Agricultural Economics
H. Fouladi; H. Amirnejad; S. Shirzadi Laskookalayeh
Abstract
IntroductionIn recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other ...
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IntroductionIn recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other hand, the destructive effect of excessive use of chemical fertilizers and pesticides on water, soil, health of ecosystems, humans and other living beings is undeniable. For this reason, the void of using an efficient model that can provide all economic and environmental aspects at the same time was completely felt. The aim of this study was to provide an optimal cropping pattern using the integrated method of Goal and Grey planning. For this purpose, the farmers of the agronomy sub-sector of Tajan Basin were selected as the statistical population. In this regard, time series information was collected from the aggregation of the average data of 401 settlements located in this area during the years 2017-2021 from the annual reports of experts. Materials and MethodsThe Linear Programming (LP) Model quantifies an optimal way of integrating constraints to satisfy the objective function to optimize crop production and profits for irrigation farmers. To use LP, one must convert the problem into a mathematical model. To do this, an objective such as maximizing profit or minimizing losses is required. The model must also include decision variables that affect those objectives, and constraints that limit what user can do. Therefore, the LP Model is a single-objective method. Goal Programming (GP) is an extension of LP in which targets are specified for a set of constraints. GP is used to perform three types of analysis: Determining the required resources to achieve a desired set of objectives. Determining the degree of attainment of the goals with the available resources. Providing the best satisfying solution under a varying amount of resources and priorities of the goals. Thus, the GP model is a multi-objective method. The Grey system theory is identified as an effective methodology that can be used to solve uncertain problems with partially known information. Grey modelling approach uses accident data for estimating the model parameters. The model can reflect the dynamics, balance the conflicting the multidimensional targets of cropping patterns, and promoting the sustainable use of cultivated land. For achieving different goals in unstable economic and environmental conditions, we used a Goal-Grey model that was obtained from the integration of Goal programing and Grey Programing. The Goal-Grey model, by considering the uncertainty in the data, leads to overlap between the economic and environmental goals and provides the scope of cultivation for the selected products. Results and DiscussionBy estimating the Linear Programming (LP) Model, crops like wheat and canola are removed from the cropping pattern, while the cultivation areas for barley and high-yielding long-grain rice increase by 644% and 31%, respectively. In contrast, the cultivation areas for high-quality long-grain rice and maize decrease by 89% and 10%, respectively. Implementing this model boosts the gross profit of farmers in the Tajan region by 14% solely through adjusting the crop composition, without altering the current input levels. Additionally, the findings show that applying the LP Model results in fertilizer savings of 5%, 13%, and 10% for phosphate, nitrogen, and potash, respectively. The amount of herbicide and fungicide consumption in the LP Model is exactly equal to the current model of the region. However, the implementation of this model will lead to a 5% increase in the consumption of insecticides poison. The amount of irrigation water consumption in the LP Model was calculated to be 2% less than the current model of the region. In addition, the results indicate that by estimating the Goal-Grey Model, only canola is removed from the cropping pattern. Also, in order to achieve the defined goals in this study, the cultivation area of wheat and maize should be equal to 208 and 7356 hectares respectively. However, the flexibility of input usage enables adjustments to other crop cultivation areas, facilitating high-quality long-grain rice production on 970 to 18,157 hectares. Plus, the cultivation area of long-grain rice can vary from 7654 to 9995 hectares. In this model, barley can be removed from the crop composition like the linear pattern or cultivated on a maximum of 2553 hectares. The implementation of the Goal-Grey model will lead to a maximum 2% increase in the gross profit of the farmers of Tajan region compared to the current model of this region. Also, by implementing the Goal-Grey Model, on average, phosphate, nitrogen, and potash fertilizer consumption is saved by 16, 27, and 20 percent, respectively. In addition, with the implementation of the Goal-Grey Model, the consumption of agricultural pesticides will decrease from 733 to 355 thousand liters on average. ConclusionThe LP Model is designed based on current regional conditions; however, as a single-objective model with fixed parameters, it lacks the flexibility to offer an adaptable program for farmers during drought or wet periods or when inputs are limited. Findings indicate that under current conditions, there is excessive use of chemical inputs and irrigation water. By accounting for data uncertainty, the Goal-Gray model addresses these limitations, balancing economic and environmental objectives and defining a cultivation range for selected crops. Acknowledgement We are grateful to the experts of agronomy management and plant conservation management of Mazandaran Province Agricultural Jihad Organization and Sari City Agricultural Jihad Management who cooperated in data collection. This article is taken from the preliminary results of a doctoral dissertation with material and intellectual rights related to Sari Agricultural Sciences and Natural Resources University, which is gratefully acknowledged.
