دانشگاه اصفهان برنامه ریزی فضایی 2228-7485 16 3 2026 09 23 Foresight of the Saffron Value Chain in the Face of Climate Change and Global Market Fluctuations in Qaen County آینده‌پژوهی زنجیره ارزش زعفران در برابر تغییرات اقلیمی و نوسانات بازار جهانی در شهرستان قائنات 1 28 30513 10.22108/sppl.2026.148989.1890 FA زهرا غفاری مقدم استادیار گروه اقتصاد کشاورزی، پژوهشگاه زابل، زابل، ایران علی سردارشهرکی استاد اقتصاد کشاورزی دانشگاه سیستان و بلوچستان و محقق پژوهشکده زعفران؛ دانشگاه تربت حیدریه، ایران محمد حجی پور دانشیار گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه بیرجند، بیرجند، ایران Journal Article 2026 05 23 Abstract Saffron as a strategic agricultural and pharmaceutical commodity plays a vital role in Iran’s agricultural economy and non-oil exports. With Iran accounting for over 96% of global saffron production, the country holds the most influential position in the international supply of this product. However, in recent years, the saffron value chain has encountered a range of complex economic, environmental, and structural challenges that have threatened its sustainability and competitiveness. The present study aimed to identify key drivers and develop future scenarios for the saffron value chain. Adopting a foresight approach, this research employed a survey-based methodology. Data were collected through questionnaires and semi-structured interviews with 20 experts in the saffron sector during 2024–2025. The data were analyzed by using structural analysis and Scenario Wizard software. The results indicated that global price fluctuations, competition from rival countries, trade restrictions (sanctions), shifting consumer preferences, and expansion of e-commerce constituted the primary drivers shaping the saffron value chain. In addition, climatic variables—particularly recurrent droughts—acted as critical limiting factors affecting production sustainability. Ultimately, 7 compatible scenarios were developed, among which 3 principal scenarios were identified: "relatively stable", "competitive with growth opportunities", and "challenging". The findings suggested that while most probable futures were situated within moderate or favorable conditions, intensification of international competition and economic instability could undermine Iran’s global position in the saffron market. In conclusion, this research underscored the necessity of scenario-based policymaking, strengthened branding efforts, adoption of modern marketing strategies, and enhanced resilience of the saffron value chain in the face of future uncertainties. Keywords: Saffron Value Chain, Foresight, Structural Analysis, Scenario Planning. Introduction Saffron, recognized as the world’s most expensive spice and a strategic commodity within Iran’s non-oil export portfolio, occupies a distinguished position in the country’s agricultural economy. With a share exceeding 96% of global saffron production and the advantage of several millennia of indigenous cultivation knowledge, Iran appears to hold an unparalleled competitive edge. Nevertheless, evidence indicates that the value chain of this product has encountered numerous structural, environmental, and economic challenges in recent years, which have seriously threatened both production sustainability and Iran’s standing in international markets. Despite being the world’s primary saffron supplier, Iran captures only a modest share of the final value added—a consequence largely attributed to raw material exports, absence of robust national branding, severe price volatility, and inequitable competition in global markets. Compounding these economic difficulties, climate change—manifested through declining rainfall, deteriorating groundwater quality, and recurrent drought episodes in key production regions, such as Greater Khorasan and Qaenat County—has further endangered production security. The current saffron value chain constitutes a complex and interconnected system, in which even minor fluctuations in a single variable—such as exchange rates or precipitation levels—can reverberate throughout the entire chain. Consequently, the lack of a comprehensive and forward-looking analytical framework capable of assessing the interactions among these economic and environmental variables represents a significant gap in macro-level planning. Materials & Methods This research adopted a strategic foresight approach, employing a combination of structural analysis and scenario-building methodologies to