Title
Rice (Oryza sativa L.) Bioeconomy: A Comprehensive DEA Analysis
Authors
Abstract
Purpose: The research focuses on analyzing the technical efficiency of a sample of 612 rice (Oryza sativa L.) producers in Ecuador during the year 2019. The objective is to explore productivity and efficiency gaps to guide decision-making and sustainable policies in the rice sector.
Design/methodology/approach: By employing Data Envelopment Analysis (DEA)- a non-parametric method used to measure productive performance- this study includes BCC model (which reflect pure technical efficiency by comparing farm only against others of similar sizes for variable returns to scale) and CCR model (which reflect overall technical efficiency, including the effect o the farm scale assumes constant returns to scale). It also incorporates Super Efficiency and Bootstrapping methods to ensure robust statistical validation.
Findings: The results reveal significant technical inefficiencies; the mean technical efficiency (the ability to produce maximum output with inputs) under the BCC model was 0.3641, while the CCR model was 0.2817. These findings indicate that rice producers could potentially reduce their input consumption (seeds, fertilizers, labor, water) by 63.59% to 71.83% without compromising current output levels, highlighting a critical need for improved resource management.
Implications: The findings highlight the need for cooperative initiatives and sustainable farming practices to enhance productivity in Ecuador’s rice bioeconomy.
Originality: This research pioneers the application of DEA combined with bootstrapping in the Ecuadorian rice sector, offering a novel framework for the agricultural policy and resource allocation.
Keywords
Rice efficiency, DEA, Bootstrapping, Sustainability, Bioeconomy
Classification-JEL
O30, O38, O47
Pages
91-120
Special Issue on
How to Cite
Castro-Alvarez, M., Zuniga-Gonzalez, C. A., Mercado-Curi, W. F., & Andrade, R. S. (2026). Rice (Oryza sativa L.) Bioeconomy: A Comprehensive DEA Analysis. Advances in Decision Sciences, 29(2), 91-120.