Agricultural Economics
J. Hosseinzad; M. Raei Jadidi
Abstract
Introduction: In recent years, the problem of water scarcity is becoming one of the most challenging issues with the economic development and population growth that have involved many sectors due to its importance and economic status and has received increasing attention from governments and international ...
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Introduction: In recent years, the problem of water scarcity is becoming one of the most challenging issues with the economic development and population growth that have involved many sectors due to its importance and economic status and has received increasing attention from governments and international research organizations. This emphasizes the need for optimal allocation of mentioned resources to balance socio-economic development and save water. Therefore, the aim of this study is to develop an uncertainty-based framework for agricultural water resources allocation and calculate the amount of water shortage after allocation and also risk evaluation of agricultural water shortage. The developed framework will be applied to a real case study in the Marand basin, northwest of Iran. Perception of the amount and severity of risk on the system can be a good guide in the optimal allocation of resources and reduction of damage.Materials and Methods: Since various uncertainties exist in the interactions among many system components, optimal allocation of agricultural irrigation water resources in real field conditions is more challenging. Therefore, introduction of uncertainty into traditional optimization methods is an effective way to reflect the complexity and reality of an agricultural water resources allocation system. Among different methods, inexact two-stage stochastic programming (ITSP) has proved to be an effective technique for dealing with uncertain coefficients in water resources management problems. ITSP is incapable of reflecting random uncertainties that coexist in the objective function and constraints. Considering the risk of violating uncertain constraints and the stochastic uncertainty of agricultural irrigation water availability on the right hand side of constraints and uncertainties related to economic data such as the revenue and penalty in the objective function which are expressed as probability distributions, the CCP method and Kataoka’s criterion are introduced into the ITSP model, thus forming the uncertainty-based interactive two-stage stochastic programming (UITSP) model for supporting water resources management. A set of decision alternatives with different combinations of risk levels applied to the objective function and constraints can be generated for planning the water resources allocation system. In the next step, on the basis of results of UITSP agricultural irrigation water shortage risk evaluation can be conducted by using risk assessment indicators (reliability, resiliency, vulnerability, risk degree and consistency) and the fuzzy comprehensive evaluation method.Results and Discussion: A series of water allocation results under different flow levels and different combinations of risk levels were obtained and analyzed in detail through optimally allocating limited water resources to different irrigation areas of Marand basin. The results can help decision makers examine potential interactions between risks related to the stochastic objective function and constraints. Furthermore, a number of solutions can be obtained under different water policy scenarios, which are useful for decision makers to formulate an appropriate policy under uncertainty.The results show that the dry season, i.e., July, August and September are the peak periods of water allocation and demand in Marand basin, which in these months, despite the higher water demand, the amount of water allocation in the current situation is less, which leads to more water shortages in these months. However, the results show that by increasing the efficiency of irrigation and water allocation using the developed framework, the amount of agricultural water allocation and demand is almost balanced and in addition to reducing water shortages, it leads to control over extraction from wells. Also, the goals of the regional water organization, which is reducing the amount of water allocated in the agricultural sector, will be achieved. Comparison with actual conditions shows that the allocation of water resources using the developed framework reduces water shortages while allocation becomes more efficient. Furthermore, the net system benefits per unit water increase which will demonstrate the feasibility and applicability of the developed framework. Results of evaluation of agricultural irrigation water shortage risks indicate that the water shortage risks in the Marand basin are in the category of serious or critical risk level. Therefore, if the current trend of allocation and exploitation of water resources continues, with the population growth, climate change, increasing demand for agricultural products and changing the probability of available water in the future, the water shortage risk would increase to the unbearable risk level. The continuation of