elucidate the dynamics of the saffron value chain in Qaenat County. The research implementation process comprised two complementary stages: Structural Analysis of the System (Modeling Influence and Dependence Dynamics) In this stage, MICMAC software was utilized to accurately identify key variables and map the structure of their interrelationships. The operational procedure included the following steps: Expert panel formation:A Delphi-based approach was employed, drawing upon specialists in agriculture, economics, market analysis, and policymaking. Direct impact matrix:Experts assessed the pairwise relationships among the drivers within an n×n matrix. Scores were assigned on a scale from 0 (no influence) to 3 (strong influence), allowing for a systematic determination of the impact of each variable on others. Output analysis:By calculating row sums (influence power) and column sums (dependence power) within the software, the position of each variable on the strategic map of the system was determined. This analysis enabled identification of "key" drivers—characterized by high influence and high dependence—that exerted the greatest shaping force on the future of the value chain, distinguishing them from other variables. Scenario Building Based on Cross-Impact Balance (CIB) Logic Following the identification of key drivers, Scenario Wizard software was employed to construct alternative futures, moving beyond linear prediction. The steps in this phase were as follows: Defining plausible states:For each key driver, possible states—favorable, intermediate, and unfavorable—were defined based on upstream policy documents and expert judgment. Measuring cross-impacts:Through a comparative questionnaire, experts scored the intensity and direction of interactions among the different states of key factors on a scale from +3 to −3, where positive values denoted mutual reinforcement and negative values indicated mutual weakening. Extracting consistent scenarios:Using the Cross-Impact Balance algorithm, the software assessed the internal consistency of the defined states. The output of this model consisted of scenarios that were logically coherent and collectively represented "plausible and consistent futures" for the saffron value chain on the 1405 horizon (2026–2027). Research Findings The findings derived from the structural exploration and relationship network analysis revealed that the saffron value chain in Qaenat County exhibited remarkably high dynamism. Based on the data extracted from the cross-impact matrix, the system demonstrated a 70% saturation level, indicating strong interconnections among its components—such that any change in a key element rapidly propagated across all dimensions of the system. The distribution of variables on the influence–dependence plane further underscored the system's high sensitivity to external drivers. Among these, "global price fluctuations" and "competition from rival countries" were identified as the most powerful driving forces, exerting a guiding influence on the future trajectory of the system. Climatic factors, such as "drought and reduced rainfall", while highly influential, acted more as contextual variables due to their stable and long-term nature, providing a risk backdrop that amplified the effects of economic drivers. In line with this structural analysis, examination of the logical space of future possibilities delineated a broad configuration for the future of the saffron value chain. Among all possible combinations of states for the key drivers—encompassing favorable, intermediate, and unfavorable conditions—7 highly consistent scenarios were extracted. Statistical analysis of these scenarios indicated a favorable potential for the development of the saffron value chain in the region: 74% of probable configurations fell within the favorable state, 21% corresponded to the intermediate state (representing a continuation of the status quo with modest growth), and only 5% were classified as unfavorable. This probability distribution suggested that, despite serious climatic threats, the inherent economic capacities and opportunities arising from modern marketing approaches could effectively offset negative environmental impacts. Accordingly, 3 selected key scenarios with the highest structural consistency are elaborated below to inform future planning: Scenario 2 (Relative Stability and Gradual Strengthening of the Value Chain): This scenario presented the most realistic mid-term