Key factors affecting the technical efficiency of bee farms in the province of La Pampa (Argentina): A two-stage DEA approach

Abstract

This paper analyzes how decision-making, management capacity and technology adoption by beekeepers, affect the technical efficiency (TE) of Argentinean beekeeping through the case study of the province of La Pampa (Argentina). The assessment of TE is currently receiving ever-growing attention as an indicator of sustainability and usage of sufficient natural resources in beekeeping activities. This study aimed to identify the key factors affecting the technical efficiency of bee farms in the province of La Pampa. The study included a sample of 40 bee farms and estimated their TE score through an input-oriented Data Envelopment Analysis (DEA) model. In a second stage, Tobit regression was determined to evaluate the technical inefficiency determinants. This paper found that most beekeeping production units have low TE levels. Only 25 % of bee farms produce either at or close to the frontier. The Tobit model revealed that variables such as marital status, educational level, primary family income, source information usage, planning and health area, affect positively on pure technical efficiency. These results are considered to be of great interest for structured beekeeping systems on small-scale and family farms, as well as for political decision-makers, regarding a public program in apiculture. Highlights: Argentina is the leading country in America, exporting honey worldwide. DEA approach and Tobit model based on a two-stage analysis is a useful tool when evaluating livestock production systems. Bee farmers in La Pampa (Argentina) are operating below the production frontier, which indicates there is still scope for improvement. Variables such as marital status, educational level, primary family income, source information usage, planning and health area, affect positively on technical efficiency. Beekeepers should be encouraged by the government to improve their efficiency through training programs to ease decision-making and management, therefore enhancing productivity.

Drivers linking farmers’ decision-making with farm performance: A systematic review and future research agenda

“…The second topic is the role of management and farm structure . In addition to studying management capacity, this research topic also considers farm size, diversification, production system, intensification, and other farm characteristics [ 58 ], as well as the technological level of the farm [ 59 , 60 ]. The third topic, farmers' emotional attachment to their business , addresses how non-economic factors, including socioemotional wealth, influence the decision to remain in the business even when returns are minimal [ 32 ].…”

“… • Management activities including farm recordkeeping, production planning, and using records in decision-making are associated with farm performance. [ 58 ] [ 59 ] [ 60 ] Farmers' emotional attachment to their business This research topic seeks to better understand o farmers' goals, motivations, and the role of emotional attachment (feelings and emotions) on farm survival. • Farms were able to remain in the business despite minimal economic returns by engaging in four different strategic behaviors: diversifying the business, increasing levels of debt, sacrificing family time and activities, and accepting less than optimal business performance.…”

What Affects the Production Technology of Labor-Intensive Agricultural Industries in the Context of Labor Aging? An Empirical Study Based on the Garlic Production in Lanling

“…Mezgebo, GK used a Tobit model to determine the influencing factors of sesame production [28]. Angon, E estimated the TE score through the input data encirclement analysis (DEA) model, and in the second stage, Tobit regression determined the determinants of the inefficiency of the evaluation technique [29].…”

“…The DEA model used in this paper is a variable-scale model, which is used to analyze the technical efficiency of agricultural production to obtain the relative comprehensive technical efficiency, pure technical efficiency, and scale efficiency values and analyze the effectiveness of the selected decision-making unit on this basis. The DEA model usually has two forms, the first is the CCR model with constant return to scale, and the second is the BCC model with variable return to scale [28,29].…”

Management improvement of the supply chain of perishable agricultural products by combining the Scor model and AHP methodology. The ecuadorian flower industry as a case study

“…By achieving integration, the SC functions as one single unit directly driven by customer demand (12,18,26). In this sense, several models have been developed to measure SC performance, and the SCOR model stands as a powerful tool to evaluate SC's activities and performance, optimizing production, distribution, and sales processes (1,17).…”