this process threatens all investments and economic foundations of this study area. Therefore, the risk of water shortage in the future should be managed by improving the water-saving technologies and also changing the cultivation pattern to drought resistant crops.Conclusion: In this study, an uncertainty-based framework for agricultural water resources allocation and risk evaluation was developed, including model optimization of agricultural water and risk evaluation of water shortage. The developed framework is capable of fully reflecting multiple uncertainties. The developed framework will be helpful for managers in gaining insights into the tradeoffs between system benefits and related risks, permitting an in-depth analysis of risks of agricultural irrigation water shortage under various scenarios. The assessment of agricultural water shortage risk based on the results of the optimization model helps decision makers to obtain in-depth analysis of agricultural irrigation water shortage risk under various scenarios. In application of the developed framework to Marand basin, series of results of agricultural water resources allocation expressed as intervals, and agricultural water shortage risk evaluation levels under different flow levels and also different combinations of risk levels are generated. Comparison between optimal results and actual conditions of agricultural irrigation water allocation demonstrates the feasibility and applicability of the developed framework. Results of evaluation of agricultural irrigation water shortage risks indicate that the water shortage risks in the Marand basin are in the category of serious or critical risk level. Therefore, effective risk management measures should be taken first for different irrigation areas of Marand basin.
F. Kashiri Kolaei; S.A. Hosseini Yekani; S.M. Mojaverian
Abstract
Introduction: Selecting suitable crops for cultivation in a non-certain environment is considered as an important management topic in the agricultural sector. Despite the multiple application of probability theory in quantifying uncertainty in the form of risk programming, validity of this theory depends ...
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Introduction: Selecting suitable crops for cultivation in a non-certain environment is considered as an important management topic in the agricultural sector. Despite the multiple application of probability theory in quantifying uncertainty in the form of risk programming, validity of this theory depends on the existence of frequency for uncertain variable. For events that cannot be measured by frequency, the only solution is to use subjective judgment of persons in the domain field rather than historical data. Some experts have mistakenly considered subjective judgmentl as a subjective probability and thus used the probability theory to quantify subjective judgmental. But based on existing evidence, the quantification of subjective judgment should be carried out in another theory called the uncertainty theory. In uncertainty theory, in addition to using the belief degree rather than frequency for calculating mathematical moments, the expected value of multiplicative variables will be different with their corresponding relations in the probability theory. Considering these conditions and having in mind that the agricultural sector is always faced with uncertain variables such as price of crops and weather conditions like rainfall, in this study the revenue uncertainty measures of major crops in the Goharbaran region of Sari have been calculated and compared. There are different measures for uncertainty, which in the present study variance and Tail Value at Risk (TVaR) have been used.
Materials and Methods: The first step in the application of the uncertainty theory is the elicitation of the belief degree or subjective judgments of the farmers about the crop's price and rainfall during the crop season. To elicit the uncertainty distribution of these variables based on the subjective judgments of farmers, 120 farmers were randomly selected in 2018. After eliciting the farmers' beliefs about uncertain rainfall and prices in the cdf method, it was necessary to select the number of belief degree which current practice was based on previous studies in this field. After calculating the above subjective judgments, while assuming linear, zigzag, normal and normal forms for uncertainty distribution, the parameters of each function were calculated using the least squares method. Among the forms of uncertainty distribution functions, the best form of the uncertainty distribution for each crop's price and rainfall was selected by comparing the RMSE indexes. Subsequently, by calculating a causal relationship between rainfall and crop yield, inverse uncertainty distribution of yield was also extracted. Given the inverse uncertainty distribution functions of crop price and yield, required parameters such as expected revenue, variance and TVaR of revenue at 95% confidence were calculated based on operational laws of uncertainty theory and probability theory. Eviews and Matlab software were used to estimate the yield response function and the uncertainty distribution functions, respectively.
