Efficiency of agricultural systems in Morocco: A meta-frontier analysis of resource use and water management New!

Context: To improve agricultural productivity and water sustainability in water-scarce regions, it is essential to understand the efficiency and diversity of farming practices
Objective: This study aims to assess the diversity and efficiency of farming systems in Morocco’s Chtouka-Massa plain. It focuses on resource management, agricultural intensification, and water use, identifying inefficiencies and proposing sustainable solutions.

Methods: Using Principal Component Analysis and Hierarchical Clustering, we classify 40 farm households into three distinct typologies: (i) extensive cereal-arboriculture systems, (ii) semi-intensive mixed cereal-vegetable systems, and (iii) intensive vegetable farming systems. A meta-frontier approach combined with Data Envelopment Analysis (DEA) is then applied to assess disparities in resource efficiency, technological performance, and environmental sustainability among these typologies. Results and conclusions: Our results show that extensive cereal-arboriculture systems exhibit the highest resource efficiency—particularly in water, nitrogen, and labor—but achieve the lowest gross margins due to limited  agricultural intensification. Semi-intensive mixed systems demonstrate moderate efficiency but consume the largest amounts of water, largely sourced from subsidized private wells. Intensive vegetable farming systems, while generating the highest gross margins, are the least efficient due to high input costs, reliance on desalinated water, and labor-intensive practices. Targeted policy interventions are needed to optimize resource use and promote sustainable practices adapted to each farming typology. Significance: This study provides actionable insights for policymakers aiming to enhance the sustainability of agricultural systems and groundwater resources in arid and semi-arid regions. The findings support the need for targeted policies to enhance groundwater management.