Abstract
Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides . Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. Of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21·9% for the Australian and 22·1% for the South American model. Of the six cross‐continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture‐related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.
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Claims 9
Claim · model-written summary
Models using assessment-day and preceding 24-hour weather had the highest prediction success.
- Taxon
- Stylosanthes scabra
Stylosanthes scabrae1 - Outcome
- prediction successe0
- Direction
- higheste0
- Comparator
- higheste0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
Cross-continent predictions from all-site continental models succeeded on more than 75% of days.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Outcome
- correctly predicted disease severitye0
- Value
- more than 75% of dayse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
The Australian model had 21·9% overall prediction error.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Outcome
- overall prediction errore0
- Value
- 21·9%e0
Not reportedMaterial
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the overall prediction error was 21·9% for the Australian and 22·1% for the South American model”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
The South American model had 22·1% overall prediction error.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Outcome
- overall prediction errore0
- Value
- 22·1%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the overall prediction error was 21·9% for the Australian and 22·1% for the South American model”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
The model excluding Planaltina was the most accurate, exceeding 85% prediction success.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Outcome
- prediction successe0
- Direction
- most accuratee0
- Value
- >85%e0
- Setting
- Planaltina in Brazile0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
The model excluding Carimagua was least accurate, with 54% success.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Outcome
- successe0
- Direction
- least accuratee0
- Value
- 54%e0
- Setting
- Carimagua in Colombiae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the model without Carimagua in Colombia was the least accurate, with only 54% success”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
Moisture-related weather variables were most important in all ANN models.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Outcome
- most important weather variablese0
- Comparator
- all ANN modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“moisture‐related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
High and low anthracnose risk sites occurred on both continents.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Interactor
- Colletotrichum gloeosporioides
Colletotrichum gloeosporioidese1pathogen - Outcome
- high and low anthracnose riske0
- Setting
- both continentse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Sites with high and low anthracnose risk are present in both continents”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”
Claim · model-written summary
Weather at Brazilian and Colombian diversity centres appeared no more conducive to serious anthracnose than Australian conditions.
- Taxon
- Stylosanthes scabraStylosanthes scabrae1
- Interactor
- Colletotrichum gloeosporioidesColletotrichum gloeosporioidese1pathogen
- Outcome
- serious anthracnose developmente0
- Direction
- do not appear to be more conducivee0
- Comparator
- conditions in Australiae0
- Setting
- Brazil and Colombiae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development”
e1“Stylosanthes scabra caused by Colletotrichum gloeosporioides”