cultural settings and enjoys diversified climatic types. The area has good climate which is an opportunity to diversify agricultural practice and grow many varied type of crops and raise different livestock. For the purpose of this study 400 food crop producing farmers in production year 2019/20 were randomly selected from four districts. The data collection instrument focused on socioeconomic factors, production level, capital resource and agrochemicals used.
 
Analytical framework and empirical model
 
The data in this study was fitted into Cobb-Douglas function specified as:
Where
ln = Natural logarithms.
β’s  = Coefficients of parameters to be estimated.
Yi = Total value output in US dollar.
Xi = Factors of productions (farm size, labour, fertilizer and other agrochemical, farm equipment and oxen).
vi = Idiosyncratic error that arises from measurement errors in input use and/or yield of production independent and identically distributed as N (0,σv2) random variables.
ui = Non-negative random variables in measuring the technical inefficiency of individual household.

Technical inefficiency effects model:
 
Where
Ui= Inefficiency effect.
i= Coefficients of climate change adaptation strategies and socioeconomic factors.
Zji= Climate change adaptation strategies and socio-economic factors (i.e. hypothesized efficiency changing  variables).

The non-negativity assumption of the inefficiency term (ui), the underlying distribution is non-normal and the error terms are asymmetrically distributed. Hence, the ordinary least squares (OLS) estimator is inefficient. Coelli, (1995) argues that there are two main benefits of using frontier functions as the ones in equation Cobb-Douglas rather than OLS (average functions).

First, frontier functions are based on best performing producers and therefore can show the technology used in production, whereas average functions only shows technology of an average producer. Secondly, frontier functions show the best-practicing technology, whereas average functions show efficiency of producers within the whole group.
       
Lovell, (1993) also argued that MLE technique provides more efficient estimates than the OLS, besides its guarantee of non-negativity assumptions on the error terms. Thus, unknown parameters of frontier models are estimated using the maximum likelihood estimation (MLE) technique.

RESULTS AND DISCUSSION

Test for model specifications
 
Before estimating model parameters checking whether the stochastic production frontier is more appropriate than a conventional production function, i.e. testing whether there exist technical inefficiency in the production process or not is important. In this test the null hypothesis is given as:
 
H0: g = δ0 = δ1 =...δ9 = 0, which states that inefficiency effects are absent from the frontier model, was rejected for the food crop farmers (Table 1).

Table 1: Generalised log likelihood-ratio tests of null hypothesis.


 
Maximum likelihood estimates for parameters of the two estimated models are presented in Table 2. The result shows that farm size, labour, fertilizer and other agrochemicals and oxen are highly significant at 1 per cent level of probability while farm equipment is significant at 5 per cent of significance. Similarly, the study of (Kidane and Ngeh, 2015; Mango et al., 2015; Sherzod et al., 2018) explained that farm area and fertilizer; (Sherzod et al., 2018; Wollie, 2018) labour and (Wollie, 2018) reported that ox are significant 1 per cent .  

Table 2: MLE of production function


 
The estimated value for the γ for the food crop farmers was 0.669 and the values is significant at 1 per cent level of probability. These values indicate that technical inefficiency is highly significant in the food crop production activities in the study area. These results confirm the findings of (Ajibefun et al. 2006; Chiona et al. 2014; Kidane and Ngeh, 2015; Mango et al., 2015; Sherzod et al. 2018; Wollie, 2018) that technical inefficiency is significant in the food crop production activities.
 
Technical efficiency estimates
 
The technical efficiency shows the ability of farmers to derive maximum output from the inputs used in food crop production. The technical efficiency estimates are presented and discussed subsequently (Table 3). The results show high variability in technical efficiency among the food crop farmers in the study area. The computed technical efficiency varies from 0.38 to 0.92 with a mean of 0.79. The result of mean efficiency is similar to the finding of (Pradhan and Mukherjee, 2018). The variation in the level of the technical efficiencies in food crop production implies there is opportunity to improve the current level of technical efficiency by 21 per cent.

Table 3: Distribution of technical efficiency estimates.


 
Impact of adaptation to climate change on technical efficiency
 
This section presents analysis of climate change adaptation strategies that influence technical efficiency in food crop production in the study area.
 
An inverse and statistically significant (at 1 per cent) relationship is found between crop diversification and technical inefficiency of food crop farmers (Table 4). This suggests that food crop farmers in study area who did not plant multiple type of crop experienced higher technical inefficiency. This is can be due to the reason that farmer engaged in multiple crop type is more efficient in allocating their resources like labour and land than his/her counterpart. Thus the adaptation of multiple crop type adaptation strategy would help the farmers to increase their level of technical efficiency.

