Agricultural Economics
M. Mardani Najafabadi; A. Abdeshahi; E. Ahani
Abstract
IntroductionThe relationship between economic development and the environment is known as one of the most important issues facing societies. If in the context of sustainable development, economic and environmental activities are considered together, the environment and economic development are two complementary ...
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IntroductionThe relationship between economic development and the environment is known as one of the most important issues facing societies. If in the context of sustainable development, economic and environmental activities are considered together, the environment and economic development are two complementary factors and, as a result, it will lead to ecological balance. In this case, economic activities will not disturb this balance. Presently, the imperative of safeguarding the environment and attaining sustainable development has ascended to a prominent position on the agendas of diverse societies, Iran included. This commitment is underscored by the execution of comprehensive economic, social, and cultural initiatives aimed at fostering long-term ecological resilience and balanced societal progress. Therefore, to preserve the environment and meet the goals of sustainable development, as well as to guide and rationally manage plans and projects, especially in the agricultural sector, serious measures should be taken. Therefore, this study was carried out to evaluate the operational, environmental, and eco-efficiency of the major agricultural products of the irrigation and drainage networks of Gotvand.The irrigation and drainage network of Gotvand is located in the southwest of Iran in Khuzestan province. This network is designed to irrigate lands located in three regions of Gotvand, Aghili, and Dimcheh, enclosed between two rivers, Karun and Lor. According to the official statistics of government organizations, the consumption of fertilizers and chemical poisons in the lands covered by this network is 3.6 times the average limit in Iran. The excess irrigation water in this network is returned to the rivers by the built-in drains and causes water pollution downstream of the network. Therefore, considering that environmental protection is one of the most important aspects of sustainable development, it is very important to investigate the effects of the use of pesticides and chemical fertilizers in agriculture and to introduce solutions to improve the efficiency of the environment in the study area. Materials and MethodsEco-efficiency includes operational and environmental impacts, which are presented as the ratio of the weighted sum of outputs to the weighted sum of inputs (operational inputs + environmental inputs). However, since agricultural activities are carried out in uncertain environmental conditions, there is uncertainty regarding inputs and outputs. The uncertainty in some of the effective input and output parameters in the ranking of networks, and as a result, the inaccuracy of the model calculation results, and the need to pay attention to the use of uncertainty models, make it more obvious. Therefore, in the present study, to include the conditions of uncertainty and risk, the robust data envelopment analysis (RDEA) model was used, which is one of the most powerful and useful models in conditions of uncertainty. The required data were collected by completing a questionnaire of the Gotvand, Aghili, and Dimche regions using a simple random sampling method in 2019. Results and DiscussionThe alfalfa producers in the Gotvand region assigned the highest environmental and Eco-efficiency by obtaining points in the range of 81 to 89 percent and 90 to 96 percent, respectively. The rice crop in the Aghili region had the highest types of operational efficiency based on different levels of deviation probability in the range of 77-87%, environmental efficiency in the range of 80-90%, and environmental-economic efficiency in the range of 87-95%. Dimanche sugarcane region has the highest average of efficiency types for different levels of deviation probability by obtaining points in the range of 78 to 90, 80 to 89, and 87 to 95 respectively for operational, environmental, and Eco-efficiency. Comparing the results of technical efficiency with environmental efficiency shows the lack of attention and skill of farmers in the correct and optimal use of production inputs. Therefore, it is necessary to hold educational and promotional classes to empower farmers to improve production methods and optimal consumption of inputs to improve farmers' income and increase their profits. Given that a substantial portion of energy consumption within the agricultural sector is attributed to fuels and diesel, optimizing energy usage and promoting the adoption of newer, less polluting energy sources emerge as crucial imperatives. Enhancing environmental efficiency in this context involves a strategic focus on reducing reliance on traditional, environmentally taxing energy forms in favor of more sustainable alternatives. ConclusionThe average operating efficiency in all different probability levels for the studied products in Goutvand , Aghili, and Dimche areas, except for beans in the Gatund area, was estimated to be lower than the average environmental efficiency. This shows the lack of ability and skill of farmers to produce a certain product with the lowest amount of input, while the farmers of these areas pay great attention and care to environmental issues.
Agricultural Economics
M. Mardani Najafabadi; A. Abdeshahi; F. Yavari; F. Naghibeiranvand
Abstract
Introduction: The cultivation of edible mushrooms is expanding rapidly due to its nutritional and medicinal values as well as its economic benefits. However, lack of knowledge and principled management may cause many problems for producers or even bring them closer to the bankruptcy brink. The first ...
