Evaluating The Efficiency Of Cumin Planting Using The Dea Method (case Study: Agricultural Lands Of Talkhooncheh City)

By Author: Mohsen heydarizadeh
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Introduction
The increase in the size of the human population and as a result the increase in the need for food, shelter, and other products has led to an unprecedented increase in the rate of conversion of natural environments into agricultural lands, farms, and urban areas [1]. The exploitation of agricultural products is given considerable attention in research related to agricultural and rural issues, and it is considered an important indicator for national food security, maintaining the livelihood of farmers, and sustainable development[2]. And super-subsistence guarantees high performance [3]. Cumin is one of the spice seeds that is widely used by people all over the world [4]. Cumin is the second most popular spice in the world after black pepper and is well known for its aroma, and therapeutic and medicinal properties [5]. Cumin has an aromatic smell and a spicy taste, which makes it a great addition to foods such as soups, pickles, meats, bread, sauces, snack foods, and cakes. It is also used as a flavoring agent in various kinds of cheeses and food industries [6]. Cumin is directly cultivated as a traditional ...
... spice in India, Iran, Turkey, Egypt, and China. Medicines can also be used to treat indigestion, stomach headache, abdominal pain, and other diseases [7]. However, in some cases, obesity is treated with different drugs. Due to the many complications of these solutions, there is an increase in the use of herbal medicines, including sub-greens, to control excess weight [8]. Cumin is a great herb to add to meals to promote weight loss by increasing cells [9]. Cumin and fennel are curry compounds and are widely used in seasonings and herbal medicines [10]. In addition, studies have shown that fennel extract has great potential to replace some agricultural pesticides and some diseases. In the control of insect chastity, as an environmentally compatible insecticide, compared to traditional insecticides, they can directly prevent bees from being killed [11]. Cumin is a flowering plant from the chrysanthemum family. The value of cumin seeds is carbohydrates, receiving, growing, thiamin, riboflavin, niacin, vitamin B6, vitamin E, and some minerals such as calcium, iron, magnesium, phosphorus, potassium, sodium, zinc, and other rare elements [12]. Cumin is also well known for its antioxidant properties, and its methanol extract contains 9 mg of total phenolics per gram of dry weight [13]. The Total phenolic food of cumin is 0.23 grams equivalent to gallic acid 100 grams of dry weight [14]; Therefore, due to their antioxidant and antimicrobial properties that improve the shelf life of food, they are recommended as a preservative [15]. By increasing the amount of bile acid production and consumption and increasing digestive enzymes (amylase, trypsin, chymotrypsin, and lipase) in the pancreas, cumin acts as a food digestion stimulant [16]. North Asia, North Africa, and especially India and Iran are known as the largest producers and manufacturers of cumin products [17]. The main area of cumin production in Iran includes arid and semi-arid areas in central, eastern, and southeastern provinces[18].
Data Envelopment Analysis (DEA) is a non-principled statistical method based on the concept of relative efficiency presented by other experts[19]. Data envelopment analysis (DEA) is based on mathematical programming and has been significantly used to evaluate the relative performance of similar decision-making units (DMU). In addition, the ability to analyze the results of DEA has given the wide application of the method in various fields of increase[20]. Data Envelopment analysis (DEA) is used to evaluate the efficiency of decision-making units (DMU) in a multi-input and output production system. In general, DEA models are either input-oriented (decreasing inputs, keeping outputs unchanged) or output-oriented (increasing the level of outputs with a given level of inputs). To determine the efficiency of crop and livestock production devices from DEA in many cases Studies have been used in agriculture[21]. DEA can evaluate the technical efficiency of multiple decision-making units, and DEA is widely used in agriculture, finance, transportation, and other fields[22]. It is a powerful tool that has been used in various problems, where managers want to perform performance evaluation and analyze decision-making options [23].
