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Stepwise Regression Models-Based Prediction for Leaf Rust Severity and Yield Loss in Wheat

Authors :
Yasir Ali
Ahmed Raza
Sidra Iqbal
Azhar Abbas Khan
Hafiz Muhammad Aatif
Zeshan Hassan
Ch. Muhammad Shahid Hanif
Hayssam M. Ali
Walid F. A. Mosa
Iqra Mubeen
Lidia Sas-Paszt
Source :
Sustainability; Volume 14; Issue 21; Pages: 13893
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Leaf rust is a devastating disease in wheat crop. The disease forecasting models can facilitate the economic and effective use of fungicides and assist in limiting crop yield losses. In this study, six wheat cultivars were screened against leaf rust at two locations, during three consecutive growing seasons. Subsequently, the stepwise regression analysis was employed to analyze the correlation of six epidemiological variables (minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity, rainfall and wind speed) with disease severity and yield loss (%). Disease predictive models were developed for each cultivar for final leaf rust severity and yield loss prediction. Principally, all epidemiological variables indicated a positive association with leaf rust severity and yield loss (%) except minimum relative humidity. The effectiveness of disease predictive models was estimated using coefficient of determination (R2) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R2 values of all disease predictive models for each of the tested cultivars were high, evincing that our regression models could be effectively employed to predict leaf rust disease severity and anticipated yield loss. The validation results explained 99% variability, suggesting a highly accurate prediction of the two variables (leaf rust severity and yield loss). The models developed in this research can be used by wheat farmers to forecast disease epidemics and to make disease management decisions accordingly.

Details

ISSN :
20711050
Volume :
14
Database :
OpenAIRE
Journal :
Sustainability
Accession number :
edsair.doi.dedup.....776efacfb2612f055dbab672d1a719c0
Full Text :
https://doi.org/10.3390/su142113893