Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity

Year 2024, , 25 - 43, 25.03.2024

Hüseyin Fatih Atlı

https://doi.org/10.31015/jaefs.2024.1.4

Cited By: 5

Abstract

Productivity is one of the most important measures used to determine the growth and development level of countries or sectors. A wide variety of projects have been planned and implemented to increase agricultural productivity. The productivity to be obtained in agriculture; Soil conditions, climate, seeds, fertilizer, pesticides, labor and agricultural mechanization directly affect it. Agricultural mechanization is the realization of agricultural activities by using energy together with agricultural tools and machines. Agricultural mechanization; It is an important agricultural production technology that helps increase agricultural productivity. Due to the inadequate maintenance planning of agricultural machinery, agricultural machinery cannot be utilized at the desired level in agricultural production. Most agricultural equipment is subject to frequent changes in speed and direction of movement while operating. Damage that can be seen on a single machine; It also causes other machines to malfunction. During the year, especially in the months when agricultural activity is high, excessive working tempo can cause tractors to malfunction. The breakdown of tractors causes disruptions in agricultural activities. In addition, the breakdown of tractors increases the repair costs. Since there is no tractor maintenance planning, farmers face interruptions in agricultural activities due to tractor malfunction. However, tractor malfunctions may cause cost and economic losses. For these reasons, there is a need for appropriate maintenance planning of agricultural machinery in order to continue agricultural activities without disruption. Maintenance planning; It consists of a set of preventive activities to improve the reliability and availability of any system. The main purpose of this study is to determine and rank the importance level weights of the criteria that are important for agricultural machinery maintenance planning using the fuzzy AHP method. Fuzzy AHP method, which provides ease of application, was preferred in determining the Criterion Weights. The research proposes a framework to determine the weights of appropriate criteria for care planning selection through a combined approach of fuzzy multi-criteria decision making involving relevant stakeholders. On the basis of the prioritization of criteria of tractor maintenance planning (TMP), it was found from the ranking that checking for all fluid levels (TMP1) ranked first. This respectively is followed by checking for general conditions (TMP4), checking for tires and wheels (TMP2) and checking for batteries (TMP3). With the results of the study, a guide was created for farmers and other stakeholders, as well as decision makers, to help plan the maintenance of machines in better working conditions. It is also thought that this study will be encouraging for other studies.

Keywords

Agricultural Marketing, Agricultural Productivity, Agricultural Mechanization, MCDM, fuzzy AHP

Ethical Statement

Ethics committee approval is not required.

References

• Adamopoulos, T., & Restuccia, D. (2014). The size distribution of farms and international productivity differences. American Economic Review, 104(6), 1667-1697. https://doi.org/10.1257/aer.104.6.1667
• Adamopoulos, T., & Restuccia, D. (2020). Land reform and productivity: A quantitative analysis with micro data. American Economic Journal: Macroeconomics, 12(3), 1-39. https://doi.org/10.1257/mac.20150222
• Afsharnia, F., Asoodar, M. A., & Abdeshahi, A. (2014). The effect of failure rate on repair and maintenance costs of four agricultural tractor models. International Journal of Agricultural and Biosystems Engineering, 8(3), 286-290.
• Akdemir, B. (2013). Agricultural mechanization in Turkey. IERI Procedia, 5, 41-44. https://doi.org/10.1016/j.ieri.2013.11.067
• Akinbamowo, R. O. (2013). A review of government policy on agricultural mechanization in Nigeria. Journal of Agricultural Extension and Rural Development, 5(8), 146-153.
• Alston, J. M., Andersen, M. A., James, J. S., & Pardey, P. G. (2010). Persistence Pays: US Agricultural Productivity Growth and the Benefits from Public R & D Spending. https://doi.org/10.1007/978-1-4419-0658-8
• Alston, J. M., & Pardey, P. G. (2014). Agriculture in the global economy. Journal of Economic Perspectives, 28(1), 121-146. https://doi.org/10.1257/jep.28.1.121
• Altuntaş, E. (2016). Türkiye ‘nin Tarımsal Mekanizasyon Düzeyinin Coğrafik Bölgeler Açısından Değerlendirilmesi. Turkish Journal of Agriculture-Food Science and Technology, 4(12), 1157-1164 (in Turkish). https://doi.org/10.24925/turjaf.v4i12.1157-1164.972
• Altuntaş, E., & Demirtola, H. (2004). Ülkemizin tarımsal mekanizasyon düzeyinin coğrafik bölgeler bazında değerlendirilmesi. GOÜ. Ziraat Fakültesi Dergisi, 21 (2), 63-70 (in Turkish).
• Amare, D., & Endalew, W. (2016). Agricultural mechanization: Assessment of mechanization impact experiences on the rural population and the implications for Ethiopian smallholders. Engineering and Applied Sciences, 1(2), 39-48. : https://doi.org/10.11648/j.eas.20160102.15
• Amini Khoshalan, H., Torabi, S. R., Hoseinie, S. H., & Ghodrati, B. (2015). RAM analysis of earth pressure balance tunnel boring machines: A case study. International Journal of Mining and Geo-Engineering, 49(2), 173-185.
• Arslankaya, D., & Göraltay, K. (2019). Current Approaches in Multi-Criteria Decision Making Methods. Iksad.
