Keywords
production constraints
hierarchical clustering
stochastic frontier analysis
ranked data
maize-farming
low-hill agriculture
How to Cite
Abstract
The study examines how farmer-perceived production constraints cluster influence technical efficiency and maize output in the low-hill zone of Himachal Pradesh. This paper analyses 432 maize-farming households from Kangra, Mandi, and Hamirpur districts using hierarchical clustering based on the Salama–Quade weighted rank correlation. The study identifies six distinct constraint typologies and evaluates differences in technical efficiency and maize output across clusters. The results show that Cluster C5 (Mechanization-constraint salient) records the highest mean technical efficiency (0.86), while Cluster C4 (Land Fragmentation) has the lowest (0.52), with the overall mean efficiency around 0.60. The study finds that larger landholdings and improved seed adoption are associated with lower inefficiency, while institutional variables do not have a significant effect in the full model. Substantial spatial variation in cluster profiles is observed across districts. Climate variability and seed-access cluster are more prominent in Mandi, while wildlife and pest and disease pressures are more acute in Kangra and Hamirpur. These results indicate that farm performance in low hill maize systems is shaped by locally specific constraint environments. Overall, the findings suggest that performance differences across maize systems are closely linked to the specific constraints faced by farmers, underscoring the need for more targeted policy support.
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