Literature Review
Rathi et al used the DEA method and Interval DEA and analyzed the relative efficiency of life insurance companies in India. They used the interval DEA model in measuring the efficiency of life insurance companies under imprecise input and output. Empirical results of conventional DEA models and interval DEA models have been calculated to track the performance of the decision-making unit at a level [24]. Khannaev used data envelopment analysis (DEA) under the assumptions of constant returns to scale and variable returns to scale and analyzed the cost efficiency of Uzbek banks from 2013 to 2018 and found that the cost efficiency of Uzbek banks is increasing on average and also found that banks that are directly or indirectly state-owned have more efficiency on average and private banks have the lowest efficiency[25]. Sufang has set the main objective of his study to estimate and analyze the total factor productivity of the tea industry in China to discover the source of technical productivity , and collected his study data from 18 major tea-producing provinces in China from 2015 to 2019. Data Envelopment Analysis (DEA) program and the DEA Malmquist index have been used to estimate efficiency scores. The results have shown that TFP is increasing and there is a big difference in TFP in different provinces. The main reason for the improvement of TFP is the increase in technological change and technical productivity affected by net technical efficiency and scale efficiency. The findings indicate that elderly farmers with better education combine their previous knowledge in the field of agriculture with the use of appropriate agricultural methods and can achieve production efficiency [26]. Vörösmarty et al. summarized the findings of supplier evaluation and supplier evaluation articles using data envelopment analysis (DEA) published between 2009 and 2018 to examine how DEA, one of the most common methods of supplier evaluation and supplier selection process and related decisions. Provides support to management. Their literature review includes 54 articles that propose the use of a form of DEA to support supplier management decisions. They used descriptive and multivariate statistics to cluster the reviewed articles. A limited number of articles have been identified that are more practice-oriented and support strategic decision-making in supplier management. Papers dealing with sustainability show their focus only on green factors, but in most cases, this means an additional criterion in the evaluation, and finally, it was found that sustainability issues do not generate new versions of the DEA model. [27]. Xiao et al. presented a two-stage network DEA framework including government and industry sectors and measured the environmental productivity of 84 resource-based cities during the post-financial crisis period of 2007 to 2015. The results have shown that the average environmental productivity in China's resource-based cities has shown a promising increase, and there is a positive relationship between government efficiency and production efficiency. The trend of decreasing government efficiency in the central, western, and northeastern regions after 2014 shows the low quality of the public sector in the use of financial income. The network's two-step DEA framework has helped to gain more insight into the internal management deficiencies of the government and industrial sectos, and to increase their cooperation to accurately improve environmental efficiency [28]. Afzalinejad has used conventional modeling in developed DEA to develop a new radial model for evaluating efficiency in the presence of adverse outputs. This method has enabled more accurate modeling of the problem. A new window has been opened to this topic and more reliability than existing methods has been shown. The new performance measure is more accurate and provides higher resolution. Evaluating the efficiency of segmentation is another advantage of the model. It has also provided credible goals with greater practicability for any inefficient decision-making unit. This model has been investigated with an obvious approach and has been used to evaluate the performance of 28 countries in economic, social, and environmental dimensions. The results have shown that only one country achieves efficiency and the average environmental efficiency is significantly lower than operational efficiency among the studied countries [29]. Cong et al. presented a new energy supply efficiency evaluation model of integrated energy systems based on comb-based measurement data envelopment analysis (SBM-DEA) Monte Carlo integration for energy saving and optimization. The integrated energy systems’ extensive energy supply data is verified by Monte Carlo F-test. Then, the cooling load, heating load, electric load, and internal combustion engine load are considered as input, and cooling income, heating income, and electricity sales are considered desirable output and carbon tax as undesirable output to build the energy supply efficiency evaluation model of integrated energy systems with The use of SBM-DEA is determined for energy optimization and carbon tax reduction. The results have shown that the proposed method has high discrimination to achieve effective decision-making units (DMU). In addition, energy optimization and carbon tax reduction of integrated energy systems have been achieved [30]. Xiao et al. built three models according to the variation of the DEA structure under the mean-variance framework, and to describe the parameter uncertainly in the proposed DEA models, they considered the portfolio return expectation and covariance as interval values, and the two-level programming models and corresponding equivalent models were also used for Obtaining the lower and upper bounds of portfolio efficiency is presented. 