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1.
J Econ Entomol ; 104(5): 1613-21, 2011 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-22066191

RESUMEN

The plum curculio, Conotrachelus nenuphar (Herbst), is a key pest of pome and stone fruit in eastern and central North America. For effective management of this insect pest in commercial apple (Malus spp.) orchards in the northeastern United States and Canada, one of the greatest challenges has been to determine the need for and timing of insecticide applications that will protect apple fruit from injury by adults. In a 2004-2005 study, we assessed the efficacy and economic viability of a reduced-risk integrated pest management strategy involving an odor-baited trap tree approach to determine need for and timing of insecticide use against plum curculio based on appearance of fresh egg-laying scars. Evaluations took place in commercial apple orchards in seven northeastern U.S. states. More specifically, we compared the trap-tree approach with three calendar-driven whole-block sprays and with heat-unit accumulation models that predict how long insecticide should be applied to orchard trees to prevent injury by plum curculio late in the season. Trap tree plots received a whole-plot insecticide spray by the time of petal fall, and succeeding sprays (if needed) were applied to peripheral-row trees only, depending on a threshold of one fresh plum curculio egg-laying scar out of 25 fruit sampled from a single trap tree. In both years, level of plum curculio injury to fruit sampled from perimeter-row, the most interior-row trees and whole-plot injury in trap tree plots did not differ significantly from that recorded in plots subject to conventional management or in plots managed using the heat-unit accumulation approach. The amount of insecticide used in trap tree plots was reduced at least by 43% compared with plots managed with the conventional approach. Advantages and potential pitfalls of the bio-based trap tree approach to plum curculio monitoring in apple orchards are discussed.


Asunto(s)
Monitoreo del Ambiente/métodos , Malus , Odorantes/análisis , Feromonas/farmacología , Gorgojos/efectos de los fármacos , Animales , Monitoreo del Ambiente/economía , Monitoreo del Ambiente/instrumentación , Hemípteros/fisiología , Insecticidas , New England , New York , Dinámica Poblacional , Estaciones del Año , Temperatura , Gorgojos/fisiología
2.
Plant Dis ; 95(9): 1179-1186, 2011 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-30732062

RESUMEN

Several disease forecast models have been developed to guide treatment of the sooty blotch and flyspeck (SBFS) disease complex of apple. Generally, these empirical models are based on the accumulation of hours of leaf wetness (leaf wetness duration [LWD]) from a biofix at or near the phenological growth stage petal fall, when apple flower petals senesce and drop. The models recommend timing of the initial fungicide application targeting SBFS. However, there are significant differences among SBFS forecast models in terms of biofix and the length of LWD thresholds. A comparison of models using a single input data set generated recommendations for the first SBFS fungicide application that differed by up to 5 weeks. In an attempt to improve consistency among models, potential sources for differences were examined. Leaf wetness (LW) is a particularly variable parameter among models, depending on whether on-site or remote weather data were used, the types of sensors and their placement for on-site monitors, and the models used to estimate LW remotely. When SBFS models are applied in the field, recommended treatment thresholds do not always match the method of data acquisition, leading to potential failures. Horticultural factors, such as tree size, canopy density, and cultivar, and orchard site factors such as the distance to potential inoculum sources can impact risk of SBFS and should also be considered in forecast models. The number of fungal species identified as contributors to the SBFS disease complex has expanded tremendously in recent years. A lack of understanding of key epidemiological factors for different fungi in the complex, and which fungi represent the most challenging management problems, are obvious issues in the development of improved SBFS models. If SBFS forecast models are to be adopted, researchers will need to address these issues.

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