Journal of Agricultural Economics & Development

Abstract Abstract
Title
Policies and Strategies for Mitigation of Greenhouse Gases Emissions in Agricultural Sector of Iran
Introduction
Greenhouse gases such as carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and F-gases (Chlorofluorocarbons (CFCs), Hydrofluorocarbons (HFCs), and Perfluorocarbons (PFCs) such as C₂F₆, CF₄, SF₆, and NF₃) play a key role in increasing global warming and intensifying climate change through their absorption and re-emission of thermal radiation. Among economic sectors, agriculture significantly contributes to these emissions via crop cultivation, enteric fermentation in livestock, application of chemical and organic fertilizers, rice paddies, and energy consumption. The agricultural sector of Iran contributes a considerable amount to the total greenhouse gases emissions, thereby playing an important role in climate change. This study aims to quantify greenhouse gases emissions from Iran’s agricultural sector and identify policies and strategies for emission mitigation.
Materials and Methods
Greenhouse gases emissions from agricultural sector of Iran were estimated using data on livestock, fertilizer amount use, rice cultivation, and energy consumption. All emissions were converted to CO₂-equivalent (CO₂eq) using Intergovernmental Panel on Climate Change (IPCC) Global Warming Potential (GWP) values, allowing aggregation of total emissions and comparison across sources. GWP indicates how much energy one ton of a given gas absorbs over a specific time period (usually 100 years) relative to one ton of CO₂. Carbon dioxide, with a GWP value of 1, serves as the baseline and can remain in the atmosphere for thousands of years. If a gas has a GWP of 10, it means that one kg of that gas traps ten times more heat than one kg of CO₂.
Results and Discussion
Studies indicate that agricultural sector of Iran contributes significantly to greenhouse gases emissions. Major sources include chemical and organic fertilizers (18.3 Mt CO₂eq), enteric fermentation in livestock (20 Mt CO₂eq), on farm electricity application (19 Mt CO₂eq), natural gases usage (11 Mt CO₂eq), fossil fuel consumption for agricultural machinery (8 Mt CO₂eq), and rice cultivation (1 Mt CO₂eq), totaling 78 Mt CO₂eq. Indirect emissions from soil carbon release, livestock grazing, and crop residue burning are estimated about 25 Mt CO₂eq. Overall, total agricultural emissions are approximately 103 Mt CO₂eq, representing about 10% of Iran’s total greenhouse gases emissions. These findings indicate that official estimates by organizations such as IPCC and United Nations Framework Convention on Climate Change (UNFCCC), relying on approximate data and lacking large-scale field measurements, likely underestimate actual emissions, highlighting the need for revision and careful consideration in national policy-making.
Conclusion
The increase in greenhouse gases emissions has significantly impacted the global climate, including a 1.6 °C rise in average temperature, glacial melting, altered precipitation patterns, increased evaporation, and depletion of surface and groundwater resources. While Iran and several other countries examined in this study such as India, Mexico, Turkey, Pakistan, and Egypt were unable to reduce emissions between 2005 and 2023, experiences from the United States demonstrate that supportive legislation and dedicated funding at the policy level can effectively reduce greenhouse gases emissions. Inefficient use of nitrate fertilizers is one of the main sources of greenhouse gases emissions in the agricultural sector, especially in countries like Iran, where nitrogen use efficiency is low. In this context, allocating a portion of Iran’s gross domestic product (GDP) to support low carbon agriculture is recommended. Strategies such as using sustained- or controlled-release fertilizers (SRF/CRF), promoting renewable energy, gradually transitioning from traditional to industrial livestock systems, applying methane-reducing additives and vaccines, and implementing integrated farming systems like agroforestry can be effective. Achieving sustainable, low-carbon agriculture in Iran requires a comprehensive approach based on real data, improved input management and production practices, renewable energy infrastructure, and legal, financial, and research support for low-carbon technologies.
Acknowledgement
This research is financially supported by the National Agriculture and Water Strategic Research Center of the Iran Chamber of Commerce, Industries, Mines and Agriculture (ICCIMA) under grant No. ص/10/2/960. Any findings, inferences and opinions, expressed in this study are those of the authors and do not necessary reflect the official standing of the ICCIMA. The authors declare that they don’t have any conflict of interests relative to this study.
Keywords: Carbon sequestration, Climate change, Global warming, Methane, Nitrous oxide.
Keywords: Carbon sequestration, Climate change, Global warming, Methane, Nitrous oxide.
Keywords: Carbon sequestration, Climate change, Global warming, Methane, Nitrous oxide.

Abstract Introduction
Price volatility in livestock products has always been one of the most significant concerns for policymakers and consumers due to their pivotal role in food security and the supply of societal protein. These products constitute a considerable portion of Iranian households' expenditure basket.
In Iran, economic challenges such as international sanctions, currency fluctuations, the COVID-19 pandemic, and domestic policies like the elimination of subsidized exchange rates have exacerbated the instability of food prices, disproportionately pressuring low-income households .This continuous inflation erodes purchasing power, increases inequality and poverty rates, and can lead to social discontent.
Price changes in these commodities directly and negatively impact the purchasing power of various social strata, especially low-income groups, through two channels: on one hand, they harm the production sector and create financial pressure, and on the other, they reduce consumers' life stability by limiting their ability to plan. These fluctuations increase market risks by creating uncertainty about future prices. Consequently, investment planning becomes difficult, which can lead to a reduction in long-term production and an increased reliance on imports. Food inflation significantly affects household purchasing power and economic welfare and is considered a primary concern for policymakers

Persistence food inflation, characterized by long memory processes, indicates the prolonged impact of price shocks, which complicates efforts to restore price stability . The presence of long memory implies that a shock can have enduring effects on the variable. The goal of the current research is to provide a comprehensive picture of the price persistence of livestock products (beef, mutton, chicken, egg and milk) in Iran.
Considering that the Iranian economy has been affected by significant shocks in recent years, this study also investigates the impact of these specific shocks—including the U.S. withdrawal from the Joint Comprehensive Plan of Action (JCPOA), the COVID-19 pandemic, and Preferential Exchange Rate Removal —on price persistence at both levels (farm and retail). These findings help policymakers, with a deeper understanding of price dynamics, devise more effective strategies for managing price volatility in this critical sector.
Materials and Methods
In the present study, the time-series analysis approach using the ARFIMA (p,d,q) model is employed to investigate the price persistence of livestock products at the farm and retail market levels and to assess the degree of persistence of the shocks' impact. This model is a generalization of the traditional ARIMA models, and its main advantage is its ability to account for fractional and long dependencies, which characterize many economic phenomena. As a powerful econometric framework, the ARFIMA model allows for the analysis of whether a price shock has transitory effects or whether its impacts persist over time and influence future price levels.
The parameter d in the ARFIMA model indicates the level of dependency (or memory) between observations. A higher value of d signifies greater persistence and durability between observations, suggesting that the effects of the shocks remain on prices for a longer duration. The data used include the prices of livestock products (beef, lamb, poultry, eggs, and milk) at the farm and retail levels for the period from April 2014 to March 2024 which were collected from the State Livestock Affairs Logistics Company.
Results and Discussion
The model results indicate that the prices of all livestock products exhibit long memory. This means that past price shocks maintain their effects on future prices for a prolonged period. The analyses reveal significant differences in price persistence between the farm and retail levels. Specifically, the degree of integration (d) for meat is lower at the retail level than at the farm level, and for milk, it is lower at the farm level than at the retail level. This finding is entirely consistent with the results of seasonal unit root tests and indicates the differentiated functioning of market mechanisms at each level.
The results showed that COVID-19 significantly affected price persistence at the farm level, whereas the shock from the Preferential Exchange Rate Removal had lasting effects on price persistence at the retail level. In contrast, the shock from the U.S. withdrawal from the JCPOA had no significant effect on any of the variables.
Conclusion
Based on the obtained results, the following suggestions are presented for policymakers:
Since the results indicate that price persistence patterns are not uniform across different livestock products and market levels (farm and retail), policy approaches must be targeted and tailored to the specific characteristics of each product.
The results show that the degree of integration for lamb and beef is higher at the farm level than at the retail level. Therefore, to protect livestock producers from market fluctuations and external shocks, the government can implem

