A dynamic multi-stage network data envelopment analysis approach for evaluating performance of wheat farms

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2023-07-20 DOI:10.1108/jm2-09-2022-0213

Shahin Rajaei Qazlue, Ahmad Mehrabian, K. Khalili-Damghani, M. Amirkhan

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Abstract

Purpose Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions. Design/methodology/approach A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods. Findings The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method. Originality/value To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

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基于动态多阶段网络数据包络分析的小麦农场绩效评价方法

目的由于小麦工业在经济中的重要性，对小麦生产过程进行真实的性能测量是必不可少的。本文的目的是开发一个与小麦生产过程完全兼容的数据包络分析（DEA）模型，以便管理者和农民能够使用它来评估小麦农场的战略决策效率。设计/方法论/方法建立了一个动态多阶段网络DEA模型，用于评估短期（两年）和长期（八年）小麦生产农场的效率。研究结果表明，由于缺乏长期规划和过度依赖雨水，大多数调查区域的效率不稳定，并且随着时间的推移，区域的效率呈Z字形变化。在所研究的区域中，只有Hashtrood区域具有高且稳定的效率，其他区域可以效仿该区域的培养方法。独创性/价值据作者所知，本研究是第一次使用动态多阶段网络DEA，考虑每隔一年的种植方法和农业部门的直接-间接投入。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.