مجله اقتصاد و توسعه کشاورزی
سال سی و پنجم شماره 2 (پیاپی 51، تابستان 1400)

In recent decades, due to increase in population and demand for agricultural products, creating new forms of energy in the agricultural sector and improper use of inputs due to lack of proper management, this economic sector has become an energy consuming sector. So far, various studies have been conducted to measure energy efficiency and cost in the agricultural sector. In most studies conducted in Iran, energy efficiency for the production of various crops has been calculated based on the final product besides material wastage has not been considered in terms of energy and cost.

Flow Cost Accounting (MFCA) is an environmental management tool that can help farmers completely understand the financial and environmental consequences of using materials and energy and provide opportunities to achieve them, as well. Unlike most environmental management systems such as ISO 14001, which, despite their impact on reducing environmental damage, do not explicitly help increase farmers' incomes and even impose additional costs on farms, the implementation of the MFCA, by striking a balance between the environment and the economy, would have significant results in increasing energy and material productivity for many farms.The main purpose of implementing MFCA in potato production is to quantify and identify the losses of agricultural inputs, which leads to effective management of residues and emissions in different stages of crop production. All output materials, including agricultural products and wastes in different stages of production, are calculated and measured in this method. Materials and Methods According to ISO 14051, the MFCA is a management tool that helps farmers recognize and reduce the potential environmental and financial consequences of product development. Likewise, this tool provides opportunities for achieving environmental and financial improvements through the transparency of processes. Accordingly, MFCA can provide important information at various stages of the cycle of Plan-Do-Check-Act (PDCA) (figure 1).   The use of materials and energy in the agricultural sector is tracked and evaluated through the development of the material and energy flow model (in terms of physical units such as mass and volume) in the method of MFCA (figure 2). In this phase, the raw materials consumed, the energy used, costs, as well as the emissions to atmosphere, soil, and water are quantified. Within the system boundaries, the following assumptions and limitations are adopted:System boundaries do not include: construction of factory buildings, vehicles, machines and equipment, etc. System boundaries do not include: transportation Energy balance analysis is a method to identify and evaluate various energy flows that take part in the production system. This analysis determines how efficient the energy is used by establishing the relationship between energy inputs and energy output. This relationship estimates whether energy is lost, gained, or would remain the same.   Figure (2): Material flow model for potato production within the MFCA boundary  

According to the results, the highest amount of energy input comes from fossil fuels and nitrogen fertilizer. Based on the energy and economic indices calculated by the two accounting methods (i.e. conventional and material flow cost accounting), it was found that the total value of potato production based on conventional accounting is 7,195$ per hectare, while this figure is 8,212$ per hectare based on material flow cost accounting method. Energy efficiency in farms, applying conventional energy accounting, was calculated to be 2.65, while this index, using material flow cost accounting, was calculated to be 2.22. The difference between energy efficiency and cost-benefit ratio is attributed to the negative production value obtained in the potato production process in Hamadan province, Iran. Potato growers can increase their income up to 1,016$ per hectare through management measures. If the negative production is reduced, the cost-benefit ratio will increase by 0.57 in the production process.

Costing energy and energy flows through a comprehensive assessment of energy and costs helps to foster the relationship between the economy and the environment. Using the suggested solutions can save a significant amount of money on reducing negative products. MFCA recognizes the material and energy waste, and, farmers, by applying it, enhance their awareness of the usual losses in the field. Farmers, also, can improve processes on their farm and reduce production costs based on a rational assessment.

Among the various available tools in the field of natural resources and environmental management, the payment for ecosystem services (PES) is one of the market-based methods that is considered worldwide to protect the environment and ecosystem. PES is an important method for effective management of natural resources and public goods and one of the tools for managing degraded ecosystems and related environmental and economic services. Considering that Sefidrood is considered as the most important and valuable source of agricultural water supply and aquatic environment in Guilan province, and also the water quality of this important river is in a bad and very bad condition, this study was conducted using PES economic tools through payments by rice consumers in Guilan province to rice farmers and thus encouraging them to take environmentally friendly measures (organic agriculture) to reduce pollution of the Sefidrood River. 

This research was conducted using a choice experiment method. In our CE, each PES alternative is described by a set of attributes that include distribution of payments, contract duration, implementing organization, monitoring times, possibility to cancel and payments. First, to investigate the effect of different attributes of PES scheme on rice consumers' willingness to pay and their marginal utility, a conditional logit model was used to compare the results of random parameter logit model and latent class models with a base model. Then, the RPL and LC model was used to further investigate the invisible heterogeneity that exists in the behavior of respondents. The RPL model is an advanced model that allows attributes coefficients to change randomly among respondents. Therefore, instead of estimating a fixed coefficient for each attribute, two coefficients are estimated, which together describe the distribution of heterogeneous preferences of the respondents for this attribute.

