Abstract
Food security is a global challenge. With rising world population and demand for food being compounded by resource and arable land constraints, raising the efficiency of food production and use has become increasingly important. While much of the research on food security is focused on farm efficiency and productivity, most neglect post-harvest (PH) handling which is critical in determining the availability of food. In this study, we employ the network Data Envelopment Analysis (DEA) model to evaluate the PH efficiency of milling, using data from Kenya’s rice processing industry. The results show lower efficiency scores when using a network DEA model, which reflects its greater discriminatory power when compared to the standard DEA approach. The study also quantified sources of productive efficiency using a fractional regression model and identified storage space and distance to market as having an impact on drying efficiency; while experience, age of mill, servicing and energy type influenced milling efficiency. The results suggest that policy makers should focus on investing in drying technologies and storage facilities to improve drying efficiency. To improve milling efficiency, policy recommendations include enhancing millers’ access to better technologies, investing in reliable sources of energy and providing PH handling workshops to reduce PH losses.
Similar content being viewed by others
Notes
For studies that use the Malmquist productivity index at industry level, see Lee (2013).
Paddy here refers to unhulled rice grain, which is threshed but unmilled rice grain.
References
Adler, N., Liebert, V., & Yazhemsky, E. (2013). Benchmarking airports from a managerial perspective. Omega, 41, 442−458.
Affognon, H., Mutungi, C., Sanginga, P., & Borgemeister, C. (2015). Unpacking postharvest losses in sub-Saharan Africa: A meta-analysis. World Development, 66, 49–68.
Akther, S., Fukuyama, H., & Weber, W. L. (2013). Estimating two-stage network slacks-based inefficiency: An application to Bangladesh banking. Omega, 41, 88−96.
Ali, J., Singh, S. P., & Ekanem, E. (2009). Efficiency and productivity changes in the Indian food processing industry: Determinants and policy implications. International Food and Agribusiness Management Review, 12, 43–66.
Atera, E. A., Onyango, J. C., Azuma, T., Asanuma, S., & Itoh, K. (2011). Field evaluation of selected NERICA rice cultivars in western Kenya. African Journal of Agricultural Research, 6(1), 60–66.
Avkiran, N. K. (2015). An illustration of dynamic network DEA in commercial banking including robustness tests. Omega, 55, 141−150.
Bagamba, F., Ruben, R., & Rufino, M. (2007). Determinants of banana productivity and technical efficiency in Uganda. In M. Smale & W. K. Tushemereirwe (Eds.), An economic assessment of banana genetic improvement and innovation in the Lake Victoria region of Uganda and Tanzania (pp. 109–128). USA: International Food Policy Research Institute. Washington D.C.
Bandara, J. S., & Yiyong Cai, Y. (2014). The impact of climate change on food crop productivity, food prices and food security in South Asia. Economic Analysis and Policy, 44, 451–465.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429−444.
Coelli, T., Rao, D. S. P., O'Donnell, C. J., & Battese, G. (2005). An introduction to efficiency and productivity analysis. New York, USA: Springer.
FAO. (1996). Rome declaration on world food security and world food summit plan of action. In World food summit, November 13–17, 1996. Rome: Italy.
FAO. (2002). The state of food insecurity in the world 2001. Italy: Rome.
Färe, R. (1991). Measuring Farrell efficiency for a firm with intermediate inputs. Academia Economic Papers, 19, 329–340.
Färe, R., & Grosskopf, S. (1996). Intertemporal production frontiers: With dynamic DEA. Boston, USA: Kluwer Academic Publishers.
Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34, 35–49.
Fleming, E., & Lummani, J. (2001). Analysis of the technical efficiency of cocoa smallholders in the gazelle peninsula, East New Britain Province. Occasional Paper No. 7. Centre for Economic Policy Analysis, University of New England, Armidale, Australia.
Floros, J. (2010). Feeding the world today and tomorrow: the importance of food science and technology. Comprehensive Reiews in. Food Science and Food Safety, 9, 572.
Fu, W.-G., Sun, S., & Zhou, Z.-Y. (2011). Technical efficiency of food processing in China: The case of flour and rice processing. China Agricultural Economic Review, 3, 321−334.
Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38, 398−409.
Government of Kenya (GoK). (2010). Agricultural sector development strategy 2010–2020. Nairobi, Kenya: Ministry of Agriculture.
Gunatilake, H., & Gopalakrishnan, C. (2010). Technical efficiency of sawmilling and the conservation of natural forests: Evidence from Sri Lanka. Journal of Natural Resources Policy Research, 2, 149–169.
Herbert, F. L., & Thomas, R. S. (2004). Network DEA: Efficiency analysis of organizations with complex internal structure. Computers & Operations Research, 31, 1365–1410.
Hodges, R. J., Buzby, J. C., & Bennett, B. (2011). Postharvest losses and waste in developed and less developed countries: Opportunities to improve resource use. The Journal of Agricultural Science, 149, 37−45.