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Introduction: The cultivation of edible mushrooms is expanding rapidly due to its nutritional and medicinal values as well as its economic benefits. However, lack of knowledge and principled management may cause many problems for producers or even bring them closer to the bankruptcy brink. The first step to improve the efficiency of units is finding an appropriate method to measure it. Data Envelopment Analysis (DEA) is one of the methods that is widely used to evaluate the relative efficiency of a homogenous set of DMUs. Despite the many advantages of this model, the high sensitivity of DEA to even a small change in the data reduces the validity of its results. In fact the conventional DEA assumes that input and output data are without any deviation. However, the observed values of the input and output data in real-life problems are sometimes imprecise or vague. So In this paper, to deal with uncertainty in data the linear robust optimization framework of Bertsimas and Sim (2004) was used to compare technical efficiency of Iranian mushroom-producing provinces and determine the optimum use of inputs.Materials and Methods: According to the purpose of this study, a robust data envelopment analysis (RDEA) model with imprecise inputs and outputs was used. The method is based on the robust optimization approach of Bertsimas and Sim (2004) which with the introduction of the conservative parameter (Γ) for each constraint, adjusts robustness in an optimisation model against the level of conservatism of the solution. The value of Γ is dependent on the maximum probability of constraint violation (p) and numbers of uncertain data in every constraint (n). So this RDEA model allows adjustment of level of robustness of the solution to trade-off between protection against constraint violation and conservatism of efficiency scores. In order to estimate the models, the GAMS software was used and related data was gathered from Statistical Center of Iran.Results and Discussion: In this paper to distinguish the causes of technical inefficiency, pure technical efficiency and scale efficiency were measured. According to the results of this model, at all levels of P, the pure technical efficiency was higher than the scale efficiency and technical efficiency, and its value was higher than 98% in all cases. This indicates that mushroom producers have a high level of knowledge and skills in this field and shows that the cause of low technical efficiency of the producers is their non-optimal scale. In addition, according to the results of both RDEA and DEA models, the most important input that has caused the inefficiency of the units is the "seed cost" input and with optimal use of this input, the cost of that can be reduced by about 70% (in ε=0.1 and P=1). Another result of this study is that with the reduction of the Probability of constraint violation, the rate of technical efficiency has decreased. For example in ε=0.1, if P is reclined from 1 (no protection against uncertainty) to 0.8 and 0.1, the average technical efficiency is reduced from 93% to 89% and 85% respectively. Also when ε is increased from 10 to 20 and 30 percent (in P=0.1) the average technical efficiency is reduced from 85 to 83 and 82 percent. On the contrary by reducing P, the percentages of reduction compare to the actual value is increased. For instance by reducing P from 1 to 0.8 and 0.1 the percentages of reduction of "seed cost" are decreased from 70% to 78% and 80% respectively. This results highlights the importance of using RDEA models to more conformity of the results to the real world.Conclusion: Based on the results the low technical efficiency of the producers is because of their non-optimal scale. Therefore, it is recommended to consider the optimal size unit for those who want to enter this activity. On the other hand, the policymakers should improve access to facilities so the small units could enlarge their unit if it's necessary. Also considering the experience of successful mushroom farms, self-reliance in production of mushroom seeds can greatly reduce inefficiency of the units. Eventually considering that the level of uncertainty has a great impact on the efficiency results and the optimal level of inputs, future researches on the appropriate level of uncertainty according to the real conditions of production can improve the results of the RDEA model.
A. Hashemi Nejad; A. Abdeshahi; M. Ghanian; B. Khosravipour
Abstract
Introduction: One of the most important challenges facing the world is how to feed expected population by 2050. Despite trying to increase food production over the past half-century, food security has been a strategic issue and an important goal of agricultural policies in many countries by challenges ...
