Potato late blight disease prediction using meteorological parameters in Northern Himalayas of India

and ABSTRACT Weather parameters play an important role in the spread of potato late blight of caused by Phytophthora infestans (Mont.) de Bary has historically been serious disease of potatoes through worldwide, including India. Due to spatial variation in prevailing weather conditions, its severity varies from region to region. Disease development process and the weather parameters are well understood and have been utilized for disease developing forecasting models and decision support system. Therefore, an experiment was conducted for two consecutive cropping seasons (2017 & 2018) to develop a forecasting model against late blight of potato using stepwise regression analysis for Northern Himalayas in India. Maximum and minimum temperature, relative humidity, rainfall and wind speed appeared to be most significant factors in the potato late blight disease development. The meteorological conditions conducive for the development of potato late blight disease were characterized. Maximum and minimum temperatures in the range of 15.0 – 28.0°C and 2.0 – 12.0°C were found favorable for potato blight disease. Similarly, relative humidity, rainfall and wind speed in the range of 85 - 95 per cent, 15.5 - 20.75 mm and 1.0 - 5.5 Km h -1 , respectively, were conducive for potato late blight disease which are helpful in disease development.

Late blight of potato is caused by Phytophthora infestans (Mont.) de Bary, is a devastating disease of major concern all over the world which causes heavy losses of potato. Phytophthora infestans is a water mold lower fungus, and infects potato crop via seed and soil during cool and wet weather. Infection of shoots can be caused by mycelium growing from the tuber into the developing shoot or via sporangia and zoospores formed on the tuber surface under moist conditions. Phytophthora infestans has a worldwide distribution and is found wherever potatoes are grown (Goss et al., 2014). The severity of a potato late blight disease outbreak is strongly influenced by prevailing environmental conditions on the pathogen lifecycle and its aggressiveness within the local pathogenic population (Doster, 1989). Economic importance of this disease in different regions resulted that the disease takes the highest toll of potato in Sub-Saharan Africa (44% crop loss) followed by South-East Asia (35% crop loss).
Potato late blight management has always been a challenge for both scientists and farming community. For effective management of potato late blight, efforts should be made to slow down the disease progress particularly reduction in the initial inoculum. There is a need to develop forecast/warning services for predicting the time of appearance of disease and optimizing use of fungicides without risking the crop and human health. These models predict outbreak or changes in intensity of one or more diseases on the basis of weather information, crop, pathogens (Shtienberg, 2010). In India, Singh et al., (2000) developed location specific computerized forecast model (JHULCAST) for Western U.P. using hourly temperature ( 0 C), relative humidity (%) and daily precipitation (mm) as input parameters. Since then the model has been calibrated for predicting late blight appearance in Punjab (Arora et al., 2012) and Tarai region of Uttarakhand (Pundhir et al., 2014). Therefore, the present investigations has been undertaken to study the correlation between potato late blight disease and environmental variables and to develop disease prediction model through multiple regression for timely management of disease. Present examinations will help to forecast the late blight of potato in Northern Himalaya in India and assist the growers for spray schedule and reduces the costs involved by eliminating the unnecessary sprays and ABSTRACT Weather parameters play an important role in the spread of potato late blight of caused by Phytophthora infestans (Mont.) de Bary has historically been serious disease of potatoes through worldwide, including India. Due to spatial variation in prevailing weather conditions, its severity varies from region to region. Disease development process and the weather parameters are well understood and have been utilized for disease developing forecasting models and decision support system. Therefore, an experiment was conducted for two consecutive cropping seasons (2017 & 2018) to develop a forecasting model against late blight of potato using stepwise regression analysis for Northern Himalayas in India. Maximum and minimum temperature, relative humidity, rainfall and wind speed appeared to be most significant factors in the potato late blight disease development. The meteorological conditions conducive for the development of potato late blight disease were characterized. Maximum and minimum temperatures in the range of 15.0 -28.0°C and 2.0 -12.0°C were found favorable for potato blight disease. Similarly, relative humidity, rainfall and wind speed in the range of 85 -95 per cent, 15.5 -20.75 mm and 1.0 -5.5 Km h -1 , respectively, were conducive for potato late blight disease which are helpful in disease development. Vol. 23,No. 3 labour cost. The disease severity data was recorded at 7 days interval from initiation of symptoms with the help of four -level scale based on area under the disease progress curve (Jenkins and Jones, 2003). The area under disease progress curve (AUDPC) was calculated for each variety/ plot from the severity data using mid-point method (Campbell and Madden, 1990 Wilcoxson et al. (1975).

