Regression Modeling Strategies to Predict and Manage Potato Leaf Roll Virus Disease Incidence and Its Vector

Yasir Ali, Ahmed Raza, Hafiz Muhammad Aatif, Muhammad Ijaz, Sami Ul-Allah, Shafeeq ur Rehman, Sabry Y. M. Mahmoud, Eman Saleh Hassan Farrag, Mahmoud A. Amer and Mahmoud Moustafa
Additional contact information
Yasir Ali: College of Agriculture, Bahauddin Zakariya University, Multan, Bahadur Sub-Campus Layyah, Layyah 31200, Pakistan
Ahmed Raza: Department of Plant Pathology, University of Agriculture, Faisalabad, Depalpur Campus, Okara 56300, Pakistan
Hafiz Muhammad Aatif: College of Agriculture, Bahauddin Zakariya University, Multan, Bahadur Sub-Campus Layyah, Layyah 31200, Pakistan
Muhammad Ijaz: College of Agriculture, Bahauddin Zakariya University, Multan, Bahadur Sub-Campus Layyah, Layyah 31200, Pakistan
Sami Ul-Allah: College of Agriculture, Bahauddin Zakariya University, Multan, Bahadur Sub-Campus Layyah, Layyah 31200, Pakistan
Shafeeq ur Rehman: School of Environmental and Civil Engineering, Dongguan University of Technology, Dongguan 523820, China
Sabry Y. M. Mahmoud: Biology Department, College of Sciences, University of Hafr Al-Batin, P.O. Box 1803, Hafr Al-Batin 31991, Saudi Arabia
Eman Saleh Hassan Farrag: Department of Clinical Laboratory Sciences, College of Applied Medical Science, University of Hafr Al-Batin, P.O. Box 1803, Hafr Al-Batin 31991, Saudi Arabia
Mahmoud A. Amer: Plant Protection Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11362, Saudi Arabia
Mahmoud Moustafa: Department of Biology, Faculty of Science, King Khalid University, Abha 61421, Saudi Arabia

Agriculture, 2022, vol. 12, issue 4, 1-13

Abstract: The potato leaf roll virus (PLRV) disease is a serious threat to successful potato production and is mainly controlled by integrated disease management; however, the use of chemicals is excessive and non-judicious, and it could be rationalized using a predictive model based on meteorological variables. The goal of the present investigation was to develop a disease predictive model based on environmental responses viz. minimum and maximum temperature, rainfall and relative humidity. The relationship between epidemiological variables and PLRV disease incidence was determined by correlation analysis, and a stepwise multiple regression was used to develop a model. For this purpose, five years (2010–2015) of data regarding disease incidence and epidemiological variables collected from the Plant Virology Section Ayub Agriculture Research Institute (AARI) Faisalabad were used. The model exhibited 94% variability in disease development. The predictions of the model were evaluated based on two statistical indices, residual (%) and root mean square error (RMSE), which were ≤±20, indicating that the model was able to predict disease development. The model was validated by a two-year (2015–2017) data set of epidemiological variables and disease incidence collected in Faisalabad, Pakistan. The homogeneity of the regression equations of the two models, five years (Y = −47.61 − 0.572x 1 + 0.218x 2 + 3.78x 3 + 1.073x 4 ) and two years (Y = −28.93 − 0.148x 1 + 0.510x 2 + 0.83x 3 + 0.569x 4 ), demonstrated that they validated each other. Scatter plots indicated that minimum temperature (5–18.5 °C), maximum temperature (19.1–34.4 °C), rainfall (3–5 mm) and relative humidity (35–85%) contributed significantly to disease development. The foliar application of salicylic acid alone and in combination with other treatments significantly reduced the PLRV disease incidence and its vector population over control. The salicylic acid together with acetamiprid proved the most effective treatment against PLRV disease incidence and its vector M. persicae .

Keywords: regression model; epidemiological variable; M. persicae; PLRV; management (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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