IMPACT OF WEATHER PARAMETERS ON SHOOT FLY (ATHERIGONA SOCCATA.RONDANI) OF SORGHUM IN KHARIF SEASON

NSave Nature to Survive QUARTERLY 9(1&): 99-104, 015 IMPACT OF WEATHER PARAMETERS ON SHOOT FLY (ATHERIGONA SOCCATA.RONDANI) OF SORGHUM IN KHARIF SEASON S. T. PAVANA KUMAR* 1, A. B. SRINATH REDDY 1, MINAKSHI MISHRA, ADAM KAMEI, DEBASIS MAZUMDAR 3 AND SHEKHARAPPA 4 1 Agriculture Research Station, Anantpur, Andhra pradesh - 515 001 Department of Plant Pathology, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal - 741 5 3 Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya, Mohanppur-7415, West Bengal 4 M.A.R.S, U.A.S, Dharwad-580005 e-mail: pvnkmr65@gmail.com ABSTRACT INTRODUCTION Sorghum [Sorghum bicolor (L.) Moench] is one of the most important food and fodder crop in the world because of its adaptation to a wide range of ecological conditions, suitability for low input cultivation and diverse uses. It is the fifth major cereal crop of world following wheat, rice, maize and barley in terms of production and utilization. It is cultivated in many parts of Asia and Africa, where its grains are used to make flat breads that form the staple food of people of diverse cultures. The grains can also be popped in a similar fashion to popcorn. More than 150 insect species have been reported as pests on this crop. Among different insect pests, the shoot fly, Atherigona soccata Rondani is a serious pest particularly in late sown crop and its incidence was moderate to high (30-50%) at Dharwad, Parbhani, Akola, Indore, Surat and Udaipur areas (Anonymous, 010). Considering seriousness of this pest, an attempt has been made to study the weather factors associated with incidence of the shoot fly to predict its occurrence and to develop precise management practices against it. A model is a concise way of representing any system of their reality in a symbolic and simplified form (Campbell et al., 1998). Here is a complex system represented after summarizing it in a simple form, depicting the salient features of the system. The empirical linear regression model, which are based on observed values are used commonly to represent the relationship between the pests and the environment. Due to variation in the agro climatic conditions of different regions, insects show varying trends in their incidence pattern and extent of damage to the crop. Besides, different weather factors also play a key role in determining the incidence and dominance of a particular pest or pest complex (Meena et al., 013). Available scientific literature shows that not much information is available especially on population dynamics and influence of various environmental factors on the fluctuation of shoot fly for dharwad region. Hence a region oriented study on shoot fly population dynamics would give an idea about peak period of their activity and may be helpful in developing pest management strategy. Many of the researchers reported about the correlation between sorghum shoot fly Atherigona soccata. Rondani infestation and using some ecological factors (Kandalkar et al. 001 and Balikai and Venkatesh 001) and studied on effect of sowing dates on the incidence of insect pests and productivity of sorghum (Ameta et al., 004), but fails to study the effect of weather parameters with shoot fly after each and every week of the crop emergence and failed to study the forecasting of the shoot fly incidence using important weather parameters. Hence the present study focused on these aspects to get insight of the shoot fly incidence. Incidence pattern of pest may not be restricted due to changing weather condition. To know the association between different weather parameters viz, Maximum temperature, Minimum temperature, Relative humidity in morning, Relative humidity in evening, Rainfall and shoot fly in kharif season. Correlation study revealed that, rainfall found an important weather parameter which was having negative association with the egg population, while the effect of higher Maximum temperature, Minimum temperature, Relative humidity in morning as well as in evening found positive. Stepwise regression analysis was carried out to know the important weather parameter in incidence of shoot fly. KEY WORDS Shoot fly egg Dead heart percentage Weather parameters, Correlation, Stepwise regression analysis Received : 30.08.014 Revised : 17.1.014 Accepted : 13.01.015 *Corresponding author 99