Abstract In the present era, various factors have affected the socio-economic structures of rural communities. Among them, climate change, as a powerful exogenous shock, increases risks such as food insecurity and population displacement, and the asymmetric impact of these shocks on different groups, especially women and children, can lead to an exacerbation of existing inequalities, especially gender inequalities. The rural labor market, as the core of livelihoods, is affected by these developments. Despite extensive studies on women's vulnerability, the analysis of the dynamics of the ratio of women's economic participation to men as a macro and structural indicator has received less attention. The aim of this research is to analyze scientific evidence to explain the economic mechanisms through which climate change affects the gender ratio of participation in the rural labor force.
In this study, using the generalized moments method (GMM), educational, economic, social, and climatic factors have been investigated on the ratio of female to male participation in the labor market of 30 provinces of Iran during the period 2015-2017. The results show that the Dumartin index as an indicator of climate change, agricultural value added, urbanization rate, divorce rate, and rural women's wage rate have a positive and significant effect, and the Gini coefficient has a negative and significant effect on female to male participation in rural areas.
Materials and Methods
In this study, using data collected from the Statistical Center of Iran during 2015 to 2017 and for 30 provinces (except Alborz province), the dynamic panel method and the generalized method of moments (GMM) were used to investigate the effect of climate change on the ratio of women to men in rural areas. In this study, the ratio of women to men's economic participation in rural areas was considered as the dependent variable, the Dumartin index (climate change index) as the independent variable, and the variables of agricultural value added, urbanization rate, divorce rate, rural women's growth rate, and Gini coefficient were considered as control variables.
Results and Discussion
The results show that the Dumartin index as an indicator of climate change has a significant effect and the improvement of the Dumartin index, which indicates the improvement of humidity and precipitation, increases the ratio of women's economic participation rate to men's in rural areas. The dependent variable has a positive and significant effect on the value of the dependent variable with a lag and indicates stability over time. The value added of the agricultural sector, urbanization rate, divorce rate and fertility rate of rural women have a positive and significant effect and the Gini coefficient has a negative and significant effect on the ratio of women's participation to men in rural areas.
Conclusion
The findings of this study suggest that climate change is not a gender-neutral phenomenon, but rather acts as a reinforcer of existing inequalities. This process not only increases women's structural vulnerability, but can also reverse the gains made over decades of efforts to empower them economically
This study, which aims to provide an in-depth and multidimensional analysis of the impacts of climate change on the economic participation ratio of women to men in rural areas, has reached some key and noteworthy findings. The final conclusion is that climate change, as a powerful structural force, is reshaping the rural labor market in complex ways, and its impact on the gender participation ratio is the product of a dynamic interaction between opposing economic and social forces. The study showed that the increase in this ratio, which at first glance can be interpreted as a positive development towards gender equality, is often a “statistical mirage” that hides deep economic distress, forced migration of men, and an increased disproportionate burden on women. Therefore, relying solely on this indicator without understanding its underlying mechanisms can lead policymakers to misleading conclusions and decisions.
This study, which aims to provide an in-depth and multidimensional analysis of the impacts of climate change on the economic participation ratio of women to men in rural areas, has reached some key and noteworthy findings. The final conclusion is that climate change, as a powerful structural force, is reshaping the rural labor market in complex ways, and its impact on the gender participation ratio is the product of a dynamic interaction between opposing economic and social forces. The study showed that the increase in this ratio, which at first glance can be interpreted as a positive development towards gender equality, is often a “statistical mirage” that hides deep economic distress, forced migration of men, and an increased disproportionate burden on women. Therefore, relying solely on this indicator without understanding its underlying mechanisms can lead policymakers to misleading conclusions and decisions.
Achieving economic stability and sustainable and equitable development in the era of climate change requires a fundamental rethinking of policy approaches. Policies must move beyond a “gender-blind” or even “gender-sensitive” logic and toward a “gender-transformative” approach. This approach requires interventions that not only respond to the different needs of women and men, but also actively seek to change the structures, norms, and power relations that perpetuate inequality. Accordingly, a set of specific policy recommendations can be made:
First, invest in infrastructure that reduces women’s work pressure: Prioritizing projects such as piped water supply, electrification, and access to clean energy in rural areas directly frees up women’s time and removes the largest structural barrier to their economic participation.
Second, support diversification into climate-resilient livelihoods: Instead of focusing solely on agriculture, the government should support the development of non-farm small and medium-sized businesses in rural areas and ensure that women have equal access to skills training, credit, and markets to participate in these new sectors.

Abstract Introduction
The exchange of agricultural goods is essential for maintaining worldwide food security, allowing nations to address local limitations caused by climate change, water shortages, and technological difficulties. Such commerce not only boosts farmers' earnings and supports rural development but also improves productivity by optimizing resource allocation. In Iran, agriculture has historically been a crucial component of the economy, contributing significantly to gross domestic product and employment levels. Among the various agricultural subsectors, horticultural products represent one of the most rapidly expanding areas in global agricultural trade. As the global focus shifts towards healthier diets, sustainable farming, and green development, these products have increasingly become vital drivers of economic growth and sources of foreign currency earnings. Within the category of horticultural crops, apples make up a notable portion of Iran's production and exports. From 2013 to 2023, apples represented an average of 17% of the country’s horticultural output, consistently ranking among the top three horticultural products, and their export trend during this timeframe indicates substantial potential for further growth.
In recent years, one strategy implemented to enhance Iran's agricultural exports is the "exchange of apple exports for banana imports," which was launched in 2016 with the purpose of fostering the growth of apple exports and securing the foreign currency necessary for importing bananas. Since this mechanism has been applied to apples over the last few years, an important question emerges: given the current limitations and difficulties, has its execution led to a significant change in the quantity of apple exports and banana imports? Furthermore, this research aims to offer insights that can improve Iran's trade and agricultural policies, aiding policymakers in crafting more balanced and effective approaches for the sustainable growth of the agricultural sector. A review of existing literature reveals that no previous research has investigated the impact of the apple–banana barter mechanism on apple exports and banana imports. As a result, the current study intends to evaluate the influence and effectiveness of this mechanism on the amounts of apple exports and banana imports from 2013 to 2023, utilizing the Fully Modified Ordinary Least Squares (FMOLS) method.

Materials and Methods
The primary aim of this research is to assess the efficiency of the “apple-banana barter” mechanism. This mechanism is closely linked to the export supply of apples and the demand for imported bananas, and its impacts are examined through an apple export supply function and a banana import demand function. The apple export supply function is defined as a function of the relative price of apple, domestic apple production, the real exchange rate, and a dummy variable indicating the barter mechanism. The banana import demand function is structured as a function of the relative price of bananas, real national income, domestic banana production, the banana import tariff, and the dummy variable for the barter mechanism. Since some variables - like apple exports, relative apple prices, banana imports, relative banana prices, and the real exchange rate - are available quarterly, while others - such as the domestic production of apples and bananas and real national income - are annual, the study utilizes the Mixed Data Sampling-Fully Modified Ordinary Least Squares (MIDAS-FMOLS) method for the years 2013-2023.

Results and Discussion
The findings suggest that the “apple-banana barter” mechanism has positively impacted the volume of apple exports and banana imports in Iran, thereby confirming the primary hypothesis of the study. The estimation of Iran’s apple export supply function indicates that the relative price of apples, the real exchange rate, and domestic production have a positive and significant effect on apple exports, whereas trade and economic crises reduce the supply of Iranian apple exports. Furthermore, the estimation of Iran’s banana import demand function shows that the relative price of bananas, domestic production, the real exchange rate, and trade-economic crises have a negative and significant effect on the volume of banana imports, while real national income has a positive and significant impact.