• Aryal, J. P., Maharjan, S., & Erenstein, O. (2019). Understanding factors associated with agricultural mechanization: A Bangladesh case. World Development Perspectives, 13, 1-9. https://doi.org/10.1016/j.wdp.2019.02.002
• Asoegwu, S. N., & Asoegwu, A. O. (2007). An overview of agricultural mechanization and its environmental management in Nigeria. Agricultural Engineering International: CIGR Journal. https://cigrjournal.org/index.php/Ejounral/article/view/961
• Atlı, H. F. (2022). Multı-criteria decision-making approach to supply chain collaboration in agriculture sector, Niğde Ömer Halisdemir University, Graduate School of Social Sciences, PhD Thesis, Niğde, Turkiye, 247 pp. https://doi.org/10.13140/RG.2.2.32265.62569
• Atlı, H. F. (2024). Bulanık ARAS (B-ARAS) yönteminin sistematik bir incelemesi ve Meta-Analizi. Socıal Scıence Development Journal, 9(42), 1-16 (in Turkish). http://dx.doi.org/10.31567/ssd.1107
• Bakır, M., & Atalık, Ö. (2021). Application of fuzzy AHP and fuzzy MARCOS approach for the evaluation of e-service quality in the airline industry. Decision Making: Applications in Management and Engineering, 4(1), 127-152. https://doi.org/10.31181/dmame2104127b
• Barabady, J., & Kumar, U. (2008). Reliability analysis of mining equipment: A case study of a crushing plant at Jajarm Bauxite Mine in Iran. Reliability engineering & system safety, 93(4), 647-653. https://doi.org/10.1016/j.ress.2007.10.006
• Baudron, F., Sims, B., Justice, S., Kahan, D. G., Rose, R., Mkomwa, S., ... & Gérard, B. (2015). Re-examining appropriate mechanization in Eastern and Southern Africa: two-wheel tractors, conservation agriculture, and private sector involvement. Food Security, 7, 889-904. https://doi.org/10.1007/s12571-015-0476-3
• Bayramoğlu, Z. (2010). Tarımsal verimlilik ve önemi. Selcuk Journal of Agriculture and Food Sciences, 24(3), 52-61 (in Turkish).
• Belton, B., Win, M. T., Zhang, X., & Filipski, M. (2021). The rapid rise of agricultural mechanization in Myanmar. Food Policy, 101, 102095. https://doi.org/10.1016/j.foodpol.2021.102095
• Benin, S. (2015). Impact of Ghana's agricultural mechanization services center program. Agricultural economics, 46(S1), 103-117. https://doi.org/10.1111/agec.12201
• Biggs, S., & Justice, S. (2015). Rural and agricultural mechanization: A history of the spread of small engines in selected Asian countries.
• Binswanger, H. (1986). Agricultural mechanization: a comparative historical perspective. The World Bank Research Observer, 1(1), 27-56. https://doi.org/10.1093/wbro/1.1.27
• Bose, D., Chattopadhyay, S., Bose, G., Adhikary, D., & Mitra, S. (2012). RAM investigation of coal-fired thermal power plants: a case study. International Journal of Industrial Engineering Computations, 3(3), 423-434. http://dx.doi.org/10.5267/j.ijiec.2011.12.003
• Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247.
• Burney, J. A., Davis, S. J., & Lobell, D. B. (2010). Greenhouse gas mitigation by agricultural intensification. Proceedings of the national Academy of Sciences, 107(26), 12052-12057. https://doi.org/10.1073/pnas.0914216107
• Bustos, P., Caprettini, B., & Ponticelli, J. (2016). Agricultural productivity and structural transformation: Evidence from Brazil. American Economic Review, 106(6), 1320-1365. https://doi.org/10.1257/aer.20131061
• Cassidy, E. S., West, P. C., Gerber, J. S., & Foley, J. A. (2013). Redefining agricultural yields: from tonnes to people nourished per hectare. Environmental Research Letters, 8(3), 034015. https://doi.org/10.1088/1748-9326/8/3/034015
• Çekel, H., & Acar, A. İ. (2023). Traktör Tasarımında Güvenilirlik Merkezli Bakım Yönteminin Uygulanabilirliği. Turkish Journal of Agriculture-Food Science and Technology, 11(9), 1721-1730 (in Turkish). https://doi.org/10.24925/turjaf.v11i9.1721-1730.6312
• Chan, F. T., & Kumar, N. (2007). Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega, 35(4), 417-431. https://doi.org/10.1016/j.omega.2005.08.004
• Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3), 649-655. https://doi.org/10.1016/0377-2217(95)00300-2
• Chand, R., Prasanna, P. L., & Singh, A. (2011). Farm size and productivity: Understanding the strengths of smallholders and improving their livelihoods. Economic and Political Weekly, 5-11. https://www.jstor.org/stable/23018813
• Clarke, L. J. (2000). Strategies for Agricultural Mechanization Development: The roles of the private sectore and the Government. https://hdl.handle.net/1813/10216
• Coelli, T. J., & Rao, D. P. (2005). Total factor productivity growth in agriculture: a Malmquist index analysis of 93 countries, 1980–2000. Agricultural Economics, 32, 115-134. https://doi.org/10.1111/j.0169-5150.2004.00018.x
• Connor, D. J., Loomis, R. S., & Cassman, K. G. (2011). Crop ecology: productivity and management in agricultural systems. Cambridge University Press.
