Evaluating the Efficiency of Sugarcane Harvesting Units Using a Combined Approach to Data Envelopment Analysis and Data Mining

Document Type : Research Article-en

Author

• N. Monjezi

Biosystems engineering Dept., Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

https://doi.org/10.22067/jam.2020.58695.0

Abstract

Every organization needs an evaluation system in order to be aware of the level of performance and desirability of its units. It is more important for agricultural companies, including agro-industries. In this study, 20 sugarcane harvesting units were selected. After modeling based on input-oriented CCR and BCC models, efficiency values for sugarcane harvesting units were calculated and the CART decision tree was used to extract rules to predict the efficiency of these units. The results of a study of 20 sugarcane harvesting units in the CCR model showed that 6 units had an efficient score and 14 units had an inefficient score, and their technical efficiency score was in the range of 0.73-0.95. The results of the BCC model study also showed that out of a total of 20 sugarcane harvesting units, 8 units had efficient scores. As can be seen, in the BCC model, more units are introduced as efficient units and there is less dispersion between inefficient units. Also, the distribution of efficient units in the BCC model is less than the CCR model. The average technical efficiency, pure technical efficiency, and scale efficiency were 93%, 88%, and 93%, respectively. Also, the accuracy of the decision tree model for technical efficiency and pure technical efficiency was 86% and 93%, respectively.

Keywords

• CART
• DEA
• Harvest
• Sugarcane

20.1001.1.22286829.1401.12.1.4.4

Open Access

©2022 The author(s). This article is licensed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

1. Banker, R. D., A., Charnes, and W. W. Cooper. 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science 30 (9): 1078-1092.
2. Charnes, A., W. W., Cooper, and E. Rhodes. 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2: 429-44.
3. Chiang, T. C., P. Y., Cheng, and F. Y. Leu. 2017. Prediction of technical efficiency and financial crisis of Taiwan’s information and communication technology industry with decision tree and DEA. Soft Computing 21 (18): 5341-5353.
4. Elhami, B., Akram, and M. Khanali. 2016. Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches. Information Processing in Agriculture 3: 190-205.
5. Everinghama, Y. L., C. W., Smyth and N, G. Inman-Bamber. 2009. Ensemble data mining approaches to forecast regional sugarcane crop production. Agricultural and Forest Meteorology 149: 689-696.
6. Ferraro, D. O., D. E. Rivero and C. M. Ghersa. 2009. An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees. Field Crops Research 112: 149-157.
7. Jenhani, I., N. B. Amor, and Z. Elouedi. 2008. Decision Trees as Possibilistic Classifiers. International Journal of Approximate Reasoning 48 (3): 784-807.
8. Jeysenthil, K. M. S., T. Manikandan and E. Murali. 2014. Third Generation Agricultural Support System Development Using Data Mining. International Journal of Innovative Research in Science, Engineering and Technology 3 (3): 9923-9930.
9. Kaab, , M. Sharifi, H. Mobli, A. Nabavi-Pelesaraei, and K. Chau. 2019. Use of optimization techniques for energy use efficiency and environmental life cycle assessment modification in sugarcane production. Energy 181: 1298-1320.
10. Khai, H., and Yabe. 2011. Technical efficiency analysis of rice production in Vietnam. Journal of the International Society for Southeast Asian Agricultural Sciences 17: 135-146.
11. Li, N., Y. Jiang, H. Mu, and Z. Yu. 2018. Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA), Energy 164: 1145-1160.
12. Liu, X., P. Guo, and S. Guo. 2019. Assessing the eco-efficiency of a circular economy system in China’s coal mining areas: emergy and data envelopment analysis, Journal of Cleaner Production 206: 1101-1109.
13. Medar, R. A., and S., Rajpurohit. 2014. A survey on Data Mining Techniques for Crop Yield Prediction. International Journal of Advance Research in Computer Science and Management Studies 2 (9): 59-64.
14. Rahman, M. T., R., Nielsen, M. A., Khan, and M., Asmild. 2019. Efficiency and production environmental heterogeneity in aquaculture: A meta-frontier DEA approach. Aquaculture 509: 140-148.
15. Ramesh, D., and B. Vishnu Vardhan. 2013. Data Mining Techniques and Applications to Agricultural Yield Data. International Journal of Advanced Research in Computer and Communication Engineering 2 (9): 3477-3480.
16. Raorane, A. A., and R. V. Kulkarni. 2012. Data Mining: An effective tool for yield estimation in the agricultural sector. International Journal of Emerging Trends and Technology in Computer Science 1 (2): 75-79.
17. Toloo, M., B. Sohrabi, and S. Nalchigar. 2009. A New Method for Ranking Discovered Rules from Data Mining by DEA. Journal of Expert Systems with Applications 36 (4): 8503-8508.
18. Torgo, L. 2011. Data Mining with R: Learning with Case Studies. CRC Press, Boca Raton.
19. Ullah, , T. Silalertruksa, P. Pongpat, and S. H. Gheewala. 2019. Efficiency analysis of sugarcane production systems in Thailand using data envelopment analysis. Journal of Cleaner Production 238: 1-11.
20. Wanke, P., and C. P., Barros. 2016. Efficiency drivers in Brazilian insurance: A two-stage DEA meta-frontier-data mining approach. Economic Modelling 53: 8-22.
21. Yoneyama, Y., , Suzuki, R., Sawa, K., Yoneyama, G. G. Power, and Araki, T. 2002. Increased plasma adenosine concentrations and the severity of preeclampsia. Obstetrics & Gynecology 100 (6): 1266-1270.
22. Wu, D. 2009. Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert Systems with Applications 36: 9105-9112.