Agricultural Economics
S. Kalhori; L. Abolhasani; M. Sabouhi Sabouni; M. Sarkhosh
Abstract
IntroductionGiven the rapid process of industrialization, expansion of agriculture, increased reliance on fossil fuels, and the intensification of climatic conditions, air quality has rapidly deteriorated in recent years. One of the most important issues and challenges facing the world today is air pollution, ...
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IntroductionGiven the rapid process of industrialization, expansion of agriculture, increased reliance on fossil fuels, and the intensification of climatic conditions, air quality has rapidly deteriorated in recent years. One of the most important issues and challenges facing the world today is air pollution, particularly PM2.5 pollution. This problem has evolved into one of the most complex and serious dilemmas affecting the lives of people worldwide. Exposure to high levels of air pollution has negative health implications. The present study aims to measure the willingness to pay of Mashhad city residents for the improvement of PM2.5 pollution and identify the factors influencing this willingness to pay. Materials and MethodsThis study used contingent valuation and the multiple-bound discrete choice model to calculate individuals' willingness to pay. The research focused on the certainty level of "definitely yes" and generated 13 different proposals ranging from 10,000 Toman to 200,000 Toman. The ordered logit regression model was employed to analyze the factors influencing the willingness of Mashhad citizens to pay for air quality improvement. The study collected 343 questionnaires from Mashhad city residents, considering variables such as education level, age, gender, marital status, family size, presence of children, chronic respiratory diseases and individuals' income. The dependent variable was the public's willingness to pay for improving air quality regarding PM2.5. Results and DiscussionThe study found that a significant portion of respondents were willing to pay for air quality improvement. About 22.45% were willing to pay less than 10,000 Toman, 60.06% were willing to pay between 45,000 and 58,000 Toman, 5.83% were willing to pay between 95,000 and 120,000 Toman, and 11.66% were willing to pay between 155,000 and 200,000 Toman. The average willingness to pay for PM2.5 pollutant improvement in Mashhad was estimated to be 55,488 Toman. Education, age, respiratory diseases, income, and family size were found to affect willingness to pay. Conclusion Improving air quality and reducing pollution requires costly efforts and collaboration from society. This research examines individuals' willingness to financially contribute to air quality enhancement. Factors influencing their willingness to pay are also studied. Based on the findings, it is recommended that the government and municipal authorities impose taxes and levies on polluting sectors, considering the calculated value of air pollution and its sources. Educational programs tailored to diverse educational backgrounds, along with technology and social media, can raise environmental awareness among youth. Developing cost-effective public transportation systems and providing discounts for low-income individuals can also help reduce pollution. Financial programs and incentives for cleaner resources are another solution for improving air quality.
Agricultural Economics
A. Sani Heidary; M. Daneshvar Kakhki; M. Sabouhi Sabouni; H. Mohammadi
Abstract
Introduction
Considering being located in arid and semi-arid regions of the world, Iran is influenced by the most severe impacts of drought. Drought is considered a major threat to the livelihood of rural households. During the recent drought, rural households faced significant losses and hardships, ...
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Introduction
Considering being located in arid and semi-arid regions of the world, Iran is influenced by the most severe impacts of drought. Drought is considered a major threat to the livelihood of rural households. During the recent drought, rural households faced significant losses and hardships, underscoring their lack of preparedness for this natural hazard. Consequently, every society must take proactive measures to manage changes, mitigate threats, and respond effectively. A review of the country's drought management programs reveals that policymakers have consistently prioritized increased production, even amid the critical conditions of recent droughts. This focus on boosting production to meet the basic needs of a growing population has taken precedence over enhancing rural households' livelihoods and resilience. However, improving rural households' resilience in drought conditions hinges on prioritizing their capacity for adaptability and flexibility. Therefore, considering the sensitivity of the issue of resilience as a dominant approach effective on the dimensions of life and livelihood of rural households on the one hand and the lack of a comprehensive study on its underlying factors, on the other hand, this research seeks to answer two questions: First, what is the resilience level of rural households against drought? Second, what factors influence the resilience levels of rural households in drought conditions?
