Assessment of Weather Parameters on Per Cent Disease Incidence and Forewarning Models of Fusarium Wilt in Pigeonpea as Influenced by Different Sowing Windows

Pigeonpea [Cajanus cajan (L.) Millspaugh] is one of the major pulse crops of the tropics and subtropics. Pigeonpea is grown in an area of 4.43 m ha with a production of 4.25 m tons the productivity of 960 kg ha^-1 in India (Anonymous, 2019). Pigeonpea is grown throughout the country, except the hilly regions where the winter temperature is very low. In India, Maharashtra, Andhra Pradesh and Gujarat are the major pigeonpea growing states. 
       
The production and productivity of this crop has remained stagnant over the past three decades due to its vulnerability to biotic and abiotic stresses. Major cause of low productivity is the losses due to diseases. Among the diseases, wilt and sterility mosaic are important. Recent surveys have indicated that major losses in the pigeonpea are due to wilt disease which is caused by Fusarium udum Butler var. cajani. Losses ranging between 0.2 to 100 % have been estimated from India (Butler, 1906, Gade, 2002 and Suresh, 2013).
      
Wilt caused by F. udum is a devastating disease of pigeonpea gaining importance day by day due to increasing drought conditions in the country. This disease can occur at any stage of plant development, from young seedling to the pod-filling stage (Choudhary, 2010). Though the disease goes unnoticed in early stages and symptoms are yellowing followed by drying of leaves and finally death of few branches or entire plant. The chemical control of this disease is not only expensive but also ineffective owing to the seed as well as soil-borne nature of the pathogen.
       
Keeping these facts in view, the correlation between weather parameters and PDI of Fusarium wilt per net plot on different pigeonpea varieties at different sowing windows and development of forewarning models of Fusarium wilt per net plot was studied during 2017-18 and 2018-19.

The experiment was laid out in split plot design with three replications at Department of Agricultural Meteorology farm, College of Agriculture, Pune during 2017-18 and 2018-19. The treatments comprised of four varieties viz., Vipula, Rajeshwari, BDN 711 and ICPH 2740 as main plot and four sowing windows viz., 24^th MW (11^th to 17^th June), 26^th MW (25^th June to 01^st July), 28th MW (9^th to 15^th July) and 30^th MW (23^rd to 29^th July) as sub plot treatments. The geographical location of the site (Pune) was 18°32¢N, latitude; 73°51E, longitude and 559 m above mean sea level (MSL) situated in the sub-tropical region (Plain Zone). The soil is medium black having depth of about 1 m. The average annual rainfall of Pune is 675 mm. Out of total rainfall, about 75 per cent is received during south-west monsoon, while remaining is received from north-east monsoon. In the month of July and August, maximum temperature ranged from 26 to 30°C. The minimum temperature varied from 6 to 10oC during winter from November to middle of February. The humidity during monsoon i.e., from June to September is quite high during morning (about 85 to 93%) and the evening humidity is generally ranged between 43 to 83 per cent. Urea and Di-ammonium phosphate were used as sources of Nitrogen (N) and Phosphorus (P), respectively and applied as per recommendation i.e., 25 kg N and 50 kg P. The seeds were treated with Thiram@ 4 g per kg of seed followed by Rhizobium and phosphate solubilizing bacteria @ 10 g per kg of seed.
       
The PDI was correlated with the weather parameters viz., maximum temperature (T_max), minimum temperature (T_min), rainfall (mm), morning relative humidity (RH I), evening relative humidity (RH II), bright sunshine hours (BSS), wind speed (WS)  and evapotranspiration (EP) using standard statistical procedure as suggested by Gomez and Gomez (1984). The multiple linear regression analysis was also worked out between PDI and weather parameters using prediction equation,
 

 
Where,
Y= Per cent disease incidence, ‘a’ as constant and ‘b’ as regression coefficients of independent variable ‘X’.

