Modelling of the effects of environmental factors on rice grain discoloration incidence in Corrientes province, Argentina

Modelling of the effects of environmental factors on rice grain discoloration incidence in Corrientes province, Argentina

Rice grain discoloration (RGD) is a disease of complex aetiology for which there are no resistant varieties. Due to the need to better define the environmental conditions that favour the disease, the aims of this work were to (i) identify the predominant fungi associated, (ii) determine the meteorol...

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Bibliographic Details
Published in:Journal of phytopathology Vol. 171; no. 1; pp. 12 - 22
Main Authors: Dirchwolf, Pamela M., Moschini, Ricardo C., Gutiérrez, Susana A., Carmona, Marcelo A.
Format: Journal Article
Language:English
Published: Berlin Wiley Subscription Services, Inc 01-01-2023
Subjects:
Air temperature
Alternaria padwickii
Cereal crops
Discoloration
Environmental conditions
Environmental effects
Environmental factors
Flowering
logistic regression models
Logit models
Microdochium albescens
Rice
Weather
weather variables
Argentina
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Summary:Rice grain discoloration (RGD) is a disease of complex aetiology for which there are no resistant varieties. Due to the need to better define the environmental conditions that favour the disease, the aims of this work were to (i) identify the predominant fungi associated, (ii) determine the meteorological variables most closely related, and (iii) develop preliminary weather‐based models to predict binary levels of RGD incidence. After analysing 123 rice grain samples under natural infection conditions from rice‐cropping regions throughout Corrientes province, Argentina, we found that RGD was mainly associated with Alternaria padwickii (14.2%) and Microdochium albescens (13.7%). The strongest associations between weather variables and RGD incidence were observed in a susceptible critical period (Scp) that extended from the rice flowering stage until 870 accumulated degree days (Scp lasting 32 days, ±7 days). The binary response logistic model including the weather variables DPrecT (which combined the effect of the simultaneous daily occurrence of precipitation lower than 12 mm and air temperature between 13 and 28°C), and DDMnT (sum of the exceeding amounts of daily min temperature from 23°C), was the most appropriate, showing prediction accuracy (PA) values of 84.6%. The univariate model that included DPrecT presented a PA of 82.1%. The logistic regression techniques here used to develop weather‐based models to estimate the probabilities of occurrence of binary levels of RGD can not only help to clarify and quantify the environmental effect on the development of RGD but also be useful tools to be included in future management strategies.
ISSN:0931-1785
1439-0434
DOI:10.1111/jph.13150