Results and Discussion: In this study, after collecting the belief degree of farmers in the studied area about different levels of price and rainfall, three groups of comprehensive beliefs about prices and rainfall were determined by goodness of fit test. Then, according to the relationship between crop yield and rainfall, the inverse function uncertainty distribution is also calculated. With the uncertainty distribution function of crops price and yield, the expected revenue, variance (standard deviation) and TVaR measure for revenue per hectare of crops were calculated and compared with the uncertainty theory as well as probability theory. Based on the results of this study, the amount of the above measures varied in different belief degree groups, which is due to differences in the uncertainty distribution parameters. Also, based on the results of this study in all groups of beliefs for all crops, the probability theory compared to the uncertainty theory has estimated the variance approximately more than 30% less, which is a significant result. In other words, applying probability theory to belief modeling will lead to erroneous and misleading results. In the case of the TVaR measure in binary multiplicative variable conditions, the use of probability theory and uncertainty theory in calculating TVaR does not yield conflicting results.
Conclusion: The purpose of this study was to compare the results of applying probability theory for modeling belief degree rather than uncertainty theory in order to illustrate the necessity of using uncertainty theory in belief degree modeling. Studying the effect of probability theory in modeling the belief degree also suggests that the application of probability theory in the presence of two uncertain variables has no significant effect on expected values and TVaR but has a significant effect on variance size. Based on the results of the present study, assuming the binary multiplicative variable, in calculating higher mathematical moments such as variance, the results of probability theory and uncertainty theory make a considerable difference. This demonstrates the need to promote the uncertainty theory in belief degree modeling. In other words, basic training in the belief degree modeling method should be considered.
M. Mardani; 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.
M. Jafari Sani; B. Hayati; J. Nematian; M. Ghahremanzadeh
Abstract
Introduction: Qaleh Chay dam basin is one of the largest irrigation regions for food production in Ajabshir and household livelihood mostly depends on agriculture but the occurrence of drought periods and extraction of underground water has led to a reduction in surface water and underground aquifers. ...
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Introduction: Qaleh Chay dam basin is one of the largest irrigation regions for food production in Ajabshir and household livelihood mostly depends on agriculture but the occurrence of drought periods and extraction of underground water has led to a reduction in surface water and underground aquifers. Continuing this process will reduce the agricultural production and consequently the region will encounter economic crisis. On the other hand, the uncertainties of various factors such as rainfall and temperature, which are not easily quantified, would affect agricultural resource system. in current study in order to response to mentioned crisis and uncertainties, interval two-stage stochastic programming (ITSP) has been proposed for water allocation of Ajbashir Qaleh chay dam among agricultural products and the results have been compared with extended ITSP.
Materials and Methods: Interval two-stage stochastic programming (ITSP) is an effective alternative to deal with uncertainties and it can be formulated as follows:
Subject to:
(water availability constraint)
(water allocation goal constraint)
(non- negativity and technical constraint)
where = system benefit; = net benefit to crop per m3 of water allocated; = promised target of water allocation quantity for crop ; = deficit to crop per m3 of water not delivered; = water deficit to crop when the flow is ; = the total amount of flow that take values with probabilities ; = water loss rate in transport process; = the maximum allowable allocation for crop ; = the total amount of crops; = type of crop. Extended ITSP is an effective alternative to cope with water scarcity. The model can be formulated as follows:
Subject to:
Where = cost of increasing 1 m3 water for crop while using alternative ; = total number of alternatives; = available amount of water for crop while using alternative ; is a binary decision variable that takes 1 if crop when using alternative and the seasonal flow is .
Results and Discussion: The data for the selected products (wheat, barley, potato, onion, grape, walnut, almond and apple) were collected from Regional Water Authority and Agriculture Jihad Organization of East Azarbaijan in 2015-16, and in some cases, completed by a questionnaire. The model was written in the GAMS package. Results of ITSP showed that under the low flow level, the total amount of water allocated to all crops would be zero with the exception of almonds where the final allocation of water for it would be [3.64, 20.61]. therefor,Under the medium flow level, the allocation of water for potato, onions, walnuts, almonds and apples would be[0, 5.49], [0, 28.57], [1.30, 35.71], 31.43 and 20 ×1000 m3 respectively and it would be zero for others. Finally under high flow level there would be no water shortage for all products. Water shortages may occur when the seasonal water flows do not be adequate for the promised water allocation for each crop. In such cases users will have to utilize supplementary resources. The results of extended ITSP showed that for wheat, barley, onion, grape and almond the third alternative under low level and the first one under medium flow level can be used. For potato and apple under low level the first alternative and under medium flow level the third one can be applied. Both the first and the third alternative could be utilized for walnut if the flow level was low. Finally, comparing the value of the objective function of ITSP and extended ITSP showed that with the utilization of supplementary resources for satisfying the water needs, the net profit of the system decreases slightly.