The data regarding correlation between weather parameters and incidence of Fusarium wilt on different pigeonpea varieties at different sowing windows are given in Table 1 and 2 and forewarning models for prediction of disease are given in Table 3. The overall linear multiple regression analysis was worked out between PDI of W0 week with weather parameters of two week prior (W-2) for all different treatment combinations.
Correlation between weather parameters and PDI of Fusarium wilt in different pigeonpea varieties at different sowing windows and forewarning models for prediction of incidence of Fusarium wilt are given below:






 
Vipula
 
During first sowing window (24^th MW), PDI for two week prior (W-2) was significantly positively correlated with minimum temperature (0.622* and 0.553) and evening relative humidity (0.589* and 0.651*) whereas, it was negatively correlated with maximum temperature (-0.237 and -0.609) and bright sunshine hours (-0.445 and -0.592) during kharif seasons of 2017-18 and 2018-19, respectively.
       
The prediction equation for Vipula and 24^th MW indicated that an increase of one unit of minimum temperature, morning relative humidity and evening relative humidity increased the Fusarium wilt by 1.967, 1.576 and 0.297 units, respectively. These weather parameters collectively increased the Fusarium wilt to an extent of 82.8% (R²=0.828).
       
During second and third sowing window (26^th, 28^th MW), PDI for two week prior (W-2) was non- significant positively and negatively correlated with weather parameters during the period of study.
       
During fourth sowing window (30^th MW), PDI for two week prior (W-2) was significantly positively correlated with maximum temperature (0.839** and 0.664), morning relative humidity (0.521 and 0.097) and bright sunshine hours (0.406 and 0.526), whereas, it was negatively correlated with evening relative humidity (-0.281 and -0.464) during kharif seasons of 2017-18 and 2018-19, respectively.
       
The prediction equation for Vipula and 30^th MW indicted that an increase of one unit of maximum temperature, minimum temperature, morning relative humidity, evening relative humidity, rainfall and bright sunshine increased the Fusarium wilt by 1.742, 1.558, 0.810, 0.092, 0.014 and 1.856 units, respectively. These weather parameters collectively increased the Fusarium wilt to an extent of 95.5% (R²=0.955).
 
Rajeshwari
 
During first, second and third sowing window (24^th, 26^th and 28^th MW), PDI for two week prior (W-2) were non-significant positively and negatively correlated with weather parameters during the period of study.  
       
During fourth sowing window (30^th MW), PDI for two week prior (W-2) was significantly positively correlated with maximum temperature (0.751** and 0.762), morning relative humidity (0.517 and 0.028) and bright sunshine hours (0.354 and 0.623*), whereas, it was negative correlated with evening relative humidity (-0.208 and -0.576) during kharif seasons of 2017-18 and 2018-19, respectively.
       
The prediction equation for Rajeshwari and 30^th MW indicated that an increase of one unit of maximum temperature, minimum temperature, morning relative humidity, evening relative humidity, rainfall and bright sunshine increased the Fusarium wilt by 2.090, 0.952, 0.543, 0.164, 0.022 and 1.648 units, respectively. These weather parameters collectively increased the Fusarium wilt to an extent of 92.1% (R²=0.921). 
 
BDN 711
 
During first, second and third sowing window (24^th, 26^th and 28^th MW), PDI for two week prior (W-2) were non-significantly positively and negatively correlated with weather parameters during the period of study.
       
During fourth sowing window (30^th MW), PDI for two week prior (W-2) was positively correlated with maximum temperature (0.790** and 0.635*) and bright sunshine hours (0.398 and 0.469), whereas, it was negative correlated with evening relative humidity (-0.276 and -0.423) and wind speed (-0.720* and-0.545) during kharif seasons of 2017-18 and 2018-19, respectively.
       
The prediction equation for BDN 711 and 30^th MW indicated that an increase of one unit of maximum temperature, minimum temperature, morning relative humidity, evening relative humidity, rainfall and bright sunshine hours increased the Fusarium wilt by 2.332, 1.172, 0.834, 0.174, 0.018 and 1.548 units, respectively. These weather parameters collectively increased the Fusarium wilt to an extent of 93.6% (R²=0.936).
 
