Comparative study of forecasting models for stripe rust of wheat
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Date
2021-03
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Sher-e-Kashmir University of Agricultural Sciences and Technology Jammu, J&K
Abstract
The investigations were conducted to predict the spatial and temporal changes in the latent period and number of generations of Puccinia striiformis f. sp. tritici across different prefectures, under four representative concentration pathway emissions scenarios (RCPs) viz., 2.6, 4.5, 6.0 and 8.5 in the three future periods (2020, 2050 and 2080) by using Growing Degree Days (GDD) approach. Daily maximum and minimum temperatures for 2020, 2050 and 2080 were generated from MarkSim® DSSAT weather file generator, whereas, baseline data for the year 1975 of the selected locations were downloaded from Indian Meteorological Department (IMD), Govt. of India. Under the influence of climate change, model outputs exhibited an increase in maximum and minimum temperatures at Jammu, Hisar, Ludhiana, Dhaulakuan and Meerut except for Leh, where maximum and minimum temperatures decreased by ±5.60°C and ±5.85°C, respectively. Maximum reduction of 110, 49, 36, 35 and 40 per cent was observed in the duration of latent period (days) of Puccinia striiformis f. sp. tritici in Jammu, Hisar, Ludhiana, Dhaulakuan and Meerut, respectively, during 2080 under RCP 8.5 scenario. However, 25 per cent increase was observed in the latent period in Leh. Maximum increase in the number of infection cycles with 49, 27, 20, 21, 24 and 20 per cent were recorded by RCP 6.0 scenario in Jammu, Hisar, Ludhiana, Dhaulakuan, Meerut and Leh, respectively, in three future time periods over the baseline period.
Relationship of meteorological parameters with the onset and progress of stripe rust of wheat was investigated to develop forewarning models by the time series and multiple linear regression. ARIMA (2,1,1) (1,1,1)7 with minimum temperature (oC) and rainfall (mm) with lag 1, adjusted best having maximum accuracy of 96.00 per cent in predicting stripe rust of wheat for short-term period. Severity of stripe rust had highly significant positive correlation (0.89 and 0.91; 0.91 and 0.75) with the maximum and minimum temperatures, whereas, morning relative humidity had significantly negative correlation (-0.84 and -0.80) in 2005-17 and 2017-2019, respectively. Rainfall had non-significant correction with the disease during 2005-17 and 2017-19, respectively. Model viz., Y= -502.1392+ 0.6373X1+ 8.5741X2+ 3.0402X3+ 1.4227X4+ 0.5764X5 and Y= 322.5683+ 9.4103X1-4.1446X2-2.5589X3-0.7089+ 0.2609X5 were developed by multiple regression for 2005-17 and 2017-2019, and were highly significant in the prediction of stripe rust of wheat. Both the models exhibited that 91 and 89 per cent variation in the disease severity was influenced by the maximum and minimum temperatures, maximum and minimum relative humidity and rainfall. Eighty four per cent increase in spore concentration of Puccinia striiformis f. sp. tritici was recorded from 51st to 7th SMW (Standard Meteorological Week) during 2017-2019
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Preferred for your work. Khushboo, S. S. 2021. Comparative Study of Forecasting Models for Stripe Rust of Wheat. Ph. D. Thesis. Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, India.