Materials and Methods
The statistical population of this study is 16,817 rural households in Zehak city, located in Sistan and Baluchistan province, which are strongly influenced by different climatic events such as drought, excessive heat, low rainfall and 120-day winds. A stratified random sampling method was used to determine the sample size. According to Cochran's formula, the sample size is estimated to be 376 households. Data were collected by completing multidimensional questionnaires along with semi-structured interviews from households in 2023. To measure the resilience capacity of rural households, the theoretical framework of TANGO based on the estimation of the three capacities of absorption, adaptation and transfer was used through the factor analysis method, in which attitudinal and mental aspects of resilience are also taken into account. Finally, partial proportional odds model has been used to evaluate the influencing factors on the resilience capacity of rural households.
Results and Discussion
The results of the state of resilience capacity of rural households in the region indicated that the average value of their resilience capacity is 26.27, which shows the low level of resilience capacity in the region. Also, the households of the region are in a bad situation based on the absorption, adaptation and transmission capacities, and the households of the region have a stronger transmission capacity than the absorption and adaptation capacity against drought. The results of grouping the resilience capacity of households reveal that 32.45% are in the vulnerable group, 28.19% are in the relative resilience group, 22.61% are in the resilient group and 16.76% are in the high resilience group. The results show that more than 60% of households are at very low levels of resilience. Finally, the partial proportional odds model results demonstrated that the variables of education of the head of the household, skill level in agricultural activities, savings, household income, number of household contacts with agricultural extension, membership of the head of the household in social groups and access to microcredits have a positive effect and variables of the value of the loss of agricultural products and the number of livestock lost have a negative effect on the resilience capacity of rural households against drought.
Conclusion
According to the findings, policy-makers should prioritize strengthening the variables that determine the resilience capacity and its dimensions in the implementation of drought management programs so that households can absorb drought shocks without damaging their basic components. Policy-makers should also target specific categories of risks, dimensions of vulnerability and resilience in different time periods (before, during, and after shock) in order to choose comprehensive strategies to build and increase resilience. For instance, before a shock, better access to early detection of emerging climate risks could help farmers plan their cropping activities accordingly. Access to climate information allows for forward-looking adaptation that reduces the impact of shocks and increases resilience.
Agricultural Economics
M. Mirchooli; M. Ghorbani; M. Sabouhi Sabouni
Abstract
IntroductionThe dependence of agriculture on environmental conditions has caused the activity in this sector to face natural and unnatural risks. After several years of agricultural insurance activity in Razavi Khorasan province, most of the pistachio farmers are not insured. Drought insurance is one ...
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IntroductionThe dependence of agriculture on environmental conditions has caused the activity in this sector to face natural and unnatural risks. After several years of agricultural insurance activity in Razavi Khorasan province, most of the pistachio farmers are not insured. Drought insurance is one of the methods that has become important to cover the risks of drought and lack of water resources in order to compensate part of the gardeners' losses. The main issue from a managerial perspective is risk management. The use of agricultural insurance, which is one of the risk management tools, will ensure financial security and stability for farmers. Given that insurance is a tool for risk management, and given the uncertainty and risks of climate change in agriculture, insurance can be a very adaptable tool to water scarcity. Agricultural insurance is considered as a useful and appropriate solution to deal with natural hazards. . Drought insurance is an important factor in off-farm drought risk management that can mitigate the effects of this inevitable phenomenon. Insurance as one of the risk management tools can increase the risk-taking of farmers and, consequently, increase the sense of security in farmers, the necessary ground for proper and efficient use of factors of production and investment in the use of new technology and thus increase productivity in agriculture provide. The effects of water scarcity can be summarized as follows; Loss of production and income, abandonment of busy crops (with high water demand) and decline in agricultural employment, on the other hand, intensifies the over-exploitation of groundwater aquifers, which has tempted many farmers to do so meet your water needs. Access to water in the study area is one of the important variables affecting pistachio yield and quality as well as the survival of pistachio trees. This variable directly affects the profitability of producers and gardeners may suffer losses from this vital input. For this reason, gardeners' behavior in relation to regular pistachio insurance can affect access to water and make more farmers inclined to drought insurance. Materials and MethodsThis research seeks to answer the question that with 5% reduction in available water, pistachio growers in Sabzevar city, whether these people are willing to accept pistachio drought insurance or not, and if so, what is the extent of this desire. The Probit pattern is one of the most suitable econometric patterns for censored observations. This model was first proposed by Tobin (1958) to estimate the demand for durable goods. Subsequently, Arab Mazar and Schmidt (1979), Brown and Mufit (1982), Madela and Nelson (1982), and Hard (1975) worked on and developed the model, validating its high capability. This pattern was named by Goldberger (1964) as the Tobit or Probin Tobin model. Assume that y is the level of activity or action desired and xi are factors that generally affect the level of activity or action in question, namely:Also assume that one group of the observed observations performs the desired