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Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states

Overview of attention for article published in International Journal of Biometeorology, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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1 news outlet
twitter
2 tweeters

Citations

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10 Dimensions

Readers on

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29 Mendeley
Title
Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states
Published in
International Journal of Biometeorology, May 2016
DOI 10.1007/s00484-016-1165-4
Pubmed ID
Authors

Verona O. Montone, Clyde W. Fraisse, Natalia A. Peres, Paulo C. Sentelhas, Mark Gleason, Michael Ellis, Guido Schnabel

Abstract

Leaf wetness duration (LWD) plays a key role in disease development and is often used as an input in disease-warning systems. LWD is often estimated using mathematical models, since measurement by sensors is rarely available and/or reliable. A strawberry disease-warning system called "Strawberry Advisory System" (SAS) is used by growers in Florida, USA, in deciding when to spray their strawberry fields to control anthracnose and Botrytis fruit rot. Currently, SAS is implemented at six locations, where reliable LWD sensors are deployed. A robust LWD model would facilitate SAS expansion from Florida to other regions where reliable LW sensors are not available. The objective of this study was to evaluate the use of mathematical models to estimate LWD and time of spray recommendations in comparison to on site LWD measurements. Specific objectives were to (i) compare model estimated and observed LWD and resulting differences in timing and number of fungicide spray recommendations, (ii) evaluate the effects of weather station sensors precision on LWD models performance, and (iii) compare LWD models performance across four states in the USA. The LWD models evaluated were the classification and regression tree (CART), dew point depression (DPD), number of hours with relative humidity equal or greater than 90 % (NHRH ≥90 %), and Penman-Monteith (P-M). P-M model was expected to have the lowest errors, since it is a physically based and thus portable model. Indeed, the P-M model estimated LWD most accurately (MAE <2 h) at a weather station with high precision sensors but was the least accurate when lower precision sensors of relative humidity and estimated net radiation (based on solar radiation and temperature) were used (MAE = 3.7 h). The CART model was the most robust for estimating LWD and for advising growers on fungicide-spray timing for anthracnose and Botrytis fruit rot control and is therefore the model we recommend for expanding the strawberry disease warning beyond Florida, to other locations where weather stations may be deployed with lower precision sensors, and net radiation observations are not available.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
Unknown 28 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 24%
Student > Ph. D. Student 6 21%
Student > Master 4 14%
Unspecified 3 10%
Professor 2 7%
Other 7 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 45%
Unspecified 8 28%
Engineering 3 10%
Biochemistry, Genetics and Molecular Biology 1 3%
Computer Science 1 3%
Other 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 July 2016.
All research outputs
#1,858,552
of 12,223,436 outputs
Outputs from International Journal of Biometeorology
#240
of 752 outputs
Outputs of similar age
#57,197
of 277,502 outputs
Outputs of similar age from International Journal of Biometeorology
#9
of 30 outputs
Altmetric has tracked 12,223,436 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 752 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.7. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,502 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.