S. T. PAVANA KUMAR et al., The basic idea of the study was to know the relationship between weather parameters and occurrence of shoot fly for particular crop season. All weather parameters are associated to each other but only in the favorable environment the shoot fly keep its activity and infest the crop. Keeping it in view present study attempted to extract the important weather parameters on the incidence of shoot fly and the year wise as well separate days after emergence of the crop (7, 14, 1 and 8) models based on the important weather parameters were developed. Hence the week wise prediction models will be developed which helps in forecasting for taking up the proper plant protection measures. MATERIALS AND METHODS The data base for this study was based on the experiment conducted by entomologists of All India Coordinated Sorghum Improvement Project at Main Agricultural Research Station, University of Agricultural Sciences, Dharwad. The experimental data was collected form experiments of All India Coordinated Sorghum Crop Improvement Project conducted during the period Kharif 000-010. Weather data was collected from meteorological observatory of Main Agricultural Research Station, Dharwad. The data collected on insect pest egg counts for 7 days after emergence (DAE) and dead heart percentage data for 14, 1 and 8 days after emergence of the crop for Kharif season for eleven years separately, they conducted an experiment under the natural conditions at Main Agricultural Research Station, Dharwad. The data collected on shoot fly eggs and dead heart based on objectives under study. The required weather data was collected from meteorological observatory of MARS, Dharwad for eleven years on the following weather parameter viz., Maximum Temperature (MAX) in degree Celsius (0 C), Minimum Temperature (MIN) in degree Celsius (0 C), Relative Humidity morning (RH1) in percentage (%), Relative Humidity evening (RH) in percentage (%), Rainfall (RF) in millimeter (mm) and Average Weekly Weather data of preceding week prior to the development of shoot fly infestation (Egg and Dead heart development) was used. The analysis included transformation of data, correlation and stepwise regression analysis. The detail of the methods is given as follows, Preliminary analysis Using the available data for eleven years regarding shoot fly egg population and per cent dead heart as variables, statistical analysis was done. Insect egg count was transformed using square root transformation and per cent dead heart due to shoot fly by using Arc sine transformation. Correlation analysis Correlation (Karl Pearson, 1867-1936) measures the degree of closeness or association between two variables and the strength of the relationship between them. In correlation we assume that both variables (X and Y) should be random and normally distributed. Correlation coefficient measures the strength of the linear relationship between two variables X and Y. It is calculated by using following formula, r = Xi X i Yi XiYi n ( Xi) ( Yi ) Yi n n Where, r = correlation coefficient Y- Shoot fly egg and Dead heart X- Weather parameters Testing correlation coefficient: The significance of Correlation coefficient (r) is tested using t- test. Test statistic is as follows, Where, r- Correlation coefficient. n- Sample size. And calculated t-value is compared with table t-value for (n-) degrees of freedom. Stepwise regression In multiple regression (Galton, 1894) when a number of variables are involved, many of them will not contribute much to the dependent variable. So elimination of these variables has to be done and there are different methods available now. In the method of backward regression, after entering all the variables in the model, variables which contribute least are eliminated one by one. The general form of Multiple Regression Equation is given by, Y=a+b 1 X 1 +b X +b 3 X 3 +.....