activity and the other group (the rest) does not perform the desired activity. As mentioned earlier, the values of xi and yi are visible for the first group. While for the second group only xi values are available and yi values are zero.In Hackmann's proposed two-step method for estimating the Tobit model, it is assumed that one set of variables may influence the decision to participate in the activity and another set of variables may affect the amount of activity performed after the decision is made. Therefore, two different sets of variables can be included in the Probit model, which are not necessarily barriers to aggregation. Therefore, two different sets of variables can be included in the Tobit model, which are not necessarily barriers to aggregation. Because it does not have a one-step model of this flexibility, it assumes that the variables influencing a person's decision to engage in an activity are the same as the variables that determine the amount of activity, if this is not necessarily the case. Hackman's two suggested steps are:Step 1: In the first step, the variables that affect the decision of gardeners in accepting pistachio drought insurance are identified and placed in a model with a binary dependent variable (zeros and ones); This means that the positive values of the dependent variable that indicate the tendency to accept pistachio drought insurance become the number one, and the dependent variable that does not tend to accept the drought insurance is set to zero. The number one means the decision to perform the activity and zero means the non-performance of the activity. At this stage, in order to identify the factors influencing the individual's decision, the Probit Model is used and estimated by the maximum likelihood method. The first step is to create a new variable inverse of the Mills ratio to enter the second step. In other words, this variable is the first and second stage communication bridge.Step 2: In the second stage, the measures affecting the willingness to participate in drought insurance after the decision is made along with the inverse Mills ratio variable are placed in a classical regression model. The dependent variable in the second stage is the amount of garden area likely to be allocated to drought insurance.Reasons to use the Tobit model: Many econometric models face two types of errors, either due to the use of specific observation data or due to the structural features of the models: first, the error due to incorrect sample selection, which usually occurs in using classical regression models, and second, the same error Assuming effective variables in the decision stage and the amount of activity performed after the decision is made (decision and action or intention and action), which usually occurs in regression models with binary and multiple responses. The Tobit model has been developed to prevent the occurrence of these two types of errors in studies.The first error is the error of incorrect sample selection; in the sense that in many econometric models, information is obtained only from observations that have acted on the activity and omits observations that have refused to do that activity. Therefore, these models are not able to assess the reaction of observations that did not act on the independent variable changes. Tobit model (type one) solves this problem in terms of observations that have performed the desired activity as well as other observations. Under these conditions, the effect of changes in independent variables on both the total observations and on the observations of the activity can be calculated separately.The second error means that the factors that influence a person's decision to perform an activity are not necessarily the same as the factors that determine the amount and level of activity desired, and can be two different sets of variables. The Tobit model (type two, Hackett or Hackman two-stage) solves this problem by separating the factors influencing the decision and the amount of activity. Results and DiscussionThe data show that the response of pistachio growers to the reduction of available water in the next 2 and 5 years is that all gardeners will insure their pistachio orchards with a 5% reduction in available water, but in terms of area under cultivation, only 39% Gardeners will increase their arable land in the next 2 years and 33% of gardeners in the next 5 years. The reaction of gardeners who did not have a history of pistachio insurance to accept pistachio insurance and increase or decrease the area under pistachio orchard in exchange for a 5% reduction in available water in the next 2 and 5 years shows that about 51% of gardeners face a 5% reduction in water in 2 And in the next 5 years, they will insure their pistachio orchards, and about 60% of gardeners will increase their cultivation in the next 2 or 5 years in the face of a 5% reduction in available water. The results of the evaluation of gardeners' reaction to the continuation of the horticultural profession in the face of a 5% reduction in available water in the next 2 years will cause 34% of gardeners not to continue this profession and 51% of gardeners will not continue this profession in the next 5 years. In the long run, water shortages can reduce the incentive for gardeners to grow pistachios. The reaction of gardeners to pistachio insurance against the reduction of available water quality shows that only 1.38 percent of the total population in the face of reduced quality of available water reduce the level of their insured garden and about 30% of them faced with declining available water quality, they will increase the level of their insured garden; And the rest of the gardeners (about 68.6%) do not change their insured level in the face of declining water quality.ConclusionAccording to the obtained information, the variables as gardener's age, ownership, relationship between gardener's field of study and agriculture, location, variety of cultivation, existence of insured pistachio garden in the neighborhood, frequency of risk, total water available to each gardener and garden life of each gardener in the first stage (Probit Model) have positive coefficients; which indicates the positive effect of these variables on the probability of willingness to accept pistachio drought insurance. In the second stage (linear regression), the variables of pistachio horticulture history, frequency of risk, garden life and total number of water hours available to gardeners have positive coefficients, which indicate the positive effect of these variables on the dependent variable of the second stage, is the tendency to accept pistachio drought insurance.
