Predicting Stripe Rust Severity in Wheat Using Meteorological Data with Environmental Response Modeling

Abstract

Objective: The main objective of current investigation was to develop a predictive disease model based upon meteorological data, viz., maximum temperature, minimum temperature, rainfall, relative humidity, and wind speed to predict stripe rust severity (%). Methods: Five years' data of stripe rust severity on three wheat varieties, namely SA-42, Sandal-73, and Barani-70, continuously cultivated for five years (2013–2017), were collected from experimental trials of Deputy Director of Agriculture Extension Layyah to develop a predictive disease model. For validation of the model, a research trial was conducted in the Research Area of the Department of Plant Pathology, Bahadar Sub-Campus Layyah, during the crop seasons of 2018–2019, following procedures similar to those utilized in five years investigation. The data on epidemiological variables used in the present investigation was collected from the Pakistan Meteorological Observatory at Karor-Layyah. To evaluate the association between meteorological factors and disease severity correlation and regression analysis was performed. Results: All meteorological variables contributed significantly in disease development and showed 89 % variability in stripe rust severity (%). Root means square error (RMSE) and residual (%) were used to evaluate the model's predictions. Both indices were below 20, showing that the model could accurately predict the progression of disease. The regression equations of 5 years model (Y = -63.11 + 0.96x1 + 1.72x2 + 3.72x3 + 0.43x4) and 2 years model (Y = -40.2 + 1.80x1 + 1.18x2 + 2.29x3 + 0.39x4) validated each other. Scatter plots indicated that environmental factors such as maximum temperature (12.8–22.5 °C), minimum temperature (8.7–14.8 °C), relative humidity (50–85 %), and wind speed (1.3–4.5) influenced the progression of stripe rust epidemic. Conclusion: Understanding the epidemiology of stripe rust will help us to forecast its progression, allowing wheat growers to more precisely adapt plant protection measures.

Keywords

• Epidemiology
• Regression model
• Stripe rut
• Wheat