Conclusions
Based on the research findings, to strengthen apple exports and enhance the efficiency of the barter mechanism, it is recommended that the barter system continue with necessary reforms to increase transparency, reduce rent-seeking and unnecessary restrictions, and establish continuous monitoring and a tracking system to minimize potential corruption. This mechanism should also be implemented alongside other export promotion strategies and, once operational issues are resolved, expanded to other export fruits such as kiwi and oranges. Controlling short-term exchange rate fluctuations and ensuring currency stability will improve exporters’ ability to make predictable decisions and enhance the effectiveness of the barter policy. Strengthening domestic production capacity and improving product quality - through higher orchard productivity, modern technologies, optimal input management, and the development of cold storage, sorting, and packaging infrastructure - are essential for ensuring a stable and high-quality export supply. Additionally, developing tools such as export insurance and trade support funds can mitigate the impact of economic crises, while tariff policies, exchange rates, and the barter mechanism should be adjusted considering factors affecting banana imports to maintain continuity in domestic market supply.

Abstract Title
A Comprehensive Model of Artificial Intelligence and Internet of Things Applications in Smart Agriculture with a Meta-Synthesis Approach
Introduction
Smart agriculture is rapidly becoming a cornerstone of sustainable food systems by integrating artificial intelligence (AI), the Internet of Things (IoT), and data-driven decision support. Yet many available frameworks are not tailored to the realities of semi-arid regions, where water scarcity, heterogeneous soils, and fragmented connectivity constrain deployment. This study develops a localized Smart Agriculture Model designed for Iran, organizing technologies and tools into a coherent, three-level structure that links macro domains, enabling technologies, and operational instruments. The model emphasizes practical decision-making and real-time monitoring over purely algorithm-centric approaches, aligning technical capabilities with environmental and infrastructural constraints. In doing so, it seeks to convert dispersed knowledge into an implementable blueprint for national and regional planning.
Materials and Methods
The research employs a meta-synthesis methodology to integrate qualitative and quantitative evidence on AI/IoT in agriculture. A systematic search identified 334 potentially relevant publications. Through staged screening (title, abstract, content, and methodology), 12 high-quality studies were retained for full analysis. From these sources, 44 indicators were coded and then grouped into ten technological domains that span the intelligence layer (e.g., analytical models and decision aids), the sensing and communication layer (e.g., IoT devices, wireless sensor networks, robotics), and the processing and security layer (e.g., big data platforms, cloud/edge computing, blockchain).
Objective importance weights for each indicator were computed using Shannon entropy, capturing the dispersion and informational value of concepts across the literature set. To contextualize these objective weights with local realities, expert judgments from four national specialists in digital agriculture were elicited and combined with the entropy results via a hybrid scheme. The integration coefficient was set to α = 0.4 after sensitivity testing to balance objective evidence with contextual expertise and to minimize divergence between the two weight sets. Reliability and agreement of the qualitative coding were assessed using multiple indices: Cohen’s kappa (0.784) indicated substantial agreement; additional measures, including Holsti’s percentage agreement (0.822) and Krippendorff’s alpha (0.81), further supported coding consistency.
Results and Discussion
The resulting model clarifies priorities across the three levels. At the operational tools level, IoT-based sensing achieved the highest integrated weight (W = 0.123), followed closely by artificial intelligence tools (W = 0.116) and wireless sensor network capabilities. Aggregating to the technology level shows a clear emphasis on decision-centric intelligence and field-level data acquisition. In contrast, deep learning components and broad supply-chain-oriented blockchain applications received lower weights under current conditions, reflecting limitations in data volume, standardization, and computing infrastructure.
These priorities mirror the pressing needs of semi-arid agriculture: granular monitoring of soil moisture and fertility, microclimate tracking, and optimized water and fertilizer use. The model therefore foregrounds low-power, wide-area connectivity; interoperable sensor gateways; and edge-aware analytics that support timely farm management actions. At the same time, it maintains a pathway for growth by including big data platforms, cloud/edge hybrids, and secure data stewardship, which become increasingly important as data flows expand.
A policy-relevant insight is the model’s “human–technology hybrid” orientation. Rather than replacing human expertise, AI and analytics are positioned to augment agronomists and farmers with transparent recommendations, scenario analysis, and early-warning capabilities. This orientation not only enhances adoption but also fits the realities of varying digital readiness across regions. Sensitivity analysis around α confirmed the stability of the weighting scheme (standard deviation < 0.007), indicating that the prioritization is robust to reasonable shifts in the balance between evidence and expert input.
Overall, the distribution of weights suggests a pragmatic modernization pathway: first, build reliable sensing and communications; second, couple these data streams to actionable decision support at farm and regional scales; third, progressively expand data management, cloud/edge resources, and advanced modeling as data coverage and quality improve.
Conclusion
This study proposes a localized, data-driven framework that integrates AI and IoT within a structured three-level model for smart agriculture. Using a hybrid weighting method (α = 0.4) to merge entropy-based evidence with expert knowledge, the results demonstrate that artificial intelligence tools and IoT-enabled sensing are the most impactful levers for immediate advancement in semi-arid contexts. The framework translates technological possibilities into implementable priorities: expand low-power connectivity and interoperable sensor networks; establish a national backbone for secure, standardized agricultural data; and elevate AI literacy so that farmers and extension agents can act on timely, explainable insights. While the synthesis draws on published research rather than field trials, the robustness of the weighting and agreement metrics provides confidence in the model’s near-term usefulness. Future work should validate and regionalize the weights with real-world datasets, incorporate scenario-based dynamic weighting under climate and market uncertainty, and couple the framework to simulation tools to estimate system-level impacts on productivity, resilience, and resource efficiency.
Keywords
Smart Technologies; Shannon Entropy; Hybrid Weighting; Decision Support Systems.

Abstract Introduction
The agricultural sector in the Sistan region, as the core of the rural economy, has faced increasing risks in recent years. Chronic water stress and recurrent droughts have severely affected the production of the region’s strategic crops, threatening the livelihoods of farmers and rural households. These conditions have heightened the economic and social vulnerability of local communities and underscored the need for effective risk management strategies to enhance sustainability. Among available mechanisms, agricultural insurance—as a well established financial risk transfer tool—holds significant potential to help farmers absorb drought related shocks and maintain economic stability.
Materials and Methods
This study is applied in purpose and employs a quantitative, descriptive analytical approach. The statistical population includes all 796 villages of the Sistan region with a total of 54,198 households. To obtain valid and generalizable results, villages with more than 300 households were selected as the primary criterion. Accordingly, all 24 villages meeting this population threshold were chosen as the final sample communities. The selection of these 24 villages with over 300 households was based on a logical focus on stable rural settlements with livelihood systems strongly centered on agriculture. Larger villages with higher populations typically possess more complex socio economic structures, and a substantial portion of their residents rely directly on agricultural activities. Consequently, the impacts of water stress on livelihoods in such communities are more tangible, measurable, and analytically meaningful. These characteristics make the relationships among the study’s core variables—insurance uptake, livelihood resilience, and water stress—more observable. Additionally, access to reliable information through local institutions (such as the village council, health house, and the local office of agricultural extension services) is easier in these larger villages, thereby increasing the efficiency of household level sampling among farmers. Thus, this sampling strategy not only enhances the internal validity of the research—by concentrating on communities in which the core issues are most pronounced—but also ensures the practical feasibility of conducting the survey. Based on random sampling and Cochran’s formula, 384 households were selected. Data were analyzed using SPSS software, employing confirmatory factor analysis, independent t test, and path analysis.
Results and Discussion
The confirmatory factor analysis indicated that all 25 livelihood resilience indicators in the region possess acceptable validity. These indicators were summarized into six main factors that together explained more than 92% of the variance. Among these factors, economic–financial dimensions—such as income diversification and safeguarding productive assets—played a significantly stronger role than others in explaining resilience within this agricultural community. Moreover, the results of the independent t-test showed that farmers who insured their crops exhibited higher livelihood resilience against drought. Finally, the path analysis results revealed that water scarcity stress acts as a very strong mediating variable, playing a key role in transmitting the impact of insurance on resilience. The direct effect of strategic crop insurance on livelihood resilience (2.44) was minimal compared to its indirect effect through water scarcity stress (26.34). Consequently, more than 90% of the total effect of insurance (29.80) is transmitted indirectly via the intensification or mitigation of drought conditions.