Hoff, A. (2007). Second stage DEA: Comparison of approaches for modelling the DEA score. European Journal of Operational Research, 181, 425–435.
Huang, C.-W., Ho, F.-N., & Chiu, Y.-H. (2014). Measurement of tourist hotels' productive efficiency, occupancy, and catering service effectiveness using a modified two-stage DEA model in Taiwan. Omega, 48, 49−59.
Ironkwe, A., Ewuziem, J., & Ekwe, K., 2014. Gender factors influencing technical efficiency of cassava farmers in Akwa Ibom state, Nigeria. Journal of Agriculture and Food Sciences, 11, 26–36.
IRRI (2016). http://irri.org/our-work/research/value-added-rice/postharvest
Jacob, B. G., Shililu, J., Muturi, E. J., & Novak, R. (2006). Spatially targeting Culex quinquefasciatus aquatic habitats on modified land cover for implementing an integrated vector management (IVM) program in three villages within the Mwea Rice scheme, Kenya. International Journal of Health Geographics, 5, 1–18.
Julian, P., Mark, B., & Sarah, M. (2010). Food waste within food supply chains: Quantification and potential for change to 2050. Philosophical Transactions of the Royal Society, London: Biological Sciences, 365(1554), 3065−3081.
Kader, A. A., & Rolle, R. S. (2004). The role of PH management in assuring the quality and safety horticultural crops. FAO Agricultural Services Bulletin, 152, 52.
Kagin, J., Taylor, J. E., & Yúnez-Naude, A. (2016). Inverse productivity or inverse efficiency? Evidence from Mexico. The Journal of Development Studies, 52, 396−411.
Kalirajan, K., & Shand, R. (1985). Types of education and agricultural productivity: A quantitative analysis of Tamil Nadu rice farming. Journal of Development Studies, 21, 232−243.
Kao, C. (2014). Efficiency decomposition in network data envelopment analysis with slacks-based measures. Omega, 45, 1−6.
Kao, C., & Hwang, S.-N. (2008). Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185, 418–429.
Kennedy, P. E. (2003). A Guide to Econometrics (5th ed.). Cambridge: MIT Press, MA, USA.
Kiplagat, J. K., Wang, R. Z., & Li, T. X. (2011). Renewable energy in Kenya: Resource potential and status of exploitation. Renewable and Sustainable Energy Reviews, 15, 2960−2973.
Kneip, A., Park, B. U., & Simar, L. (1998). A note on the convergence of nonparametric DEA estimators for production efficiency scores. Econometric Theory, 14, 783–793.
Kneip, A., Simar, L., & Wilson, P. W. (2008). Asymptotics and consistent bootstraps for dea estimators in nonparametric frontier models. Econometric Theory, 24(6), 1663–1697.
Kumar, N., & Basu, P. (2008). Perspectives of productivity growth in Indian food industry: A data envelopment analysis. International Journal of Productivity and Performance Management, 57, 503−522.
Lee, B. L. (2013). Productivity performance of Singapore’s retail sector: A two-stage non-parametric approach. Economic Analysis and Policy, 43, 67−77.
Lee, B. L., & Worthington, A. C. (2016). A network DEA quantity and quality-orientated production model: An application to Australian university research services. Omega, 60, 26−33.
Li, Y.-J., Chen, Y., Liang, L., & Xie, J.-H. (2012). DEA models for extended two-stage network structures. Omega, 40, 611−618.
Liu, J.-S., & Lu, W.-M. (2010). DEA and ranking with the network-based approach: A case of R&D performance. Omega, 38, 453−464.
Liu, Y.-N., & Wang, K. (2015). Energy efficiency of China's industry sector: An adjusted network DEA (data envelopment analysis)-based decomposition analysis. Energy, 93, 1328−1337.
Liu, J.-S., Lu, L.-Y.-Y., Lu, W.-M., & Lin, B.-J. (2013). A survey of DEA applications. Omega, 41, 893–902.
Liu, W.-B., Zhou, Z.-B., Ma, C.-Q., Liu, D.-B., & Shen, W.-F. (2015). Two-stage DEA models with undesirable input-intermediate-outputs. Omega, 56, 74–87.
Lozano, S., & Gutiérrez, E. (2014). A slacks based network DEA efficiency analysis of European airlines. Transportation Planning and Technology, 37, 623−637.
Matthews, K. (2013). Risk management and managerial efficiency in Chinese banks: A network DEA framework. Omega, 41, 207−215.
Mburu, S., Ackello-Ogutu, C., & Mulwa, R. (2014). Analysis of economic efficiency and farm size: A case study of wheat farmers in Nakuru District, Kenya. Economics Research International, 2014, 1−10.
McDonald, J. (2009). Using least squares and Tobit in second stage DEA efficiency analyses. European Journal of Operational Research, 197, 792–798.
Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11, 619−632.