To confirm the CL model, the independence of irrelevant alternatives assumption was performed using the Hausman-McFadden test. Given that the value of chi-square statistics has become large and significant, therefore, the CL model is not suitable for investigating the effect of attributes on consumer’s willingness to pay, and more advanced models should be used. For this reason, RPL and LC models are estimated. According to the results of the RPL model, the highest willingness to pay is related to the monitoring times therefor indicating that consumers are willing to pay 1347 Tomans for more monitoring. The amount of willingness to pay for the duration of contract and distribution of payments is equal to 1326 and 914 Tomans, respectively, which indicates if the contracts are short-time and also more payments are made to low-income rice farmers, the willingness to pay will increase to 1326 and 914 Tomans, respectively. Based on the results of the LC model, in the first class, except for the contract duration, all other attributes were not statistically significant. In the second class, the distribution of payments, the contract duration and the monitoring times with a positive sign and the implementing organization with a negative sign are significant. Class membership coefficients for organic rice consumers indicate that the likelihood of being in second class depends significantly on the respondents' age, gender, and level of education.

The results of RPL and LC models confirm the existence of heterogeneity in the preferences of organic rice consumers. Therefore, appropriate methods can be used to differentiate organic products and thus improve the utility of consuming these products. Consumers were also more inclined to have a short-time and high monitoring scheme, this result is not unexpected due to the novelty of the scheme. Therefore, it is recommended to start short-time schemes with high monitoring. Consumers also tended to make more payments to low-income rice farmers, so it is recommended that lower-income rice farmers be given priority in implementing the PES scheme. The results of both model showed that the distribution of payments and monitoring times had the highest priority for consumers in choosing the PES scheme, respectively. Therefore, in order to increase the participation of consumers in such schemes, it is recommended to include these attributes in the schemes. Also, although PES is not designed as a tool to reduce poverty, it can increase the incomes of low-income rice farmers and help their livelihoods. Given that such schemes have not yet been implemented in Iran, it is suggested that in order to increase consumer participation, various levels of attributes should be provided to the respondents.

One of the principal requirements for sustainable agriculture is efficient energy use. Energy use in agriculture has been increasing in response to the growing global population, limited arable land and desire for higher living standards. It should be noted that agriculture contributes significantly to atmospheric GHG emissions, with 10-12% of the net global CO2 (carbon dioxide) emissions. The scientific community believes global warming will pose one of the major environmental challenges in the future, with the bulk of GHG originating from fossil fuel consumption. Kiwifruit is an economically important fruit crop in northern Iran, because the northern region of Iran is a suitable, natural habitat for kiwifruit cultivation. The high kiwifruit production in Iran has reached a point that Iran is now well-known on global markets and in recent years this fruit has contributed a large share to agricultural exports. More recently, Mazandaran horticulturists have been encouraged to produce more kiwifruit. Increased production leads to greater energy consumption by Iranian kiwifruit orchards due to the added application of inputs, such as fertilizers and fuel. Besides, where there is no clear energy consumption pattern in agricultural production, especially fruit orchards, a lot of energy dissipates in the fruit production cycle. Therefore, it seems necessary to provide a model for the energy consumption of kiwifruit orchards in Mazandaran Province to prevent excessive energy utilization. Energy analysis is one of the methods has been used to evaluate the status of agricultural production. In this regard, many researchers have used Data Envelopment Analysis (DEA) for optimization the energy consumption in agricultural productions. 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. Although DEA is a powerful tool to measure efficiency but the uncertainty in the applied data in this model is inevitable and there is need to use different models that be able to control this uncertainty.

In this study, in order to determine the efficiency of kiwifruit orchards in Mazandaran province and in terms of uncertainty of input data, the Robust Data Envelopment Analysis model (RDEA) and Fuzzy Interval Data Envelopment Analysis model (FIDEA) were used. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. The required data were collected by distributing and completing a questionnaire and face-to-face interview using random sampling method in 1397-98.

The results showed that the average technical efficiency of all kiwi fields in RDEA model at three levels of probability include: 10, 50 and 100% is equal to 0.93, 0.96 and 0.98%, respectively. The results of FIDEA model showed that if the level of parameter α (optimal use of production factors) increases, the average efficiency of kiwi fields will increase. The highest energy savings are related to chemical pesticides and the lowest amount of savings is related to the chemical fertilizers and electricity inputs, respectively. So, holding the training courses on the correct and optimal use of production inputs from an economic and managerial point of view and improving the level of knowledge of farmers and factors involved in kiwi production in Mazandaran province can improve the efficiency and save energy consumptions.

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 problems are often imprecise or vague. To deal with imprecise data, this study uses RDEA and FIDEA approaches as a way to quantify vague data in DEA models. It is shown that the approaches can be a useful tool in DEA models without introducing additional complexity into the problem. A case study of kiwifruit orchard units is presented to illustrate the reliability and flexibility of the models. As a result, efficiency decreases as the constraint violation probability increased. 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.