The
Where, Si = Amount of disease at i th time, i ranges from 1 to n; S i-1 = Amount of disease at (i-1) th time. ; t 2 -t 1 = Number of days between two observations.; n = Number of successive evaluation of disease The data recorded was analyzed using SPSS v.17 (statistical software). Correlation between environment parameters (maximum temperature, minimum temperature, rainfall, relative humidity and wind speed) and disease severity were determined by correlation analysis. Disease predicting model for potato late blight based on two years environmental variables was developed using step wise regression analysis. Before subjecting the data to regression analysis, to help standardize AUDPC values across years, the metric was converted into the relative AUDPC (rAUDPC) as described (Fry et al., 1983). Then the regression models were applied for analyzing the effects of weather parameters on disease progress. The prediction equation and stepwise multiple regression analysis was done by using the following: Where, Ŷ = predicted severity; a = intercept; bi = regression coefficient for xi (i = 1 to … n); xi = independent variables (i = 1… n); e = random error.
Multiple regression models was evaluated by the co-efficient of determination (R 2 ) and correlation coefficient (r), regression coefficient, test for significance of regression co-efficient, standard error and residual sum of squares (RSS).

Weather parameters and their influence on severity on late blight
Potato tubers were planted in the first week of June in 2017 and 2018. The crop was grown and maintained according to the standard organic agronomic management practices and was observed regularly for initiation of late blight symptoms. Disease initiated at pre-flowering stage (35 days after planting) during the 2 nd week of July in both the cropping seasons (2017 and 2018). Disease progresses with the passage of time. Weather parameters for the two consecutive years (2017 and 2018) were collected for correlation and prediction model. Minimum temperature 130C and 15.5 along with minimum relative humidity 75% and 78% was recorded in the early stages of infection process and maximum temperature 28 and 30.6 along with There was not wide range in resistance reactions among varieties tested with having rAUDPC values less than 0.2 in resistant variety and between 0.6 -0.8 in most of the other varieties. There was a good distribution between high and low values, with several having intermediate values. Yearly average of rAUDPC values indicated that the varieties had similar relative resistance levels in both the years but had slightly higher rAUDPC values in the first year due to more rainy events. The findings were in accordance with the results reported by Das et al., 2011 that variation in environmental variables affected the potato blight disease. Cool weather (12-15°C) and high humidity (>90%) with heavy dewfall or rains alternating with warm winds favor the rapid development of fungal foliar disease of potato (Lal et al., 2016). The timing and duration of each event is very important and the relationship between the disease development and meteorological factors were the main components of disease forecasting system. A significant correlation of potato late blight severity with maximum temperature, minimum temperature, rainfall and relative humidity was found with all the tested varieties during two cropping seasons of 2017 and 2018 (Table 1). Multiple regression model developed on the basis of coefficient of determination  Y=6.5 +0.45x 1 +0.02x 2 +0.05x 3 +0.13x 4 +0.38X 5 ; R 2 =0.82 Where Y=Potato late blight disease severity; x 1 = maximum temperature; x 2 = minimum temperature; x 3 = rainfall, x 4 =relative humidity and x 5 = wind speed.
It is evident from the model that major factors responsible for the occurrence of potato late blight disease were maximum temperature, minimum temperature, rainfall and wind speed prevalent at that time. It indicated that with one unit change in maximum temperature, minimum temperature, rainfall, relative humidity and wind speed there would be probable change of 0.45, 0.02, 0.05, 0.13 and 0.38 units in potato late blight disease severity, respectively. The participation of wind speed would be so significant in disease prediction (Table 2).
Forecasts in certain regions are developed in response to specific cultural procedure adopted, prevailing environment of that region and to grower's responses in that region. It is well established fact of forecast that work well in some locations, may not work in other locations (Singh et al., 2000). Thus, the region specific models with significant variables were developed by stepwise regression on four potato varieties separately to predict potato late blight disease severity during two years. Out of five variables used, three of them viz., maximum temperature, rainfall and relative humidity exerted significant influence in the development of disease actively while minimum temperature and wind speed appeared as the main contributing meteorological variables in the stepwise regression analysis. When these three meteorological variable models were used to predict potato late blight disease severity, there was a fairly good R 2 value, low C (p) value and low RMSE value obtained (Table 3). During two years of period, all the four varieties performed similarly with small variation in the meteorological conditions. As the maximum and  minimum temperature increased from 15°C to 28°C and 2°C to 12°C, respectively, the disease severity also increased. Maximum and minimum temperatures in the range of 15-28°C and 2-12°C, respectively were found favourable for potato blight disease. Similarly, relative humidity, rainfall and wind speed in the range of 85-95 per cent, 15.5-20.75 mm and 1-5.5 Kmh -1 , respectively were conducive for potato late blight disease. Initially significant correlation was observed between relative humidity and disease development. Maximum disease severity was observed at 90 per cent relative humidity in Gurez local variety. Weather conditions favorable for disease development were characterized during two years (2017 and 2018). Maximum and minimum temperatures in the range of 15-28°C and 10.0-12.5°C were found favorable for blight disease.

CONCLUSION
The findings of the present study reveal that three weather parameters i.e. temperature (maximum and minimum), relative humidity (morning and evening) and rainfall significantly influences the late blight of potato in Northern Himalayas. High disease severity of potato late blight was recorded during peak periods of humidity (August and September). Increase in temperature along with relative humidity (90%) confers more congenial factors for disease progress. Therefore, formulated disease prediction model may predict the severity of potato late blight for Northern Himalayas particularly for high altitude. Present data and their analysis may further guide the researchers to devise and develop management strategies for late blight of potato in the hilly temperate region.