Agricultural Economics
Sh. Zarif Moradian; M. Sabouhi Sabouni; M. Daneshvar Khakhki
Abstract
Introduction Given the growing global hunger in recent years, creating and increasing resilience among disadvantaged and impoverished communities, emphasized in the 2030 Sustainable Development Agenda, is a significant concern to most countries. The term resilience is generally considered as the ...
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Introduction Given the growing global hunger in recent years, creating and increasing resilience among disadvantaged and impoverished communities, emphasized in the 2030 Sustainable Development Agenda, is a significant concern to most countries. The term resilience is generally considered as the capacity of a system to withstand various risks, and household resilience can be defined as the ability to return to the previous level of living conditions after a shock. Since one of the most critical shocks that farmers have faced in Iran is drought, the present study aimed to estimate the effect of drought on rural farmers’ household resilience in a selected village in Qalandar Abad district in Iran. Materials and Methods The factor analysis method was used to estimate the components of the Resilience Index Measurement and Analysis (RIMA) of the Food and Agriculture Organization of the United Nations (FAO), and the Mimic (Multiple indicators_ multiple causes) method was used to estimate the latent variable of resilience. RIMA, which considers resilience as a latent variable, includes four main components of Access to Basic Services (ABS), Assets (AST), Social Safety Nets (SSN), and Adaptive Capacity (AC). Also, according to the purpose of the study on estimating the resilience of rural households with the MIMIC method, at least two food security indicators at the household level, as multiple indicators of resilience, are required. The food security indices used in the calculations of this study include the Household Hunger Scale Index and the household Food Consumption Score. The samples included 149 farmers randomly selected from Hossein Abad Rekhneh Gol village, and data were collected through interviews with the household head. To reveal the effect of different shocks on the rural resilience households, self-reported information such as drought, livestock loss, and the characteristics of the households were used through an Ordinary Last Square regression. Results and Discussion In the first stage, each of the pillars of resilience, including Access to Basic Services, Assets, Social Safety Nets, and Adaptive Capacity, which are considered latent variables, shows a higher correlation between the variables, and the calculated pillars indicate the greater importance of that variable in each of the resilience components. According to the results, among the variables that constitute the pillar of access to basic services, "the distance from the household to the health center" variable correlates with this pillar, which indicates its high importance. In addition, the "attending school years" is one of the most important variables in forming and creating the adaptive capacity of a household to the crises ahead. The agricultural water availability and the total yield during a year play an important role in creating the asset pillar. Regarding creating the social safety nets pillar, as we expected, the governmental cash transfers, through monthly subsidies, the Imam Khomeini Relief Foundation, and the State Welfare Organization of Iran, is the most crucial variable. The results obtained from the food consumption (FCS) score index showed that 117 out of 149 studied households are within the acceptable threshold, 28 households are on the borderline, and four households are in a poor food consumption situation. The Hunger Scale Index showed that out of 149 households, 62 households are on the little to no hunger threshold, while 81 households are on the moderate hunger and six households are on the severe hunger threshold. Also, based on the results of the MIMIC model, among the calculated pillars, household assets is the most important. The increase of one standard deviation unit in AST will increase 0.06 standard deviation units in the resilience capacity index. Adaptive capacity and social safety nets pillars also play a significant role in creating resilience for rural households. Thus, increasing one standard deviation in the AC and SSN led to an increase in the magnitude of the resilience by 0.04 and 0.03 standard deviations, respectively. Finally, the effect of different shocks on the rural resilience households showed that variables such as drought, livestock loss, and gender of household head (being female) have a negative effect on their resilience. The size of the household has a positive impact, which means that the more family members, the more resilience. Conclusion One of the critical goals of underdeveloped and developing countries, is to eradicate poverty and achieve sustainable development. In Iran, like other developing countries, smallholder farmers are known to be vulnerable to environmental and economic changes such as climate change, rising prices of agricultural inputs, etc. Therefore, adopting and implementing policies that lead to a fair income distribution for vulnerable people is essential. Estimating the RIMA makes it possible to rank households based on their strengths, weaknesses, and current needs. Budget allocation and the policy time duration are two limiting factors that may optimize using the RIMA results. The present study examined the RIMA and the effect of drought on the calculated index for the first time in Iran for a specific region. Since the ranking of households based on resilience requires awareness of all vulnerable households' situations, the definition of short-term and long-term projects in the future development plans is essential. To identify "the most vulnerable groups" and "the most important challenges and shocks," these scheduled projects are vital for budget allocation prioritization.