The rapid acceleration of inflation over the past decade has increased the cost of living in the metropolitan area of Tehran. The World Food Program (WFP) report shows that Tehran province has the highest rate of welfare inequality compared to other provinces in Iran, and a significant portion of the province's population has only abdominal satiety. This has led to an increase in short stature, cardiovascular disease, cancer, obesity, diabetes, tooth decay and gastrointestinal diseases among Tehran families. In this regard, although with the decision of the Working Group on Health and Food Security and the participation of various agencies, programs have been carried out to improve the level of health and nutrition of the residents of the province, but the prevalence of various deficiencies and diseases may be due to lack of nutrients in the food basket and as a result of food insecurity.

Since ensuring health and food security is one of the strategic goals of the 20-year vision document of the country, in the present study, households living in Tehran province were first divided into three lower income deciles, four middle deciles and three upper income deciles according to the raw data of the income expenditure of the Statistics Center of Iran. Then based on the classification of the commodity group of the Statistics Center of Iran and using the nutritional performance matrix, the level of nutrient intake in different income deciles of Tehran province in 2018 has been investigated. Then, using the Matching method, nutrient consumption, the diversity and food security of the province's income deciles have been analyzed.

The results showed that the level of calcium, iron and vitamin C intake among households in the lower three deciles of Tehran province is very low and an adult received only about 14, 487 and 75 mg of these three nutrients per day. As for other nutrients, an adult in the lower three deciles of income received protein and vitamin A 72 mg and 551 micrograms, respectively which is at the minimum daily requirement and only carbohydrates and vitamin B1 has been received 339 g and 1.5 mg, respectively which is above the minimum daily requirement. It should be noted that in all income deciles, the level of carbohydrate intake is more than triple the daily threshold required by an adult (130 g). Finally, according to the results, the lower three deciles of income have lower diversity and food security than the other deciles.

Considering the current situation of receiving micronutrients in Tehran province, the culture and nutritional literacy of households has a great importance and role that responsible organizations can play an important role in promoting it through culture as well as specialized and general education. At the same time, the stability of food prices along with the provision of cash grants and targeted food packages can increase the consumption and food diversity of households and significantly increase food security in poor households, especially women and children. In this regard, considering that a significant part of the country's resources is wasted annually in the form of hidden and non-targeted subsidies, Iran ranks first in the world in the payment of non-targeted food and fuel subsidies, therefore, targeting subsidies and paying cash and non-cash subsidies with proper identification of the target community, in accordance with the conditions of the country is an important issue that should be considered by policy makers.

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.

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.

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.

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.

Iran imported 9 million tons of corn, from Switzerland, Emirate, England and, Netherlands in 2017 so these regions are now the major sources of corn import for Iran. Among the multiple risks, Iran's corn imports encountered systematics and unsystematic risk. Systematic risks are the risks carried by entire assets within a system and cannot be diversified. They are also called non-diversifiable risks, beta risks, and market risks. Specific risks are risks that are unique to an individual asset. In a portfolio, specific risks can be mitigated by using a diversification strategy. Terms or phrases which can be used conterminously with specific risks are diversifiable, unique, unsystematic, or idiosyncratic risks. International price fluctuation and internal policy comprise the risk of Iran's corn imports. Risk specification and management of Iran's corn imports are important since corn, as an input of livestock production, makes the risk of these industries and hence the price of red meat and poultry. The corn imports systematic risks refer to the risks caused by global corn price fluctuations. The systematic risks usually result from the unpredictable fluctuation of global corn demand or a concerted action taken by major corn exporters. All corn importing countries are liable to these risks. Global corn prices fluctuate when the global consumption of corn grows quickly and an imbalance of supply and demand ensues. Worldwide fluctuation is the risk brought about which cannot be avoided by diversification; all the corn importing countries will be affected by this risk. The specific risks to corn imports refer to the risks resulting from corn exporting countries. When a corn exporting country stops its exports due to the policies, climate, production decrease as a result of disease and the other production risk, or other factors, it will bring losses to the corn importing countries. These failures result in a specific risk to corn imports. Since such failures cannot take place in all countries at the same time, and most of them have delayed effects on global corn prices, diversification can be adopted in order to reduce the specific risks to corn importing countries.This paper tries to answer the following questions: What risks will Iran face in terms of corn import systematic or unsystematic risk? What is the relationship between global corn prices and the import prices of Iran's corn imports? Can diversification really minimize Iran's corn import risks?