Factor analysis showed that all 25 livelihood resilience indicators in the region had acceptable validity. These indicators were summarized into six main factors, which together explained more than 92% of the variance. Among these factors, the economic-financial dimensions, such as income diversification and the preservation of productive assets, played a much more prominent role than the other dimensions in explaining resilience in this agricultural community. On the other hand, the results of the independent samples
𝑡-test indicated that farmers who insured their crops had a higher level of livelihood resilience against drought. Finally, the path analysis results showed that water stress acted as a very strong mediating variable and played the main role in transferring the effect of insurance to resilience. The direct effect of strategic crop insurance on livelihood resilience (2.44) was negligible compared with its indirect effect through water stress (26.34), such that more than 0.90 of the total effect of insurance (29.80) was transmitted indirectly through the intensification or mitigation of drought conditions.
Conclusions
Agricultural insurance can serve as a catalytic mechanism for strengthening livelihood resilience. Farmers who use insurance demonstrate markedly better resilience outcomes across all dimensions. Crucially, the effectiveness of insurance is primarily indirect, operating through its ability to enhance households’ capacity to manage and withstand the impacts of drought. This finding positions agricultural insurance not as a supplementary service but as a central strategy for protecting productive assets and breaking the cycle of climate induced poverty in arid regions.

Abstract افزایش تنوع کشت علاوه بر تقویت تنوع زیستی و بهبود کارکردهای اکولوژیکی، موجب ارتقای حاصلخیزی خاک، بهبود چرخه مواد غذایی، کاهش شیوع آفات و بیماری‌ها، افزایش کارایی استفاده از منابع و تقویت پایداری تولید می‌شود. از لحاظ اقتصادی نیز تنوع کشت به عنوان یک راهبرد مؤثر مدیریت ریسک، وابستگی کشاورزان به یک محصول خاص را کاهش داده و آنان را در برابر نوسانات اقلیمی، شوک‌های قیمتی و مخاطرات تولید مقاوم‌تر می‌سازد. در نتیجه، تنوع کشت می‌تواند همزمان به بهبود امنیت غذایی، افزایش درآمد خانوارهای کشاورز و ارتقای تاب‌آوری نظام‌های کشاورزی کمک کند. با وجود اهمیت بالای تنوع کشت در ابعاد اقتصادی، اجتماعی و زیست‌محیطی، هنوز شناخت کاملی از عوامل مؤثر بر شکل‌گیری و شدت تنوع کشت در سطح مزرعه وجود ندارد. همچنین، اغلب مطالعات پیشین با استفاده از مدل‌های متعارف اقتصادسنجی انجام شده‌اند و میان عوامل مؤثر بر ورود کشاورزان به تنوع کشت و عوامل تعیین‌کننده شدت تنوع تمایز قائل نشده‌اند. به منظور دستیابی به اهداف پژوهش، داده‌های مورد نیاز از طریق میدانی و تکمیل پرسشنامه ساختاریافته جمع‌آوری شد. حجم نمونه با استفاده از فرمول کوکران تعیین گردید و بر این اساس، 325 بهره‌بردار کشاورزی به عنوان نمونه آماری انتخاب شدند. اطلاعات مورد نیاز از طریق مصاحبه حضوری با کشاورزان گردآوری و سپس با استفاده از مدل رگرسیون بتای صفرمتورم به بررسی همزمان عوامل مؤثر بر احتمال قرار گرفتن کشاورزان در وضعیت تک‌کشتی و عوامل مؤثر بر شدت تنوع کشت پرداخته است.

نتایج مدل رگرسیون بتای صفرمتورم نشان داد که عوامل مؤثر بر تنوع کشت از کانال‌های متفاوتی عمل می‌کنند. در بخش احتمال قرار گرفتن در وضعیت تنوع صفر (تک‌کشتی)، متغیرهای تجربه کشاورزی، سطح سواد سرپرست خانوار، مقیاس تولید (سطح زیرکشت)، تعداد قطعات زمین، مالکیت دام، شرکت در کلاس‌های آموزشی و ترویجی و دسترسی به اعتبارات اثر منفی و معنادار داشتند؛ به این معنا که این عوامل احتمال قرار گرفتن کشاورزان در وضعیت تک‌کشتی را کاهش داده و زمینه ورود آنان به تنوع کشت را فراهم می‌کنند. در بخش شدت تنوع کشت، متغیرهای تجربه کشاورزی، بعد خانوار، سطح سواد سرپرست خانوار، دسترسی به منابع آب، تعداد قطعات زمین، مالکیت دام، مالکیت تراکتور و ادوات کشاورزی و برخورداری از درآمدهای غیرکشاورزی دارای اثر مثبت و معنادار بودند، در حالی که فاصله تا نزدیک‌ترین بازار اثر منفی و معناداری بر شدت تنوع کشت داشت. این نتایج نشان می‌دهد که منابع تولید، سرمایه انسانی، دسترسی به امکانات تولیدی و شرایط بازار نقش مهمی در توسعه و تعمیق تنوع کشت ایفا می‌کنند. همچنین، نتایج اثرات نهایی کلی نشان داد که تجربه کشاورزی، بعد خانوار، سطح سواد سرپرست خانوار، مقیاس تولید، دسترسی به منابع آب، تعداد قطعات زمین، مالکیت دام، دسترسی به اعتبارات و درآمدهای غیرکشاورزی اثر مثبت و معناداری بر شاخص کلی تنوع کشت دارند، در حالی که فاصله تا بازار اثر منفی و معناداری بر تنوع کشت بر جای می‌گذارد. در مقابل، اثر کلی مالکیت تراکتور و ادوات کشاورزی و نیز شرکت در کلاس‌های آموزشی و ترویجی از نظر آماری معنادار نبود. همچنین در بخشی از مطالعه به بررسی ارتباط غیرخطی تجربه و مقیاس تولید بر تنوع پرداخته شد که نتایج نشان داد تجربه کشاورزی اثر مثبت اما غیرخطی بر تنوع کشت دارد، به گونه‌ای که اثر آن تا حدود 45 سال تجربه، افزایشی بوده و پس از آن روند کاهشی پیدا می‌کند. بر اساس یافته‌های پژوهش، پیشنهادهای سیاستی زیر قابل ارائه است:

1- توسعه سرمایه انسانی و خدمات آموزشی–ترویجی هدفمند

با توجه به ارتباط مثبت و معنادار تحصیلات، مشارکت در دوره‌های آموزشی–ترویجی و تجربه کشاورزی (تا نقطه آستانه) با تنوع کشت و همچنین کاهش اثر تجربه در سطوح بالاتر، پیشنهاد می‌شود برنامه‌های آموزشی و ترویجی به‌صورت هدفمند و متناسب با ویژگی‌های بهره‌برداران طراحی شوند. این برنامه‌ها باید علاوه بر انتقال دانش فنی، بر ارتقای مهارت‌های مدیریتی، تحلیل اقتصادی محصولات جایگزین و ترویج فناوری‌های نوین تمرکز داشته باشند و برای کشاورزان کم‌تجربه و باتجربه، رویکردهای آموزشی متفاوتی اتخاذ شود.

2- توسعه نظام‌های اعتباری و تقویت سرمایه‌گذاری در زیرساخت‌های تولید

با توجه به ارتباط مثبت و معنادار دسترسی به اعتبارات، مالکیت تراکتور و ادوات کشاورزی و دسترسی به منابع آب با تنوع کشت، پیشنهاد می‌شود سیاست‌های حمایتی بر توسعه تسهیلات اعتباری هدفمند، سرمایه‌گذاری در مکانیزاسیون، توسعه سامانه‌های نوین آبیاری و تأمین تجهیزات تولید برای بهره‌بردارانی که قصد تنوع‌بخشی به الگوی کشت دارند، متمرکز شود تا محدودیت‌های سرمایه‌ای و فنی کاهش یابد.