outlook, promising a combination of stable global prices and gradual improvements in export infrastructure. Under these conditions, competition with rival countries—such as Afghanistan and Spain—reached a point of equilibrium, while the Qaenat saffron brand supported by modern marketing strategies and e-commerce platforms succeeded in maintaining its market share. In this scenario, the value chain transitioned away from its traditional state toward greater digitalization though changes occurred incrementally and without major economic shocks. Relative exchange-rate stability played a pivotal role in enhancing predictability of profitability for farmers and provided a conducive environment for local investment in processing industries. Scenario 3 (Growth Opportunity and Competitiveness): This scenario portrayed a highly dynamic and competitive future, in which innovation and quality took precedence. Despite intensified international competition, global demand for high-quality saffron continued to rise. The adoption of modern technologies in processing and packaging facilitated the creation of substantial value added within the chain. Shifting consumer preferences toward organic products and those with geographical traceability presented an unparalleled opportunity for Qaenat to establish itself as the global hub of premium saffron. Under these conditions, the value chain—through alternative markets and digital trade platforms—generated new revenue streams and demonstrated considerable resilience against trade restrictions and sanctions. Scenario 7 (Challenging): This scenario depicted a situation in which the convergence of international economic pressures and severe fluctuations in global prices drove the system toward instability. In this future, escalating production costs resulting from sanctions and exchange-rate volatility coupled with declining purchase prices due to market saturation by competitors severely undermined the profit margins of Qaenat producers. This scenario served as a warning that, in the absence of proactive defensive strategies—such as the establishment of price-support funds, aggressive branding initiatives, and institutional synergy—the value chain might face structural disruption and lose its central role in the regional economy and livelihoods. Discussion of Results & Conclusion The final analyses of this research, which integrated structural analysis (MICMAC) and scenario-building approaches, indicated that the future of the saffron value chain in Qaenat County was concurrently shaped by climatic constraints and global market dynamics. The findings confirmed that while climatic factors—particularly drought and reduced rainfall—acted as structural and unavoidable limitations on production capacity, economic variables and market conditions played the decisive role in determining the success or failure of the chain in the long term. In effect, the structure of the saffron value chain in Qaenat was no longer solely dependent on production; it had become increasingly contingent upon market conditions. The assessments revealed that variables, such as global price fluctuations, international competition, exchange rate volatility, and trade sanctions, were the primary determinants shaping the future of this industry, accounting for over 65% of future changes and fluctuations. In the scenario-building phase, 74% of the extracted scenarios pointed to a favorable future for the chain, suggesting that the saffron value chain of Qaenat, despite external pressures, possessed considerable development potential. However, the remaining 25%—comprising intermediate and challenging scenarios—served as a cautionary reminder that this favorable trajectory would not materialize automatically without deliberate planning. Indeed, achieving such an outcome necessitated a fundamental shift in perspective from "traditional production" toward "active, market-oriented management". The research results underscored that the key to success lay not in expanding cultivated area, but in strengthening the industry's resilience against exchange-rate fluctuations and competitive pressures—a goal that could be pursued through the adoption of modern marketing strategies and effective utilization of e-commerce platforms. Based on these findings, 5 main strategies for developing the saffron value chain in Qaenat are proposed: Branding and Identity-Building: To preserve competitive advantage against emerging rivals, it