Patrick, G. F., & Kehrberg, E. W. (1973). Education and Agricultural Development: Eastern Brazil. Agricultural Experiment Station Research Bulletin, 906, Purdue University, 13p
Qushim, B., Gillespie, J., Paudel, K., & Mcmillin, K. (2016). Technical and scale efficiencies of meat goat farms in the USA. Applied Economics, 48, 608−620.
Ramalho, E. A., Ramalho, J. J., & Henriques, P. D. (2010). Fractional regression models for second stage DEA efficiency analyses. Journal of Productivity Analysis, 34, 239–255.
Ramalho, E. A., Ramalho, J. J., & Henriques, P. D. (2011). Alternative estimating and testing empirical strategies for fractional regression models. Journal of Economic Surveys, 25, 19–68.
Rickman, J. F. (2002). Grain quality from harvest to market. Japan International Research Center for Agricultural Sciences (JIRCAS) International Symposium, 11, 94–98.
Rubinstein, R. Y., & Glynn, P. W. (2009). How to deal with the curse of dimensionality of likelihood ratios in Monte Carlo simulation. Stochastic Models, 25, 547−568.
Rungsuriyawiboon, S., & Zhang, Y. (2018). Examining the economic performance of Chinese farms: A dynamic efficiency and adjustment cost approach. Economic Analysis and Policy, 57, 74−87.
Satin, M. (1997). The state of food and agriculture. In Food and agriculture Organization of the United Nations (FAO). Agro-Industries and Postharvest Management Service, Rome: Italy.
Sibiko, K. W., Owuor, G., Birachi, E., Gido, E., Ayuya, O., & Mwangi, J. (2012). Analysis of determinants of productivity and technical efficiency among smallholder common bean farmers in eastern Uganda. Current Research Journal of Economic Theory, 53, 44–55.
Simar, L. (2007). How to improve the performances of DEA/FDH estimators in the presence of noise? Journal of Productivity Analysis, 28, 183−201.
Simar, L., & Wilson, P. W. (1998). Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49–61.
Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136, 31−64.
Simar, L., & Wilson, P. W. (2008). Statistical inference in nonparametric frontier models: Recent developments and perspectives. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 421–521). Oxford, UK: Oxford University Press.
Simar, L., & Wilson, P. W. (2011). Two-stage DEA: Caveat emptor. Journal of Productivity Analysis, 36, 205–218.
Simon, J., Simon, C., & Arias, A. (2011). Changes in productivity of Spanish university libraries. Omega, 39, 578−588.
SPORE. (2011). Post-harvest management: Adding value to crops. The magazine for agric and rural development in African. Caribbean and Pacific (ACP) countries, 152, 13–19.
Subramaniam, S. K., Husin, S. H., Yusop, Y., & Hamidon, A. H. (2009). Machine efficiency and man power utilization on production lines. In L. Trilling, D. Perkins, D. D. Dionysios, L. Perlovsky, K. Davey, D. Landgrebe, M. A. Marino, D. L. Russell, S. H. Collicott, M. Ceccarelli, & J. W. Lund (Eds.), EHAC'09 proceedings of the 8th WSEAS international conference on electronics, hardware, wireless and optical communication. World scientific and engineering academy and society (WSEAS) (pp. 70–75). USA: Wisconsin.
Thanassoulis, E., Portela, M. C. S., & Despić, O. (2008). Data envelopment Analsyis: The mathematical programming approach to efficiency analysis. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency and productivity growth (pp. 251–420). Oxford, UK: Oxford University Press.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197, 243−252.
Tone, K., & Tsutsui, M. (2014). Dynamic DEA with network structure: A slacks-based measure approach. Omega, 42, 124−131.
Wang, K., Huang, W., Wu, J., & Liu, Y.-N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5−20.
Wilson, P., Hadley, D., Ramsden, S., & Kaltsas, I. (1998). Measuring and explaining technical efficiency in UK potato production. Journal of Agricultural Economics, 49, 294−305.
Wolde-Rufael, Y. (2005). Energy demand and economic growth: The African experience. Journal of Policy Modeling, 27, 891−903.
World Bank. (2011). Missing food: The case of postharvest grain losses in sub-Saharan Africa. Washington, DC, USA: The World Bank 116p.
Yodfiatfinda, D. S. B., Nasir, M. S., Zainalabidin, M., Ariff, M. H., & Zulkornain, Y. (2012). The empirical evaluation of productivity growth and efficiency of LSEs in the Malaysian food processing industry. International Food Research Journal, 19, 287–295.
Yu, M.-M. (2010). Assessment of airport performance using the SBM-NDEA model. Omega, 38, 440−452.
Acknowledgements
The authors acknowledge financial support for data collection involved in this research from Australian Awards Africa. The views expressed in the article, however, are solely of the authors and do not necessarily reflect the views of the supporting agency.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest statement
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Majiwa, E., Lee, B.L., Wilson, C. et al. A network data envelopment analysis (NDEA) model of post-harvest handling: the case of Kenya’s rice processing industry. Food Sec. 10, 631–648 (2018). https://doi.org/10.1007/s12571-018-0809-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12571-018-0809-0