Conclusion: In this paper, ITSP method was used to allocate water to agriculture products. The results showed that there was water scarcity for products on drought and normal years. Users can utilize supplementary resources to cope with water scarcity. An extended ITSP method is based on retrieving water shortage and its results revealed that the system net benefit decreases as supplementary water reservoirs were used for water shortages. Based on the results obtained, highlighting the irrigation efficiency is recommended.
A. Azari; A. Azari
Abstract
Introduction: The existence of a variety of natural and unnatural hazards in agricultural activities have caused farmers to face uncertain and vulnerable situations. In this regard, income protection insurance is one of the new insurance policies that covers the fluctuations of yield and price, simultaneously. ...
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Introduction: The existence of a variety of natural and unnatural hazards in agricultural activities have caused farmers to face uncertain and vulnerable situations. In this regard, income protection insurance is one of the new insurance policies that covers the fluctuations of yield and price, simultaneously. The main objective of this research is to design a pattern of income protection insurance for strategic agricultural crops such as rain-fed barley and compare it with yield protection crop insurance in Hamadan province, for different insurance coverage levels in order to reduce the farmers' income fluctuations. Another objective of this research is to investigate the use of the time series analysis techniques in predicting the price and yield variables of the selected crop and to determine the minimum income, in order to pay the farmers for the damages. Also, determining the best model for simulating yield and price parameters, and finally estimating the income of the rain-fed barley, under the influence of the uncertainty are other objectives of this research.
Materials and Methods: In this research, the data were analyzed in order to extract the best predictive model. A fair premium offer for agricultural products requires, a prediction of the performance of various products as well as the price fluctuations in the future. In this study, this prediction was made through predicting the price and performance time series using the ARIMA various models test. The best prediction model based on the 22-year and 32-year statistics of price and performance variables was chosen using the Akaike information criterion (AIC). Then, the expected damages, fair premium and real premium of the product of interest, were calculated once by using the yield insurance method without applying the price-performance relationship and once by using the income insurance method by applying the price-performance relationship of 2017. In order to take into account the uncertainty of the deviation parameter from the mean of the yield data, about 10,000 random samples of the deviations (the residuals of errors) were generated by the Monte Carlo algorithm substitution and accordingly, the product performance was simulated. Each of these simulations could be the actual performance of the product in the upcoming year, according to which, the expected damages and subsequently the fair and real premiums will be estimated for the following year. All of these steps were done using coding in the MATLAB software.
Results and Discussions: The results of the time series analysis indicated that the price of barley was estimated to be about 10706 Rials in the upcoming year of 1396. The rain-fed barley’s yield was also predicted to be1476 kg/ha in the same year. By simulating the farmer’s actual income using the Monte Carlo method and by considering 10,000 iterations for implementing the forecasting model. The average payable compensation payments (fair premiums) for the upcoming year, for rain-fed barley and for 50, 60, 70, and 80 percent coverage levels, were obtained equal to 42371.9, 122972.9, 288375.8, and 580106.3 Rials per hectare, respectively. Using the results of time series analysis of the price of barley product, the forecast for next year of 1396 is about 10706 Rials. The yield of barley for the next year is expected to be 1476 kg ha-1. By simulating the actual earnings of farmers in the Monte Carlo method and considering 10,000 times the repeat of the implementation of the forecast model, the average payable compensation (fair premium) in the following year for the production of barley at the surface of the coatings 50, 60, 70 and 80 percent were calculated 42371.9, 2929.92, 8837.258, and 59.50106 Rials per hectare, respectively. Accordingly, the amount of the real premium of rain-fed barley, by using the operating insurance method and without applying the price- performance relationship was obtained equal to 48503.5, 141131, 331905.4 and 668566.4 Rials per hectare for 50, 60, 70, and 80 percent coverage levels, respectively. On the other hand, the amount of rain-fed barley’s real premium was obtained 47079.9, 136636.5, 320417.6, and 644562.6 Rials per hectare, by using the income insurance method and by applying the price-performance relationship, for 50, 60, 70, and 80 percent coverage levels, respectively. By deducting 71% government subsidy from this, the amount of premium for each farmer would cost 15536.4, 45090.1, 105738.8, and 212705.6 Rials, respectively.