3- بهبود دسترسی به بازار و توسعه زیرساخت‌های بازاریابی

با توجه به ارتباط منفی و معنادار فاصله از بازار با تنوع کشت، پیشنهاد می‌شود توسعه شبکه حمل‌ونقل روستایی، ایجاد بازارهای محلی، تقویت زنجیره ارزش، توسعه صنایع تبدیلی و بهبود دسترسی کشاورزان به اطلاعات بازار در اولویت سیاست‌گذاری قرار گیرد تا هزینه‌های مبادله کاهش یافته و انگیزه تولید محصولات متنوع افزایش یابد.

4- حمایت از نظام‌های تولید تلفیقی و تقویت تاب‌آوری اقتصادی خانوارها

با توجه به ارتباط مثبت و معنادار مالکیت دام و درآمد غیرکشاورزی با تنوع کشت، پیشنهاد می‌شود توسعه نظام‌های تلفیقی زراعت–دامداری، حمایت از تولید محصولات علوفه‌ای، استفاده بهینه از بقایای گیاهی و ایجاد فرصت‌های درآمدی مکمل در مناطق روستایی مورد حمایت قرار گیرد تا توان مالی، ظرفیت مدیریت ریسک و پایداری اقتصادی خانوارهای کشاورز افزایش یابد.

5- اجرای سیاست‌های منطقه‌محور در مدیریت اراضی و بهره‌برداری از ظرفیت‌های ساختاری مزارع

با توجه به ارتباط مثبت و معنادار اندازه مزرعه، تعداد قطعات زمین و دسترسی به منابع آبی با تنوع کشت، پیشنهاد می‌شود سیاست‌های مدیریت اراضی با رویکرد منطقه‌محور طراحی شوند؛ به‌گونه‌ای که ضمن توسعه زیرساخت‌های آبی، از ظرفیت ناهمگنی قطعات زمین برای تخصیص بهینه محصولات متناسب با ویژگی‌های خاک، منابع آب و شرایط توپوگرافی بهره گرفته شود و در اجرای برنامه‌های یکپارچه‌سازی اراضی، مزیت‌های تنوع فضایی مزارع نیز حفظ گردد.

6- طراحی بسته‌های سیاستی یکپارچه برای توسعه پایدار تنوع کشت

با توجه به اینکه نتایج پژوهش نشان داد عوامل انسانی، اقتصادی، ساختاری، زیرساختی و نهادی به‌صورت هم‌زمان بر تنوع کشت اثرگذار هستند، پیشنهاد می‌شود سیاست‌های توسعه تنوع کشت در قالب بسته‌های یکپارچه شامل آموزش، اعتبار، توسعه بازار، بهبود زیرساخت‌های تولید، مدیریت منابع آب و حمایت از نظام‌های تولید تلفیقی طراحی و اجرا شوند؛ زیرا تمرکز بر یک ابزار سیاستی به‌تنهایی نمی‌تواند زمینه توسعه پایدار و افزایش تاب‌آوری نظام‌های زراعی را فراهم کند. با وجود کارایی مدل رگرسیون بتای صفرمتورم، این پژوهش با محدودیت‌هایی همراه است. استفاده از داده‌های مقطعی، امکان بررسی تغییرات زمانی و پویایی رفتار کشاورزان را محدود می‌کند. بنابراین، پیشنهاد می‌شود مطالعات آتی با بهره‌گیری از داده‌های تابلویی (پانلی) و پوشش مناطق مختلف، عوامل مؤثر بر تنوع کشت را در طول زمان و در شرایط مختلف بررسی کنند.

Abstract Abstract
Introduction:The agricultural sector is considered one of the important pillars of sustainable development in various countries due to its fundamental role in ensuring food security, creating employment, preserving natural resources, and supporting economic development. In addition to meeting food demand, this sector has an impact on the growth of production, creation of added value and the development of rural areas through extensive links with other economic sectors. Agriculture is considered a cornerstone of national economies due to its vital role in ensuring food security, creating employment, preserving natural resources, and contributing significantly to non-oil exports. This fundamental status underscores the sector’s substantial value to the national economy through extensive backward and forward linkages. Furthermore, agriculture possesses a significant potential to stimulate production in other economic sectors; consequently, a decline in agricultural output can reduce value-added across the broader economy. In several developed nations, the agricultural sector ranks as the primary source of employment, positioning it as a pivotal component that contributes to socio-economic stability. The deep and multifaceted interconnections of the agricultural sector with other economic domains establish it as a driving force for growth and sustainable development at national and regional levels. However, climate change, manifesting in phenomena such as consecutive droughts, devastating floods, unprecedented temperature increases, and disruption of regular rainfall patterns, poses a serious threat to the sector’s productive capacity, presenting structural challenges to food security and the livelihoods of its stakeholders. Chaharmahal and Bakhtiari Province, as a significant agricultural hub, due to its climatic sensitivities and heavy reliance on surface and groundwater resources, necessitates a transition towards “climate-smart agriculture.” The primary objective of this research is to conduct a multi-dimensional evaluation (economic, socio-environmental, and technical-institutional) of climate change adaptation strategies and to provide a scientific decision-making framework for local stakeholders.
Materials and Methods: A hybrid decision-making framework integrating the Fuzzy Delphi Method (FDM), Fuzzy Analytic Hierarchy Process (FAHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed. First, potential climate adaptation strategies were identified through a comprehensive review of national and international literature. Subsequently, the Fuzzy Delphi Method was applied with the participation of nine experts (five university faculty members and four specialists from the Agricultural Jihad Organization) to screen and confirm the ten most appropriate adaptation strategies. In the second stage, the relative weights of the economic, social, environmental, and technical–institutional criteria and their corresponding sub-criteria were determined using FAHP based on the judgments of sixteen experts (five university faculty members, six agricultural specialists, and five managers from the Agricultural Jihad Organization). Finally, the selected adaptation strategies were prioritized by integrating the FAHP-derived weights with the TOPSIS method.
Results: The findings from the FAHP analysis indicate that the “economic” and “environmental” criteria are the most critical components in the innovation adoption process. Specifically, the sub-criterion of “Financial Return and Profitability” with an overall weight of 0.1685 and “Sustainable Water Resource Management” with a weight of 0.1364 were identified as the primary priorities. In the final ranking, the option of cultivating climate-resilient crop varieties secured the first rank with a score of 0.719. This strategy directly addresses farmers’ livelihood concerns and climatic limitations by reducing water requirements and maintaining yield stability. Crop rotation combined with cover cropping ranked second with a score of 0.700, highlighting the importance of ecosystem-based approaches and the enhancement of soil health (weight 0.0848). The options of conservation tillage (with a score of 0.668) and smart irrigation (with a score of 0.654) also achieved subsequent ranks. Conversely, technologies such as the Internet of Things, due to their need for specialized knowledge and technical infrastructure, and crop insurance, owing to its reactive nature (compensation for losses rather than prevention), received the lowest priority.
Conclusion: The findings indicate that economic performance, sustainable water resource management, and institutional capacity are the key determinants in prioritizing climate-smart agricultural strategies. Among the evaluated options, cultivation of climate stress-tolerant crop varieties and crop rotation with cover crops emerged as the most effective adaptation strategies. These findings suggest that policies promoting Climate-Smart Agriculture should prioritize economically viable, resource-efficient, and practically implementable strategies. Accordingly, strengthening agricultural extension services, promoting the development and dissemination of climate-resilient crop varieties, and designing targeted economic incentives for the adoption of prioritized adaptation measures can significantly facilitate the transition toward climate-smart agriculture.
Keywords: Climate Change; Climate-Smart Agriculture; Multi-Criteria Evaluation; FAHP; TOPSIS
JEL Classification: Q10; Q15; Q18; Q54