• Da Silva, C. A. G., de Sá, J. L. R., & Menegatti, R. (2019). Diagnostic of failure in transmission system of agriculture tractors using predictive maintenance based software. AgriEngineering, 1(1), 132-144. https://doi.org/10.3390/agriengineering1010010
• Dale, V. H., & Polasky, S. (2007). Measures of the effects of agricultural practices on ecosystem services. Ecological economics, 64(2), 286-296. https://doi.org/10.1016/j.ecolecon.2007.05.009
• Daum, T., & Birner, R. (2020). Agricultural mechanization in Africa: Myths, realities and an emerging research agenda. Global food security, 26, 100393. https://doi.org/10.1016/j.gfs.2020.100393
• Daum, T., Villalba, R., Anidi, O., Mayienga, S. M., Gupta, S., & Birner, R. (2021). Uber for tractors? Opportunities and challenges of digital tools for tractor hire in India and Nigeria. World Development, 144, 105480. https://doi.org/10.1016/j.worlddev.2021.105480
• Davis, K., Nkonya, E., Kato, E., Mekonnen, D. A., Odendo, M., Miiro, R., & Nkuba, J. (2012). Impact of farmer field schools on agricultural productivity and poverty in East Africa. World development, 40(2), 402-413. https://doi.org/10.1016/j.worlddev.2011.05.019
• De Araujo Zanella, A. R., da Silva, E., & Albini, L. C. P. (2020). Security challenges to smart agriculture: Current state, key issues, and future directions. Array, 8, 100048. https://doi.org/10.1016/j.array.2020.100048
• Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparison. International journal of approximate reasoning, 21(3), 215-231. https://doi.org/10.1016/S0888-613X(99)00025-0
• Diao, X., Silver, J., & Takeshima, H. (2016). Agricultural mechanization and agricultural transformation (Vol. 1527). Intl Food Policy Res Inst.
• Doğan, M. (2012). Türkiye ziraatinde makineleşme: traktör ve biçerdöverin etkileri. Coğrafya Dergisi, (14) (in Turkish).
• Durczak, K., Ekielski, A., Kozłowski, R., Żelaziński, T., & Pilarski, K. (2020). A computer system supporting agricultural machinery and farm tractor purchase decisions. Heliyon, 6(10). https://doi.org/10.1016/j.heliyon.2020.e05039
• Elhaki, O., & Shojaei, K. (2020). Observer‐based neural adaptive control of a platoon of autonomous tractor–trailer vehicles with uncertain dynamics. IET Control Theory & Applications, 14(14), 1898-1911. https://doi.org/10.1049/iet-cta.2019.1403
• Emami, M., Almassi, M., & Bakhoda, H. (2018). Agricultural mechanization, a key to food security in developing countries: strategy formulating for Iran. Agriculture & Food Security, 7(1), 1-12. https://doi.org/10.1186/s40066-018-0176-2
• Emerick, K., De Janvry, A., Sadoulet, E., & Dar, M. H. (2016). Technological innovations, downside risk, and the modernization of agriculture. American Economic Review, 106(6), 1537-1561. https://doi.org/10.1257/aer.20150474
• Emrouznejad, A., & Marra, M. (2017). The state of the art development of AHP (1979–2017): A literature review with a social network analysis. International journal of production research, 55(22), 6653-6675. https://doi.org/10.1080/00207543.2017.1334976
• Erdal, Ö. Z. (2005). Ege Bölgesi'nde meydana gelen traktör kazalarının tarımsal iş güvenliği açısından değerlendirilmesi. Ege Üniversitesi Ziraat Fakültesi Dergisi, 42(2), 191-202 (in Turkish).
• Eren, A. (2021). Implementation of Official Development Assistance to the Balkans and Eastern European Countries with the Support of Bulanık AHP and Bulanık Moora, Gazi University, Graduate School of Science Sciences, Master's Thesis, Ankara, Turkiye, 96 pp.
• Ertuğrul, İ. (2007). Bulanik Analitik Hiyerarşi Süreci ve Bir Tekstil Işletmesinde Makine Seçim Problemine Uygulanmasi. Hacettepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(1), 171-192 (in Turkish).
• FAO (2023). Sustainable Agricultural Mechanization. Retrieved in October, 16, 2023 from https://www.fao.org/sustainable-agricultural-mechanization/en/
• Fathollahzadeh, H., Mobli, H., Rajabipour, A., Minaee, S., Jafari, A., & Tabatabaie, S. M. H. (2010). Average and instantaneous fuel consumption of Iranian conventional tractor with moldboard plow in tillage. ARPN Journal of Engineering and Applied Sciences, 5(2), 30-35.
• Fu, Y. K., Wu, C. J., & Liao, C. N. (2021). Selection of in-flight duty-free product suppliers using a combination fuzzy AHP, fuzzy ARAS, and MSGP methods. Mathematical Problems in Engineering, 2021, 1-13. https://doi.org/10.1155/2021/8545379
• Galiev, I., Khafizov, C., Adigamov, N., & Khusainov, R. (2018, May). Increase of efficiency of tractors use in agricultural production. In 17th International Scientific Conference Engineering for rural development Proceedings (Vol. 17, No. 23-25, p. 373). https://doi.org/10.22616/ERDev2018.17.N482
• Galiev, I., Khafizov, K. A., Khusainov, R., & Faskhutdinov, M. (2020, May). Ensuring possibility of functioning of tractors in agricultural production taking into account residual resources of their units and systems. In 19th International scientific conference engineering for rural development proceedings (Vol. 18, pp. 48-53). https://doi.org/10.22616/ERDev.2020.19.TF012
• Ghadikolaei, A. S., & Esbouei, S. K. (2014). Integrating FAHP and Fuzzy ARAS for evaluating financial performance. Bol. Soc. Paran. Mat, 32(3), 163-174. https://doi.org/10.5269/bspm.v32i2.21378
• Goldsmith, P. D., Gunjal, K., & Ndarishikanye, B. (2004). Rural–urban migration and agricultural productivity: the case of Senegal. Agricultural economics, 31(1), 33-45. https://doi.org/10.1111/j.1574-0862.2004.tb00220.x
• Gollin, D. (2010). Agricultural productivity and economic growth. Handbook of agricultural economics, 4, 3825-3866. https://doi.org/10.1016/S1574-0072(09)04073-0
• Gollin, D., Lagakos, D., & Waugh, M. E. (2014). The agricultural productivity gap. The Quarterly Journal of Economics, 129(2), 939-993. https://doi.org/10.1093/qje/qjt056
• Gollin, D., Parente, S., & Rogerson, R. (2002). The role of agriculture in development. American economic review, 92(2), 160-164. https://doi.org/10.1257/000282802320189177
• Gornall, J., Betts, R., Burke, E., Clark, R., Camp, J., Willett, K., & Wiltshire, A. (2010). Implications of climate change for agricultural productivity in the early twenty-first century. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 2973-2989. https://doi.org/10.1098/rstb.2010.0158
• Gu, Y. K., & Huang, K. Q. (2010). A using reliability evaluation model for diesel engine based on fuzzy neural network. Advances in neural network research and applications, 145-152. https://doi.org/10.1007/978-3-642-12990-2_17
• Gupta, S., Khosravy, M., Gupta, N., & Darbari, H. (2019). In-field failure assessment of tractor hydraulic system operation viapseudospectrum of acoustic measurements. Turkish Journal of Electrical Engineering and Computer Sciences, 27(4), 2718-2729. https://doi.org/10.3906/elk-1807-165
• Houmy, K., Clarke, L. J., Ashburner, J. E., & Kienzle, J. (2013). Agricultural mechanization in sub-Saharan Africa: guidelines for preparing a strategy (Vol. 22). Food and Agriculture Organization of the United Nations (FAO).
