{'fraction_non_alphanumeric': 0.038708363043699706, 'fraction_numerical': 0.020882143220943263, 'mean_word_length': 4.9395039322444045, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Development of weather based rice yellow stem borer prediction model for the Cauvery command rice areas, Karnataka, India

Abstract Relationship of weather parameters viz., maximum temperature (Tmax, °C), minimum temperature (Tmin, °C), rainfall (RF, mm), morning relative humidity (RH1, %), evening humidity (RH2, %), and sunshine hours (SSH), during seven years at Mandya (Karnataka) was individually explored with peaks of rice yellow stem borer (YSB) Scirpophaga incertulas (Walker) light trap catches. The peaks of YSB trap catches exhibited significant correlation with Tmax of October 3rd week, Tmin of November 1st week, RF of October 2nd week, RH1 of November 4th and RH2 of November 1st week, and SSH of October 4th week. Weather-based prediction model for YSB was developed by regressing peaks of YSB light trap catches on mean values of different weather parameters of aforesaid weeks. Of the weather parameters, only Tmin, RF, and RH1 were found to be relevant through stepwise regression. The model was validated satisfactorily through 8-year independent data on weather parameters and YSB light trap catch peaks (R 2 = 0.90, p < 0.0002).


PUBLIC INTEREST STATEMENT
Insect pests are a major bottleneck in realizing yield potential of the crop. Accurate prediction of insect emergence and development is essential for effective pest management despite being quite challenging. Improperly timed pesticide applications are expensive, harmful to natural enemy populations, and also contaminate the environment. Proper forewarning and monitoring techniques are indispensible in pest management, their absence is only felt when sudden pest outbreaks catch use unaware. Pest forecasting models facilitate understanding of factors that affect pest population dynamics. These can thus be used for predicting pest outbreaks. In our paper, we developed a forewarning model for yellow stem borer, a serious pest of rice at Mandya, Karnataka, India. This would prove useful in forewarning likely occurrence of the pest in Mandya region, thereby paving way for timely action and prevention of yield losses due to the pest.
Both biotic and abiotic factors are believed to be responsible for pest population dynamics . Climatic factors such as temperature, rainfall, and relative humidity greatly influence the outbreak of the insect population (Chen, Liu, & Zeno, 1968;Heong, Manza, Catindig, Villareal, & Jacobsen, 2007;Siswanto, Dzolkhifli, & Elna, 2008). Population dynamics of YSB like any other species are thus liable to fluctuate according to the dynamic condition of its environment (Chen et al., 1968;Khaliq, Javed, Sohail, & Sagheer, 2014). Quantification of important mortality factors, both biotic and abiotic is needed to reliably forecast insect populations. Besides, knowledge of the seasonal abundance and population build up trend is essential to ensure timely preparedness to tackle impending pest problems and prevent crop losses (Das et al., 2008).
Pest population density may be the resultant of weather parameters of several preceding weeks or months. It thus becomes worthwhile to explore relationship of pest population with weather parameters of several preceding weeks. Several workers have analyzed influence of weather factors on YSB population and observed temperature, humidity, and rainfall to be important ones (Bhatnagar & Saxena, 1999;Das et al., 2008;Joshi, Ram, & Singh, 2009;Mandal, Roy, & Saha, 2011;Sharma, Atsedewoin, & Fanta, 2011). However, such a study has not been attempted for Mandya region of Karnataka. As pest-weather relations are empirical and location-specific in nature and thus need to be established individually for different regions.
Keeping this in view, present study was undertaken to develop and validate weather-based YSB prediction model for the Cauvery Command areas of Mandya, Karnataka, India.

Materials and methods
Data on YSB light trap catches and weather parameters viz., maximum temperature (T max ) (°C), minimum temperature (T min ) (°C), rainfall (RF) (mm), morning relative humidity (RH 1 ) (%), evening humidity (RH 2 ) (%), and sunshine hours (SSH) for 15 years across 1991-2005 for Mandya region (76.19′-76.20′ S and 12.13′-13.4′ E) of Karnataka, India was collected from Annual Progress Reports of Directorate of Rice Research (DRR), Hyderabad, India. Weekly weather data and log transformed YSB light trap catches for seven years (N = 7) were used for model formulation. Linear correlation coefficients were computed between the YSB trap catch peaks during different years and weekly mean values of each of the weather parameters individually. The correlation coefficients between YSB light trap catch peaks and weekly values of weather parameters were explored from 1st week of July onwards until attainment of peak YSB catch. Most important week with respect to each of the weather factors could thus be identified and relevant values were used to develop a multiple linear pest-weather model by regression of the YSB trap catch peaks on different weather parameters using SAS statistical software. The model was validated through eight-year independent data (N = 8) set on weather and YSB trap catch peaks. Model performance was evaluated by comparing observed and predicted YSB trap catch peaks.