30 US industry portfolios have been selected and some empirical analyzes have been performed on different subsets of data to find out which model is more robust in dealing with the impact of parameter uncertainty on portfolio performance and their ranking. Finally, some power tests are presented to further examine the consistency of the findings [31]. Khodadadipour has presented a new stochastic model called predicted ranking criterion by using an input-oriented data envelopment analysis (DEA) model with unfavorable outputs. The proposed model has used statistical techniques to evaluate the efficiency of decision-making units (DMU) with random data. Based on the proposed model, an efficient stochastic crossover DEA (SDEA) model is proposed for DMU ranking and discrimination. Then, due to the non-uniqueness of the resulting optimal solution, a stochastic model for ranking priorities is introduced by which mutual efficiency evaluation is performed using an aggressive approach. Finally, the proposed models have been implemented to evaluate 32 thermal power plants. The results have shown the application of the proposed models [32]. Liu et al. presented a new probabilistic linguistic decision-making method with a consistency improvement algorithm and data envelopment analysis (DEA) with cross-productivity. In the first step, the concept of order compatibility of probabilistic linguistic preference relation (PLPR) is presented. Order consistency is useful for decision-makers to make quick and efficient decisions in certain situations. Then, based on the multiplicative PLPL composition, a compatibility improvement algorithm is developed to transform unacceptable multiplicative compatible PLPRs into acceptable ones. In addition, a DEA model is developed to obtain the preference vector of option weights from the acceptable multiplicative adaptive PLPR. Meanwhile, for options with equal priority weight, a DEA cross-efficiency model has been used to further differentiate and obtain the final ranking of the options. Finally, a numerical example of emergency logistics distribution selection is presented to demonstrate the effectiveness and applicability of the proposed method [33]. Amin et al. discussed the role of alternative optimal solutions in data envelopment analysis (DEA) models to evaluate mutual efficiency in portfolio selection in their study and it was found that the combination of alternative optimal solutions to build the mutual efficiency matrix is the result of the average variance portfolio selection method. Improves This improvement means that it is possible to create securities with lower risk and higher expected returns if alternative optimal solutions are investigated. The proposed method for selecting the stock portfolio has been applied in the Tehran stock market [34]. Yeşilyurt et al. presented a unique method to transform multiple outputs into a single virtual output, which uses a proposed method close (or even depending on the target parameters at the cost of computation time and resources) to obtain efficiency scores from the computed single and to Efficiency scores from multiple DEA outputs. Allows SFA to be used with a virtual outlet. The proposed method has been validated using a simulation study, and its applicability has been demonstrated using a real-world application using a hospital dataset from Turkey [35]. Luo et al. developed a general efficiency measurement model for the construction industry, and then a target data envelopment analysis model was used to determine the optimal routes for low-efficiency provinces by achieving stage targets in different regions. In different regions of China, a comparison has been made about construction productivity, resource utilization, and recommendations for further improvement in three regions with different levels of economic development. Analytical and experimental results have provided information for policy-making and strategic planning by gradually improving the efficiency of China's regional construction industry [36]. Mohsin et al. used a data set collected from forty-eight countries in five different regions. Data envelopment analysis (DEA) and difference-in-difference (DID) methods have been used to observe the effect of energy reforms. According to the DEA results, Nepal, Bangladesh, and Singapore performed poorly in improving energy efficiency due to reforms, while Uzbekistan performed the least in this area. DID results have shown that sub-Saharan African countries have performed better after implementing energy reforms in the region. These results have confirmed that energy modification can be a good means to achieve a high level of energy efficiency and reduce unit energy cost. The results have shown that there is a 13.2% improvement in energy efficiency after electricity reforms. Based on empirical results, this study has identified specific policy outcomes [37]. Mustafa et al. compared the technical efficiency of ports in South Asia and the Middle East with ports in East Asia and determining ways to increase efficiency and optimize management. Cross-sectional data for 2018 were collected for 15 container ports each in the South, Middle East, and East Asia regions and sorted into input and output variables. The data has been analyzed through DEA-CCR and DEA-BCC models. The results have shown that only one port from the United Arab Emirates and India among the ports of Central and South Asia in the CCR model has increased by 47% with the number of efficient ports in the BCC model. While in the East Asia region, two Chinese ports and one from South Korea became efficient in the CCR model with a 33% increase in the BCC model. Lian Yungang Port has become the most prominent among the efficient ports. The average yield for the East Asia region (CCR: 0.524, BCC: 0.901) is similar to the yield for the South Asia and Middle East region (CCR: 0.517, BCC: 0.906) [38]. Shi et al. have proposed a new comb-based network data envelopment analysis (SBM-NDEA) model to evaluate the performance of manufacturing processes that have a complex structure containing series and parallel processes. The evaluated method is illustrated by evaluating Chinese commercial banks during the years 2012 to 2016. The operating process of these banks has been divided into the processes of deposit production and deposit use, which are connected serially, while the process of using the deposit is further divided into the processes of making profits and saving deposit interest, which are connected in parallel. The overall return is broken down into deposit production and deposit productivity. The deposit with higher productivity has been divided into profitability and deposit reserve interest, respectively. Empirical results have shown that the overall inefficiency is mainly achieved through the profit generation process. Also, the results have estimated the adjustment of variables for the network process of an inefficient bank [39].
