• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.163-166
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• 2001

One of the most important factors influencing the outbreak and severity of foliar diseases is the duration of wetness from dew deposition, rainfall, or irrigation. Models may provide good alternatives for assessing leaf wetness duration (LWD) without the labor, cost, and inconvenience of making measurements with sensors.(omitted)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.93-96
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• 2001

Estimation of leaf wetness duration (LWD) facilitates assessment of the likelihood of outbreaks of many crop diseases. Models that estimate LWD may be more convenient and grower-friendly than measuring it with wetness sensors. Empirical models utilizing statistical procedures such as CART (Classification and Regression Tree; Gleason et al., 1994) have estimated LWD with accuracy comparable to that of electronic sensors.(omitted)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.50-53
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• 2003

Models have been developed to estimate leaf wetness duration (LWD) using conventional weather observations, e.g., air temperature, water vapor pressure, and wind speed, which are relatively invariant over space (Pedro and Gillespie, 1982; Gleason et al., 1994; Francl and Panigrahi, 1997).(omitted)

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2003.09a
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• pp.54-57
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• 2003

Implementation of disease-warning systems often results in substantial reduction of spray frequency (Lorente et al., 2000; Madden et al., 2000). This change reduces the burden of pesticide sprays on the environment and can also delay the development of fungicide and bactericide resistance. To assess the risk of outbreaks of many foliar diseases, it is important to quantify leaf wetness duration(LWD) since activities of foliar pathogen depend on the presence of free water on host crop surface for sufficient periods of time to allow infection to occur.(omitted)

• Research in Plant Disease
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• v.9 no.3
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• pp.131-136
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• 2003

The effects of temperature, duration of wetness period, and fungicides on the spore germination, appressorium formation, acervulus formation and lesion development by Colletotrichum spp., cause of soybean anthracnose, and their pathogenicity were assessed in controlled environment. C. gloeosporioides was highly pathogenic on inoculated soybean seeds as high as C. truncatum, whereas remarkably low on the soybean leaves. Spore germination, appressorium formation and mycelial growth of C. gloeosporioides were best at $25^{\circ}C$, but C. truncatum was best at $30^{\circ}C$. It has also done at $15^{\circ}C$, even though it was much retarded. C. truncatum showed high sensitivity to the fungicides, fluazinam and benomyl, meanshile C. gloeosporioides showed to fluazinam and triflumizole. At least 8 hrs. of wetness period was requird for the pathogen to develop lesions at $30^{\circ}C$. When the wetness period was 32 hrs. lesion size of was larger at $25^{\circ}C$ than $30^{\circ}C$, however it was traceable at $20^{\circ}C$. Different sensitivity of Colletotrichum spp. to fungicides suggests that proper fungicide is required to effective control of soybean anthracnose ingected multiply with Colletotrichum spp.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.378.3-378
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• 2001

• The Plant Pathology Journal
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• v.17 no.6
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• pp.372.2-372
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• 2001

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.188-189
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• 2019

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.1
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• pp.23-28
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• 2002

Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

• Journal of Ginseng Research
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• v.40 no.1
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• pp.90-96
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• 2016

Background: Knowledge on microclimate conditions under artificial shades in a ginseng field would facilitate climate-aware management of ginseng production. Methods: Weather data were measured under the shade and outside the shade at two fields located in Gochang-gun and Jeongeup-si, Korea, in 2011 and 2012 seasons to assess temperature and humidity conditions under the shade. An empirical approach was developed and validated for the estimation of leaf wetness duration (LWD) using weather measurements outside the shade as inputs to the model. Results: Air temperature and relative humidity were similar between under the shade and outside the shade. For example, temperature conditions favorable for ginseng growth, e.g., between $8^{\circ}C$ and $27^{\circ}C$, occurred slightly less frequently in hours during night times under the shade (91%) than outside (92%). Humidity conditions favorable for development of a foliar disease, e.g., relative humidity > 70%, occurred slightly more frequently under the shade (84%) than outside (82%). Effectiveness of correction schemes to an empirical LWD model differed by rainfall conditions for the estimation of LWD under the shade using weather measurements outside the shade as inputs to the model. During dew eligible days, a correction scheme to an empirical LWD model was slightly effective (10%) in reducing estimation errors under the shade. However, another correction approach during rainfall eligible days reduced errors of LWD estimation by 17%. Conclusion: Weather measurements outside the shade and LWD estimates derived from these measurements would be useful as inputs for decision support systems to predict ginseng growth and disease development.