H. Ailbakhshi; A. Dourandish; M. Sabouhi Sabouni
Abstract
Introduction: Understanding the temporal and spatial fluctuations of climatic parameters (such as temperature, precipitation, relative humidity, etc.) and its impact on agricultural sector is essential for managing agricultural resources and adopting appropriate strategies. Precipitation directly affects ...
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Introduction: Understanding the temporal and spatial fluctuations of climatic parameters (such as temperature, precipitation, relative humidity, etc.) and its impact on agricultural sector is essential for managing agricultural resources and adopting appropriate strategies. Precipitation directly affects the production of dry crops by supplying the required moisture for the plant, and indirectly affects the production of aquatic crops through supplying surface and underground water resources. Climate change has an effect on temperature and precipitation distribution and consequently affects the plants water requirement and agricultural water consumption. Overall, climate change is influenced by both temperature and precipitation. Due to the changing rainfall pattern and average temperature of the atmosphere, this phenomenon can damage the production of agricultural products that maintain the major food sources of the country. Given the important role of agriculture in the country's economy and the existence of the ongoing water crisis and drought in the country, climate change can have major impacts on their aggravation. The purpose of the present study is to investigate the effects of climate change and water scarcity on agricultural production, price and income in Iran.
Materials and Methods: The multi-market model, sometimes referred to as the "finite general equilibrium" or "multi-market partial equilibrium model", has reduced the complexities of computable general equilibrium (CGE) models. The AMM template was used for this purpose. To simulate the effects of climate change, crop yields were calculated using yield response coefficients. Then, the demand function of different products was calculated using estimated elasticities and finally climate change has been simulated for 2025.
Results and Discussion: The results showed that climate change would increase, yield of rainfed wheat, blue barley, dry barley and maize grain in semi-arid climate and subtropical climate, in addition dry barley and barley products in warm semi-arid climate and subtropical climate, and finally rainfed barley and corn products Grain in temperate semi-arid climates and subtropicals climate by 2025 relative to current levels. Climate change also would decrease yields of dry wheat and barley in temperate semi-arid climates and subtropicals, and also wheat in warm and semi-arid climates and subtropicals for 2025 compared to the present value. The results also showed that climate change would expand the water available for the blue wheat crop in the semi-arid climate and sub-climates, besides the blue barley crop in the semi-arid, semi-arid, and temperate semi-arid climates for 2025 compared to the present value. Climate change also might reduce the amount of water available for the blue wheat crop in the climate and sub-arid and semi-arid sub-climates, therefor the corn yield in the cold and semi-arid sub-climates and sub-climates for 2025 compared to the present value. The results also revealed that climate change would diminish cultivation of maize crop in semi-arid climate and temperate climates in addition irrigated and rainfed wheat crop in warm and semi-arid sub-climate and also rainfed wheat crop in semi-arid climate by 2025 relative to the present situation. Also the area under cultivation of blue barley and dry barley crops in warm and semi-arid climates, cold and semi-arid climates, and blue wheat crop in semi-arid climates and rainfed wheat crop in temperate and semi-arid climates would decrease by 2025.
Conclusion: The results also demonstrated that with the climate change, the amount of maize crop production in cold and semi-arid climates and sub-climates, and the production of blue and dry wheat crops in warm and semi-arid climates, cold semi-arid, temperate and dry semi-arid climates for 20 years would decrease relative to current value. Also, the production of irrigated and rainfed barley in warm and semi-arid climates, sub-climates and temperate semi-arid climates for 2025 would increase compared to the present situation. Thus the first hypothesis of the study: "Climate change and water scarcity reduces agricultural production" is not approved in Iran. The results also explained that with the climate change the prices of wheat, barley and maize crops in the semi-arid and temperate climates for the year 2025 would also rise, so the second hypothesis of the study "Climate change and scarcity of water resources will increase the prices of agricultural products in Iran” is confirmed. The results also show that with climate change, farmers 'incomes in cold and semi-arid climates, temperate and warm semi-arid climates would increase by 2025 relative to their present value, so the third research hypothesis that "climate change and water scarcity reduces farmers' income" In Iran, " is not confirmed. The results also indicated that wheat, barley and maize exports remained negative by the creation of net climate change for 2025 and that the country's climate change created an importer of these products.