Theoretical foundations of research
Performance measurement is an important management task. It not only shows a unit's past performance compared to other similar units so that it can distribute appropriate rewards, but also identifies unfavorable factors so that improvements can be made to improve future performance. Various models have been proposed to measure efficiency[40]. The DEA model was a method to evaluate efficiency based on the concept of relative efficiency, which is currently applied in many fields. Data envelopes can be obtained by evaluating the relative effectiveness between multiple input and output decision units using a mathematical programming model [41]. The decision-making units that were at the top of the limit were considered effective combinations and the efficiency value was 1, while the decision-making units that were not at the top of the limit were inefficient and the efficiency value was between 0 and 1 [42]. In the literature, measuring the relative production efficiency of a set of manufacturers or decision-making units (DMUs) generally requires the same types of possible inputs and the same outputs used by these DMUs. In general, the best DMU performance in the set of efficiency scores will be one, while the performance of other DMUs varies between zero and one compared to this best performance [43]. Data envelopment analysis (DEA) is based on mathematical programming and has been widely used to evaluate the relative performance of similar decision-making units (DMU). In addition, the ability to analyze DEA results has continuously increased the application of the method in various fields [44]. The DEA model was a method to evaluate efficiency based on the concept of relative efficiency, which is currently applied in many fields. DEA can be obtained by evaluating the relative effectiveness between multiple input and output decision units using a mathematical programming model [45]. DEA uses mathematical programming tools to evaluate the efficiency of each member of a set of DMUs relative to other members in that set. In the process of evaluating the performance of each DMU, DEA creates an efficient frontier where the best-performing units are located. Other units that are not on the efficient frontier are said to be inefficient. Also, DEA provides the possibility to determine how DMUs should be changed for efficiency through the radial design to the boundary [46].
methodology
In 1978, "Edward Rhodes" based on the work of Farrel (1957) in his doctoral thesis under the guidance of two of the greatest professors in operations research, namely " William Cooper and Abraham Charnes", a new method for evaluating the performance and efficiency of decision-making units called Data envelopment analysis (DEA) This method, which is a generalization of Farrel's efficiency measurement method, due to its mathematical basis and extraordinary ability to calculate efficiency, was quickly welcomed by the world's scientific and research communities as well as the managers of various organizations; and grew very quickly; so that The publication of hundreds of different scientific articles shows the ability, application and unique capability of this method in various fields.
The use of the Data envelopment analysis model, for the relative evaluation of the units, requires the determination of two basic characteristics, the nature of the model and the yield to the scale of the model, each of which is explained below.
The essence of the DEA model is:
a) The nature of the input: If we try to minimize the inputs by keeping the output level constant in the evaluation process, the nature of the used pattern is the input.
b) The nature of the output: if we try to increase the level of the output by keeping the level of the inputs constant in the evaluation process, the nature of the model used is the output.
In the DEA model, from the input point of view, we seek to obtain technical inefficiency as a ratio that must be created in the inputs so that the unit in question reaches the efficiency limit.
The types of DEA models in general are: The CCR model and the BCC model.
The most important DEA model is the Charnes, Cooper, and Rhodes model, known as CCR, which was introduced in 1978 and can consider multiple outputs with multiple inputs at the same time in a unit measurement of efficiency and present it as a score among similar organizational units.
Return to scale represents the link between the changes in inputs and outputs of a system. One of the capabilities of the DEA method is the use of different patterns corresponding to returns to different scales, as well as the measurement of returns to the scale of units.
Constant returns to scale: that is, every multiple of inputs produces the same multiple of outputs. The CCR model assumes constant returns to the scale of units.
Variable returns to scale: that is, any multiple of inputs can produce the same multiple of outputs or less or more than that in outputs. The BCC model assumes variable returns to scale.
To implement DEA, the following steps are necessary:
• Determining a set of targets for comparison
• Determining evaluation characteristics, distinguishing between outputs and inputs
• Collect data, showing characteristic values for each objective
• Analyzing and interpreting the results
In this article, an input-oriented approach and return to fixed scale have been used.
Formula 1 shows the constant return to the scale-input-oriented-initial (multiplicative) model:
MAX Z_0=∑_(r=1)^s▒u_r y_r0
St: (1)
∑_(i=1)^m▒〖v_i x_i0=1〗
∑_(r=1)^s▒〖u_r y_rj-∑_(i=1)^m▒〖v_i x_ij≤0〗〗
u_r,v_i≥0 (j=1,2,…,n)

Case study
The planting of cumin has received a special welcome from the farmers of the Islamic Republic of Iran to the farmers of Talkhooncheh. Cumin is known as one of the most important medicinal plants in the Islamic Republic of Iran. Cumin is registered not only in the list of the oldest medicinal plants in the world; but also in the list of the most recent sources. Cumin has characteristics such as a short growing season, low water requirement, non-interference of its growing season with other agricultural products, high economic justification compared to other products, and suitability for cultivation in arid and semi-arid areas.