Abstract Among different food groups, animal-based products systematically exhibit the highest greenhouse gas emission intensities. Life cycle assessment (LCA) evidence indicates that beef and lamb have the greatest emission intensities, while poultry, eggs, and dairy products are associated with comparatively lower levels, although they still emit substantially more greenhouse gases than plant-based food sources. These differences are primarily driven by processes such as enteric fermentation, methane emissions, feed production, and land-use change associated with livestock feed cultivation. Consequently, the composition of household food baskets plays a critical role in determining total emissions, and even modest shifts in consumption patterns can significantly alter dietary carbon footprints. In high-income countries, dietary transitions are largely influenced by environmental awareness, health policies, and changing consumer preferences. In contrast, in developing countries, consumption patterns are more strongly shaped by economic variables such as income levels, relative food prices, and economic uncertainty. Therefore, reductions in high-emission food consumption do not necessarily reflect deliberate pro-environmental behavior and may instead result from declining welfare and reduced purchasing power. This issue is particularly important in developing economies where macroeconomic shocks can substantially reshape dietary structures. Accordingly, analyzing emission trends without accounting for macroeconomic conditions and structural changes in consumption may lead to misleading interpretations. However, a review of the literature reveals that environmental studies have primarily focused on carbon footprint quantification, while food economics research has concentrated on the impacts of economic shocks, with limited systematic integration between these two strands of research. As a result, the relative contribution of behavioral change versus economic pressure in driving dietary carbon footprint dynamics remains insufficiently understood.Despite the growing body of literature in life cycle assessment, index decomposition analysis, and food economics, several critical gaps persist. LCA-based studies have mainly estimated emission intensities but have not addressed the drivers of temporal variation. Moreover, applications of the Logarithmic Mean Divisia Index (LMDI) in Iran have largely been confined to the energy and industrial sectors, with no prior studies examining animal protein consumption systems. In addition, most sustainable diet studies implicitly assume that dietary change is driven by informed consumer choice, neglecting the role of macroeconomic conditions, while food economics studies have rarely considered the environmental consequences of economic shocks. Consequently, a conceptual gap remains between food consumption patterns, macroeconomic shocks, and carbon footprint dynamics. This study addresses these gaps by focusing on the animal protein consumption basket, applying the LMDI method to decompose activity and structural effects, and integrating environmental analysis with macroeconomic shocks in Iran, including inflation, exchange rate fluctuations, and economic sanctions. This integrated framework enables the separation of environmentally motivated behavioral changes from those induced by economic constraints.
The present study examined the evolution of the carbon footprint associated with animal protein consumption among Iranian households over the period 1992–2024 by applying the Logarithmic Mean Divisia Index (LMDI) decomposition method to quantify the contributions of the activity effect (changes in consumption level) and the structural effect (changes in the composition of the consumption basket) across six distinct economic sub-periods. The results indicate that variations in the carbon footprint were driven primarily by macroeconomic shocks including inflation, international sanctions, exchange rate fluctuations, and changes in household purchasing power rather than by environmental policies or deliberate pro-environmental behavioral change. During periods of economic expansion, rising animal protein consumption constituted the main driver of emission growth, whereas during periods of instability, reduced consumption and economically induced shifts in dietary composition contributed to declining carbon footprints. Throughout the study period, red meat remained the largest contributor to emission variability, while poultry served as the principal substitute. However, the decline in dairy consumption observed in the later years also points to adverse economic consequences for food security and dietary quality. Overall, the findings suggest that the recent reduction in emissions should not necessarily be interpreted as evidence of a transition toward a sustainable food system; rather, it largely reflects economic constraints and reduced access to high-quality protein sources. Achieving the simultaneous objectives of emission reduction, food security, and public health therefore requires integrated policy interventions that combine lowering emission intensity in livestock production, restructuring the protein consumption basket, and maintaining the economic accessibility of nutritious foods. In this regard, policies such as promoting sustainable dietary patterns, improving productivity and reducing emission intensity in the livestock sector, employing price-based and informational instruments (e.g., carbon labeling), and integrating climate policies with food-security and nutritional-equity objectives are recommended. In addition, simultaneous monitoring of environmental and nutritional indicators is essential for a comprehensive assessment of food-system sustainability.

Abstract Studying the consumption behavior of urban and rural households provides the basis for proper economic planning in order to identify and differentiate the different groups of consumer goods consumed by households, and by calculating demand elasticity, it assists economic planners in choosing economic policies, such as paying and eliminating subsidies, and creating social welfare. Statistical studies are related to household budgets and estimating household demand for major groups and subgroups of household budgets. The diversity of consumption patterns carried out by households means that the welfare effects of changes in the price of goods are different for households with different incomes. The aim of the present study is to investigate the consumption behavior of urban households in Khuzestan Province. In this regard, composite data on different groups of household consumption goods were collected and, using a composite almost ideal demand system, the seemingly unrelated regression (SUR) method was used to investigate the consumption behavior of households during the period 1401-1393.Today, studies on household budgets and the estimation of consumer demand are of great ‎importance for economic policymaking. Assessing household behavior in response to changes in prices ‎and income is important for producers, who aim to supply goods favored by households, and for ‎governments and policymakers, who seek to understand consumption patterns as a key tool for ‎achieving developmental goals and efficiently managing human resources.In many countries, the ‎implementation of inappropriate economic policies often leads to economic instability and ‎macroeconomic imbalances, such as chronic budget deficits, persistent foreign trade deficits, rising ‎inflation, and, in general, negative economic growth. Consequently, policymakers implement a set of ‎economic measures to address these challenges.‎‏ ‏Moreover, governments require a clear ‎understanding of essential goods and their demand in order to regulate markets and allocate ‎resources effectively. This, in turn, requires a precise understanding of how individuals respond to ‎changes in economic conditions, since household consumption decisions directly affect resource ‎allocation. Examining the consumption behavior of urban and rural households provides a foundation ‎for accurate economic planning, including the classification of different commodity groups and the ‎estimation of demand elasticities. Such analyses help policymakers design economic interventions, ‎including the implementation or removal of subsidies, and promote social welfare.‎

Materials and Methods
This study aims to investigate the behavior of urban households in Khuzestan province. In this ‎regard, various commodity groups, family consumer panel data collected using Almost Ideal Demand ‎System composition, Seemingly Unrelated Regression (SUR), during the period 2014-2023, consumer ‎behavior has been studied in households. In the Seemingly Unrelated Regression (SUR) method, each ‎equation is first estimated using Ordinary Least Squares (OLS). After obtaining the residuals, an ‎estimate of the variance-covariance matrix of the disturbance terms is constructed. Then, the ‎coefficients of the equations are estimated using Generalized Least Squares (GLS). One of the ‎strengths of the Almost Ideal Demand System (AIDS) is that it allows testing of theoretical restrictions.‎
The homogeneity hypothesis was tested for each equation using the Wald test . The results ‎of the homogeneity tests across different commodity groups in urban areas indicate that urban ‎consumers do not exhibit money illusion in the consumption of clothing, housing, and transportation. ‎Analysis of the own-price elasticities for different commodity groups shows that the uncompensated ‎own-price elasticities are negative for all commodity groups, and the law of demand is not violated in ‎any group. In the commodity groups for urban households in Khuzestan Province, used for ‎estimation, are as follows: Food, beverages, tobacco, and Clothing, footwear, and housing, fuel, lighting, and ‎Healthcare and medical services, Transportation and communication, Culture, recreation and ‎education, and Other goods.‎

Results and Discussion
Overall, commodities including food and beverage and tobacco products group with the group of ‎housing and fuel and lighting, healthcare, transportation and communication, entertainment and ‎alternative education gross and gross supplements are a group of clothing and footwear. Group ‎Clothes and footwear with the group of Housing and fuel and lighting, transport and communication ‎, with health care and recreation education, gross alternative and complementary gross. Housing, ‎fuel and lighting, transport and communication, with gross substitutes and supplements, including health ‎and entertainment, and education gross. Healthcare entertainment, education, and transporthave a gross ‎alternative and complementary relationship. Transport and communication with entertainment, ‎education, and gross supplements‏.َ‏