is essential to register Qaenat saffron with international geographical indications and stringent quality export standards. This would ensure that the real value added of the product accrues to the region itself. Economic Risk Management: Severe fluctuations in global prices and exchange rates pose a serious threat to the livelihoods of small-scale farmers. To address this, establishment of support funds and price-stabilization mechanisms is critical to preventing financial shocks to producers. Development of Digital Marketing: Adoption of e-commerce platforms can reduce unnecessary intermediaries, establishing a direct link between Qaenat producers and global markets. This approach would gradually increase farmers' share of sales profits. Adaptation to Climate Change: Under conditions of limited water resources, smart farm management and improved water-use efficiency represent the primary defensive strategy for sustaining production and coping with environmental challenges. Flexible (Scenario-Based) Policymaking: Policymakers should not rely solely on a single roadmap; rather, they must develop clear operational plans tailored to different conditions—favorable, intermediate, and challenging. This can only be achieved through close collaboration among farmers, exporters, and scientific institutions, ensuring that Qaenat saffron not only survives future turbulence, but also strengthens its global position. چکیده زعفران به‌عنوان محصولی راهبردی، جایگاهی حیاتی در اقتصاد کشاورزی و صادرات غیرنفتی ایران دارد؛ به‌گونه‌ای که ایران با تولید بیش از ۹۶ درصد زعفران جهان، تأثیرگذارترین نقش را در عرضه جهانی این محصول ایفا می‌کند. با این وجود، زنجیره ارزش زعفران در سال‌های اخیر با چالش‌های پیچیده‌ای در ابعاد اقتصادی، محیطی و ساختاری مواجه بوده که پایداری و رقابت‌پذیری آن را تهدید کرده است. هدف این پژوهش، شناسایی پیشران‌های کلیدی و تدوین سناریوهای آینده برای زنجیره ارزش زعفران است. این پژوهش با رویکرد آینده‌پژوهی و روش پیمایشی انجام شده است. داده‌ها از طریق پرسشنامه و مصاحبه با 20 نفر از خبرگان حوزه زعفران در سال‌های ۱۴۰۳–۱۴۰۴ گردآوری و با استفاده از روش تحلیل ساختاری و نرم‌افزار Scenario Wizard تحلیل شدند. نتایج نشان داد که نوسانات قیمت جهانی، رقابت کشورهای رقیب، محدودیت‌های تجاری (تحریم‌ها)، تغییر ترجیحات مصرف‌کنندگان و توسعه تجارت الکترونیک، از اصلی‌ترین پیشران‌های تأثیرگذارند. همچنین، متغیرهای اقلیمی مانند خشکسالی‌های مکرر، به‌عنوان عوامل محدودکننده، نقش کلیدی در پایداری تولید دارند. در نهایت، هفت سناریوی سازگار استخراج شد که سه سناریوی «نسبتاً باثبات»، «رقابتی با فرصت رشد» و «چالش‌زا» به‌عنوان سناریوهای اصلی شناسایی شدند. یافته‌ها حاکی از آن است که اگرچه بیشتر آینده‌های محتمل در وضعیت میانه یا مطلوب قرار دارند، تشدید رقابت‌های بین‌المللی و بی‌ثباتی‌های اقتصادی می‌تواند جایگاه جهانی زعفران ایران را با تهدید مواجه کند. در مجموع، این پژوهش بر ضرورت اتخاذ سیاست‌گذاری‌های سناریومحور، تقویت برندسازی، بهره‌گیری از بازاریابی نوین و افزایش تاب‌آوری زنجیره ارزش زعفران در برابر عدم‌قطعیت‌های آینده تأکید می‌ورزد

https://sppl.ui.ac.ir/article_30513_d6b12cdc2102f0a872b01f363f9e6774.pdf

دانشگاه اصفهان برنامه ریزی فضایی 2228-7485 16 3 2026 09 23 Modeling and Spatial Mapping of Cutaneous Leishmaniasis (CL) Distribution in Isfahan Province Using Machine Learning Algorithms مدل‌سازی و پهنه‌بندی توزیع مکانی بیماری سالک در استان اصفهان با استفاده از الگوریتم‌های یادگیری ماشین 29 50 30595 10.22108/sppl.2026.148536.1880 FA فاطمه عباسی دانش‌آموخته کارشناسی ارشد سنجش از دور و GIS، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران رضا ذاکری نژاد استادیار گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران عبدالله سیف دانشیار گروه جغرافیای طبیعی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران Journal Article 2026 04 25 Abstract Cutaneous Leishmaniasis (CL) is a significant vector-borne parasitic disease with substantial public health, social, and economic impacts, particularly in endemic regions like Iran. This retrospective study aimed to model the spatial susceptibility of CL in Isfahan Province, Iran, from 2021 to 2023, utilizing 6,353 confirmed cases reported by local health centers. A comprehensive set of independent variables—including climatic indicators, topographic factors, vegetation indices (NDVI, EVI), land use, population density, and distance from water bodies—were integrated and analyzed within a Geographic Information Systems (GIS) framework. Spatial modeling was performed using LightGBM and Classification and Regression Trees (CART) algorithms with model performance evaluated through overall accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The LightGBM model demonstrated superior predictive performance (AUC=0.992, accuracy=96.55%) compared to the CART model (AUC=0.975, accuracy=96.65%). Spatial susceptibility mapping identified central and northwestern regions—including Isfahan, Khomeynishahr, Najafabad, Falavarjan, Barkhar, Mobarakeh, Kashan, and Aran and Bidgol—as high-risk areas. Variable importance analysis revealed that land use, seasonal average wind speed, distance from water bodies, and seasonal Enhanced Vegetation Index (EVI) were the most influential environmental predictors. These findings underscore the critical role of climatic and environmental factors in CL distribution and provide actionable spatial insights for targeted public health planning and vector control interventions in Isfahan Province. LightGBM is recommended as the preferred model for future susceptibility mapping endeavors. Keywords: Modeling, Cutaneous Leishmaniasis, Geographic Factors, Data Mining. Introduction Cutaneous Leishmaniasis (CL) is one of the most important vector-borne diseases, posing significant public health, social, and economic challenges worldwide. It is a parasitic disease that can manifest in cutaneous, mucocutaneous, or visceral forms (Shabanpour et al., 2022). CL is transmitted by female sandflies (Phlebotominae) and its causative agent is a protozoan parasite of the genus Leishmania. Transmission occurs from animal reservoirs, mainly domestic and wild rodents, to humans, causing skin ulcers that may persist for up to a year on the face, hands, and feet. These lesions impose psychological and social burdens, affecting patients' quality of life. In Iran, CL occurs in urban (dry) and rural (wet) forms. In urban areas, humans are the main reservoir, and Phlebotomus sergenti transmits Leishmania tropica. In rural areas, wild rodents act as reservoirs and Phlebotomus papatasi transmits Leishmania major (Mollalo et al., 2018; Shirzadi, 2012). Iran is considered an endemic focus with official records of approximately 20,000 new cases annually although actual numbers are likely higher (Shirzadi, 2012; Rathi & Hanafi-Bojd, 2006). Geographic factors play a critical role in disease distribution and Geographic Information Systems (GIS) provide tools for spatial analysis and visualization of CL patterns, facilitating public health planning and policy decisions (Bayatani & Sadeghi, 2012). Materials & Methods This retrospective study was conducted in Isfahan Province, Iran, from 2021 to 2023, by using all confirmed CL cases reported by health centers of Isfahan Province and Kashan County, totaling 6,353 records. The dependent variable was CL occurrence, while independent variables included climatic indicators (minimum, maximum, and mean temperature, precipitation, relative humidity, soil moisture, solar radiation, and wind speed), topographic factors (elevation, slope, and aspect), vegetation indices (NDVI and EVI), land use, population density, and distance from water bodies. Data were preprocessed, integrated, and analyzed in ArcGIS 10.8.2. Spatial modeling was performed by using LightGBM and CART algorithms in RStudio 2025.06.14 with 70% of the data used for training and 30% for testing. In the LightGBM model, parameters were set as follows: learning rate=0.05, number of trees (nrounds)=600, num_leaves=31, max_depth=−1, min_data_in_leaf=10, feature_fraction=0.8, bagging_fraction=0.8, and bagging_freq=5. In the CART model, the complexity parameter (cp) was set to 0.001, minsplit=20, minbucket=7, and maxdepth=30. Model performance was evaluated by using overall accuracy, sensitivity, specificity, ROC curves, and the area under the curve (AUC). High-risk areas were mapped and classified into 5 susceptibility classes, ranging from very low to very high and the relative importance of environmental variables was assessed for each model. Research Findings The spatial modeling of CL in Isfahan Province yielded distinct performance outcomes for the two algorithms employed. The LightGBM model demonstrated an overall accuracy of 96.55%, a Kappa coefficient of 0.9309, sensitivity of 95.44%, specificity of 97.59%, and an area under the curve (AUC) of 0.992. In comparison, the CART model achieved an overall accuracy of 96.65%, a Kappa coefficient of 0.9142, sensitivity of 97.61%, specificity of 94.00%, and an AUC of 0.975. Spatial susceptibility mapping based on the LightGBM-generated sensitivity map categorized Isfahan Province into 