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Evaluation of strains of Bacillus thuringiensis as biological control agents of the adult stages of the carrot weevil, Listronotus oregonensis (Coleoptera:Curculionidae)Saade, Fabienne Eugenie Joseph January 1993 (has links)
No description available.
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Mathematical models for the population dynamics and management of the carrot weevil, Listronotus oregonensis (LeConte) (Coleoptera:Curculionidae)Zhao, Dingxin January 1990 (has links)
No description available.
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Mapping spatial distribution of a disease forecasting model using precipitation and relative humidity measurements provided by weather radarLaurence, Helene. January 2001 (has links)
Many applications of remote sensing in agriculture have been developed since 60 years but mostly since the launch of Landsat 1 in 1972. With time, improvements in spatial, spectral and temporal resolution have been made and generated a resurgence of remote sensing popularity. Combined with agricultural systems modelling, remote sensing data such as weather radar measurements can help to obtain an accurate tool in real-time for agricultural decision-makers. Indeed, precipitation and relative humidity (RH) could become available for the agricultural decision-makers using the McGill Doppler S-band radar. At present, precipitation measurements are available with a spatial resolution of 1 km up to a range of 240 km and RH data could be available with a resolution of a few kilometres up to a range of 40 km. Both weather variables could be available with a time scale of 5 min if requested. These measurements would compensate for the actual lack of a dense weather station network prevailing in southern Quebec. / So far, the reliability of weather radar measurements has been tested by the scientific community for precipitation data but has never been tested for the RH data. In this study, a comparison between RH measured at three weather stations and RH calculated from weather radar measurements was made using consecutive time interval of 240 hours in 1997 and 336 hours in 1998. A valid t-test designed for simple linear regression analysis with two time series as dependent and explanatory variable, and based on the first-difference ratios (FDR) of the time series clearly showed that RH calculated from radar measurements is comparable to the one measured at weather stations. Thereafter, the possibility of integrating weather radar measurements (precipitation and RH) in a geographic information system (GIS) to map the variability of a crop disease was verified. Results indicated the potential of weather radar measurements in agriculture.
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Mapping spatial distribution of a disease forecasting model using precipitation and relative humidity measurements provided by weather radarLaurence, Helene. January 2001 (has links)
No description available.
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