اقتصاد و توسعه کشاورزی

Understanding farmers' mental models (MM) is crucial for interpreting their behavior; however, these models, are shaped by technical, economic, and social factors, have often been overlooked. Using a case study from Kangavar County, Western Iran, this study investigates the MM of farmers and experts regarding wheat seed usage and proposes optimization strategies using a mixed-methods approach. Qualitatively, phenomenological analysis of interviews with 16 participants (selected via purposive and snowball sampling) identified five key factors shaping farmers' mental models (MM): work context, climate, experienced risks, available resources, and subjective norms. Experts' rationale for seed recommendations centered on three themes: academic knowledge, professional experience, and user characteristics. Quantitatively, a structured questionnaire administered to a separate sample of 255 farmers (stratified proportional sampling) and 50 experts (complete enumeration) revealed significant disparities. Independent samples t-tests revealed statistically significant differences (p < 0.01) across all major mental model (MM) domains. Responses were initially scored as percentages (0–100) based on item agreement, then standardized to a 100-point scale. Mean scores were substantially higher among farmers in experienced risks (74.02 vs. 15.80), subjective norms (66.24 vs. 18.93), and work context (65.67 vs. 35.30), indicating stronger perceived influence of experiential and social factors. Conversely, experts scored higher on academic knowledge (75.28 vs. 41.71), reflecting greater reliance on scientific evidence.The Delphi method identified and prioritized key strategies for optimizing seed use, with "preparing a suitable seedbed" (weighted average: 0.148) and "using certified seeds" (weighted average: 0.143) ranked highest. These findings underscore the critical gap between farmer and expert models and emphasize the need for targeted interventions that integrate experiential knowledge with scientific evidence to bridge this divide, fostering more sustainable seed usage practices.

Climatic conditions are key determinants of agricultural productivity, affecting both production levels and food security. This study aims to decompose total factor productivity growth in Iran’s agricultural sector by explicitly incorporating climatic variables. True Random Parameters Stochastic Production Frontier (TRP-SPF) model and the Maximum Likelihood method were used to estimate parameters in two scenarios, with and without climate variables which are evaluated by  and  models, respectively. The results showed that the variables of cultivated area, tractors, and technology have a positive and significant impact on production in both models, and the highest effect observed for cultivated area. In the  model, the variables of temperature and precipitation also made significant contributions to higher production. The average technical efficiency in the  model was estimated at 56.5%, and in the model with climatic variables, it decreased to 43.3%. These results confirmed the significant impact of climatic variables on production and suggested that agricultural output could be increased by more than 50% with the same resources and technology, minimizing inefficiency factors and the adverse effects of climatic variables. Decomposition of the Climatic Adjusted Total Factor Productivity (CATFP) index into four components, including climatic effects, scale effects, technical efficiency, and technological progress, revealed that climatic effects have the greatest impact, accounting for 26.4%, while the other three components contributed almost equally to improving productivity. Given the significant impact of climatic variables on agricultural productivity, this study recommends strategies adapting better with climatic conditions of different regions.

Livestock grazing pressure in East Azerbaijan province, located in the northwest of Iran, is 2.5 times higher than the capacity of rangelands. This study employed a two-stage Delphi technique to identify and rank supervisory, natural, economic, cultural, legal, administrative, and infrastructure factors. The second Delphi round results indicated that, from the experts’ point of view, poverty among rural residents on the outskirts of rangelands (scoring 4.7), insufficient support of the Agriculture Organization in providing livestock feed and allocation of loans to livestock farmers (scoring 4.53), conversion of rangelands into agricultural and horticultural lands to gain higher profits (scoring 4.43), overgrazing and long term grazing (scoring 4.4), were the important factors contributing to rangeland degradation. Comprehensive legal, legislative, executive, and monitoring measures to prevent overgrazing and premature grazing were identified as key solutions to reduce rangeland degradation. Among economic factors, poverty among rural residents on the outskirts of rangelands and their livelihood dependence on rangelands were recognized as the most significant factors in the destruction of rangelands and forests. Additionally, collaboration with the Agriculture Organization in providing alternative forage for livestock, strengthening the monitoring mechanisms of relevant organizations, and enhancing education and awareness levels among the public, regarding the protection of natural resources have been highlighted as other important strategies in reducing the level of rangeland degradation.

Agricultural products, are mostly perishable, and ensuring their quality through methods like cold storage or optimal conditions can present significant economic challenges for retailers and small-scale producers. In some cases, these products lose their quality and marketability within a span of less than 24 hours. The main challenge in adopting this strategy is determining the timing and amount of the price discount. To optimize price discounting, four functions need to be estimated: a) a time-dependent quality function, b) a price change function, c) a demand function that is a function of price and quality, and d) an objective function, which is considered here as the retailer's profit function. We employ dynamic pricing models that integrate factors such as quality degradation, inventory levels, and consumer behavior. The research data was collected from three fruit and vegetable stores in Gorgan city. The time period was three months, comprising all working days. Each seller was asked to report the price and quantity of sales on an hourly basis from the beginning to the end of the working day. In this way, about 1000 data were recorded from each store. Tomato was selected as case study due to their widespread consumption, continuous supply, and rapid perishability. The results show that price discounts can significantly enhance profitability while reducing the risk of unsold and wasted products. Specifically, the findings indicate that implementing price discounts every 3 to 4 hours yielded the highest profitability. The average discount is 35%, leading to an 82% increase in gross profit. In contrast, shorter discount intervals (every 1 to 2 hours) with an average discount of 18% improved gross profit by 73%. On the other hand, longer discount intervals (every five to seven hours) with an average discount of 46% resulted in a 66% increase in gross profit. Furthermore, this study further emphasizes how crucial consumer perception is to the effectiveness of dynamic pricing methods. The perception of lower product quality brought on by excessive discounting may have a detrimental effect on long-term store credit and customer trust. Perishable product waste can be reduced through the implementation of price reduction strategies. However, the results indicate that sellers’ profits increase in the absence of price reductions. Future research could enhance this framework by incorporating additional factors that influence product quality, such as temperature, humidity, and product variety; examining the effects of consumer price expectations on demand; extending the model to other perishable products, including meat and dairy; and leveraging advanced technologies, such as blockchain and artificial intelligence, to improve dynamic pricing and inventory management.