• Houssou, N., Diao, X., Cossar, F., Kolavalli, S., Jimah, K., & Aboagye, P. (2013). Agricultural mechanization in Ghana: is specialization in agricultural mechanization a viable business model? (Vol. 1255). Intl Food Policy Res Inst.
• Hrytsaienko, H., Hrytsaienko, I., Bondar, A., & Zhuravel, D. (2019). Mechanism for the Maintenance of Investment in Agriculture. In Modern Development Paths of Agricultural Production: Trends and Innovations (pp. 29-40). Cham: Springer International Publishing.
• Hudec, M. (2016). Fuzziness in information systems. Switzerland (CHE): Springer Nature. https://doi.org/10.1007/978-3-319-42518-4
• Iqbal, M. A., Iqbal, A., Afzal, S., Akbar, N., Abbas, R. N., & Khan, H. Z. (2015). In Pakistan, agricultural mechanization status and future prospects. American-Eurasian Journal of Agricultural & Environmental Sciences, 15(1), 122-128. https://doi.org/10.5829/idosi.aejaes.2015.15.1.12500
• Irz, X., Lin, L., Thirtle, C., & Wiggins, S. (2001). Agricultural productivity growth and poverty alleviation. Development policy review, 19(4), 449-466. https://doi.org/10.1111/1467-7679.00144
• Işık, E., Güler, T., & Ayhan, A. (2003). Bursa iline ilişkin mekanizasyon düzeyinin belirlenmesine yönelik bir çalışma. Uludağ Üniversitesi Ziraat Fakültesi Dergisi, 17(2), 125-136 (in Turkish).
• Jiang, M., Hu, X., Chunga, J., Lin, Z., & Fei, R. (2020). Does the popularization of agricultural mechanization improve energy-environment performance in China’s agricultural sector?. Journal of Cleaner Production, 276, 124210. https://doi.org/10.1016/j.jclepro.2020.124210
• Jurca, V. (2012). Maintenance Management systems in agricultural companies in the czech republic systemy zarządzania utrzymaniem ruchu w przedsiębiorstwach rolnych w republice czeskiej. spis treści-contents, 14(3), 233.
• Kaviani, M. A., Peykam, A., Khan, S. A., Brahimi, N., & Niknam, R. (2020). A new weighted fuzzy programming model for supplier selection and order allocation in the food industry. Journal of Modelling in Management, 15(2), 381-406. https://doi.org/10.1108/JM2-11-2018-0191
• Keršulienė, V., & Turskis, Z. (2014a). An integrated multi-criteria group decision making process: selection of the chief accountant. Procedia-Social and Behavioral Sciences, 110, 897-904. https://doi.org/10.1016/j.sbspro.2013.12.935
• Keršulienė, V., & Turskis, Z. (2014b). A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer. Journal of Business Economics and Management, 15(2), 232-252. https://doi.org/10.3846/16111699.2014.903201
• Khodabakhshian, R. (2013). Maintenance management of tractors and agricultural machinery: Preventive maintenance systems. Agricultural Engineering International: CIGR Journal, 15(4), 147-159.
• Kilic, T., Palacios-Lopez, A., & Goldstein, M. (2015). Caught in a productivity trap: A distributional perspective on gender differences in Malawian agriculture. World development, 70, 416-463. https://doi.org/10.1016/j.worlddev.2014.06.017
• Koçtürk, D., & Avcıoğlu, A. (2007). Türkiye’de bölgelere ve illere göre tarımsal mekanizasyon düzeyinin belirlenmesi. Tarım Makinaları Bilimi Dergisi, 3(1), 17-24 (in Turkish).