Table 4: MLE of determinants of technical inefficiency.


 
The estimated coefficient for improved crop varieties is negative and significant at 5 per cent (Table 4). This implies an increase in use of improved crop varieties tends to increase technical efficiency. Policy and programmes regarding the development and provision of improved crop varieties would help the farmers in the study area to left up the level of technical efficiency.

An inverse and statistically significant relationship is found between technical inefficiency and adjusting planting dates of the respondents (Table 4). This implies an increase in adjusting planting dates tends to increase technical efficiency (or decrease technical inefficiency). Adjusting planting dates helps farmers to reduce the loss due to changing climate. Thus farmers capable to adjust their planting dates experienced higher technical efficiency. The availability of information on climate change can help the farmers to adjust the time of planting dates i.e. increase the level of technical efficiency. 
 
A negative and statistically significant relationship, at 10 per cent was found between irrigation practices and technical inefficiency in food crop production in the study area (Table 4). This implies that an increase in irrigation practices tends to increase technical efficiency. In the study area there is no functional irrigation technology. In the study area there is financial constraint that limits small scale famers to adopt irrigation technology. Government policy toward the development of irrigation schemes can help farmers in the study area to increase their level of technical efficiency.
 
The result shows that the coefficient for land fragmentation is positive and significant at 1 per cent level of probability (Table 4). For the positive significant coefficient, it implies that an increase in land fragmentation tends to increase the level of the technical inefficiency (i.e. decrease technical efficiency). The finding shows that land fragmentation decrease level of technical efficiency in the study area. Land fragmentation is related to the use of time and other resource for agricultural production. In the study area farmers with more fragmented farm land are inefficient in using the agricultural resources. As policy intervention land consolidation farming can reduce the problem of land fragmentation and increase the level of technical efficiency.
 
Analysis of policy variables that affect technical inefficiency
 
The impact of selected climate change adaptation strategies (i.e. improved crop varieties, adjusting planting dates, land fragmentation, irrigation practices) on mean technical efficiency of food crop farmers in the study area after simulation is presented in Table 5. These variables are determinants of technical efficiency that could be influenced by policy implementation to improve the technical efficiency. The simulation is done with an increase in the value of these selected variables at 5 per ent, 10 per cent and 20 per cent. The results of the simulations are presented in Table 5.

Table 5: Simulation results of variation in policy variables on mean technical efficiency.


 
The results of simulation of policy variables show that the level of mean technical efficiency would increase with rising level of improved crop varieties, adjusting planting dates and irrigation practices (Table 5). An increase in the availability of improved crop varieties from 5 per cent through 20 per cent raised the mean technical efficiency from 77 per cent to 79 per cent (Table 5). Thus in the study area government can play a great roll to increase the level of technical efficiency of farmers. By institutional arrangement either government or the private sector can provide improved crop varieties, climate change resisting and those crop having short time harvest, to influence current level of technical efficiency.
 
Information on changing climate helps farmers to adjust planting dates of crops. With increase in information regarding the climate and farming experience farmers adjust the planting time. Thus, with increase in practices of adjusting the planting dates from 5 per cent through 20 per cent the mean technical efficiency raised from 79 per cent to 81 per cent.
 
Irrigation is one mechanism farmers practices in response to changing climate mainly rainfall. With increase in irrigation practices from 5 per cent through 20 per cent the mean technical efficiency raised from 79 per cent to 82 per cent. Training and facilitating the adoption of new technology regarding irrigation would increase the level of technical efficiency in the study area.
 
The climate change awareness as one policy variable shows that an increase in climate change awareness from 5 per cent through 20 per cent raised the mean technical efficiency from 80 per cent to 84. The dissemination of information concerning climate change helps farms to adjust time planting crops. Thus, concerned body, the government, private sector and NGOs can provide information of changing climate that would increase the level of technical efficiency of farmers in the study area.
 
Education as one policy variable shows that, an increase in level of education from 5 per cent through 20 per cent raised the mean technical efficiency from 80 to 83 per cent. Thus providing training and technology on irrigation by concerned body would increase the level of technical efficiency of farmers in the study area.

