scholarly journals Comparing surface wetness inside and outside grape canopies for regionwide assessment of plant disease risk

2005 ◽  
Vol 58 ◽  
pp. 80-83
Author(s):  
W.R. Henshall ◽  
R.M. Beresford ◽  
R.W. Chynoweth ◽  
P. Ramankutty

Wetness duration measured by flat plate sensors inside and outside a grape canopy was recorded from DecemberMarch Sensors outside the canopy generally recorded longer wetness duration than sensors inside the canopy For days with rain short wetness durations detected by outside sensors were not detected by inside sensors because of sheltering by the canopy When wetness arose solely from dew duration inside was much shorter than outside for prolonged wet periods Wetness was used to calculate infection periods according to two botrytis bunch rot risk models Agreement between sensors was worse inside the canopy than outside although on occasions when rainfall exceeded 10 mm there was greater uniformity between sensors For regionwide disease risk monitoring wetness duration measured outside leaf canopies at standard meteorological sites would give a worstcase estimate of disease risk Regression equations are presented that allow estimation of inside wetness duration from outside wetness duration for rainy and nonrainy days

EDIS ◽  
2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Thiago Borba Onofre ◽  
Clyde W. Fraisse ◽  
Natalia A. Peres ◽  
Janise McNair

Leaf wetness duration is an essential input in disease prediction models and decision support systems in Florida and elsewhere. Incorrect installation or lack of regular maintenance of leaf wetness sensors may lead to errors in plant disease risk monitoring and negative impacts on yield. This 7-page publication provides detailed guidelines for the proper installation and maintenance of leaf wetness sensors and describes the most common problems found in field installations as well as potential solutions. Written by T. B. Onofre, C. W. Fraisse, N. A. Peres, and J. McNair, and published by the UF/IFAS Department of Agricultural and Biological Engineering, February 2020.


2007 ◽  
Vol 60 ◽  
pp. 128-132 ◽  
Author(s):  
K.S. Kim ◽  
R.M. Beresford ◽  
W.R. Henshall

To improve the implementation of weatherbased disease risk models a spatial interpolation method was investigated to provide weather estimates for specific sites Two sites in the HortResearch horticultural weather station network one in Marlborough and one in Hawkes Bay were selected as validation sites Interpolated weather data were estimated for these sites from November to March in 200304 and 200405 using actual weather data from nearby stations that were selected as natural neighbours using the geometrical technique Voronoi tessellation Wetness duration was also estimated using interpolated weather data as inputs to an empirical wetness model Air temperature estimates were comparable to actual measurements but wetness duration was overestimated When interpolated and actual data were used as inputs to the grape botrytis model Bacchus predicted risks were comparable to each other for short periods rather than the whole growing season This suggests that risk of botrytis bunch rot could be predicted reliably at a specific site using the spatial interpolation method


2021 ◽  
pp. 115059
Author(s):  
Amir Ashraf ◽  
Sajid Ali ◽  
Ismail Shah

2007 ◽  
Vol 102 (1-3) ◽  
pp. 145-151 ◽  
Author(s):  
Amy M. Kilbourne ◽  
Edward P. Post ◽  
Mark S. Bauer ◽  
John E. Zeber ◽  
Laurel A. Copeland ◽  
...  

2020 ◽  
Author(s):  
Zefang Tang ◽  
Yiqin Yu ◽  
Kenney Ng ◽  
Daby Sow ◽  
Jianying Hu ◽  
...  

AbstractAs Electronic Health Records (EHR) data accumulated explosively in recent years, the tremendous amount of patient clinical data provided opportunities to discover real world evidence. In this study, a graphical disease network, named progressive cardiovascular disease network (progCDN), was built based on EHR data from 14.3 million patients 1 to delineate the progression profiles of cardiovascular diseases (CVD). The network depicted the dominant diseases in CVD development, such as the heart failure and coronary arteriosclerosis. Novel progression relationships were also discovered, such as the progression path from long QT syndrome to major depression. In addition, three age-group progCDNs identified a series of age-associated disease progression paths and important successor diseases with age bias. Furthermore, we extracted a list of salient features to build a series of disease risk models based on the progression pairs in the disease network. The progCDN network can be further used to validate or explore novel disease relationships in real world data. Features with sufficient abundance and high correlation can be widely applied to train disease risk models when using EHR data.


2015 ◽  
Vol 44 (suppl_1) ◽  
pp. i8-i8
Author(s):  
E. Verly-Jr ◽  
V. T. Baltar ◽  
R. M. Fisberg ◽  
D. M. Marchioni

Technometrics ◽  
2019 ◽  
Vol 62 (2) ◽  
pp. 249-264 ◽  
Author(s):  
Lu You ◽  
Peihua Qiu
Keyword(s):  

Author(s):  
Ronald S. LaFleur ◽  
Laura S. Goshko

Cardiovascular disease (CVD) continues to be a leading cause of death. Accordingly, risk models attempt to predict an individual's probability of developing the disease. Risk models are incorporated into calculators to determine the risk for a number of clinical conditions, including the ten-year risk of developing CVD. There is significant variability in the published models in terms of how the clinical measurements are converted to risk factors as well as the specific population used to determine b-weights of these risk factors. Adding to model variability is the fact that numbers are an imperfect representation of a person's health status. Acknowledgment of uncertainty must be addressed for reliable clinical decision-making. This paper analyzes 35 published risk calculators and then generalizes them into one “Super Risk formula” to form a common basis for uncertainty calculations to determine the best risk model to use for an individual. Special error arithmetic, the duals method, is used to faithfully propagate error from model parameters, population averages and patient-specific clinical measures to one risk number and its relative uncertainty. A set of sample patients show that the “best model” is specific to the individual and no one model is appropriate for every patient.


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