5 risk classes ranging from very low to very high. Peripheral areas, particularly eastern counties, such as Naein and Khur and Biabanak, exhibited very low susceptibility. In contrast, central and some northwestern regions demonstrated higher susceptibility to CL. Variable importance analysis revealed that the most influential environmental predictors in the LightGBM model were land use, seasonal average wind speed, distance from water bodies, and seasonal Enhanced Vegetation Index (EVI) values. The CART model showed consistent spatial patterns, identifying land use, wind speed, summer relative humidity, and EVI as key predictors. Overall, the findings confirmed that climatic and environmental factors—including land use, 3-year average EVI, relative humidity, and wind speed—were key determinants of CL distribution in the study area with high-risk areas primarily concentrated in central and northwestern regions, including Isfahan, Khomeynishahr, Najafabad, Falavarjan, Barkhar, Mobarakeh, Kashan, and Aran and Bidgol. Discussion of Results & Conclusion The performance evaluation of the LightGBM model indicated an overall accuracy of 96.55%, a Kappa coefficient of 0.9309, sensitivity of 95.44%, specificity of 97.59%, and an AUC of 0.992. The CART model achieved an overall accuracy of 96.65%, a Kappa coefficient of 0.9142, sensitivity of 97.61%, specificity of 94.00%, and an AUC of 0.975. The sensitivity map generated by LightGBM categorized Isfahan Province into 5 risk classes. Peripheral areas, especially eastern counties, such as Naein and Khur and Biabanak, exhibited very low sensitivity, whereas central and some northwestern regions showed higher susceptibility. Variable importance analysis revealed that the most influential factors in LightGBM included land use, seasonal average wind speed, distance from water bodies, and seasonal EVI values. Similarly, the CART model showed consistent spatial patterns with land use, wind speed, summer relative humidity, and EVI as key predictors. The performance evaluation showed that the LightGBM model (AUC=0.992) outperformed the CART model (AUC=0.975) in discriminating between infected and healthy areas. Therefore, LightGBM is recommended as the superior model for spatial susceptibility mapping of cutaneous leishmaniasis in Isfahan Province. CART, due to its simpler structure and interpretability, can serve as a baseline model for comparing other machine learning algorithms. Spatial sensitivity mapping revealed that high-risk areas were primarily concentrated in central and northwestern regions, including Isfahan, Khomeynishahr, Najafabad, Falavarjan, Barkhar, Mobarakeh, Kashan, and Aran and Bidgol. Modeling results confirmed that climatic and environmental factors, including land use, 3-year average Enhanced Vegetation Index (EVI), relative humidity, and wind speed, were key determinants of cutaneous leishmaniasis distribution. These findings are consistent with previous studies (Mollalo et al., 2018; Kosgei, 2024). چکیده لیشمانیوز پوستی از مهم‌ترین بیماری‌های منتقله از ناقل در ایران است و استان اصفهان به‌عنوان یکی از کانون‌های فعال آن، نیازمند شناسایی نواحی پرخطر جهت بهینه‌سازی برنامه‌های پیشگیری و کنترل است. هدف این پژوهش، مدل‌سازی و پهنه‌بندی حساسیت مکانی بروز لیشمانیوز پوستی در شهرستان‌های استان اصفهان طی سال‌های ۱۴۰۰ تا ۱۴۰۲ با استفاده از الگوریتم‌های یادگیری ماشین است. در این مطالعه گذشته‌نگر، متغیرهای اقلیمی، پوشش گیاهی، توپوگرافی، فاصله از آبراهه، کاربری اراضی و تراکم جمعیت به‌عنوان متغیرهای مستقل و موارد تأییدشده بیماری به‌عنوان متغیر وابسته در نظر گرفته شدند. داده‌ها پس از پیش‌پردازش در ArcGIS 10.8 با الگوریتم‌های LightGBM و CART در RStudio 2025.06.14 مدل‌سازی شدند. نتایج نشان داد مدل LightGBM با دقت 55/96%، ضریب کاپای 09/93% و سطح زیر منحتی (AUC) برابر 992/0 نسبت به مدل مدل CART با دقت 65/%96، ضریب کاپای 42/%91 و سطح زیر منحنی (AUC) برابر 975/0 عملکرد بهتری داشت. براساس مدل LightGBM به‌عنوان مدل برتر، پهنه‌بندی حساسیت مشخص کرد 61/%93 مساحت استان در کلاس بسیار کم‌خطر و تنها 43/%3 در کلاس‌های خطر زیاد و بسیار زیاد قرار دارد که عمدتاً در بخش‌های مرکزی و شمال‌غربی استان شامل شهرستان‌های اصفهان، خمینی‌شهر، نجف‌آباد، فلاورجان، برخوار، مبارکه، کاشان و آران و بیدگل متمرکز شده‌اند. مهم‌ترین عوامل مؤثر در پیش‌بینی بیماری شامل کاربری اراضی، سرعت باد، شاخص EVI و فاصله از آبراهه بودند. این یافته‌ها با شناسایی دقیق نواحی پرخطر، به برنامه‌ریزی هدفمند اقدامات پیشگیرانه و کنترلی در سطح استان کمک می‌کند.

https://sppl.ui.ac.ir/article_30595_9311de3328d81123ded8aa1ff6240591.pdf