1Graduated Master Student, Water Engineering Department, College of Agriculture, Shiraz University, Iran (Postal code: 7144165186).
2Assistant Professor, Water Engineering Department, College of Agriculture, Shiraz University, Shiraz, Iran (Postal code: 7144165186).
3Professor, Water Engineering Department, College of Agriculture, Shiraz University, Shiraz, Iran (Postal code: 7144165186).
Clustering was used to divide dryland farming areas in western Iran into homogeneous sub-regions to identify dryland farming potential, considering drought impacts. Clustering utilized eight algorithms/four indices to detect optimal number of clusters. Ward’s algorithm validated by Silhouette index, produced the best result by detecting 7 dryland farming clusters. Based on similar P/ETo values, four sub-regions were recognized among 7 clusters. Northwestern sub-region was ranked first, followed by central, northeastern and southern sub-regions. Drought impact analysis led to 6 optimal clusters by Ward’s algorithm, validated by Silhouette index. Ranking criteria utilized drought characteristics, obtained from 3- to 12- months SPI analysis. Northwestern sub-region and parts of central sub-region, with respectively first and second rankings for dryland farming, are also least affected by droughts. Areas in central sub-region with good dryland farming potential can be strongly impacted by droughts. Northeastern and southern sub-regions respectively ranked third and fourth for dryland farming, were severely affected by droughts. In conclusion, areas with highest dryland farming potential were impacted minimally by drought, while areas with lowest potential were strongly affected by droughts. However, sub-regions with good dryland farming potential were be severely influenced by drought. Therefore, drought analysis should be considered for dryland farming management.