L. Ravand; A. Dourandish; M. Sabouhi Sabouni
Abstract
Introduction: Globalization is an inevitable process that one of its consequences is the liberalization of trade and the reduction of protectionism. Trade liberalization causing the heavily interdependent economics of the countries around the world to reduce customs and trade barriers to a minimum level, ...
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Introduction: Globalization is an inevitable process that one of its consequences is the liberalization of trade and the reduction of protectionism. Trade liberalization causing the heavily interdependent economics of the countries around the world to reduce customs and trade barriers to a minimum level, and make financial transfers between countries easily done. Rice is the most important and strategic crop after wheat and plays a significant role in trade and food security of the world and Iran. Because it provides more than 20% of human total daily calories and Almost two thirds of the world's population depends on rice for food. The total Production of Rice in Iran during 2012-2013 was about 2.3 million ton and about 93% of rice products are yielded in Gilan, Mazandaran, Khuzestan, Golestan and Fars provinces. About 88% of total production of rice in Iran is allocated to domestic consumption while just 12% of goes to the world market. Total consumption of rice in Iran is about 3.2 million ton.
Materials and Methods: The model that is used in this study is Agricultural sector partial equilibrium model with endogenous prices. The data used in present study are the average of production, consumption, export, import and area under cultivation quantity, which export and import prices for long, medium and short grain rice and import tariffs for two growing years of 2011-12 and 2012-13 are considered. These information are provided from agriculture jihad organization of Iran, Customs Office of the Islamic Republic of Iran and the book of export-import regulations. In current research, at first simple linear demand function is calibrated for long, medium and short grain rice based on demand price elasticity. The supply function involves two parts: domestic supply function and export supply function. The calibration of domestic supply is done by a Maximum Entropy integrated PMP method and calibration of export supply function is based on export supply price elasticity. It should be noted that the demand elasticities used in current study are captured from various studies and the export supply elasticities are taken as unity following Aydın et al (2004). Constraints that are used in the model are comprised of the constraints of area under cultivations, water, chemical fertilizer, variable costs, constraint of commodity balance and constraints of calibration includes area under cultivation and export.
Results and Discussion: The investigated scenarios are the reduction of import tariffs for rice by 10, 25, 50, 75, 90 and 100 percent. The results of present study showed that The area under cultivation of long and medium grain rice, compared to the base year (2012 and 2013 will decrease about 0.61 and 3.38 percent), in Mazandaran province, about 0.49 and 9.18 percent in Gilan province, about 2.82 and 4.32 percent in Golestan province , about 90 and 0.6 percent in Khuzestan province and about 24.47 and 2.47 in Fars province t. Short grain rice in Golestan province will decrease about 22.93 percent and in Fars province will decrease about 43.33 percent. Generally, with decreasing tariff rates, the long, medium and short grain rice, compared to the base year (2012 and 2013), will decrease about 21.5, 4 and 11.5 percent, respectively. Also, the consumption of long, medium and short grain rice will increase by 0.5, 1.1 and 0.7 percent, respectively. The average import of long, medium and short grain rice will increase by 5, 11 and 33.5 percent, respectively and Exports of long, medium and short grain rice also will increase by about 7.7%, 11.7% and 11.43% as a result of tariff cuts. Also, the net social welfare due to the reduction of rice tariff rates relative to the base year, will increase about 0.2 percent. The average welfare of consumers will increase about 1% and the welfare of producers will decrease about 1.7 percent compared to the base year. Also the welfare of the state will increase about 9.5 percent compared to the base year.
Conclusions: Considering small cultivated pieces of land, the high cost of production, the lack of relative advantage in the production of some types of rice and also the high waste of factories in the country, trade liberalization can be fruitful. Considering the importance of product advantages in producing, exporting and importing as well the best quality of Iranian rice, creating new technologies and new planting methods such as hydroponic cultivation, which leads to increased performance and increased water productivity per unit area, and also planting kinds of rice which have advantages in each province would conclude increasing the welfare of rice producers.