The results of the simulation of land fragmentation climate change adaptation variables show with increase in land fragmentation from 5 per cent through 20 per cent the mean technical efficiency declined from 72 per cent to 63 per cent. Thus to revert this value the government has to take some measures to reduce land fragmentation.
 

CONCLUSION

This study analyzed the effects of climate change adaptation measures and policy variables on technical efficiency of food crops producer in Kellem Wollega. Adaptation strategies such as multiple crop type, improved crop varieties, adjusting planting dates and irrigation have significant contribution on household food crop production technical efficiency in the study area. On the other hand, households that use land fragmentation adaptation strategy experience a reduction in technical efficiency in the study area. Policy/decision makers should therefore consider adaptation strategies that would improve household food crop production technical efficiency. The impact of selected climate change adaptation strategies on mean technical efficiency after simulation has shown that with appropriate policy the level of technical efficiency food crop farmers can be increased. The connection between productivity with the implementation of specific farm-level adaptive practices, as well as with actions that ease adoption barriers deserves additional analyses.
 
Conflict of interest: None.
 

REFERENCES


  1. Adger, W.N. et al. (2003). Adaptation to climate change in the developing world. Progress in Development Studies. 3(3): 179-195. DOI: 10.1191/1464993403ps060oa.

  2. Ahmed, M.H. et al. (2018). Measuring maize production efficiency in the eastern Ethiopia: Stochastic frontier approach. African Journal of Science, Technology, Innovation and Development. 10(7): pp. 779-786. DOI: 10.1080/20421338.2018.15 14757.

  3. Aigner, D. and Chu, S.F. (1986). On estimating the industry production function. American Economic Review. 58: 826-839.

  4. Aigner, D., Lovell, C.A.K. and Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics. 6(1): 21-37. DOI: 10.1016/0304- 4076(77)90052-5.

  5. Ajibefun, I.A., Daramola, A.G. and Falusi, A.O. (2006). Technical efficiency of small scale farmers: An application of the stochastic frontier production function to rural and urban farmers in Ondo State, Nigeria. International Economic Journal. 20(1): 87-107. DOI: 10.1080/10168730500515498.

  6. Boko, M. et al. (2007). Africa. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Inter governmental Panel on Climate Change. [M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, (Eds)], In: Cambridge University Press. Available at: https://cgspace.cgiar.org/handle/10568/17019 (Accessed: 10 December 2018).

  7. Bradshaw, B., Holly, D. and Barry, S. (2004). Farm-level adaptation to climatic variability and change: Crop diversification in the Canadian Prairies. Climatic Change. 67: 119-141. DOI: 10.1007/s10584-004-0710-z.

  8. Chiona, S., Kalinda, T. and Tembo, G. (2014). Stochastic frontier analysis of the technical efficiency of smallholder maize farmers in Central Province, Zambia. Journal of Agricultural Science. 6(10): 108-118. DOI:10.5539/jas.v6n10p108.

  9. Coelli, T.J. (1995). Recent developments in frontier modelling and efficiency measurement. Australian Journal of Agricultural Economics. 39(3): 219-245. DOI:10.1111/j.1467-8489. 1995.tb00552.x.

  10. Coelli, T. (1996). A Guide to FRONTIER Version 4.1: A Computer Programme for Stochastic Frontier Production and Cost Function Estimation. CEPA Working Paper No.7/96, Department of Econometrics, University of New England, Armidale. Available: http://www.uq.edu.au/economics/ cepa/frontier.php [Accessed: 13 May. 2019]

  11. Coelli, T., Rao, D.S.P. and Battese, G.E. (1998). Introduction. In: An Introduction to Efficiency and Productivity Analysis. [Coelli, T., Rao, D.S.P. and Battese, G.E. (eds)], Boston, MA: Springer US, pp. 1-9. 

  12. Coelli, T.J., Rao, D.S.P., O’Donnell, C.J. and Battese, G.E. (2005). An Introduction to Efficiency and Productivity Analysis. 2nd ed. New York, NY, United States: Springer. DOI: 10.1007/b136381 

  13. Easterling, W.E. (1996). Adapting North American agriculture to climate change in review. Agricultural and Forest Meteorology. 80: 1-54. https://doi.org/10.1016/0168-1923(95)02315-1.

  14. Fankhauser, S. (2017). Adaptation to Climate Change. Annual Review of Resource Economics. 9(1): 209-230. DOI: 10.1146/annurev-resource-100516-033554.

