Abstract
Fusarium Head Blight (FHB) is a disease of great concern in wheat (Triticum aestivum). Due to its relatively narrow susceptible phase and environmental dependence, the pathosystem is suitable for modeling. In the present work, a mechanistic model for estimating an infection index of FHB was developed. The model is process-based driven by rates, rules and coefficients for estimating the dynamics of flowering, airborne inoculum density and infection frequency. The latter is a function of temperature during an infection event (IE), which is defined based on a combination of daily records of precipitation and mean relative humidity. The daily infection index is the product of the daily proportion of susceptible tissue available, infection frequency and spore cloud density. The model was evaluated with an independent dataset of epidemics recorded in experimental plots (five years and three planting dates) at Passo Fundo, Brazil. Four models that use different factors were tested, and results showed all were able to explain variation for disease incidence and severity. A model that uses a correction factor for extending host susceptibility and daily spore cloud density to account for post-flowering infections was the most accurate explaining 93% of the variation in disease severity and 69% of disease incidence according to regression analysis.
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Claims 3
Claim · model-written summary
All four models explained variation in Fusarium head blight incidence and severity in wheat.
- Taxon
- wheat
Triticum aestivumt0 - Outcome
- disease incidence and severitye0
- Comparator
- Four modelse0
FusariumNot reportedSettingDirectionValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“fusarium head blight of wheat”
e0“Four models that use different factors were tested, and results showed all were able to explain variation for disease incidence and severity.”
Claim · model-written summary
The model accounting for extended host susceptibility and post-flowering spore density was most accurate for disease severity, explaining 93% of its variation.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- disease severitye0
- Direction
- most accuratee0
- Value
- 93%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“fusarium head blight of wheat”
e0“A model that uses a correction factor for extending host susceptibility and daily spore cloud density to account for post-flowering infections was the most accurate explaining 93% of the variation in disease severity and 69% of disease incidence according to regression analysis.”
Claim · model-written summary
The model accounting for extended host susceptibility and post-flowering spore density was most accurate for disease incidence, explaining 69% of its variation.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- disease incidencee0
- Direction
- most accuratee0
- Value
- 69%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“fusarium head blight of wheat”
e0“A model that uses a correction factor for extending host susceptibility and daily spore cloud density to account for post-flowering infections was the most accurate explaining 93% of the variation in disease severity and 69% of disease incidence according to regression analysis.”