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Introduction: One of the most important challenges facing the world is how to feed expected population by 2050. Despite trying to increase food production over the past half-century, food security has been a strategic issue and an important goal of agricultural policies in many countries by challenges including population grow, increasing demand, natural resources erosion, etc. One of the critical dimensions in achieving food security is expanding food supply chain. A food supply chain can be defined as a set of interdependent components include of input supply, production, storage, processing, marketing, distribution and consumption or as the activities from ‘farm to fork’. Bread supply chain in Iran, is one of the most important food supply chain because bread is considered as the most important food source and is staple food of choice, so it has a special place in household’s nutrition pattern that supply 46.2% and 59.3% of energy for urban and rural people. Also, wheat is the raw material of bread and one of the strategic and critical crops in Iran agriculture. More than 80% of wheat consumption in Iran is predominantly used for bread. Wheat is the staple food of the national diet of Iranian households, who draw, on average, 47% of their daily calorie from wheat products. Although the population of Iran is nearly 1% of world population, it consumes roughly 2.5% of wheat produced worldwide. But, wheat is exposed to different kinds of risks such as natural disasters, including environmental concerns and climate change, pests and diseases, market vacillations and government policy that affect bread supply chain performance. So, the objective of this study is to explore factors affecting wheat production risk in bread supply chain.Materials and Methods: In this study regression analysis was used to determine the effects of variables on wheat production risk. The used data was time series for wheat production, wheat guarantee price, harvested area, rainfall, temperature, wheat axial plan, seed consumption, wheat import and export variables during 1982-2014. In order to explore factors affecting wheat production risk, at first wheat production variance as the risk criterion was estimated by ARCH (2) Model. The used data in the study was time series and therefore applying Ordinary Least Squares method in estimating regression equation would lead to pseudo regression. Since based on Augmented Dicky-Fuller method, variables were combination of I (0) and I (1), therefore Autoregressive Distributed Lag Model has been used to determine short run and long run relationship.Results and Discussion: Results revealed that wheat production risk was affected by population, wheat imports, rainfall, wheat guaranteed prices, harvested area and wheat axial plan variables which population, import, rainfall, harvested area had a positive effect and guaranteed price and wheat axle plan had a negative effect on wheat production risk. Therefore increasing population growth, import, rainfall and harvested area would lead to risk increase while increasing price and the implementation of wheat axis plan would reduce wheat production risk. So, increasing population and consumption, have also increased wheat import in recent years. While wheat import have reduced domestic production and farmers' incentives that would lead to increased wheat production risk. The tools used by governments for increasing domestic production against wheat import and increasing producer’s incentives are guaranteed price and wheat axial plan. Another effective cause of wheat production risk was climate changes and extreme weather events. Farmers’ economic profit was influenced severely and even determined by climate changes and weather events. Also, during this period, wheat harvested area had nearly doubled. This growing trend has also increased the risk of wheat production.Conclusion: wheat is a strategic crops in Iran. So, it is necessary to reduce its production risk. Wheat production risk was reduced by applying weather-based crop insurance scheme, sustaining the guaranteed price of wheat, supporting plans such as wheat axial plan, improving policies such as wheat imports and optimizing harvested area.
A. Abdeshahi; M.R. Ghorbani
Abstract
Introduction: Providing food for people is one of the most important concerns of planners and policymakers in different communities. Protein is among the food needed for humans and lack of it in diet can lead to malnutrition and thus endanger human health. Parallel to population growth and changing consumption ...
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Introduction: Providing food for people is one of the most important concerns of planners and policymakers in different communities. Protein is among the food needed for humans and lack of it in diet can lead to malnutrition and thus endanger human health. Parallel to population growth and changing consumption patterns towards more protein substances consumption, as well as the relative expensiveness of red meat in recent decades, poultry industry has been considered in Iran and in the world. Chicken meat is one of the most important sources of protein. Thus, reducing the cost as well as increasing its production can improve nutrition level and have an effective role in human health. It is assumed in economy that the producer's goal is to maximize profits. Producers of broiler chicken are no exception to this, and although they may pursue other goals, such as their own employment and their families, they seek to maximize the profits of their assets, like any other producer. Among the most important ways of achieving this goal are the proper use of production inputs as well as optimal scale production, which is used to determine the proximity of a decision maker unit to the optimal scale. Optimal scale refers to the amount of production in which the elasticity of scale equals one, and also there is a constant return to scale in production. Hence, it should be considered that scale efficiency measures average productivity while production takes place in optimal scale. If a firm exceeds the optimal scale, it has a decreasing return to scale, and if it runs at a level lower than the optimal scale, it will have an increasing return to scale. In agricultural economics, scale efficiency is usually nonparametric and is estimated in the framework of data envelopment analysis (DEA). Although many studies have attempted to estimate technical efficiency using parametric and nonparametric methods, scale efficiency is estimated exclusively in the framework of the nonparametric technique of data envelopment analysis. Ray (1998) presented a parametric method in which the scale efficiency is calculated by estimating a production function under the variable returns-to-scale hypothesis and from the estimated scale elasticity. Regarding the technical efficiency and scale efficiency of broiler and laying chicken breeding units, numerous studies have been conducted on the mentioned subject. But all of these studies, particularly in Iran, have used the data envelopment analysis method to measure the scale efficiency. In the present study, the scale efficiency of broiler chicken production units is calculated by parametric method through estimating the stochastic frontier function for the first time in Iran.Materials and Methods: In this study, the parametric method of estimating the scale efficiency, first presented by Ray (1998), is used to calculate the scale efficiency of broiler chickens breeding units in Khuzestan province. First, the frontier production function introduced by Lovell and Schmidt (1977) and Miocene and Von den Berg (1977) is evaluated after comparing Cobb-Douglas and Translog functional forms in the framework of a translog production function. Initially, the technical efficiency of studied units is estimated by imposing the necessary assumptions and then, the scale elasticity of different production units is estimated and, the scale efficiency is computed through relation provided by Ray (1998).Results and Discussion: Based on the results, the average age of the owners of the studied production units was 46.5 years, their average experience was 18 years and their average education level was 12 years. The average capacity of the investigated poultry was 21600 pieces, the average breeding period 4 times in each year, and the length of the breeding period was about 48 days. The calculation of the production elasticity showed that broiler chicken producers use labor, food, number of chicks and water inputs in the economic area of the production and medicine, electricity and fuel inputs in the third region of production. The average scale elasticity in the units under study was 1.12, indicating that the broiler chicken producers have an increasing return to scale on average. The average scale efficiency of broiler chicken breeding units was 72% and the average technical efficiency of the producers was 88%, indicating that there was the possibility of increasing technical efficiency up to 12% with existing technology on average. The results also demonstrated that the cities of Dezful and Behbahan with an average of 81% had the highest scale efficiency and the Ramhormoz city with an average efficiency of 60% had the lowest scale efficiency. In terms of technical efficiency, Baghmalek city with an average of 95% had the highest and Shadegan city with an average technical efficiency of 76% had the lowest technical efficiency among the studied cities.