Poultry production is one of the fastest-growing industries worldwide, and poultry meat has become a primary protein source for many populations. To maximize growth in the shortest possible time, producers often rely on disease-preventing antibiotics and growth promoters. However, numerous studies have detected antibiotic residues in poultry meat, raising concerns about their contribution to antibacterial resistance in human pathogens. Consequently, consumer demand for healthier products—particularly antibiotic-free poultry—is steadily increasing. Although this trend is advancing globally, it is still in its early stages in Iran. Accordingly, in order to move towards the development of the market for healthy products, the first essential step is to identify the factors that influence household consumption patterns. This study employs a combination of three decision-making tools, including Interpretive Structural Modeling (ISM), MICMAC analysis, and Fuzzy BestWorst Method (BWM), to evaluate factors influencing the purchase of antibiotic-free poultry in Mashhad, Iran, a key market where such products are available and consumer trends are emerging, in 2025. The process begins with the ISM method, which constructs a hierarchical model to illustrate the influence and interdependence of these factors. This step is crucial for understanding their collective impact on consumer purchase intention. Subsequently, the MICMAC analysis categorizes these factors based on their levels of influence and dependence, identifying the most critical elements that create a competitive advantage. Finally, the fuzzy Best–Worst Method (BWM) is applied to assign weights to these key factors. The results indicate that consumer awareness is the highest weight, followed by attitude and subjective norms. Identifying these key factors and their interrelationships provides actionable insights for decision-makers and stakeholders for strategic planning and prioritization. Based on these findings, it is recommended that policymakers and marketers focus on increasing consumer awareness, shaping positive attitudes, and leveraging subjective norms to encourage the purchase of antibiotic-free poultry. Targeted educational campaigns, informative labeling, and marketing strategies tailored to consumer preferences can help enhance adoption and support healthier consumption patterns.

Climate change, with its gradual and complex impacts, has increasingly inflicted damages on the agricultural sector of arid and semi-arid regions in Iran, including the Zayandeh-Rud Basin. This study aims to develop strategic adaptation policies to enhance agricultural resilience in response to climate change in this basin. The research is based on the Sendai Framework for Disaster Risk Reduction (SFDRR) and the resilience criteria established by the World Economic Forum (2013). A descriptive-analytical methodology was employed, combining documentary studies, field surveys, and the Delphi policy method to achieve expert consensus. Purposive sampling targeted faculties, agricultural experts of the Ministry of Agriculture, as well as producers and exporters of agricultural products with substantial expertise in Climate change issues, adaptation policies, and resilience. Data collection instruments consisted of four structured questionnaires designed to identify strengths and weaknesses, weight resilience criteria, prioritize policies, and select strategies, complemented by semi-structured interviews. Initially, to determine the strategic position of agriculture in the Zayandeh-Rud Basin, Internal Factor Evaluation (IFE), External Factor Evaluation (EFE), and Internal-External (IE) matrices were utilized. Subsequently, the Analytic Hierarchy Process (AHP) and the multi-criteria decision-making PROMETHEE method were employed to prioritize adaptation policies aimed at enhancing resilience to Climate Change. Suitable strategies were developed and extracted through SWOT analysis, and finally, the Quantitative Strategic Planning Matrix (QSPM) was used to evaluate the relative attractiveness and feasibility of each strategy, thereby selecting the implementable final policies. Findings indicated that agriculture in the Zayandeh-Rud Basin, with a final internal factor score of 2.04 and external factor score of 2.12, is in defensive position (survival mode) and exhibits low resilience due to water transfer and scarcity, land fragmentation and subsidence, and weaknesses in modern infrastructure. The most attractive adaptation policies identified under the defensive strategy include equitable water distribution (Total Attractiveness Score (TAS=1.46), development-oriented water resource planning (TAS=1.38), optimization of water. consumption and reduction of losses (TAS=1.35), control of groundwater levels (TAS=1.23), reform of regulations related to land fragmentation (TAS=1.18), and farmer participation in water resource management (TAS=1.16(. The proposed solutions emphasize strengthening integrated and basin-based water governance, implementing participatory strategic management models, investing in modern and soft technologies, gradually revising water-related laws and regulations, and expanding research and national strategy formulation. The results of this study provide actionable, stakeholder-inclusive recommendations for policymakers to enhance sustainability and resilience in vulnerable agricultural regions.