+b n X n Where, Y-Egg and percent Dead heart a-intercept b-partial Regression Coefficients X 1, X, X 3..X n Weather parameters (MaxT, Min T, Rh1, Rh, Rf). The improvement of stepwise regression involves reexamination at every stage of the regression of the variables incorporated into the model is previous stages. The process is continued till no more variables will be admitted to the equation and no more variables are rejected. Steps followed in this procedure as given by the Draper and Smith (1936) is as follows. Some of the modeling work has been done by Mandal et al. (006), Parimala and Mathur (006), Patel et al. (007) and Vijaya Lakshmi et al. (010) RESULTS AND DISCUSSION Correlation analysis results for egg and dead heart of shoot fly 100

IMPACT OF WEATHER PARAMETERS ON SHOOT FLY indicted that high relative humidity in the morning tend to increase the oviposition of the shoot fly significantly (006) and the rainfall found to have significant (p=0.05) negative(- 0.696) association with the shoot fly caused due to washing of eggs during 005 (Table 1). This is in accordance with the study of Delobel and Lubega, 1984. In case of per cent dead heart, maximum temperature during 7 days after emergence of the crop showed negatively significant result towards dead heart development in the year 004 (-0.761 at p=0.05) but it exhibited positively significant result during 001, for 1(0.78) and 8(0.731) days after emergence of the crop at 5 per cent level of significance. The minimum temperature during 14 days after emergence of the crop showed negatively significant during 001 (-0.680 at p=0.05), 004 (-0.917 at p=0.01) and 005(-0.688 at p=0.05) but in the year 006 (0.668 at p=0.05) and 007(0.768 at p=0.05) showed positive correlation with the per cent dead heart. In 1(-0. 835 at p=0.01 in 001, -0. 853 at p=0.05 in 003) and 8(-0.701 at p=0.01 in 001) days after emergence of the crop minimum temperature associated negatively significant with the dead heart of shoot fly. The relative humidity in morning exhibited significant positive correlation with the dead heart of 14(0.755 at p=0.05) and 1(0.750 at p=0.05) days after emergence of the crop but its effect was negative for 8 (-0.701 at p=0.05) days after emergence of the crop with dead heart. For relative humidity Table 1: Correlation between egg population and weather parameters for kharif season Weather Parameters Egg Population(Yearwise) 000 001 00 003 004 005 006 007 008 009 010 MAX 0.34 0.316 0.16-0.31-0.53-0.30-0.157-0.55-0.387-0.16-0.75 MIN -0.045-0.5-0.57-0.743-0.514-0.593 0.098 0.607-0.38-0.081-0.5 RH1-0.107-0.18 0.04 0.03 0.315-0.01 0.74* -0.186 0.557 0.38 0.15 RH -0.101-0.154 0.003 0.11 0.376-0.083-0.14-0.087 0.643 0.1-0.048 RF -0.53 0.18 0.06-0.3 0.61-0.696* 0.3-0.4 0.59 0.186-0.051 *- Significant at 5 per cent level, **- Significant at 1 per cent level; MAX- maximum temperature, MIN- minimum temperature, RH1- relative humidity in morning,rh- relative humidity in evening, RF- rainfall, Egg- Egg count of Shoot Fly Table : Correlation between dead heart and weather parameters for kharif season Dae Weather Parameters Per Cent Dead Heart(Yearwise) 000 001 00 003 004 005 006 007 008 009 010 14 MAX 0.108 0.534-0.31-0.47-0.761* 0.8 0.7-0.36 0.09-0.619-0.69 1-0.381 0.78* -0.409-0.659-0.007-0.149-0.11 0.611-0.48-0.3-0.619 8 0.131 0.731* 0.8 0.085 0.019-0.13 0.178 0.16 0.564 0.587-0.137 14 MIN -0.36-0.680* 0.103-0.455-0.917** -0.688* 0.668* 0.768* 0.73 0.544-0.567 1 0.11-0.835** -0.56-0.853* -0.098-0.359-0.565-0.44-0.575 0.39-0.594 8 0.353-0.701* 0.715-0.00 0.351-0.366 0.447-0.518-0.877-0.579-0.594 14 RH1 0.059-0.4-0.163-0.34 0.755* -0.033 0.17 0.58-0.078 0.537-0.50 1 0.35-0.416-0.98-0.164-0.097 0.750* 0.364-0.336-0.306 0.67-0.06 8 0.166-0.34-0.03 0.0-0.317 0.1 0.145-0.5-0.947* -0.501-0.568 14 RH -0.08-0.69 0.54 0.44 0.338-0.47-0.06 0.656-0.106 0.495 0.344 1 0.74-0.815** 0.85 0.551-0.13 0.396 0.055-0.597 0.179 0.031 0.365 8-0.111-0.793* -0.349 0.116 0.73 0.30 0.003-0.46-0.88-0.064-0.073 14 RF 0.05-0.444 0.388-0.49 0.344-0.416 0.19 0.335-0.546 0.447-0.18 1 0.3-0.16 0.033 0.493-0.43 0.436 0.043-0.015 0.089-0.081 0.371 8 0.404-0.075 0.043-0.36-0.13-0.067 0.6-0.188-0.78-0.17 0.457 *- Significant at 5 per cent level, **- Significant at 1 per cent level. DAE- days after emergence, MAX- maximum