Conclusions: The results ultimately indicated that for the selected product, the amount of the computed premiums in the income protection insurance model is lower than the yield protection crop insurance. Accordingly, considering the risk of planting rain-fed products, applying the income insurance model will encourage farmers more. Also, in this model, the insurer, having understood the simultaneous effect of price and performance, will offer computing insurances with more certainty in different coverage levels. According to the results of this research, it is suggested that income insurance, taking into account the uncertainties that were caused by price prediction and product performance, should be used instead of functional insurance. This policy will make farmers more lucrative and leads to a better risk management system by insuring companies.
H. Azizmohammadlou
Abstract
Introduction: Risk and uncertainty are the main characteristics of agriculture sector and related activities. Risk and uncertainty can affect farmers decision making on output determination, input employment and technology selection. Analysis and understanding the behavior of farmers in risky environment ...
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Introduction: Risk and uncertainty are the main characteristics of agriculture sector and related activities. Risk and uncertainty can affect farmers decision making on output determination, input employment and technology selection. Analysis and understanding the behavior of farmers in risky environment leads to better prediction and evaluation of the result of policies in agriculture sector and therefore helps policymakers to select suitable policies for improving the status of inputs employment in this sector. The aim of this paper is to analyzethe reaction of farmers to the risk of demand uncertainty and its effect on inputs employment in Iran agriculture sector.
Materials and Methods: Data for variables included in the estimated econometric model in this paper- like interest rate, wage index, number of employees in the agricultural sector, output value, and capital stock weregathered from Iran central bank data center during the period 1974-2012. The augmented dickey fuller test is used to investigate the stationary of variables included in the econometric models of the study. In order to analysis the reaction of farmers to the risk of demand uncertainty and its effect on inputs employment in agriculture sector, two steps were taken as follows: at the first step, a demand prediction model is estimated using a first-order autoregressive process and demand uncertainty in agriculture sector is calculated by the residual of the estimated model. At the second step, the effect of demand uncertainty on capital and labor intensity is tested using Johnson cointegration approach. Schwarz and Quinn's criteria were used to determine the optimal lag numbersin vector autoregressive model. The number of co-integration vectors weredetermined using maximum eigenvalue and trace tests.
Results and Discussion: To analyzethe behavior of farmers in risky situations in terms of input employment, five possibilities or five scenarios were taken into account. First scenario: if the farmer is risk lover, labor is going to be a constant and capital increase. If, however, the farmer is risk-averse, labor is going to be constant and capital decreases. Second scenario: if farmer is risk lover, labor decreases and capital is going to be constant. Though in the case, that farmer is risk-averse, labor increases and capital is going to be constant. Third scenario: if the farmer is risk lover, labor decreases and capital increases. However, in the case, that farmer is risk-averse, labor increases and capital decreases. Fourth scenario: if the farmer is risk lover, the rate of increasein labor is less than the rate of increasein capital. In the case of risk adverse farmer, the rate of increasein labor is more than the rate of increasein capital. Fifth scenario: if farmer is risk lover, the rate of decreasing in labor is more than the rate of increasein capital. In the case of risk-averse farmer, the rate of decreasing in labor is less than the rate of increasein capital. Cointegrationtest based on eigenvalue and trace statistics in this paper confirm the presence of almost two cointegration vectors between the model variables. According to the estimated coefficients of the restricted vectors, there is a negative relationship between demand uncertainty and capital-labor ratioin long run. The coefficient of demand uncertainty in restricted vector is estimated around -0.33. This shows that as demand uncertainty increase 1%, capital- labor decrease 0.33%. These findings reveal that the firms in the agriculture sector are risk-averse and have a negative response todemand uncertainty. Separately estimation of labor and capital demand function indicates that the coefficient of demand uncertainty is respectively obtained around (-0. 14) and (-0.05). In the other words, the negative effect of demand uncertainty on capital formation is larger than the negative effect of demand uncertainty on labor employment. As demand uncertainty goes up in this sector, both labor force and capital decrease. The rate of decreasing in capital, however, is more than the rate of decreasing in labor force in the agricultural sector.