Results and discussion
The YSB trap catch peaks exhibited significant correlation with T max of October 3rd week, T min of November 1st week, RF of October 2nd week, RH 1 and RH 2 of November 4th and 1st week, respectively, and SSH of October 4th week (Table 1). As peaks of YSB light trap catches during different years occurred between December 1st week and December 4th week, weather parameters only up to November 4th week were considered for developing regression relationship. Pest-weather model between YSB light trap catches and all weather parameters was established as follows (Equation 1, Table 2).
Weather parameters explained 97% variability in YSB trap catches (p = 0.0001). However, removal of SSH did not result in an appreciable reduction in coefficient of determination (R 2 = 0.99) rather it increased, thereby indicating insignificant influence of SSH on YSB light trap catches (Equation 2, Table 2). Likewise, exclusion of RH 2 did not cause any reduction in R 2 (0.98) suggesting insignificant role of RH 2 on YSB light trap catches (Equation 3, Table 2). However, exclusion of RH 1 caused a significant reduction in R 2 (0.64), suggesting significant role of RH 1 on YSB light trap catches (Equation 4, Table 2).
Further, rainfall was excluded and model with T max , T min , and RH 1 could account for only 51% variability in YSB trap catches, thereby suggesting significant role of rainfall (Equation 5, Table 2). With the removal of T min , the model with T max , rainfall, and RH 1 explained only 60% variability in YSB light trap  Table 2). However, T max removal did not result in much reduction in R 2 thus indicating T max to be insignificant in YSB light trap catches (Equation 7, Table 2). Out of different weather parameters, only T min , RF, and RH 1 were thus revealed to be important with respect to their influence on YSB light trap catch peaks. These three factors could explain 92% variability in YSB light trap catches (p = 0.0310). Final model with T min , rainfall, and RH 1 was thus established as follows (Equation 7, Table 2) and its parameters are shown in Table 3.
The pest-weather model was validated satisfactorily (R 2 = 0.90; p = 0.0001) with independent eight-year data on weather and YSB trap catches (Figure 1    Pest-weather model clearly suggested the T min , RF, and RH 1 to be important weather parameters that influenced YSB light trap catches at Mandya, Karnataka. Besides, satisfactory validation of the model (R 2 = 0.90, p = 0.0001) with 8-year data endorsed importance of these parameters in affecting YSB population dynamics. On the other hand, T max , RH 2 , and SSH were not found to influence the YSB light trap catches. The T min , RF, and RH 1 exhibited negative relationship with YSB light trap catches. Indirectly, YSB light trap catches were indicative of its population level on rice crop in the region. The YSB has been designated as a resident pest as it hibernates in rice stubbles in the same field and previous season's pest density has influence on ensuing season's density. It thus follows that after initial emergence of YSB adults; interfield migration is important for its population build up and spread in an area. Insects being cold-blooded animals, temperature plays a crucial role in their development and distribution (Bale et al., 2002;Kinoshita & Yagi, 1930), and YSB is no exception to this thermal principle. Significant influence of T min on YSB light trap catches is understandable because moth movement and population build in a particular area takes place during night. Cooler nights seemed to have favored YSB movement.
Rainfall negatively affected YSB light trap catches directly as well indirectly through relative humidity. Heavy rainfall might adversely affect YSB population through physical destruction of its development stages, while high humidity might promote entomopathogens.
Similar results on interactive effect of rainfall, relative humidity, and mean minimum temperature on rice stem borer were also reported (Abraham, Thomas, Karunakaran, & Gopala Krishnan, 1972;Bhatnagar & Saxena, 1999). Rehman, Inayatullah, and Majid (2002) found minimum temperature, rainfall, and relative humidity to be important factors that influenced the YSB outbreaks. As reliable forewarning and monitoring tools are indispensible for pest management, the YSB prediction model would prove useful in forewarning likely occurrence of the pest in Mandya region, thereby paving way for timely action and prevention of yield losses due to the pest.