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Sampling methods as a basis for assessing the phytosanitary status of diseases and damage in blueberry production in the Colombian highland tropics

Main phytosanitary problems of blueberry in the Colombian Highland Tropics. Photo: J.G. Ramírez-Gil

Abstract

Blueberry production has significantly increased globally in recent years due to its excellent nutritional quality. In Colombia, cultivated areas have expanded, along with the rise of phytosanitary problems that are still not well-characterized, nor are there reliable sampling tools for monitoring and evidence-based decision-making. This study focuses on the symptomatological characterization, identification, and determination of the incidence of the main blueberry pathologies (diseases and disorders) in Colombia, under high-altitude tropical conditions. Additionally, various sampling methods were evaluated as a basis for developing statistically valid tools for monitoring in field conditions. The research was conducted in commercial fields across nine municipalities in the Cundinamarca and Boyaca regions of Colombia. After characterizing the main diseases and disorders, the incidence was determined under field conditions, and the best sampling strategy was evaluated based on methods such as random, systematic grid-based, and stratified sampling. Sample size determination was based on the finite population method. The intensity measures evaluated incidence and severity showed that in Colombia’s high-altitude tropics, the most important diseases were rust and shoot dieback, mechanical damage, and fruit dehydration as abiotic disorders. Stratified sampling yielded the best performance, showing the lowest coefficient of variation. Our findings provide the first characterization of blueberry pathologies, their significance, and a sampling method for evidence-based decision-making. This work is crucial as it establishes a methodology for identifying phytosanitary issues, proposes a robust sampling approach for field application, and emphasizes the need for ongoing detection of emerging diseases to enhance data-driven decision-making.

Keywords

Vaccinium corymbosum, Stratified sampling, Dieback, Rust, Incidence, Severity

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