• Kubler, S., Robert, J., Derigent, W., Voisin, A., & Le Traon, Y. (2016). A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Expert systems with applications, 65, 398-422. https://doi.org/10.1016/j.eswa.2016.08.064
• Kurukulasuriya, P., & Rosenthal, S. (2013). Climate change and agriculture: A review of impacts and adaptations. http://hdl.handle.net/10986/16616
• Lagakos, D., & Waugh, M. E. (2013). Selection, agriculture, and cross-country productivity differences. American Economic Review, 103(2), 948-980. https://doi.org/10.1257/aer.103.2.948
• Lau, H., Shum, P. K., Nakandala, D., Fan, Y., & Lee, C. (2020). A game theoretic decision model for organic food supplier evaluation in the global supply chains. Journal of Cleaner Production, 242, 118536. https://doi.org/10.1016/j.jclepro.2019.118536
• Lawry, S., Samii, C., Hall, R., Leopold, A., Hornby, D., & Mtero, F. (2017). The impact of land property rights interventions on investment and agricultural productivity in developing countries: a systematic review. Journal of Development Effectiveness, 9(1), 61-81. https://doi.org/10.1080/19439342.2016.1160947
• Lee, D. R. (2005). Agricultural sustainability and technology adoption: Issues and policies for developing countries. American journal of agricultural economics, 87(5), 1325-1334. https://www.jstor.org/stable/3697714
• Li, L., Li, S. M., Sun, J. H., Zhou, L. L., Bao, X. G., Zhang, H. G., & Zhang, F. S. (2007). Diversity enhances agricultural productivity via rhizosphere phosphorus facilitation on phosphorus-deficient soils. Proceedings of the National Academy of Sciences, 104(27), 11192-11196. https://doi.org/10.1073/pnas.0704591104
• Li, W., Wei, X., Zhu, R., & Guo, K. (2018). Study on factors affecting the agricultural mechanization level in China based on structural equation modeling. Sustainability, 11(1), 51. https://doi.org/10.3390/su11010051
• Lips, M., & Burose, F. (2012). Repair and maintenance costs for agricultural machines. International Journal of Agricultural Management, 1(3), 40-46. https://doi.org/10.22004/ag.econ.149750
• Liu, Y., Eckert, C. M., & Earl, C. (2020). A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Systems with Applications, 161, 113738. https://doi.org/10.1016/j.eswa.2020.113738
• Lobell, D. B., & Gourdji, S. M. (2012). The influence of climate change on global crop productivity. Plant physiology, 160(4), 1686-1697. https://doi.org/10.1104/pp.112.208298
• Lorencowicz, E., & Uziak, J. (2015). Repair cost of tractors and agricultural machines in family farms. Agriculture and Agricultural Science Procedia, 7, 152-157. https://doi.org/10.1016/j.aaspro.2015.12.010
• Luo, X., Liao, J., Zang, Y., & Zhou, Z. (2016). Improving agricultural mechanization level to promote agricultural sustainable development. Transactions of the Chinese Society of Agricultural Engineering, 32(1), 1-11.
• Lynch, P., Adendorff, K., Yadavalli, V. S., & Adetunji, O. (2013). Optimal spares and preventive maintenance frequencies for constrained industrial systems. Computers & Industrial Engineering, 65(3), 378-387. https://doi.org/10.1016/j.cie.2013.03.005
• Mantoam, E. J., Romanelli, T. L., & Gimenez, L. M. (2016). Energy demand and greenhouse gases emissions in the life cycle of tractors. Biosystems Engineering, 151, 158-170. https://doi.org/10.1016/j.biosystemseng.2016.08.028
• Martin, P. L., & Olmstead, A. L. (1985). The agricultural mechanization controversy. Science, 227(4687), 601-606. https://doi.org/10.1126/science.227.4687.601
• Mavi, R. K. (2015). Green supplier selection: a fuzzy AHP and fuzzy ARAS approach. International Journal of Services and Operations Management, 22(2), 165-188. https://doi.org/10.1504/IJSOM.2015.071528
• McMillan, M. S., & Rodrik, D. (2011). Globalization, structural change and productivity growth (No. w17143). National Bureau of Economic Research. https://doi.org/10.3386/w17143
• Mishra, D., & Satapathy, S. (2023). Reliability and maintenance of agricultural machinery by MCDM approach. International Journal of System Assurance Engineering and Management, 14(1), 135-146. https://doi.org/10.1007/s13198-021-01256-y
• Mittal, S., Gandhi, S., & Tripathi, G. (2010). Socio-economic impact of mobile phones on Indian agriculture (No. 246). Working paper.
• Mittal, S., & Tripathi, G. (2009). Role of mobile phone technology in improving small farm productivity. Agricultural Economics Research Review, 22, 451-460. https://doi.org/10.22004/ag.econ.57502
• Molden, D., Oweis, T., Steduto, P., Bindraban, P., Hanjra, M. A., & Kijne, J. (2010). Improving agricultural water productivity: Between optimism and caution. Agricultural water management, 97(4), 528-535. https://doi.org/10.1016/j.agwat.2009.03.023
• Molden, D., Oweis, T. Y., Pasquale, S., Kijne, J. W., Hanjra, M. A., Bindraban, P. S., ... & Zwart, S. (2007). Pathways for increasing agricultural water productivity.
• Moorehead, S. J., Wellington, C. K., Gilmore, B. J., & Vallespi, C. (2012, October). Automating orchards: A system of autonomous tractors for orchard maintenance. In Proceedings of the IEEE international conference of intelligent robots and systems, workshop on agricultural robotics.
• Mousazadeh, H., Keyhani, A., Javadi, A., Mobli, H., Abrinia, K., & Sharifi, A. (2011). Life-cycle assessment of a Solar Assist Plug-in Hybrid electric Tractor (SAPHT) in comparison with a conventional tractor. Energy conversion and Management, 52(3), 1700-1710. https://doi.org/10.1016/j.enconman.2010.10.033
• Mrema, G. C., Kienzle, J., & Mpagalile, J. (2018). Current status and future prospects of agricultural mechanization in sub-saharan Africa (SSA). Agricultural Mechanization in Asia, Africa and Latin America, 49(2), 13-30.