Conclusions: With increasing demand uncertainty in the agricultural sector, labor-intensity of production process goes up and farmers move toward using labor intensive process and technologies. It is inferred that higher level of demand uncertainty leads to debilitatinginvestment process and retard the trend of capital formation and technology development in the agricultural sector. The implication of such conclusion is that as demand uncertainty increases, capital intensity decreases in agriculture sector and production firms tend to use more labor-intensive technologies and process. This reveals the necessity of serious attention to investment and capital formation issue in this sector. Regarding the intensifying the risky environment in this sector, the government is recommended to use suitable promotion and motivation mechanisms to enhance farmers intensively for investment and output improvement.
M. Mardani; S. Ziaee
Abstract
Introduction: Several techniques are used to evaluate decision making units in DMUs with a restricted multiplier. DEA is recognized as a methodology widely used to evaluate the relative efficiency of a set of decision-making units (DMUs) involved in a production process. This approach assumes that the ...
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Introduction: Several techniques are used to evaluate decision making units in DMUs with a restricted multiplier. DEA is recognized as a methodology widely used to evaluate the relative efficiency of a set of decision-making units (DMUs) involved in a production process. This approach assumes that the input and output data of the different decision making units (DMUs) are measured with precision. Although DEA is a powerful tool to use measure efficiency, there are some restrictions that need to be considered. One important restriction involves the sensitivity of DEA to the specific data under analysis. In this paper, the linear robust optimization framework of Bertsimas and Sim is used to concentrate on the DEA with uncertain data to determine the efficiency of irrigated wheat farms in Neyshabur County.
Materials and Methods: This paper proposes a linear robust data envelopment analysis (LRDEA) model using imprecise data represented by an uncertainty set. The method is based on the robust optimization approach of Bertsimas and Sim to seek maximization of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between a constraint’s protection and its efficiency. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. The most significant uncertainties for a DEA model are input and output data that arise from errors. Application of the proposed model (LRDEA) to the case study (Neishabour district irrigated wheat farms) demonstrates the reliability and flexibility of the model. Monte Carlo simulation was implemented to examine the quality of the LRDEA model 100 random numbers were generated for each input and output of DMUs.
Results and Discussion: In this section, a case study of Neishabour county irrigated wheat farms is presented to illustrate the use of the methodology in this proposal, which consists of 95 DMUs, one input and five outputs. For the input and output data uncertainty, ten given maximums of a constraint’s violation probability were considered with respect to nominal values: 10%, 20%, up to 100% (i.e. we used Γ = 0.10, 0.20, up to1.00). The results show that the Gamma value decreases as the probability of constraint violation increases. The LRDEA model result shows how efficiency declines as the level of conservatism of the solution increases, that is, 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 of the difference between the mean of optimal and actual amount of inputs is related to the two inputs of pesticide and cultivation land in both of the DEA and RDEA models. Accordingly, holding participatory extension classes to train farmers to increase yield and optimal use of existing agricultural land with a cooperative of efficient farmers is recommended. Also, the extinction of integrated pest management (IPM) to increasing non-optimal use of pesticide in the study area is proposed. Monte Carlo simulation was implemented to examine the quality of the LRDEA model 100 random numbers were generated for each input and output of DMUs. In the simulation violation probabilities ranging from 0.1 to 1.0 (at a constant the level of ε), percentages of average conformity are quite high. . However, it declines very rapidly as P approaches 0.7.
