Validation of the phenological forecast model "RIMpro-Cydia 3" in the region of Plovdiv (Central South Bulgaria)

The study aimed to validate the forecast model "RIMpro-Cydia 3" for the region of the city of Plovdiv and through it to signalize the most appropriate periods for control of the codling moth Cydia pomonella L. (Lepidoptera: Tortricidae) at the conditions of Bulgaria. The observations of the phenology of the pest were conducted in the period 2020 – 2022 in apple orchards in the region of Plovdiv. The model used the data from the automatic weather station, installed in the Centre of Integrated Disease Management at the Experimental Field of the Agricultural University, Plovdiv. Comparing the prediction of the model with the observed values in the region of Plovdiv, the codling moth developеа two full and partial third generations per year, which overlap. Their population dynamics and density depend both on the weather conditions and on the applied plant protection measures. The second generation of the pest had a higher density and causes greater losses to the fruit crop. In this region “RIMpro-Cydia 3” model predicted correctly the development and population density of the first generation of the codling moth, while for the second generation, the model showed significant deviations in the prediction of the population density as well as the exact development periods. The farmers have to use the model data only for the forecast of the first generation of the Codling moth. The measures for efficient control of the second generation should be signalized by direct observations in the orchards. Keywords: codling moth, Cydia pomonella, phenological development, forecast model, RIMpro-Cydia 3

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Publication Details

Journal
Agricultural Sciences
Published
2024-12-30
DOI
https://doi.org/10.22620/agrisci.2024.43.008
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Validation of the phenological forecast model "RIMpro-Cydia 3" in the region of Plovdiv (Central South Bulgaria)

Stanislav Stefanov, Atanaska Stoeva, Pavlin Vasilev, R. Andreev et al.
Agricultural Sciences
Remote Sensing in Agriculture
article

Validation of the phenological forecast model "RIMpro-Cydia 3" in the region of Plovdiv (Central South Bulgaria)

Stanislav Stefanov, Atanaska Stoeva, Pavlin Vasilev, R. Andreev, Mamothibedi Tieho
article en

Abstract

The study aimed to validate the forecast model "RIMpro-Cydia 3" for the region of the city of Plovdiv and through it to signalize the most appropriate periods for control of the codling moth Cydia pomonella L. (Lepidoptera: Tortricidae) at the conditions of Bulgaria. The observations of the phenology of the pest were conducted in the period 2020 – 2022 in apple orchards in the region of Plovdiv. The model used the data from the automatic weather station, installed in the Centre of Integrated Disease Management at the Experimental Field of the Agricultural University, Plovdiv. Comparing the prediction of the model with the observed values in the region of Plovdiv, the codling moth developеа two full and partial third generations per year, which overlap. Their population dynamics and density depend both on the weather conditions and on the applied plant protection measures. The second generation of the pest had a higher density and causes greater losses to the fruit crop. In this region “RIMpro-Cydia 3” model predicted correctly the development and population density of the first generation of the codling moth, while for the second generation, the model showed significant deviations in the prediction of the population density as well as the exact development periods. The farmers have to use the model data only for the forecast of the first generation of the Codling moth. The measures for efficient control of the second generation should be signalized by direct observations in the orchards. Keywords: codling moth, Cydia pomonella, phenological development, forecast model, RIMpro-Cydia 3

Agricultural SciencesVol. 16(43)
Bulgarian National Science Fund
Openalex Percentile: Top 75%
Remote Sensing in Agriculture
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