L. Hassani; M. Daneshvar Kakhki; M. Sabouhi Sabouni
Abstract
Introduction: Over the last two decades, awareness of resilience and sustainability and also efforts to reduce unsustainable production patterns have significantly increased. Hence, it is crucial to examine the resilience and sustainability of production systems. Resilience explains how well production ...
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Introduction: Over the last two decades, awareness of resilience and sustainability and also efforts to reduce unsustainable production patterns have significantly increased. Hence, it is crucial to examine the resilience and sustainability of production systems. Resilience explains how well production systems withstand and/or rebound from aberration. Sustainability concept based on Commission’s words is: “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. The important issue relevant to resilience and sustainability and the resilience of farms/agricultural systems is, whether resilience or sustainability can be considered as a property of a system or needs to be understood as a process. Since both of them are not essentially opposed but have various theoretical and methodological implications, it is necessary to define a resilience and sustainability indicator. So, it is required to have an intelligent objective function for fairly balancing between production systems and dimensions of sustainable production to fulfill economic benefit and the resulting environmental benefit, etc. Based on the existing published literature, studies focusing on both resilience and sustainability indicators in industrial dairy farms by using multi-objective non-linear programming and swarm intelligence algorithm have not been carried out. Therefore, it is the aim of the present study to design the “automata resilience and sustainability indicator” for industrial dairy farms. The objective function has a hierarchical structure and in order to integrate these pillars into a single score, a value between zero and one, Analytic Hierarchy Process (AHP) has been used that the value of one means complete sustainability.
Material and Methods: The objective function should be maximized which has 5 main indicators including, environmental, economic, social, technological and political issues. Each indicator has some sub-indicators. So, we designed and modeled formulas for all of them. The value of objective function is normalized, therefore, its maximum possible value is "one", which indicates the complete resilience and sustainability of dairy farms. The resilience and sustainability indicator is obtained at three levels. Eight types of constraint sets are considered. Then, the model has been implemented using data of 30[1] industrial dairy farms in Khorasan-Razavi province of Iran during 2016.
Results and Discussion: The resilience and sustainability indicator across all farms was obtained 0.43 and which was low. One of the main reasons of unsustainability and inflexibility of dairy farms under study is the unsuitable use of resources and inputs. Therefore, the proposed model (Automata Resilience and Sustainability Indicator Model) was designed and optimized. Based on result the optimum resilience and sustainability achievable for the proposed dairy farm is 0.9598 (95.98%). Thus, the proposed model succeeds in determining the dairy farms' resilience and sustainability. Furthermore, it helps in setting up other operational parameters as determining the amount of cow manure produced, the man-working hours and labor expenditure. The obtained results should be further used as guidance for improving the resilience and sustainability of the manufacturing operation in dairy farms.
Conclusions: This study has introduced a formulation for a resilience and sustainability problem in process of production in the industrial dairy farm. The contribution of the proposed formulation is its ability to addresses all pillars of resilience and sustainability at the producing level. One of the main advantages of the proposed measure of resilience and sustainability is data collection that relies on data usually collected in all farms for revenue and cost analysis, cattle diet and quality control. This fact makes the model applicable to facilities introducing resilience and sustainability concepts. Thus contributes to promoting the implementation of sustainable practices in agricultural production, especially in developing countries, where still have a lack of resilience and sustainability awareness and related legislation. Using weight is important to the application of the objective function and also makes the model suitable for its intended usage in the dairy farms of developing countries. This model is applicable in the area of the optimum dairy cattle nutrition, rising profitability, reducing feed cost, decreasing GHG, managing the water and energy consumption, etc., by maximizing resilience and sustainability in dairy farms. Additionally, the results allow also for identifying the prospective measures for improving resilience and sustainability. Through results analysis, a strategy for developing resilience and sustainability can be well defined. Furthermore, the current research can be extended by integrating the model with life cycle assessment results, another producer support policies, dairy farms' capacity expansions and could also be applicable to other forms of agricultural systems by a bit changes in the decision variables and model parameters.
4- This data was gathered based on non-random sampling. Because, in non-random sampling, the sample individuals are selected among individuals who have a defined characteristics and based on researcher's opinion. The proposed model is designed for a sample dairy unit. In other words, the data obtained from non-random sampling were used only to determine the status of the studied samples.