Regionalization of dryland farming potential as influenced by droughts in western Iran

Clustering was used to divide dryland farming areas in western Iran into homogeneoussub-regions to identify dryland farming potential, considering drought impacts. Clusteringutilized eight algorithms/four indices to detect optimal number of clusters. Ward’s algorithmvalidated 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 southernsub-regions. Drought impact analysis led to 6 optimal clusters by Ward’s algorithm, validatedby 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 respectivelyfirst and second rankings for dryland farming, are also least affected by droughts. Areas incentral 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 potentialwere impacted minimally by drought, while areas with lowest potential were strongly affectedby droughts. However, sub-regions with good dryland farming potential were be severelyinfluenced by drought. Therefore, drought analysis should be considered for dryland farmingmanagement.

Regionalization of dryland farming potential as influenced by droughts in western Iran Keywords:

Dryland Farming , Drought Events , Drought Indices , Clustering Analysis