temperature, MIN- minimum temperature, RH1- relative humidity in morning RH- relative humidity in evening, RF- rainfall, DH- Dead heart Per cent Table 3: Stepwise regression models in 7 days after emergence of crop for weather parameters on egg population in kharif season Year Excluded Parameters Model SE R 000 MAX, MIN EGG=-3.717+0.549(RH1) -0.7(RH) - 0.311(RF) 0.683 0.641 001 MIN, RH EGG=-146.087+.333(MAX)+0.986(RH1)+0.455(RF) 0.949 0.531 00 RH1, RH EGG=3.047+0.347(MAX)-0.463(MIN)+0.435(RF) 0.551 0.31 003 MAX, MIN, RH EGG=-37.906+0.483(RH1) -.87(RF) 0.365 0.663 004 MAX, RF EGG=38.18-1.357*(MIN)-0.14*(RH1)+0.051*(RH) 0.13 0.871* 005 MAX, MIN, RH EGG=-.540+0.88*(RH1)-0.17*(RF) 0.17 0.9* 006 No EGG=-1.137-0.305(MAX)+0.653*(MIN)+0.163* 0.137 0.965* (RH1)-0.077*(RH)-0.019(RF) 007 MAX, RH1, RH, RF EGG=-4.879+0.338(MIN) 0.0 0.368 008 MAX, MIN, RH, RF EGG=-0.46+0.05(RH1) 0.313 0.313 009 MIN, RH EGG=-18.391+0.18(MAX)+ 0.199(RH1) -0.04(RF) 0.178 0.45 010 MAX, RH1, RF EGG=-7.084-1.7(MIN)+0.011(RH) 0.508 0.50 *- Significant at 5 percent level. SE- Standard error, R - Coefficient of determination. DAE- Days after emergence, MAX- Maximum temperature, MIN-Minimum temperature, RH1- Relative humidity morning, RH-Relative humidity evening, RF- Rainfall, DH- Dead heart Per cent, Egg- Egg count of Shoot Fly 101

S. T. PAVANA KUMAR et al., Table 4: Stepwise regression models in 14 days after emergence of crop for weather parameters on dead heart development in kharif season Year Excluded Parameters Model SE R 000 MAX, MIN %DH=-166.334+4.046(RH1) -1.817(RH) -.374(RF) 4.176 0.53 001 MAX, RF, RH1, RH %DH=30.174-1.915*(MIN) 8.635 0.46* 00 MIN, RF %DH=4.939+1.408(MAX)-0.847*(RH1)+0.838*(RH) 1.116 0.915* 003 MAX %DH=399.314-4.735*(MIN)-3.736*(RH1)+ 0.650 0.986* 0.475*(RH)+10.54*(RF) 004 MAX, RF, RH1, RH %DH=146.384-6.668*(MIN) 1.464 0.841* 005 RH %DH=-178.316+3.187*(MAX) -9.503*(MIN)+ 4.54 0.873* 3.436(RH1) -1.48*(RF) 006 MAX, RF %DH=-114.884+4.749*(MIN)+0.633(RH1) - 0.31*(RH) 1.66 0.84* 007 MAX, RF, RH1, RH %DH=-7.576+1.998*(MIN) 0.893 0.590* 008 MAX, RF, RH %DH=-4.101+3.975(MIN) -0.069(RH1) 0.975 0.574 009 MIN, RH1, RH, RF %DH=61.403-1.14(MAX) 1.990 0.384 010 MIN, RF, RH %DH=14.975-1.141(MAX)-0.791(RH1) 3.705 0.541 *- Significant at 5 percent level. SE- Standard error, R - Coefficient of determination. DAE- Days after emergence, MAX- Maximum temperature, MIN-Minimum temperature, RH1- Relative humidity morning, RH-Relative humidity evening, RF- Rainfall, DH- Dead heart per cent Table 5: Stepwise regression models in 1 days after emergence of crop for weather parameters on dead heart development in kharif season Year Excluded Parameters Model SE R 000 MIN, RH1 %DH=410.915-9.449(MAX) -1.518(RH)+ 1.738(RF) 9.780 0.43 001 MAX %DH=353.358-19.908*(MIN)+1.797(RH1)-0.797*(RH)+0.936(RF) 5.94 0.97* 00 MIN %DH=106.3-1.406*(MAX)-0.653*(RH1)+0.373(RH)-.08*(RF) 0.484 0.984* 003 MIN, RH, RF DH=303.083-4.436*(MAX)-1.907(RH1).836 0.774* 004 MIN, RH %DH=65.948-1.171(MAX)-0.8(RH1)-0.307(RF).651 0. 005 MAX, RH, RF %DH=-108.950-5.30(MIN)+.675*(RH1) 6.410 0.645* 006 RH1, RH, RF %DH=336.34-3.483*(MAX)-11.096*(MIN) 4.840 0.714* 007 MIN, RH1 %DH=-33.360+3.180(MAX) - 0.499(RH)+0.776(RF) 4.436 0.701 008 MIN, RH, RF %DH=98.95-0.665(MAX) -0.460(RH1) 1.367 0.561 009 MIN, RH1, RF %DH=15.819-3.191(MAX)-0.414(RH) 1.535 0.483 010 MIN, RH1, RH %DH=63.396-1.78(MAX)+0.38(RF) 4.301 0.491 *- Significant at 5 percent level. SE- Standard error, R - Coefficient of determination. DAE- Days after emergence, MAX- Maximum temperature, MIN-Minimum temperature, RH1- Relative humidity morning, RH-Relative humidity evening, RF- Rainfall, DH- Dead heart per cent in the evening found no significance with the dead heart in 14 DAE but found negatively significant at 1 per cent level of significance during 1 (-0.815 in 001) DAE and significant at 5 per cent level during 8 (-0.793 in 001) days after emergence of the crop. The effect of rainfall during 14, 1 and 8 days after emergence of the crop found no significance with the dead heart of shoot fly. Results are presented in the Table. In regression analysis when more number of factors involved, many of them will not contribute much to the dependent variable, so elimination