Productivity Dynamics Of Water User Associations: A DEA-Malmquist Analysis In The Context Of Indian Irrigation

Abstract

This study introduces an integrated framework for evaluating and forecasting the productivity of Water User Associations in the Rohini canal system of Uttar Pradesh, India, by combining DEA, the MPI, and machine learning techniques. The analysis covers five canals from 2018 to 2020, revealing significant variations in WUA performance. Notably, the RMC canal experienced a 17% decline in productivity, while others demonstrated improvement. OPEX emerged as the most influential factor affecting efficiency. The MPI enables decomposition of productivity into efficiency change and technological progress, offering insights into temporal and spatial disparities. For instance, Chauka WUA achieved notable gains through reduced inputs and increased outputs, whereas RMC’s decline was attributed to stagnant output despite lower inputs. Findings highlight the essential role of WUAs in improving irrigation efficiency and productivity, especially in resource-limited settings. This study bridges theoretical evaluation with practical policy recommendations, supporting the optimization of irrigation systems and advancing sustainable agricultural water management in India.