Introduction
Increasing pressure on limited water resources, pronounced climatic variability, and the growing demand for agricultural production have made accurate agroclimatic assessment an essential component of sustainable agriculture in Iran. Identifying homogeneous agroclimatic zones enables more efficient resource management, optimized cropping calendars, and reduced production risk under variable environmental conditions. Agro-ecological zoning (AEZ) provides a scientific framework to integrate climatic, topographic, and crop phenological factors in order to delineate areas with similar production potential. Barley (Hordeum vulgare L.), as one of Iran’s major cereal crops, plays a vital role in food security, livestock feed supply, and agricultural livelihoods. Barley yield and stability are highly sensitive to planting date and length of the growing period, both of which are directly influenced by climatic gradients, elevation, and rainfall patterns. Despite the importance of these parameters, national-scale agroclimatic zoning of barley based explicitly on planting date and growing period has received limited attention. Therefore, this study aimed to delineate agroclimatic zones for barley cultivation across Iran using advanced spatial interpolation and clustering techniques, providing a decision-support tool for sustainable crop planning.
 
Materials and Methods
This study adopted a spatial–analytical approach at the national scale of Iran. Point-based data on barley planting date and length of the growing period were obtained from the national Water Requirement System and related agroclimatic databases, covering approximately 790 stations distributed across the country. These data represent long-term average conditions relevant to barley phenology. To transform discrete point data into continuous spatial surfaces, the Empirical Bayesian Kriging Regression Prediction (EBK-RP) method was employed. This geostatistical technique combines regression modeling with Empirical Bayesian Kriging, allowing automatic estimation of semivariogram parameters and improved uncertainty handling, particularly in regions with sparse observations. Elevation derived from a digital elevation model (DEM) was incorporated as a covariate to enhance spatial prediction accuracy. The resulting raster layers for planting date and growing period length were then classified using the K-means clustering algorithm, a widely used unsupervised machine learning technique. The optimal number of clusters for each variable was determined using the elbow method based on within-cluster sum of squares (WCSS). Three clusters were identified for planting date and four clusters for growing period length. Finally, the classified layers were overlaid and combined to generate homogeneous agroclimatic zones for barley cultivation. To improve spatial coherence and reduce classification noise, focal statistics with a majority filter were applied.
 
Results and Discussion
The spatial distribution of barley planting dates and growing period lengths exhibited strong correspondence with Iran’s climatic and altitudinal gradients. Early planting dates were predominantly observed in the humid Caspian coastal region and parts of western and northwestern Iran, where higher precipitation, moderate temperatures, and lower frost risk prevail. In contrast, delayed planting was common in the arid and semi-arid central, eastern, and southeastern regions, reflecting dependence on late and irregular rainfall events. The length of the growing period showed an inverse relationship with temperature and aridity. Longer growing seasons (approximately 230–300 days) were identified in high-altitude and humid regions such as the Caspian lowlands, Alborz and Zagros mountain ranges, and western provinces. Shorter growing periods (around 120–170 days) characterized the hot, dry southern and eastern regions, where high temperatures and moisture stress accelerate crop development and shorten phenological stages. The integration of planting date and growing period classes resulted in twelve agroclimatic zones for barley cultivation. Among these, zones characterized by early to intermediate planting and moderate to long growing periods demonstrated the highest suitability for barley production. These zones are mainly located in northern, western, and parts of northwestern Iran, where climatic conditions provide more stable growth environments. Conversely, zones with late planting and short growing periods, largely distributed across central, eastern, and southeastern Iran, were identified as high-risk areas with significant climatic limitations for barley cultivation. These findings highlight the dominant role of climate, elevation, and moisture availability in shaping barley phenology and production potential. The spatial patterns observed are consistent with previous agro-ecological zoning studies in Iran and other semi-arid regions, reinforcing the reliability of the applied methodology.
 
Conclusion
This study demonstrated that integrating advanced geostatistical modeling (EBK Regression Prediction) with unsupervised clustering (K-means and elbow method) provides a robust framework for national-scale agroclimatic zoning of barley. The results clearly indicate that barley planting date and length of the growing period in Iran are strongly controlled by climatic–altitudinal gradients. Only a limited portion of the country—primarily humid and semi-humid northern and western regions—offers high and stable potential for sustainable barley production, while large arid and semi-arid areas face substantial climatic constraints. The derived agroclimatic zones can serve as a strategic decision-support tool for policymakers, planners, and extension services. They facilitate optimized cropping calendars, targeted selection of barley cultivars, improved water resource management, and risk reduction under climate variability. Ultimately, applying such zoning-based approaches can enhance productivity, promote sustainable land use, and support long-term food security in Iran.