scholarly journals Improving IPM Decision Support in Vegetable Crops through Weather-based Disease Advisories

HortScience ◽  
2005 ◽  
Vol 40 (4) ◽  
pp. 1110D-1110
Author(s):  
Albert Sutherland ◽  
John Damicone ◽  
Rafal Jabrzemski ◽  
Stdrovia Blackburn
Keyword(s):  
Decision Support ◽  
Site Selection ◽  
Historical Data ◽  
Disease Outbreak ◽  
Cultural Practice ◽  
Weather Data ◽  
Vegetable Crops ◽  
White Rust ◽  
Oklahoma Mesonet ◽  
Application Date

Weather-based disease advisories have allowed vegetable producers to optimize their fungicide applications. These models typically use only past weather data to identify times of potential disease outbreak. The Oklahoma Mesonet has developed a new Spinach White Rust Advisory that improves grower disease decision support by combining forecast, current, and past weather data in calculating infection periods. The decision-support component issues initial spray advisories, based on infection hour accumulation from the first true-leaf stage or from a previous fungicide application date for subsequent sprays. The advancement in this model in relation to traditional weather-based disease advisories are: incorporation of an 84-hour forecast, hourly model recalculation, cultural practice customization, user site selection from any of 110+ statewide sites, and immediate access to detailed historical data. The model is available on the Oklahoma Mesonet AgWeather website (http://agweather.mesonet.org).

Estimation of Evapotranspiration using the Modified Hargreaves Equation by Genetic Algorithm

2020 ◽  
Author(s):  
Deokhwan Kim
Keyword(s):  
Genetic Algorithm ◽  
Developing Countries ◽  
Correction Factor ◽  
Historical Data ◽  
Reference Evapotranspiration ◽  
Weather Data ◽  
Correction Coefficient ◽  
Test Bed ◽  
True Value ◽  
Daily Weather Data

<p>In this study, the Hargreaves monthly correction factor is presented to estimate the reference evapotranspiration. For the analysis, I used daily weather data from 1989 to 2018, at 67 meteorological stations located throughout the Korean peninsula.</p><p>A large number of more or less empirical methods have been developed over the last 50 years by numerous scientists and specialists worldwide to estimate evapotranspiration from different climatic variables. The FAO Penman-Monteith method is recommended as the sole ETo method for determining reference evapotranspiration. However, the Penman-Monteith method has the disadvantage of inputting a lot of weather data. In addition, there is a lack of meteorological data when using old historical data or as a test bed for developing countries.</p><p>In the case of the Hargreaves method, the reference evapotranspiration can be estimated only if the latitude, maximum and minimum temperatures of the meteorological station are known. However, the accuracy of the results is not as good as that of the Penman-monteith method. Thus, using the genetic algorithm method suggested the monthly correction factor of the Hargreaves method each station. The reference evapotranspiration amount calculated by Penman-Monteith was set as the true value, and the learning period of genetic algorithm was set from 1989 to 2013, and the validation period was set from 2014 to 2018.</p><p>In order to verify the model efficiency, the root mean square error decreased and the correlation coefficient increased when the monthly correction coefficient was applied to the reference evapotranspiration calculated by the Hargreaves method.</p><p>It is very important to estimate the reference evapotranspiration amount in order to develop the water long-term plan.</p><p>With the development of measuring equipment and technological capabilities, it is now possible to simulate the state of nature as if it were real, but many problems arise when using historical data or analyzing developing countries.</p><p>If the monthly correction coefficient suggested in this study is applied, it is possible to estimate the standard evaporation amount with a more approximate value.</p><p> </p><p>Acknowledgements</p><p> This research is supported by the Research Program (20200041-001) of Korea Institute of Civil Engineering & Building Technology </p>

The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma

2002 ◽  
Vol 11 (4) ◽  
pp. 183 ◽  
Author(s):  
J. D. Carlson ◽  
Robert E. Burgan ◽  
David M. Engle ◽  
Justin R. Greenfield
Keyword(s):  
Wildland Fire ◽  
Weather Conditions ◽  
Fire Danger ◽  
Rating System ◽  
Weather Data ◽  
Fuel Load ◽  
Usda Forest Service ◽  
University Of Oklahoma ◽  
Oklahoma Mesonet ◽  
Fire Danger Rating System

This paper describes the Oklahoma Fire Danger Model, an operational fire danger rating system for the state of Oklahoma (USA) developed through joint efforts of Oklahoma State University, the University of Oklahoma, and the Fire Sciences Laboratory of the USDA Forest Service in Missoula, Montana. The model is an adaptation of the National Fire Danger Rating System (NFDRS) to Oklahoma, but more importantly, represents the first time anywhere that NFDRS has been implemented operationally using hourly weather data from a spatially dense automated weather station network (the Oklahoma Mesonet). Weekly AVHRR satellite imagery is also utilized for live fuel moisture and fuel load calculations. The result is a near-real-time mesoscale fire danger rating system to 1-km resolution whose output is readily available on the World Wide Web (http://agweather.mesonet.ou.edu/models/fire). Examples of output from 25 February 1998 are presented.The Oklahoma Fire Danger Model, in conjunction with other fire-related operational tools, has proven useful to the wildland fire management community in Oklahoma, for both wildfire anticipation and suppression and for prescribed fire activities. Instead of once-per-day NFDRS information at two to three sites, the fire manager now has statewide fire danger information available at 1-km resolution at up to hourly intervals, enabling a quicker response to changing fire weather conditions across the entire state.

The Warning Decision Support System–Integrated Information

2007 ◽  
Vol 22 (3) ◽  
pp. 596-612 ◽  
Author(s):  
Valliappa Lakshmanan ◽  
Travis Smith ◽  
Gregory Stumpf ◽  
Kurt Hondl
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Severe Weather ◽  
Weather Data ◽  
Spatial Coverage ◽  
Analysis System ◽  
Automated Algorithms ◽  
The Individual ◽  
Integrated Information

Abstract The Warning Decision Support System–Integrated Information (WDSS-II) is the second generation of a system of tools for the analysis, diagnosis, and visualization of remotely sensed weather data. WDSS-II provides a number of automated algorithms that operate on data from multiple radars to provide information with a greater temporal resolution and better spatial coverage than their currently operational counterparts. The individual automated algorithms that have been developed using the WDSS-II infrastructure together yield a forecasting and analysis system providing real-time products useful in severe weather nowcasting. The purposes of the individual algorithms and their relationships to each other are described, as is the method of dissemination of the created products.

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