M. Mardani Najafabadi; A. Abdeshahi
Abstract
Introduction: Date is one of the strategic and economic horticultural products in Iran due to its important role in gross domestic product, employment and export. Therefore, investigating the efficiency of date producers and trying to improve their efficiency through optimum use of resources have special ...
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Introduction: Date is one of the strategic and economic horticultural products in Iran due to its important role in gross domestic product, employment and export. Therefore, investigating the efficiency of date producers and trying to improve their efficiency through optimum use of resources have special importance. Several techniques are used to evaluate efficiency of decision making units (DMUs). Data Envelopment Analysis (DEA) is recognized as a methodology, widely used to evaluate the relative efficiency of a set of DMUs. Although, DEA is a powerful tool for measuring efficiency, there are some restrictions to be considered. One of the important restrictions involves the sensitivity of DEA to uncertainty of the data in analysis. In this research, the linear robust optimization framework of Bertsimas and Sim (2004) was applied in DEA with uncertain data.Materials and Methods: Data envelopment analysis (DEA) traditionally assumes that input and output data of different DMUs are measured with precision. However, in many real applications, inputs and outputs are often imprecise. This paper applied a robust data envelopment analysis (RDEA) model using imprecise data represented by uncertain set in estimating the efficiency of date producers. The method is based on the robust optimization approach of Bertsimas and Sim (2004) to seek maximization of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to alter the degree of conservatism to let decision maker know the trade-off between constraint’s protection and its efficiency. The method incorporates the degree of conservatism in maximum probability bound for constraint violation. 85 date producers were selected by simple random sampling and necessary data were collected by completing a questionnaire.Results and Discussion: In this section, the results of evaluating date producers are presented which consists of eight inputs and one output. For denoting input and output data uncertainty, ten given maximums of constraint’s violation probability were considered with respect to nominal values: 10%, 20%,…100% (i.e. we used Γ = 0.10, 0.20,…1.00). The results revealed that Gamma value decreases as the probability of constraint violation increases. The RDEA model result showed how efficiency declines as the level of conservatism of solution increases or as the constraint violation probability decreases. According to the method, if all Gammas equal 0, then robust and original DEA models are the same. The most difference between mean of optimal and actual amount of inputs is related to four inputs including machinery, fertilizer, pesticide, and irrigation water in both DEA and RDEA models. In this regard, the government and other relevant authorities should provide producers with extension services to help them optimize inputs. The average technical efficiency for this category of producers is estimated at 90%, and this result indicates a relatively high level of technical knowledge of farmers in using current technologies. In simulating violation probabilities ranging from 0.1 to 1.0 (at a constant the level of ε), percentages of average conformity are quite high.Conclusion: Evaluating the performance of many activities by a traditional DEA approach requires precise input and output data. However, input and output data in real-world are often imprecise or vague. To deal with imprecise data, this study used a robust optimization approach as a way to quantify imprecise data in DEA models. It is shown that the Bertsimas and Sim (2004) approach can be a useful tool in DEA models without introducing additional complexity into the problem (we called robust data envelopment analysis (RDEA)). A case study of Ahvaz county date producer is presented to illustrate reliability and flexibility of the model. The problem was solved for a range of given uncertainty and constraint violation probability levels using the GAMS software. This case suggests that our approach identifies the tradeoff between levels of conservatism and efficiency. As a result, efficiency decreases as the constraint violation probability increases. Additionally the RDEA approach provides both a deterministic guarantee about the efficiency level of the model, as well as a probabilistic guarantee that is valid for all symmetric distributions.