of those variables has to be done which are really not contributing to the shoot fly development and hence stepwise regression procedure was carried out to know the important weather parameters in affecting the shoot fly oviposition and dead heart development. The regression model during 004(0.871), 005(0.9), 006(0.965) produced significant coefficient of determination for 7 DAE and all weather parameters ( MaxT, MinT, Rh1, Rh and Rf) were included in the model during the year 006, which exhibited highest significant (p=0.05) R value with less standard error (0.137) compare to all other models. The model in the year 006 could be used for the prediction of shoot fly after 7 days after emergence of the crop. (Table 3) For 14 days after emergence of the crop, models in 001, 00, 003, 004, 005, 006 and 007 produced significant coefficient of determination value and the regression model in the year 003 which included MinT, Rh1, Rh and Rf in the model with high and significant coefficient of determination value of 0.986 (p=0.05) and hence it was best fit. The weather parameter maximum temperature was excluded from the model because its contribution towards dead heart dead heart development during 003 was negligible and found not important for the prediction purpose. Results are depicted in thetable 4. Results from the Table 5 revealed that, the coefficient of determination (R ) value during 00 (0.984) found high, followed by 001 (0.97), 003 (0.774), 006 (0.714) and in the year 005 (0.645) found significant at 5 per cent level of significance. The model for 1 days after emergence of the crop during 00 which had less standard error 0.484 and exhibited highest significant R, which included MaxT, Rh1,Rh and Rf in the model. Hence it was best fit to predict the per cent dead heart for 1 days after emergence of the crop. The coefficient of determination found high for 008(0.89), followed by 006(0.855), 009(0.84), 001(0.814), 00(0.766) and 004(0.704) were significant at 5 percent level of significance, but in rest of the years it was not precise and good fit. The model in the year 008 found best fit model for 8 days after emergence of the crop and the model included single weather parameter i.e, relative humidity in morning and excluded the rest of the rest of the parameters. Hence relative humidity in morning found important for 8 days after emergence of the crop, which contributed 89 per cent variation alone in the shoot fly incidence and model 10

IMPACT OF WEATHER PARAMETERS ON SHOOT FLY Table 6: Stepwise regression models in 8 days after emergence of crop for weather parameters on dead heart development in kharif Year Excluded Parameters Model SE R 000 MIN, RF %DH=-158.653-9.756(MAX)+ 1.51(RH1) -7.435(RH) 4.63 0.485 001 MAX, RF %DH=367.974-3.560(MIN)+.837(RH1)-1.16*(RH) 9.56 0.814* 00 MAX, MIN, RH1 %DH=75.774-3.198*(RH)+14.399*(RF) 6.04 0.766* 003 MIN, RF %DH=-9.047+.089(MAX)+0.380(RH1)+0.75(RH) 1.795 0.8 004 MAX, MIN, RF %DH=85.596-3.13*(RH1)+.973*(RH) 6.914 0.704* 005 RH1, RF %DH=59.630+4.179(MAX) -11.8(MIN)+ 1.81(RH) 10.77 0.313 006 MAX, RH1 %DH=-60.157+7.369*(MIN) - 0.697*(RH)+3.65*(RF) 4.453 0.855* 007 RH, RF %DH=374.501-1.780(MAX) -7.375(MIN) -1.664(RH1) 3.6 0.47 008 MAX, MIN, RH, RF %DH=73.05-0.35*(RH1) 0.57 0.89* 009 MIN, RH1, RF %DH=-11.631+4.170*(MAX)+0.548*(RH) 1.83 0.84* 010 MAX, MIN, RH %DH=403.183-3.959(RH1)+1.568(RF) 11.870 0.575 *- Significant at 5 percent level. SE- Standard error, R - Coefficient of determination; DAE- Days after emergence, MAX- Maximum temperature, MIN-Minimum temperature, RH1- Relative humidity morning, RH-Relative humidity evening, RF- Rainfall. DH- Dead heart Per cent produced precise standard error of 0.57.