  15. Khanal, U. et al. (2018). Do climate change adaptation practices improve technical efficiency of smallholder farmers? Evidence from Nepal. Climatic Change. 147(3): 507-521. DOI:10.1007/s10584-018-2168-4.

  16. Kidane, A. and Ngeh, E.T. (2015). A comparative analysis of technical efficiency of smallholder tobacco and maize farmers in Tabora, Tanzania. Journal of Development and Agricultural Economics. 7(2): 72-79. DOI: 10.5897/JDAE 2014.0616.

  17. Klein, R.J.T. and Maciver, D.C. (1999). Adaptation to climate variability and change: Methodological issues. Mitigation and Adaptation Strategies for Global Change. (4): 189- 198. DOI: 10.1023/A:1009690729283.

  18. Kodde, D.A. and Palm, F.C. (1986). Wald criteria for jointly testing equality and inequality restrictions. Econometrica. 54(5): 1243-1248. DOI: 10.2307/1912331.

  19. Lovell, C.K. (1993). Production Frontiers and Productive Efficiency. In: The Measurement of Productive Efficiency-Techniques and Applications, [Fried, H.O., Lovell, C.K. and Schmidt, S.S., (Eds.)], Oxford University Press, Oxford. pp 3-67.

  20. Mango, N. et al. (2015). A stochastic frontier analysis of technical efficiency in smallholder maize production in Zimbabwe: The post-fast-track land reform outlook. Cogent Economics and Finance. 3(1): 1117189. DOI: 10.1080/23322039.2015.1117189.

  21. Meeusen, W. and van Den Broeck, J. (1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review. 18(2): 435. DOI: 10.2307/2525757.

  22. Mendelsohn, R. and Dinar, A. (1999). Climate change, agriculture and developing countries : does adaptation matter?. The World Bank Research Observer. 14(2 (August 1999), pp. 277-293. DOI: 10.1093/wbro/14.2.277.

  23. Mendelsohn, R., Nordhaus, W. and Shaw, D. (1994). The impact of global warming on agriculture: A Ricardian analysis. American Economic Review. 84(4): 753-71. DOI: 10.1257/ aer.89.4.1046.

  24. Oren, N. M. and Alemdar, T. (2006). Technical efficiency analysis of tobacco farming in Southeastern Anatolia. Turkey Journal of Agriculture and Forestry. 30: 165-172.

  25. Pradhan, K.C. and Mukherjee, S. (2018). Examining technical efficiency in Indian agricultural production using production frontier model. South Asia Economic Journal. 19(1): 22- 42. DOI: 10.1177/1391561418761073.

  26. Reilly, J. (1996). ‘Agriculture in a Changing Climate: Impacts and Adaptation. In: Climate Change 1995: Impacts, Adaptations and Mitigation of Climate Change. [R.T. Watson, M.C. Zinyowera and R.H. Moss (Eds.)], Cambridge University Press: Cambridge, pp. 429-467.

  27. Rosenzweig, C. and Parry, M.L. (1994). Potential impact of climate change on world food supply. Nature. 367(6459): 133-138. https://doi.org/10.1038/367133a0.

  28. Sherzod, B., Kim, K.R. and Lee, S.H. (2018). Agricultural transition and technical efficiency: An empirical analysis of wheat- cultivating farms in Samarkand Region, Uzbekistan. Sustainability. 10: 1-11. DOI:10.3390/su10093232.

  29. Smit, B. et al. (2000). An anatomy of adaptation to climate change and variability. Climatic Change. 45(1): 223-251. DOI: 10.102 3/A:1005661622966.

  30. Smit, B. and Skinner, M. (2002) ‘Adaptation Options in Agriculture to Climate Change: A Topology. Mitigation and Adaptation Strategies for Global Change. 7: 85-114. DOI: 10.1023/ A:1015862228270.

  31. Smith, B. et al. (1999). The Science of adaptation: A framework for assessment. Mitigation and Adaptation Strategies for Global Change. 4: 199-213. DOI: 10.1023/A:1009652531101.

  32. Umunakwe, P.C. et al. (2015). Indigenous practices for climate change adaptation among rural households in Imo State, Nigeria. British Journal of Applied Science and Technology. 8(1): 67-79. https://doi.org/10.9734/BJAST/2015/13193.

  33. Wollie, G. (2018). Technical efficiency of barley production: The case of smallholde farmers in Meket district, Amhara National Regional State, Ethiopia. Journal of Political Science and International Relations. 1(2): 42. DOI: 10.209 44/preprints201801.0253.v1.
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)