• Mugiyo, H., Chimonyo, V. G., Sibanda, M., Kunz, R., Masemola, C. R., Modi, A. T., & Mabhaudhi, T. (2021). Evaluation of land suitability methods with reference to neglected and underutilised crop species: A scoping review. Land, 10(2), 125. https://doi.org/10.3390/land10020125
• Myalo, O. V., Myalo, V. V., Prokopov, S. P., Solomkin, A. P., & Soynov, A. S. (2018, July). Theoretical substantiation of machine-tractor fleet technical maintenance system on the example of Omsk region agricultural enterprises. In Journal of physics: conference series (Vol. 1059, No. 1, p. 012005). IOP Publishing. https://doi.org/10.1088/1742-6596/1059/1/012005
• Myalo, O. V., Prokopov, S. P., Myalo, V. V., Soyunov, A. S., & Demchuk, E. V. (2019, September). Material and technical support of the enterprises of the agro-industrial complex of the Omsk region management and certification of the technical component of the production processes in crop production. In IOP Conference Series: Materials Science and Engineering (Vol. 582, No. 1, p. 012028). IOP Publishing.
• Myers, J. H., & Alpert, M. I. (1968). Determinant buying attitudes: meaning and measurement. Journal of Marketing, 32(4_part_1), 13-20. https://doi.org/10.1177/002224296803200404
• Najafi, P., Asoodar, M. A., Marzban, A., & Hormozi, M. A. (2015). Reliability analysis of agricultural machinery: A case study of sugarcane chopper harvester. Agricengint: CIGR journal, 17(1), 158-165.
• Nguyen, H. T., Md Dawal, S. Z., Nukman, Y., Aoyama, H., & Case, K. (2015). An integrated approach of fuzzy linguistic preference based AHP and fuzzy COPRAS for machine tool evaluation. PloS one, 10(9), e0133599. https://doi.org/10.1371/journal.pone.0133599
• Nguyen, H. T., Md Dawal, S. Z., Nukman, Y., P. Rifai, A., & Aoyama, H. (2016). An integrated MCDM model for conveyor equipment evaluation and selection in an FMC based on a fuzzy AHP and fuzzy ARAS in the presence of vagueness. PloS one, 11(4), e0153222. https://doi.org/10.1371/journal.pone.0153222
• Obinna, O., & Oluka, I. (2016). Predicting repair and maintenance costs of agricultural tractors in Nigeria. International Journal of Advancements in Research & Technology, 5(3), 154-169.
• Oğuz, C., Bayramoğlu, Z., Ağızan, S., & Ağızan, K. (2017). Tarım işletmelerinde tarımsal mekanizasyon kullanım düzeyi, Konya ili örneği. Selcuk Journal of Agriculture and Food Sciences, 31(1), 63-72 (in Turkish). https://doi.org/10.15316/SJAFS.2017.8
• Olesen, J. E., & Bindi, M. (2002). Consequences of climate change for European agricultural productivity, land use and policy. European journal of agronomy, 16(4), 239-262. https://doi.org/10.1016/S1161-0301(02)00004-7
• Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., & Lobell, D. B. (2021). Anthropogenic climate change has slowed global agricultural productivity growth. Nature Climate Change, 11(4), 306-312. https://doi.org/10.1038/s41558-021-01000-1
• Ozguven, M. M., Turker, U., & Beyaz, A. (2010). Türkiye’nin tarımsal yapısı ve mekanizasyon durumu. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG), 2010(2), 89-100 (in Turkish).
• O’Donnell, C. J. (2010). Measuring and decomposing agricultural productivity and profitability change. Australian Journal of Agricultural and Resource Economics, 54(4), 527-560. https://doi.org/10.1111/j.1467-8489.2010.00512.x
• Ömürbek, N., & Tunca, Z. (2013). Analitik hiyerarşi süreci ve analitik ağ süreci yöntemlerinde grup kararı verilmesi aşamasına ilişkin bir örnek uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 18(3), 47-70 (in Turkish).
• Özkan, B., Dengiz, O., & Turan, İ. D. (2020). Site suitability analysis for potential agricultural land with spatial fuzzy multi-criteria decision analysis in regional scale under semi-arid terrestrial ecosystem. Scientific reports, 10(1), 22074. https://doi.org/10.1038/s41598-020-79105-4
• Paman, U., Uchida, S., & Inaba, S. (2010). Economic potential of tractor hire business in Riau Province, Indonesia: A case study of small tractors for small rice farms. Agricultural Engineering International: CIGR Journal, 12(1).
• Peterman, A., Quisumbing, A., Behrman, J., & Nkonya, E. (2011). Understanding the complexities surrounding gender differences in agricultural productivity in Nigeria and Uganda. Journal of Development Studies, 47(10), 1482-1509. https://doi.org/10.1080/00220388.2010.536222
• Pickett, W., King, N., Lawson, J., Dosman, J. A., Trask, C., Brison, R. J., ... & Saskatchewan Farm Injury Cohort Study Team. (2015). Farmers, mechanized work, and links to obesity. Preventive medicine, 70, 59-63. https://doi.org/10.1016/j.ypmed.2014.11.012
• Pingali, P. (2007). Agricultural mechanization: adoption patterns and economic impact. Handbook of agricultural economics, 3, 2779-2805. https://doi.org/10.1016/S1574-0072(06)03054-4
• Pingali, P. L., Bigot, Y., & Binswanger, H. P. (1987). Agricultural mechanization and the evolution of farming systems in Sub-Saharan Africa. Johns Hopkins University Press.
• Poozesh, M., Mohtasebi, S. S., Ahmadi, H., & Asakereh, A. (2012). Determining the reliability function of farm tractors. Elixir Project Management, 47, 9074-9078.