(Table 6) Weather based forewarning of the incidence of insect pest and generation of information about critical weather sensitive phases in the life cycle of insect can guide operational and tactical strategy in insect pest management (Mandal et al. 006). Weather parameters viz., relative humidity in morning and rainfall found important in the oviposition of shoot fly after 7 days of emergence of the crop. In which high relative humidity causes high oviposition of shoot fly (increased egg population) after first week of emergence of the crop (Singh and Verma, 1988 and Karibasavaraja and Balikai, 006). Heavy rainfall after first week of emergence of the crop causes washing of eggs from the plant leading to less oviposition due to mortality of eggs and there expected a less number of adult shoot fly population in the later stage of the crop. (Delobel and Lubega, 1984) For the dead heart development, maximum temperature had negative significant effect (Karibasavaraja and Balikai, 006), when the temperature below 30ºC it favoured negatively the development of dead heart after first week of emergence of the crop, but it favoured the dead heart positively after 1 and 8 days after emergence of the crop if the temperature was > 30 0 C (Dubey and Yadav, 1980). The minimum temperature found negatively correlated with the dead heart development when it drops below 0ºC and its effect found positive in the late sown sorghum crop. The shoot fly incidence was positively favoured by relative humidity in morning when it recorded more than 60 per cent (The results are in confirmation with Singh and Verma, 1988 and Karibasavaraja and Balikai, 006) in weeks after emergence of the crop and it found negative when it drops below 60 per cent (low humid) in the morning. But relative humidity in the evening exhibited negative effect on shoot fly incidence during 1 and 8 after emergence of the crop due to low humidity during evening time and the rainfall had no significance in the dead heart formation of shoot fly but when it was combined with maximum temperature affected shoot fly egg and dead heart negatively. The results are in confirmation with Balikai and venkatesh, 001. The model Egg=-1.137-0.305 (MAX)+0.653 (MIN)+0.163 (RH1)-0.077 (RH)-0.019 (RF) could be used to predict the egg population (oviposition) during 7 days after emergence of the sorghum during the Kharif season for Dharwad region, which produced highest significant R square value (R = 0.965 at p=0.05) and least standard error (0.137) which included all the weather parameters in the model. To predict the dead heart per cent during the crop growth after 14 days of emergence of the sorghum crop was %DH=399.314-4.735(MIN)-3.736(RH1)+0.475(RH)+10.54(RF), which excluded maximum temperature from the model and produced highest significant R square at 5 per cent level (0.986) and least standard error among the models. During the 1 days after emergence of the crop, the model %DH=106.3-1.406 (MAX)-0.653 (RH1)+0.373 (RH)-.08 (RF), which excluded the minimum temperature from the model and produced significant coefficient of determination (R = 0.984 at p=0.05) with least standard error (0.484), in the same way for the 8 DAE %DH=73.05-0.35 (RH1), which excluded maximum temperature, minimum temperature, relative humidity during evening and Rainfall and obtained high R square of 0.89 at 5 per cent level with least standard error 0.57, included the parameter relative humidity in morning alone produced the significant result and rest of the parameters were of no importance during the 8 Days after emergence of the crop. The above discussion can be concluded that, the shoot fly remained active throughout the kharif season (Kulkarni et al., 1978). High rainfall after first week of emergence of the crop leading to mortality of eggs leading to less dead heart in the later growth stage of the crop and humidity in the morning and rainfall found important during oviposition but rainfall has no significance during dead heart formation. Lastly, the above step wise regression models could be used for the prediction purpose during the respective days after emergence of the sorghum crop and the date of sowing of the crop may be included in the above model as one of the independent variables for the better prediction for the Dharwad region of Karnataka. REFERENCES Ameta, O. P. and Sumeria, H. K. 004. Effect of sowing dates on the incidence of insect pests and productivity of sorghum {Sorghum bicolor}. Indian J. Agric. Res. 38(4): 78-8. Anonymous 010. 41 st Annual Sorghum Group Meeting-agm11- Dharwad (AICSIP). Balikai, R. A. 000. Seasonal incidence of sorghum shoot fly in 103

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