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

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 and Lidia Sas-Paszt
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Yasir Ali: College of Agriculture, Bahauddin Zakariya University Multan, Bahadur Sub-Campus, Layyah 31200, Pakistan
Ahmed Raza: Department of Biology, Institute of Pure and Applied Zoology, University of Okara, Okara 56300, Pakistan
Sidra Iqbal: Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Depalpur Campus, Okara 56300, Pakistan
Azhar Abbas Khan: College of Agriculture, Bahauddin Zakariya University Multan, Bahadur Sub-Campus, Layyah 31200, Pakistan
Hafiz Muhammad Aatif: College of Agriculture, Bahauddin Zakariya University Multan, Bahadur Sub-Campus, Layyah 31200, Pakistan
Zeshan Hassan: College of Agriculture, Bahauddin Zakariya University Multan, Bahadur Sub-Campus, Layyah 31200, Pakistan
Ch. Muhammad Shahid Hanif: College of Agriculture, Bahauddin Zakariya University Multan, Bahadur Sub-Campus, Layyah 31200, Pakistan
Hayssam M. Ali: Botany and Microbiology Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Walid F. A. Mosa: Plant Production Department (Horticulture-Pomology), Faculty of Agriculture, Saba Basha, Alexandria University, Alexandria 21531, Egypt
Iqra Mubeen: Key Lab of Integrated Crop Disease and Pest Management, College of Plant Health and Medicine, Qingdao Agricultural University, Qingdao 266109, China
Lidia Sas-Paszt: The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland

Sustainability, 2022, vol. 14, issue 21, 1-15

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 (R 2 ) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R 2 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.

Keywords: leaf rust; regression model; Triticum astivum L.; yield loss; disease forecast (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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