Arash Dourandish

This study presents a life cycle assessment (LCA) of drip and flood irrigation systems for wheat production in the semi-arid Central Mashhad Plain, northeastern Iran, following the ISO 14040/14044 framework. The functional unit is defined as 1 ton of wheat grain at the farm gate. Environmental impacts were assessed using the ReCiPe 2016 Midpoint (H) method. Inventory data were sourced from field surveys, the Ecoinvent database, and regional statistics. The results indicate that while drip irrigation reduces water consumption by approximately 35.3% compared with flood irrigation, it is associated with higher environmental burdens in a range of impact categories, notably marine ecotoxicity (+62%), freshwater ecotoxicity (+58%), freshwater eutrophication (+42%), fossil resource scarcity (+41%), human non-carcinogenic toxicity (+38%), and global warming potential (+19%), along with substantial increases in mineral resource scarcity, human carcinogenic toxicity, and terrestrial ecotoxicity. These trade-offs are mainly linked to the material and energy demands of the pressurized irrigation infrastructure (pipes, pumps, and filters). Sensitivity and uncertainty analyses confirm the robustness of the comparative rankings. However, the analysis is limited by the gate-to-gate system boundary and the use of background databases that may not fully reflect region-specific emission factors. These findings highlight the importance of integrated irrigation policies that address both water conservation and the broader environmental implications of irrigation modernization in semi-arid, groundwater-dependent regions.

This study aims to evaluate the resilience of Iran’s pistachio exports by investigating the structural drivers of market instability and forecasting export performance up to 2028 under various socio-economic and policy scenarios. A multi-stage hybrid approach was developed: qualitative content analysis identified 20 key drivers affecting the pistachio supply chain; Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) categorized these strategic drivers; scenario planning constructed four plausible future narratives; and an Autoregressive Distributed Lag (ARDL) model quantified future export volumes under each scenario. The structural analysis identifies "Economic Sanctions" and "Exchange Rate Volatility" as the most critical risk drivers with the highest system influence on the food export sector. The ARDL empirical findings confirm that pistachio production volume, export price, and the weighted per capita income of importing countries exert positive and significant long-term effects on export performance. Under the "Optimistic Scenario" (reduced sanctions and agrotech upgrades), exports are projected to reach 117,000 tons by 2028, whereas the "Pessimistic Scenario" forecasts a decline to 77,000 tons. The study relies on macro-level econometric and structural data; future research could integrate micro-level farm-gate dynamics and climate-induced water scarcity factors in pistachio-producing regions. The findings provide agri-food policymakers and export managers with actionable insights to mitigate supply chain vulnerabilities, design resilient international marketing strategies, and stabilize export revenues amid severe geopolitical and economic shocks.

Climate change poses substantial challenges to agricultural output, particularly in areas experiencing water scarcity, such as those found in Fars Province, Iran. This research investigates the relationship between farmers' strategies for adapting to climate change and their technical efficiency in wheat production in Marvdasht County, Iran. Using data from 345 farmers, we applied a K-means clustering algorithm to identify three distinct adaptation profiles—passive, moderate, and active—based on the number and weighted importance of strategies adopted. We subsequently employed an Endogeneity-Corrected Stochastic Frontier (ECSF) model to measure technical efficiency, taking into account unobserved heterogeneity and endogeneity. Results show that farmers in the active cluster, who adopted a broader mix of strategies, including smart irrigation, conservation tillage, and optimized fertilization, achieved the highest efficiency scores. Key determinants of efficiency included education, access to credit and extension services, climate risk perception, and social capital. Conversely, off-farm income was negatively associated with efficiency. The findings highlight the need for targeted policy interventions to promote comprehensive adaptation packages, improve access to financial and technical resources, and strengthen farmer networks. This study presents a comprehensive framework for assessing the impacts of adaptation and informing agricultural policy in climate-vulnerable regions.

In countries facing physical water shortages, the safe use of treated wastewater can increase agricultural yields. However, farmers’ willingness to reuse water in agriculture is very low. Therefore, the purpose of this study is to determine the factors that influence 217,215 Iranian farmers who use treated wastewater to adopt safe irrigation practices. This study, which developed the Theory of Planned Behavior (TPB) by including risk perception (RP) and knowledge factors, is a groundbreaking endeavor in the field of the safe use of treated wastewater at the farm level in Iran and around the world. The final model analysis was conducted based on structural equation modeling (SEM). The findings reveal that attitudes, perceived behavioral control (PBC), RP, and knowledge significantly influence farmers’ behaviors regarding safe wastewater use, while subjective norms did not impact intentions. The subjective norm in this study includes the perceived social pressure by farmers (through family, friends, the farming community, and local authorities) to perform or not perform safe behavior in using treated wastewater for irrigation. Notably, PBC was the most important component in the original TPB model, because intention has a beneficial impact on behavior. In the extended model, knowledge and risk perception emerged as critical elements. Therefore, intervention policies should prioritize enhancing farmers’ knowledge, risk perception, and perceived behavioral control to promote safe treated wastewater usage. This study offers valuable insights for developing countries in agricultural practices.

Considering the increasing demand for urban water and the low water-use efficiency in the agricultural sector, an exchange of treated urban wastewater for agricultural water could be a fundamental solution for administering sustainable management of water resources. However, implementing the intersectoral water exchange project is influenced by the economic, social, and environmental conditions of each region, especially in developing countries. Therefore, this study sought to identify the best possible strategies for exchanging treated urban wastewater for agricultural water in Mashhad city, located in Iran, a country that suffers from decreased urban water resources. Based on the interviews conducted with the experts and the strengths, weaknesses, opportunities and threats analysis, 28 influential factors were identified for the water exchange project, and eleven strategies were developed for its implementation. The results of the analytic hierarchy process (AHP) and F-TOPSIS indicated that organizing educational and awareness campaigns regarding the exchange project's goals, devising an appropriate organizational structure, enhancing treated wastewater's quality according to the WHO standards, taking the necessity of conducting social, cultural, and economic studies into account, developing consumers' awareness programs, and supporting, training, and counseling the farmers and setting the required incentives for them were among the main factors involved in the implementation of the water exchange project in Mashhad.

Purpose: Although Iran is one of the largest producers and exporters of saffron in the world, the organic saffron market in Iran is still in its early stages, and there is scarce empirical evidence in this regard. Therefore, the study's primary purpose is to segment the organic saffron market in Mashhad, Iran using neobehavioristic theory and machine learning methods.Design/methodology/approach: Considering the neobehavioristic theory of consumer behavior, the organic saffron market was segmented using crisp and fuzzy clustering algorithms. Also, to assess the relative importance of the factors affecting the intention to buy organic saffron in each market segment, a sensitivity analysis was performed on the output of the artificial neural network (ANN). A total of 400 questionnaires were collected in Mashhad, Iran in January and February 2020.Findings: In contrast to the belief that psychological factors are more important in market segmentation than demographic characteristics, findings showed that the demographic characteristics of consumers, especially education and income, are the dominant variables in the segmentation of the organic food market. Among the 4 A’s marketing mix elements, the results showed that a low level of awareness and accessibility are obstacles to organic saffron market development. Advertising, distribution channel improvement, package downsizing and online business development are suggested strategies for expanding the organic saffron market in Iran.Practical implications: The results of the present study will help policymakers and suppliers of organic saffron to identify their target markets and design short- and long-term marketing strategies to develop the organic saffron market.Originality/value: Machine learning methods and the neobehavioristic theory of consumer behavior were used to segment the organic food market.