Conclusions: Evaluating the performance of many activities by a traditional DEA approach requires a precise input and output data. However, input and output data in real-world problems are often imprecise or vague. To deal with imprecise data, this study uses a robust optimization approach as a way to quantify vague data in DEA models. It is shown that the Bertsimas and Sim approach can be a useful tool in DEA models without introducing additional complexity into the problem (we called linear robust data envelopment analysis (LRDEA)). A case study of Neishabour county irrigated wheat farms is presented to illustrate the reliability and flexibility of the proposed model. The problem was solved for a range of given uncertainty and constraint violation probability levels using the GAMS software. This example suggests that our approach identifies the tradeoff between levels of conservatism and efficiency. As a result, efficiency decreases as the constraint violation probability increased. Additionally the LRDEA 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.
M. Bagheri; A.K. Esmaieli; M. Zibaei
Abstract
This study develops a long run optimal pattern for nomadic ranchers of Fars province under climate uncertainty by simulating dynamic process of livestock and forage productivity as well as employing dynamic stochastic programming. Results indicate that the nomadic representatives do not perform, optimally. ...
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This study develops a long run optimal pattern for nomadic ranchers of Fars province under climate uncertainty by simulating dynamic process of livestock and forage productivity as well as employing dynamic stochastic programming. Results indicate that the nomadic representatives do not perform, optimally. Despite their attachments to livestock as an asset, and their life dependency on livestock, they intend to maintain their herd under any circumstance and to have large herds during any year. But results of the long run optimal pattern based on the stochastic dynamic programming model indicate that even in a wet year rangeland forage production is not enough for livestock feeding. Accordingly, nomads must adjust their herds and fit numbers of their livestock with the pasture capacity. Therefore, in the long run partial, adjusting strategies on the number of livestock rather than purchasing additional forage is recommended.
M. Bahrami Nasab; A. Dourandish; M.R. Kohansal
Abstract
Determining the optimal pattern for growing crops in accordance with the availability of the resources and risk factors as well as the uncertainty of agriculture sector would help the farmers, managers and economic planners in selecting the type of the product and the level of cultivation. Fuzzy programming ...
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Determining the optimal pattern for growing crops in accordance with the availability of the resources and risk factors as well as the uncertainty of agriculture sector would help the farmers, managers and economic planners in selecting the type of the product and the level of cultivation. Fuzzy programming model with Interval programming approach is used in this study to determine the optimal cultivation pattern in the Esfarayen county while taking terms of uncertainty into consideration. The required data were gathered via 128 questionnaire and interviews with the farmers of the region as simple random sampling in 2013. Using different levels of Alpha cut in the model made the whole parameter-related fuzzy data to access the optimization process fractionally. Models assessments results with perspective to optimistic and pessimistic conditions in accordance with various Alpha cuts show the benefit increases in optimistic conditions while decreasing in conservative ones due to the increase in the level of uncertainty and risk or expansion of fluctuations periphery due to smaller Alpha cuts. Forage maize, Red beans and wheat in most case scenarios are economical as well as optimum crops for cultivation. In order to improve the farmers’ money-making situation along with optimal usage of production sources, encouraging and supportive policies to be performed by Agriculture- Jehad (Ministry of Agriculture) of Northern Khorasan or the Esfarayen county is recommended.
M. Mardani; A. Sargazi; M. Sabouhi
Abstract
This study investigated the efficiency of wheat farms in Sistan province. The data consisted of 50 samples from crop producers in the Sistan region. Using the simple sampling method, the data was collected by completing the questionnaires. To evaluate the farm efficiencies, the two approaches of the ...
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This study investigated the efficiency of wheat farms in Sistan province. The data consisted of 50 samples from crop producers in the Sistan region. Using the simple sampling method, the data was collected by completing the questionnaires. To evaluate the farm efficiencies, the two approaches of the “data envelopment analysis” and the “optimization model including parameters to control for the degree of conservation” were combined. The results showed that the average of the study farms ‘ efficiencies in the proposed model (RDEA) is reduced by increasing probability of constraint violation (p) under the constant level of a given uncertainty (ε) .. The Monte Carlo simulation method was used to evaluate the model. The simulation results indicate a higher capability of this model with respect to the DEA model. Therefore, this method can be used to obtain the efficiency of decision-making units.