In agriculture, the evaluation of production efficiency is always of interest, and the evaluation of cumin efficiency is no exception to this rule. Considering the importance of agriculture in the Islamic Republic of Iran and considering the planting of cumin, it is important to evaluate the effectiveness of cumin, which is one of the products used by farmers. The program uses the mathematical model of Data envelopment analysis (DEA) to evaluate the efficiency of agricultural products. In this model, to evaluate efficiency, input and output factors with similar characteristics can be classified into two input and output groups. In this article, by considering the two criteria of costs and the area of planting cumin as input variables and also considering the two criteria of income and the amount of cumin crop as output variables and using DEA SOLVER software to evaluate the efficiency and ranking of agricultural fields The city of Talkhoonche, where the cumin crop is harvested, has been discussed. Table 1 and Table 2 include input and output changes, respectively, and the following information is related to 2021. Table 1 contains the input variables as follows:
Land area ( acre) Cost (Dollar) Variable Farm
50 1221 Farm No. 1
40 698.276 Farm No. 2
20 209.482 Farm No. 3
30 279.310 Farm No. 4
10 69.827 Farm No. 5
40 1326 Farm No. 6
50 872.844 Farm No. 7
30 157.112 Farm No. 8
4 174.569 Farm No. 9

Table 2 includes the output variables as follows:
Amount of harvest (kg) Income (Dollar) Variable
Farm
3500 5586 Farm No. 1
480 872.844 Farm No. 2
1600 1564 Farm No. 3
500 558.620 Farm No. 4
500 1221 Farm No. 5
2000 2793 Farm No. 6
1000 2094 Farm No. 7
1000 1396 Farm No. 8
80 111.724 Farm No. 9

In this research, 9 decision-making units were examined according to 2 inputs and 2 outputs. The type used in this research is constant return to scale and is based on the basic model and input-oriented approach.
By giving the information in the above tables to the DEA SOLVER software, the efficiency of each of the 9 farms has been obtained. The efficiency value is according to the model defined in Table 3. In addition to the efficiency value, its type can also be seen in Table No. 3:
Type of performance The amount of efficiency Farm
Strong performance 1 Farm No. 1
ineffective .304 Farm No. 2
ineffective .645 Farm No. 3
ineffective .463 Farm No. 4
Strong performance 1 Farm No. 5
ineffective .714 Farm No. 6
ineffective .353 Farm No. 7
ineffective .889 Farm No. 8
ineffective .286 Farm No. 9

If the efficiency of a unit is less than 1, it is inefficient.
If the efficiency of a unit is equal to 1 and there is no shortage in the output and no surplus in the input, it is a strong efficiency.
If the efficiency of a unit is equal to 1 and it has a deficit in the output or an excess in the input, it is poor efficiency.
Figure 1 shows the efficiency values of 9 farms in the agricultural fields of Talkhooncheh where cumin is planted and studied in this research.

In this way and by the using DEA method in this research, it was found that the efficiency of cumin crops in farm No. 1 and Farm No. 5 are efficient and the rest of the farms are inefficient.
Conclusion
Paying attention to the evaluation of the efficiency of agricultural products and especially the cumin product has been noticed due to the special position of this product and the importance of the agricultural issue for our country; Therefore, great care must be taken in the process of evaluating the efficiency of crops and this issue can directly affect the success of agriculture in the Islamic Republic of Iran. The problem of evaluating the efficiency of agricultural products is a strategic issue and it should be considered fundamentally, this process should not be based on people's taste, and instead of personal vision and experience, a mathematical analysis should be used. The method of data coverage analysis facilitates the evaluation of the efficiency of cumin and other agricultural products. In this study, to evaluate the efficiency of planting cumin in the agricultural lands of Talkhooncheh city, the information that existed among the local farmers of Talkhooncheh in 2021 was used, and the DEA method was used to evaluate the efficiency of cumin in the agricultural lands of Talkhooncheh. And it is suggested that people who are interested in evaluating the efficiency of cumin and other agricultural products use the DEA method, the obtained results showed that the DEA method is effective in evaluating the efficiency of planting cumin in the agricultural lands of Talkhooncheh and other agricultural products. And the use of the DEA method can be helpful for researchers.
Reference

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