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

Hüseyin Fatih Atlı

doi:10.31015/jaefs.2024.1.4

EN

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

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.

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English

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Marketing in Agricultural Management

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Research Article

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Hüseyin Fatih Atlı ^*
0000-0002-1397-1514
Türkiye

Publication Date

March 25, 2024

Submission Date

October 31, 2023

Acceptance Date

February 7, 2024

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Year 2024 Volume: 8 Number: 1

DOI

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

IZ

https://izlik.org/JA76EM99AF

APA

Atlı, H. F. (2024). Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity. International Journal of Agriculture Environment and Food Sciences, 8(1), 25-43. https://doi.org/10.31015/jaefs.2024.1.4

AMA

1.Atlı HF. Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity. int. j. agric. environ. food sci. 2024;8(1):25-43. doi:10.31015/jaefs.2024.1.4

Chicago

Atlı, Hüseyin Fatih. 2024. “Safety of Agricultural Machinery and Tractor Maintenance Planning With Fuzzy Logic and MCDM for Agricultural Productivity”. International Journal of Agriculture Environment and Food Sciences 8 (1): 25-43. https://doi.org/10.31015/jaefs.2024.1.4.

EndNote

Atlı HF (March 1, 2024) Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity. International Journal of Agriculture Environment and Food Sciences 8 1 25–43.

IEEE

[1]H. F. Atlı, “Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity”, int. j. agric. environ. food sci., vol. 8, no. 1, pp. 25–43, Mar. 2024, doi: 10.31015/jaefs.2024.1.4.

ISNAD

Atlı, Hüseyin Fatih. “Safety of Agricultural Machinery and Tractor Maintenance Planning With Fuzzy Logic and MCDM for Agricultural Productivity”. International Journal of Agriculture Environment and Food Sciences 8/1 (March 1, 2024): 25-43. https://doi.org/10.31015/jaefs.2024.1.4.

JAMA

1.Atlı HF. Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity. int. j. agric. environ. food sci. 2024;8:25–43.

MLA

Atlı, Hüseyin Fatih. “Safety of Agricultural Machinery and Tractor Maintenance Planning With Fuzzy Logic and MCDM for Agricultural Productivity”. International Journal of Agriculture Environment and Food Sciences, vol. 8, no. 1, Mar. 2024, pp. 25-43, doi:10.31015/jaefs.2024.1.4.

Vancouver

1.Hüseyin Fatih Atlı. Safety of agricultural machinery and tractor maintenance planning with fuzzy logic and MCDM for agricultural productivity. int. j. agric. environ. food sci. 2024 Mar. 1;8(1):25-43. doi:10.31015/jaefs.2024.1.4

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

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

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