Conclusion
The results indicate that in urban areas, food and beverage and tobacco group and group of housing ‎and fuel and lighting are essential‏.‏‎ But, the clothing group is classified as a luxury category, which ‎implies that a portion of the urban population in Khuzestan Province does not have sufficient access to ‎the required clothing.Also, healthcare and medical services is considered a luxury good. The high ‎income elasticity of the culture, recreation, and education group suggests that a considerable portion ‎of households cannot afford these goods.‎

Abstract Introduction: The environmental impacts of pesticides, genetically modified organisms and other chemicals used to increase agricultural production have raised consumers' concerns about the quality and safety of agricultural products. And now, with the increase of environmental awareness, it has criticized modern agricultural activities. These factors have encouraged consumers to consume organic agricultural products. Way of producing the organic products can increase the production costs and, finally, increase the total price of these products. So, consumers should pay more for these products than the inorganic ones. In spite of higher prices of these products, consumers are increasingly tending to consume organic products. So that, consumers tend to pay more for better and more organic and safe agricultural products. Several factors influence the consumer payment preferences for organic products. In this study, these factors are classified into four groups and their impacts on consumer preferences are examined.
Materials and Methods: This study has used the Structural Equation Modeling (SEM). Structural Equation Modeling (SEM) is a powerful collection of multivariate analysis techniques, which specifies the relationships between variables through the use of two main sets of models: Measurement model and Structural model. Measurement model tests the accuracy of proposed measurements by assessing relationships between latent variables and their respective indicators. The structural model drives the assessment of the hypothesized relationships between the latent variables, which allow testing the statistical hypotheses for the study. Additionally, SEM considers the modeling of interactions, nonlinearities, correlated independents, measurement error, correlated error terms, and multiple latent independents that each one is measured by multiple indicators. Unlike conventional analysis, SEM allows the inclusion of latent variables into the analysis and it is not limited to relationships among observed variables and constructs. It allows the study to measure any combination of relationships by examining a series of dependent relationships simultaneously while considering potential errors of measurement among all variables. SEM has several advantages over conventional analysis, including greater flexibility regarding assumptions (particularly allowing interpretation even in the face of multicollinearity). SEM allows the use of confirmatory factor analysis to reduce measurement error by testing multiple indicators per latent variable while offering superior model visualization through its graphical modeling interface. Structural Equation Modeling include six steps (data collection, model specification, identification, estimation, evaluation and modification). In the present study, the variables including marketing factors, awareness and knowledge, demographic characteristics and attitudes towards organic products are considered as latent variables that the relationship of these variables with the payment preferences is investigated. In order to collect required data, a researcher-made questionnaire and simple random sampling method has been used.
Results and Discussion: Results indicate that given the significance of factor loadings, the indicators (observed variables) such as packaging, brand, advertising, discounts and shopping incentives, familiarity with different types of products, way to get information, familiarity with organic agricultural product stores have the required accuracy to measure latent variables. Regarding the model fitting indexes and being model values in the acceptable range, we can say that the measurement and structural models fit well with the data. The results of structural model and hypothesis testing show that awareness and knowledge, demographic characteristics, attitude towards organic products have a significant effect (0.27, 0.59 and 0.21, respectively) on consumer payments preferences. In other words, increasing the awareness of consumer about organic products, increasing household size and income, the positive attitude of consumers towards the characteristics of organic products would increase consumer payments preferences. Also, marketing factors have a significant effect (0.68) on the attitude toward organic products. So that, marketing factors including packaging, brand, advertising and discounts and shopping incentives have a positive effect on the attitude toward organic products. Therefore, hypotheses 2 through 5 are supported.
Conclusion: The Findings indicate that increasing the awareness of consumer about organic agricultural products, increasing household size and income, the positive attitude of consumers towards the characteristics of organic agricultural products will increase consumer payments preferences. Therefore, it is suggested that the relevant authorities take serious action to inform about the properties and nutritional value of organic agricultural products, the differences in the labels of food products, and the existing stores supplying of organic products. Also, it is recommended that the numbers of organic supply stores are boosted, especially in areas where high-income people live.

Abstract Date is as one of the important items of the agricultural production in Iran as Iran's share of global production was 14.1% in 2012 and rank of production increased to second in the world too. In recent years, price uncertainty in date market has increased due to changes in government policies on date prices pattern, from a guaranteed buying pattern to negotiated price pattern. According to the importance of this industry in the country and issues that are always in marketing and market making of agricultural products in developing countries, This paper seeks to measure marketing margins in the industry due to the product's market power and price volatility, and to achieve this goal, the main idea of this paper based on the study Brorsen et al (11). This paper provides a conceptual and empirical framework for analyzing marketing margins in a date market facing output price uncertainty in Iran. Present study evaluated marketing margins into component reflecting the marginal cost of the processing industry, oligopoly price distortions, and an output risk component. The empirical finding is that, while marketing margin is about 33%, the coefficient for oligopoly is more than the coefficient for oligopsony. In the other words, there is an asymmetric monopoly power among buyers and sellers, Also the estimated coefficient for price risk based on exponential GARCH approach indicates, this factor would affect the marketing margin about 7 percent, if all other factors remain constant.

Abstract Introduction: The water crisis is one of the main challenges of the current century. Drought is one of the most costly natural disasters in Iran. During the past 40 years, our country has experienced 27 droughts. It seems a necessary step to deal with the consequences of drought and reducing its effects, thorough understanding and knowledge of each region's vulnerability, which is neglected in our country, unfortunately. It is necessary to study the influencing factors in determining vulnerability and makes it visible. On the other hand, due to the continuing drought conditions intensified in recent years and its impact on different economic sectors, especially the agricultural sector in the country need to assess vulnerability to drought in the country will double. Materials and Methods: Fuzzy AHP method based on the concept of fuzzy sets introduced by LotfeiZadeh. There are several ways to use fuzzy theory and hierarchical structure proposed merger. Cheng in 1996 suggested a new approach to solve problems using Fuzzy AHP calibration values within the membership and (TFNs). Extent Analysis Method proposed by Chang is one of the common ways to solve problems. In this study, we developed a method based on fuzzy analytic hierarchy Chang that has been developed by Zhu et al. and Van Alhag. Results and Discussion: Vulnerability to drought conditions is determined by factors such as economic, social and physical sensitivity to the damaging effects of drought increases. This study is designed in the hierarchy. The purpose of this study is assessing the vulnerability of the country to drought. Vulnerability of this study includes economic vulnerability, social vulnerability and physical vulnerability. Economic vulnerability to drought indicates that the economy is vulnerable to external shocks due to drought and the inability of the economy to withstand the effects of the event and recover the situation. Social vulnerability determines the capacity to deal with drought in the community and reflect the effects of drought on people's ability to cope with the event. The physical vulnerability is related to the characteristics and the structure of society, infrastructure and services that are the result of the damage caused by drought. In the present study, the economic dimension of vulnerability, including GDP per capita, value added in agriculture, value added in industry and the impact of drought on the GDP. Under the criteria of social vulnerability, population density, population growth, the rate of literacy, vulnerable populations, the costs of health and safety and the impact of drought on employment were considered. The physical dimensions of vulnerability include the rate of irrigated land and road density since the objective of this study was to assess vulnerability to drought in various provinces of the country, the required data for all provinces except for Alborz province was collected in 1391 from intelligence sources. To determine the importance of different dimensions of vulnerability as well as the sub-phase in each dimension, the questionnaire was used for paired comparisons. As for the tens of experts, specialists and professionals who have expertise using the Delphi method is incorporated. In general, the importance of physical vulnerability is more than economic and social vulnerability. On the other hand, according to the results the economic and social vulnerability is important, too. The results of this study showed that the importance of the physical vulnerability was more than the economic and social vulnerability and economic vulnerability and social importance were the same. In the economic vulnerability sub-criteria of per capita GDP, in the social vulnerability sub-criteria of population density and in the physical vulnerability sub-criteria of road density have the most importance. These findings may reflect the fact that when drought occurs, access to infrastructure, services and markets can considerably reduce the harmful effects of drought. According to the results, Semnan, Tehran and Gilan provinces jointly are economically vulnerable. On the other hand, in terms of criteria for social vulnerability, provinces of Fars, Khuzestan and Gilan were the most social vulnerable and Isfahan, Kermanshah and Ilam are the least vulnerable. Also, according to the results the province of Khuzestan, Fars and Khorasan were the most; and Yazd, Bushehr and Kohgiluyeh Boyer were the least physical vulnerability. Conclusion: In this study, in order to assess vulnerability to drought in various provinces, , after determining the hierarchy and collect relevant data, the importance of each criteria and sub-criteria were determined. In order to determine the importance of different aspects of vulnerability (the economic, social and physical) Fuzzy AHP method was used in each dimension. According to the results of this study, the province of Khuzestan, Fars and Khorasan are the most and Yazd, Bushehr and Kohgiluyeh Boyer were the least physical vulnerability. Since different provinces ‌have significant differences in vulnerability to drought and vulnerability in various aspects of economic, social and physical, in order to achieve drought management based on risk management, recommended in policy and planning make attention the effects of drought in the various provinces.