ICPH 2740
 
During first sowing window (24^th MW), PDI for two week prior (W-2) was non-significantly positively and negatively correlated with weather parameters during the period of study.
       
During second sowing window (26^th MW), PDI for two week prior (W-2) was significantly positively correlated with maximum temperature (0.586* and 0.589*) and morning relative humidity (0.487 and 0.096), whereas, it was negative correlated with evening relative humidity (-0.107 and -0.441), during kharif seasons of 2017-18 and 2018-19, respectively.

The prediction equation for ICPH 2740 and 26^th MW indicated that an increase of one unit of maximum temperature, minimum temperature, morning relative humidity, evening relative humidity and bright sunshine increased the Fusarium wilt by 1.454, 1.476, 1.294, 0.366 and 2.865 units, respectively. These weather parameters collectively increased the Fusarium wilt to an extent of 86.7% (R²=0.867).
       
During third sowing window (28^th MW), PDI for two week prior (W-2) was significantly positively correlated with maximum temperature (0.766** and 0.682*) and morning relative humidity (0.608* and 0.176), whereas, it was negative correlated with evening relative humidity (-0.325 and -0.485) during kharif seasons of 2017-18 and 2018-19, respectively.
       
The prediction equation for ICPH 2740 and 28^th MW indicated that an increase of one unit of maximum temperature, minimum temperature, morning relative humidity, evening relative humidity and bright sunshine increased the Fusarium wilt by 1.697, 1.583, 1.181, 0.214 and 2.477 units, respectively. These weather parameters collectively increased the Fusarium wilt to an extent of 93.1% (R²=0.931).
       
During fourth sowing window (30^th MW), PDI for two week prior (W-2) was significantly positively correlated with maximum temperature (0.871** and 0.919**), morning relative humidity (0.727* and 0.056), evaporation (0.054 and 0.839**) and bright sunshine hours (0.695 and 0.843**), whereas, it was significantly negative correlated with evening relative humidity (-0.618* and -0.835**) and wind speed  (-0.813** and -0.847**) during kharif seasons of 2017-18 and 2018-19, respectively.
       
The prediction equation for ICPH 2740 and 28^th MW indicated that an increase of one unit of maximum temperature, minimum temperature, morning relative humidity, rainfall and bright sunshine increased the Fusarium wilt by 0.099, 1.279, 0.305, 0.044 and 1.219 units, respectively. These weather parameters collectively increased the Fusarium wilt to an extent of 95.5% (R²=0.955). F. wilt was highly correlated with weather parameters and the above all prediction equations were corroborated with Puran et al., (2017).
       
Chhetry and Devi (2014) found that F. wilt progress is slow during the early phases of growth but accelerates during the flowering and podding stage. The rate of infection was the highest in flowering and podding stage. Chaudhary et al., 2000 also showed that flowering stage of the crop has no association with wilting but temperature and moisture and resistance level of the pigeonpea genotype together determine the course of wilt development.
       
The weather parameters viz., minimum temperature and rainfall besides rainy day played a significant role in the development of F. wilt incidence on pigeonpea crop (Patel et al., 2011). In fact, development of perithecial stage of F. udum is favoured by cloudy weather, high humidity and a combination of low and high temperature. Further, F. wilt was favoured by soil water holding capacity and soil temperatures.
       
Similar results were reported by Chandra et al., (2017) concluded that mean and maximum incidence of Fusarium wilt disease found to be significant positively correlated with maximum and minimum temperatures (°C) while morning and evening and evening RH (%), rainfall (mm) and rainy day were negatively correlated with the corresponding incidence. These results were confirmed with Usha and Dubey, 2010 and they studied that early sowing minimizes wilt incidence. Maximum and minimum ambient temperature and soil temperature were positively and significantly correlated with wilt incidence.

The correlation of weather parameters with PDI of Fusarium wilt indicated that significant positively correlation with maximum and minimum temperature. The prediction of PDI of F. wilt with multiple linear regression equations were recorded with good R² values in all treatment combinations. It could be used for forewarning models of F. wilt under Pune variable climatic conditions.

None.