• Prakash, C., & Barua, M. K. (2016). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66-78. https://doi.org/10.1016/j.spc.2016.04.001
• Qian, L., Lu, H., Gao, Q., & Lu, H. (2022). Household-owned farm machinery vs. outsourced machinery services: The impact of agricultural mechanization on the land leasing behavior of relatively large-scale farmers in China. Land Use Policy, 115, 106008. https://doi.org/10.1016/j.landusepol.2022.106008
• Rani, P., Mishra, A. R., Krishankumar, R., Mardani, A., Cavallaro, F., Soundarapandian Ravichandran, K., & Balasubramanian, K. (2020). Hesitant fuzzy SWARA-complex proportional assessment approach for sustainable supplier selection (HF-SWARA-COPRAS). Symmetry, 12(7), 1152. https://doi.org/10.3390/sym12071152
• RazaviToosi, S. L., & Samani, J. M. V. (2016). Evaluating water management strategies in watersheds by new hybrid Fuzzy Analytical Network Process (FANP) methods. Journal of Hydrology, 534, 364-376. https://doi.org/10.1016/j.jhydrol.2016.01.006
• Redreev, G. V. (2016, August). Ensuring machine and tractor aggregates operability. In IOP Conference Series: Materials Science and Engineering (Vol. 142, No. 1, p. 012085). IOP Publishing. https://doi.org/10.1088/1757-899X/142/1/012085
• Redreev, G. V., Luchinovich, A. A., Ustiyantsev, E. I., & Laskin, A. S. (2018, July). Information system of machines and tractors fleet technical service. In Journal of Physics: Conference Series (Vol. 1059, No. 1, p. 012003). IOP Publishing. https://doi.org/10.1088/1742-6596/1059/1/012003
• Redreev, G. V., Myalo, O. V., Prokopov, S. P., Solomkin, A. P., & Okunev, G. A. (2017, July). Machine-tractor aggregates operation assurance by mobile maintenance teams. In IOP Conference Series: Materials Science and Engineering (Vol. 221, No. 1, p. 012016). IOP Publishing. https://doi.org/10.1088/1757-899X/221/1/012016
• Redreev, G. V., Okunev, G. A., & Voinash, S. A. (2020). Efficiency of usage of transport and technological machines. In Proceedings of the 5th International Conference on Industrial Engineering (ICIE 2019) Volume II 5 (pp. 625-631). Springer International Publishing. https://doi.org/10.1007/978-3-030-22063-1_66
• Reimers, M., & Klasen, S. (2013). Revisiting the role of education for agricultural productivity. American Journal of Agricultural Economics, 95(1), 131-152. https://doi.org/10.1093/ajae/aas118
• Restuccia, D., Yang, D. T., & Zhu, X. (2008). Agriculture and aggregate productivity: A quantitative cross-country analysis. Journal of monetary economics, 55(2), 234-250. https://doi.org/10.1016/j.jmoneco.2007.11.006
• Robertson, G. P., & Swinton, S. M. (2005). Reconciling agricultural productivity and environmental integrity: a grand challenge for agriculture. Frontiers in Ecology and the Environment, 3(1), 38-46. https://doi.org/10.1890/1540-9295(2005)003[0038:RAPAEI]2.0.CO;2
• Rohani, A., Abbaspour-Fard, M. H., & Abdolahpour, S. (2011). Prediction of tractor repair and maintenance costs using Artificial Neural Network. Expert Systems with Applications, 38(7), 8999-9007. https://doi.org/10.1016/j.eswa.2011.01.118
• Ronaghi, M. H., & Mosakhani, M. (2022). The effects of blockchain technology adoption on business ethics and social sustainability: evidence from the Middle East. Environment, Development and Sustainability, 24(5), 6834-6859. https://doi.org/10.1007/s10668-021-01729-x
• Ruttan, V. W. (2002). Productivity growth in world agriculture: sources and constraints. Journal of Economic perspectives, 16(4), 161-184. https://doi.org/10.1257/089533002320951028
• Rybacki, P., & Grześ, Z. (2018). A method to assess reliability of seasonally operated machines using fuzzy logic principles. Journal of Research and Applications in Agricultural Engineering, 63(1).
• Saaty, T. L. (1977). Modeling unstructured decision-making-AHP. In International Conference on Mathematical Modeling.
• Saaty, T. L. (1982). The analytic hierarchy process: A new approach to deal with fuzziness in architecture. Architectural Science Review, 25(3), 64-69. https://doi.org/10.1080/00038628.1982.9696499
• Savci, S. (2012). An agricultural pollutant: chemical fertilizer. International Journal of Environmental Science and Development, 3(1), 73.
• Sergi, D. (2021). Evaluation and prioritization of public service areas with fuzzy z-numbers based decision support models for digital transformation and industry 4.0 applications, Istanbul Technical University, Graduate Education Institute, Master's thesis, Istanbul, Turkiye, 220 pp.
• Shafiee, M. (2015). A fuzzy analytic network process model to mitigate the risks associated with offshore wind farms. Expert Systems with Applications, 42(4), 2143-2152. https://doi.org/10.1016/j.eswa.2014.10.019
• Sims, B., & Kienzle, J. (2017). Sustainable agricultural mechanization for smallholders: what is it and how can we implement it?. Agriculture, 7(6), 50. https://doi.org/10.3390/agriculture7060050
• Soberi, M. S. F., & Ahmad, R. (2016). Application of fuzzy AHP for setup reduction in manufacturing industry. J. Eng. Res. Educ, 8, 73-84.