Abstract Abstract
This study investigates casual relationship between CO2 emission and gross domestic product per capita in five different country groups, using cross- country data for the period 1960-2007. To achieve the purpose, the co- integration test and error correction models are applied. Results confirm casual relationships between the two variables. A unilateral relationship between GDP and CO2 emission was founded for countries with low and upper middle income and a bilateral relationship for countries with lower middle income and OECDs nations. Using world data, the relationship between GDP and CO2 emission was found bilateral. Furthermore, estimation of long term relationship between CO2 and GDP indicates that all countries are placed at the point ASC of the Kuznets curve. This indicates that for all countries increase of income raise CO2 emission in average. However, the slope of this curve was found increasing for high level income countries and decreasing for low level income countries.
JEL: C33; O40; Q25

Abstract Abstract
In recent decades, more consumption of agricultural inputs led to growth in agricultural products in the world. However, problems occurred due to the lack of attention to environmental issues and risks of excessive consumption of chemicals on human health become a concern for people and policy makers. To solve the problem, sustainable agriculture has been considering by authorities. This study aimed at assessing the level of sustainability in some agricultural products. Identification of factors that influence sustainability in the study products was another aim of this study. The study was conducted in 2008-2009. The township of Jiroft was selected for this study, due to economic importance of agriculture in this region. The study concentrated on three products of tomato, potato and onion that play important role in the food security of the region. The statistical population of the study included all onions, potatoes and tomatoes growers (N=1320). Using the Cochran's formula, sample size was measured at 197 persons. Using the stratified random sampling method, the study interviewees were selected. The reliability of the questionnaire was evaluated by Cronbach's alpha's coefficient at 0.75. Content validity was defined using a panel of expert. According to the results, levels of sustainability for 53.2 percent of the study samples were lower than their average level, while 47.2 percent had higher levels. Results of regression model presented that the variables of educational level, knowledge of sustainable agriculture, the number of family labor force, growers’ participation in social activities, the system of private property and livestock- cultivation system have a significant positive impact on the sustainability. The only variable of the rate of cultivation presented a negative impact on the agricultural sustainability. The study recommends holding appropriate extension classes accordance to the level of growers ‘education.

Abstract Introduction One of the main targets of planners, decision makers and governments is increasing society health with promotion and production of suitable and healthy food. One of the basic commodities that have important role in satisfaction of required human food is milk. So, some part of government and producer healthy budget allocate to milk consumption promotion by using generic advertising. If effectiveness of advertising budget on profitability is more, producer will have more willing to spend for advertising. Determination of optimal generic advertising budget is one of important problem in managerial decision making in producing firm as well as increase in consumption and profit and decrease in wasting and non-optimality of budget. Materials and Methods: In this study, optimal generic advertising budget intensity index (advertising budget share of production cost) was estimated under two different scenarios by using equilibrium replacement model. In equilibrium replacement model, producer surplus are maximized in respect to generic advertising in retail level. According to market where two levels of farm and processing before retail exist and there is trade in farm and retail level, we present different models. Fixed and variable proportion hypothesis is another one. Finally, eight relations are presented for determination of milk generic advertising optimum budget. So, we use data from several resources such as previous studies, national (Iran Static center) and international institute (Fao) formal data and own estimation. Because there are several estimations in previous studies, we identify some scenarios (in two general scenarios) for calculation of milk generic advertising optimum budget. Results and Discussion: Estimation of milk generic advertising optimum budget in scenario 1 shows that in case of one market level, fixed supplies and no trade, optimum budget is 0.4672539 percent. In case of one market level and no trade, optimum budget is 0.3674844 percent. In case of one market level with trade, optimum budget according to own price trade elasticity of farm input, changed from 0.3675013 to 0.3674941 percent. In case of two market level and no trade at either market levels, optimum budget is 0.5094457 percent. In case of two market levels with trade only at retail, optimum budget according to own price trade elasticity of retail goods are changed from 0.509446 to 0.3674844 percent. In case of two market levels with trade only at farm, optimum budget according to own price trade elasticity of farm input are changed from 0.5094600 to 0.5094951 percent. In case of two market levels with trade at both retail and farm, optimum budget according to own price trade elasticity of retail goods and farm input are changed from 0.5085780 to 0.5117381 percent. This index in variable proportion hypothesis will be changed from 0.4143826 to 0.4164392.Estimation of milk generic advertising optimum budget in scenario 2 shows that in case of one market level, fixed supplies and no trade, optimum budget is 9.639368 percent. In case of one market level and no trade, optimum budget 8.9480986 percent. In case of one market level with trade, optimum budget according to own price trade elasticity of farm input, changed from 8.948178 to 8.948440 percent. In case of two market level and no trade at either market levels, optimum budget is 14.4113143 percent. In case of two market levels with trade only at retail, optimum budget according to own price trade elasticity of retail goods are changed from 14.413087 to 14.447182 percent. In case of two market levels with trade only at farm, optimum budget according to own price trade elasticity of farm input are changed from 14.413301 to 14.413689 percent. In case of two market levels with trade at both retail and farm, optimum budget according to own price trade elasticity of retail goods and farm input are changed from 14.379081 to 14.413792 percent. This index in variable proportion hypothesis will be changed from 13.294219 to 13.323525. Finally, Results indicate that milk generic advertising budget intensity index will be changed from 0.3674844 to 14.4474182 percent with mean of 0.4617576 percent for scenario 1 and 13.445766 percent for scenario 2. Conclusion: According to the results, we proposed that milk producer should spend 13.44 percent of their production cost to generic advertising. This spending can increase milk consumption and it increase health society. Moreover, it decreases the household care and remedy spending and it increases the profitability of milk production firms. Also, government could spend to milk generic advertising from healthy budget of ministry of medical health, care and education or from agricultural promotion budget of ministry of Agri-Jahad.

Abstract Abstract
One of the major risks in the agricultural sector is the price fluctuations risk of agricultural products. Futures markets for agricultural products as one of the policies and executive approach have a significant impact on reducing price fluctuations of agricultural products. So in this study, In this study the factors affecting farmers likely to participate in the future market for product sale by using cross-sectional data from 90 tomato farming Mashhad in 2009- 2010 with using of logit model estimated were analyzed. This study result shows that the firstly Hedge ratio of this product to sample between -1.25 to 5.63 variables and secondly the optimum Hedge ratio of 0.03 is. In addition to results logit model show that the age farmers and farmer debt, the statistically significant and positive effect on the probability of having farmers participate in the futures market and cultivation area, how to sell product and coefficient price changes in cash market variables to have statistically significant and negative effect on the probability participation in the futures market are farmers. The results of suggested were considered. According to the results, increase awareness of all age groups farmers, through the expansion educational and extension classes and motivate small-scale farmers (target groups) to use this market suggested.