This paper applies an improved portfolio model and diversification theory to quantify the risks for Iran's corn import risk during 2000-2019. Diversification theory often applied to the analysis of Iran's corn risks, is considered as a powerful instrument in this field of study. Firstly, for considering the systematic risk like the relationship between Iran's corn import prices and global corn prices is estimated. The Ease of Doing Business Index grading system is employed to represent the risk weight relative to each of source country, which should be able to better reflect the extent of each country's influence on Iran's corn risk. Secondly, the diversification index will be calculated and then the systematic and unsystematic risk is estimated. Finally risk index as an import ratio from source countries in order to replace the volume of imports from a country is used to solve the rapidly increasing risk as well as increasing import volume. 

Empirical results show that ever-increasing global corn prices, price fluctuations, and the increasing volume of imports are the root causes of the growth of Iran's corn import risks. The systematic risks are the primary risks to Iran's corn import risk, which the highest systematic risk accrued in 2011. The diversification indexes remain between 0.4 and 0.5, with no evidence that a linear relationship exists between the diversification index and the specific risk index. Therefore, it is not enough to just reduce the specific risks by increasing the number of source countries. It is of equal importance to import corn from countries with low-risk weights and to strike a balance among countries and regions with similar risks. From 2006 to 2011, Iran's corn import risk index remained steady, between 2.8 and 11. As the Ease of Doing Business Index grading system indicates, Iran should import less from countries with low Ease of Doing Business Index grades such as Singapore and import more from those with higher Ease of Doing Business Index grades, maintain balanced imports from countries with similar risks.

Studying Iran’s poultry sector shows that the feed costs account for a large portion of the total cost of poultry production. Besides, corn as the feed for poultry had the largest share of total feed cost. According to the governmental trade policy and exchange rate variability, corn prices fluctuate in Iranian market. However, the demand for chicken meat has increased in recent years. This is due to the relative increase in the price of red meat compared to chicken, as well as promoting the health benefits of consuming white meat (chicken and fish). However, the chicken meat market has been accompanied by price shocks and price increases, and these fluctuations are one of the main challenges of poultry industry in the country. Examining the cost of production inputs in the Iranian poultry industry shows that poultry feed costs, especially corn, accounts for the largest share.
In this study, the smooth transition autoregressive (STAR) model was used to investigate the threshold effect of corn price, as one of the most important inputs of poultry feed, on the price of chicken meat. This method is a nonlinear approach of time series analysis which evaluates the asymmetric changes in the pattern parameters with a smooth transition by considering one or more thresholds. Nonetheless, this model allows several regimes and the transition among them to examine the relationship among research variables which is more realistic than that obtained using the traditional linear regression model. To estimate the nonlinear STAR model, monthly data of corn and chicken meat prices from 1993 to 2020 were collected from the State Livestock Affairs Logistics (S.L.A.L). Several steps were performed to estimate the STAR model. First, data stationary was tested using the seasonal Hylleberg, Engle, Granger, and Yoo (HEGY) unit root test. After investigating the order of variables, the optimal number of lags was determined using the Akaike information criterion (AIC). Smooth threshold linearity versus nonlinearity test was then performed to ensure that the STAR method was appropriate. Then, the applicable forms of transfer function and transfer variable were determined. Finally, after estimating the model, the hypotheses of normality and non-autocorrelation of residuals were tested.
HEGY seasonal unit root test indicates that the logarithms of corn and chicken meat prices do not have seasonal and non-seasonal unit roots, and these data are stationary. Then, three lags are selected as the optimal number of lags using the AIC, and the first lag of chicken meat logarithm is determined as the best transition variable based on the minimum sum of squares of error. Besides, nonlinearity tests suggest that the Exponential Smooth Transition Auto-regression (ESTAR) specification with two-regime switching fits the data better. The empirical results imply that the real threshold value of chicken meat price is statistically significant and equal to 1028.6 Rails. According to the consumer price index in 2020, the nominal threshold value of chicken meat price is 28800 Rails. Therefore, 1% increase in the price of corn will increase chicken meat price by 0.4% and 1.2% in the lower and upper regimes, respectively, after three months.
In this study, the threshold effect of corn price as one of the most important inputs of poultry feed on the price of chicken meat was investigated using Smooth Transition Regression (STR) model. This study's results show that the effect of corn price on meat price is nonlinear and asymmetric. The asymmetry feature is revealed in three aspects: lagged impact, direct impact, and the strength of influence. Based on the results, it is suggested that the government adopt appropriate policies to establish the stability in corn price as one of the most important inputs of poultry industry via foreign exchange and trade policies. Thus, fluctuations in the price of chicken meat can be prevented. It is also expected that if the chicken meat price increases in one period, the price in the next period will also be affected by the relationship between the first lagged chicken meat prices and its current period prices in both regimes. Therefore, the government should prevent fluctuations in chicken meat prices to ensure food security. In this regard, in addition to implementing pricing policies affecting the price of poultry industry inputs, based on the results of the previous section, controlling the price of corn is the most important factor.