• Spinelli, R., Magagnotti, N., Nati, C., Cantini, C., Sani, G., Picchi, G., & Biocca, M. (2011). Integrating olive grove maintenance and energy biomass recovery with a single-pass pruning and harvesting machine. Biomass and bioenergy, 35(2), 808-813. https://doi.org/10.1016/j.biombioe.2010.11.015
• Subramanian, N., & Ramanathan, R. (2012). A review of applications of Analytic Hierarchy Process in operations management. International Journal of Production Economics, 138(2), 215-241. https://doi.org/10.1016/j.ijpe.2012.03.036
• Takeshima, H., Edeh, H. O., Lawal, A. O., & Isiaka, M. A. (2015). Characteristics of Private‐Sector Tractor Service Provisions: Insights from N igeria. The Developing Economies, 53(3), 188-217. https://doi.org/10.1111/deve.12077
• Takeshima, H., Hatzenbuehler, P. L., & Edeh, H. O. (2020). Effects of agricultural mechanization on economies of scope in crop production in Nigeria. Agricultural Systems, 177, 102691. https://doi.org/10.1016/j.agsy.2019.102691
• Takeshima, H., Nin-Pratt, A., & Diao, X. (2013). Mechanization and agricultural technology evolution, agricultural intensification in sub-Saharan Africa: Typology of agricultural mechanization in Nigeria. American Journal of Agricultural Economics, 95(5), 1230-1236. https://www.jstor.org/stable/24476904
• Teklewold, H., Kassie, M., & Shiferaw, B. (2013). Adoption of multiple sustainable agricultural practices in rural Ethiopia. Journal of agricultural economics, 64(3), 597-623. https://doi.org/10.1111/1477-9552.12011
• Thirtle, C., Lin, L., & Piesse, J. (2003). The impact of research-led agricultural productivity growth on poverty reduction in Africa, Asia and Latin America. World Development, 31(12), 1959-1975. https://doi.org/10.1016/j.worlddev.2003.07.001
• Toğa, N. (2006). Ülkemizin Tarımsal Mekanizasyon Durumu, Sorunları ve Çözüm Önerileri. Tarımsal Mekanizasyon, 23, 6-8 (in Turkish).
• Turskis, Z., Goranin, N., Nurusheva, A., & Boranbayev, S. (2019). A fuzzy WASPAS-based approach to determine critical information infrastructures of EU sustainable development. Sustainability, 11(2), 424. https://doi.org/10.3390/su11020424
• Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal of Computers communications & control, 10(6), 113-128.
• Ustalı, N. K., & Tosun, N. (2019). Bulanık AHP ve Bulanık WASPAS yöntemleri ile yeni ürün seçimi. Pazarlama İçgörüsü Üzerine Çalışmalar, 3(2), 25-34.
• Van Loon, J., Woltering, L., Krupnik, T. J., Baudron, F., Boa, M., & Govaerts, B. (2020). Scaling agricultural mechanization services in smallholder farming systems: Case studies from sub-Saharan Africa, South Asia, and Latin America. Agricultural systems, 180, 102792. https://doi.org/10.1016/j.agsy.2020.102792
• Vernon, D., & Meier, A. (2012). Identification and quantification of principal–agent problems affecting energy efficiency investments and use decisions in the trucking industry. Energy Policy, 49, 266-273. https://doi.org/10.1016/j.enpol.2012.06.016
• Wang Chen, H. M., Chou, S. Y., Luu, Q. D., & Yu, T. H. K. (2016). A fuzzy MCDM approach for green supplier selection from the economic and environmental aspects. Mathematical Problems in Engineering, 2016. https://doi.org/10.1155/2016/8097386
• Wang, C. N., Nguyen, N. A. T., Dang, T. T., & Lu, C. M. (2021). A compromised decision-making approach to third-party logistics selection in sustainable supply chain using fuzzy AHP and fuzzy VIKOR methods. Mathematics, 9(8), 886. https://doi.org/10.3390/math9080886
• Wiebe, K. D. (2003). Linking land quality, agricultural productivity, and food security. USDA-ERS Agricultural Economic Report, (823). https://dx.doi.org/10.2139/ssrn.757869
• Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80. https://doi.org/10.1016/j.agsy.2017.01.023
• Xu, Z., & Liao, H. (2013). Intuitionistic fuzzy analytic hierarchy process. IEEE transactions on fuzzy systems, 22(4), 749-761. https://doi.org/10.1109/TFUZZ.2013.2272585
• Yang, J., Huang, Z., Zhang, X., & Reardon, T. (2013). The rapid rise of cross-regional agricultural mechanization services in China. American Journal of Agricultural Economics, 95(5), 1245-1251. https://www.jstor.org/stable/24476906
• Yazdani, M., Pamucar, D., Chatterjee, P., & Torkayesh, A. E. (2022). A multi-tier sustainable food supplier selection model under uncertainty. Operations Management Research, 15(1-2), 116-145. https://doi.org/10.1007/s12063-021-00186-z
• Yıldırım, C., & Altuntaş, E. (2015). Tokat ilinde traktör ve tarım makinaları kullanımından kaynaklanan iş kazalarının iş güvenliği açısından değerlendirilmesi. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG), 32(1), 77-90(in Turkish).
• Zadeh, L. A. (1965). Information and control. Fuzzy sets, 8(3), 338-353.
• Zadeh, L. A. (1975). Fuzzy logic and approximate reasoning. Synthese, 30(3), 407-428.
• Zadeh, L. A. (2015). Fuzzy logic—a personal perspective. Fuzzy sets and systems, 281, 4-20. https://doi.org/10.1016/j.fss.2015.05.009
• Zavadskas, E. K., Turskis, Z., & Bagočius, V. (2015). Multi-criteria selection of a deep-water port in the Eastern Baltic Sea. Applied Soft Computing, 26, 180-192. https://doi.org/10.1016/j.asoc.2014.09.019
• Zeren, Y., Tezer, E., Tuncer, İ. K., Evcim, Ü., Güzel, E., & Sındır, K. O. (1995). Tarım alet-makine ve ekipman kullanım ve üretim sorunları. Ziraat Mühendisliği Teknik Kongresi Tarım Haftası, 95, 9-13 (in Turkish).