Development of weather based rice yellow stem borer prediction model for the Cauvery command rice areas, Karnataka, India

Prasannakumar, N. R.; Chander, Subhash; Kumar, Lomash

doi:10.1080/23311932.2014.995281

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…For the dependent variable YSB population, MINT, SSH and ERH are significantly contributing, further MINT have negative effect on YSB population and SSH and ERH have positive impact on YSB population for the data under consideration. Similar results were found in Prasannakumar et al [2]. Though the listed variables had significant influence on the Yellow Stem Borer populations, the model R 2 value for the fitted regression in the Warangal centre is low, indicating that the model is not a strong fit, for which non-linearity and high heterogeneity in dependent variables may be responsible.…”

Section: Stepwise Regression Analysissupporting

confidence: 86%

“…"Both biotic and abiotic factors are believed to be responsible for pest population dynamics" [2]. "Besides inherent biotic potential of the insect to a large extent abiotic factors like temperature, rainfall, relative humidity, sunshine hours etc.…”

Section: Introductionmentioning

confidence: 99%

“…Though the count regression data modelling approach is applied in many areas; Identifying defects in manufacturing [4], In domestic violence data [5], caries research [6], spontaneous abortion [7], modelling of death due to covid-19 in Ghana [8] and zoological data [9] but their applications are limited in insect pest modeling. Contrary to count regression modelling, the continuous linear regression model was applied in many insect pest modelling studies and as well as in agricultural data sets; weather based regression modelling of white rust disease of Indian mustard [10], regression modelling factors influencing agricultural diversification of Karnataka [11], regression modelling of biometrical characters in Sesame [12], regression analysis of growth patterns of root and shoot morphological traits in Sesame [13], weather based YSB regression model [14], modelling of YSB population under changing climatic scenarios [15].…”

Section: Introductionmentioning

confidence: 99%

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Modelling the Relationship between Weather Variables and Yellow Stem Borer Population: A Count Data Modelling Approach

Reddy¹,

Rathod²,

Kallakuri³

et al. 2022

IJECC

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Aim: This study was conducted to model the relationship between discrete dependent variable (yellow stem borer population) and continuous weather variables. Data Description: The yellow stem borer (YSB) population and standard meteorological week (SMW) wise weather variables (temperature, relative humidity, rainfall and sunshine hours) data of Warangal centre (Telangana state) generated under All India Co-Ordinated Rice Improvement Project (AICRIP) from 2013-2021 were considered for the study. The YSB population were recorded daily using light trap with an incandescent bulb and are counted as weekly cumulative catches. Methodology: The weekly cumulative trapped YSB populations and weekly averages of climatological data were considered as inputs to the models under consideration. In this study the classical linear regression i.e. step-wise multiple linear regression and count regression models such as Poisson, negative binomial, zero inflated Poisson and zero inflated negative binomial regression models were employed. Result: The empirical results revealed that the zero inflated count regression models viz., zero inflated Poisson regression and zero inflated negative binomial regression models performed better compared to the classical linear regression, Poisson and negative binomial regression models, further the negative binomial regression model outperformed all models as it yielded lowest mean square error (MSE) and highest R2 values. The average percentage reduction in accuracy of zero-inflated negative binomial regression model over classical model was around 4 percent. Conclusion: Based on the results obtained in this study, it is concluded that the zero inflated models performs better compared to classical models as they are unable to handle the presence of excess zeroes, as a result provides more prediction error and lower R2 values. Further, the models developed in this study will be of great assistance in identifying the factors influencing occurrence of YSB population in rice.

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Section: Stepwise Regression Analysissupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling the Relationship between Weather Variables and Yellow Stem Borer Population: A Count Data Modelling Approach

Reddy¹,

Rathod²,

Kallakuri³

et al. 2022

IJECC

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“…Paddy water temperature above 35°C is considered to be the major factor of Chilo suppressalis population decline in July and August in Taiwan (Chang, 1968). According to Prasannakumar (2015), weather parameters i.e. minimum temperature, rainfall, and humidity in the morning affect the YSB population outbreak in India.…”

Section: The Effect Of Planting Distance and Depth Of Puddle To Micromentioning

confidence: 99%

The Effect of Planting Distance and Depth of Water Puddle to Damage Intensity by Rice Yellow Stem Borer (Scirpophaga incertulas Walker) (Lepidoptera: Crambidae) in Relation to Microclimate Change

Nuzulullia

Martono

Trisyono

2019

JPTI

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The rice yellow stemborer (Scirpophaga incertulas) is one of main pests on rice which may attack at all growth stages. The weather was considered one of influencing factors against its infestation. Therefore, this research was conducted to recognize microclimate factors affecting the infestation of S. incertulas. The experiment was carried out in Institute for Pest Forecasting, Jatisari Karawang from January-April 2017. The investigation was arranged in Factorial of Blocked Randomized Design with four repetitions and combinations of treatment factors, i.e. planting distance (conventional and jajar legowo 2 : 1) and depth of puddle (5 cm, 0 cm, and intermittent). The observation was performed from one to 12 weeks after planting. At the same time, three weather factors (temperature, relative humidity and rainfall) were observed in four West Java regencies i.e. in Bekasi, Karawang, Subang and Indramayu. The results showed that the conventional and the jajar legowo schemes are influenced by the micro air temperature. Both planting distance and depth of water puddle treatment were influenced by the micro air temperature. Microclimate factors which influenced the intensity of infestation were air temperature, humidity, water temperature, and light intensity. The weather factor showing effects in Bekasi, Karawang, and Indramayu was rainfall. However, the three weather factors did not affect on the acreage of pest infestation in Subang.

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“…The weather based forewarning model was revalidated to judge the repeatability of model accuracy to predict the H. armigera adult population based on the weather parameters and the model was validated satisfactorily (R 2 = 0.719, RMSE=1.66%, MBE=-0.71% and MAE=1.26%) (Fig 2 ) with 2017-18 weather data and H. armigera trap catches indicating that this model can be precisely used to predict H. armigera population for New Delhi weather conditions. Pest-weather models have also earlier been developed and validated for rice yellow stem borer (Prasannakumar et al 2015), guava fruit fly (Sharma et al 2015) and sucking pests of cotton, viz. aphids, thrips and leafhoppers (Kumar et al 2018).…”

mentioning

confidence: 99%

Validation of weather based forewarning model for Helicoverpa armigera in chickpea

2019

Self Cite

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Weather based forewarning model for Helicoverpa armigera developed earlier was validated by recording the activity of H. armigera through pheromone trap catches in chickpea sown during rabi 2017â€“18 at IARI, New Delhi. The moth activity of H. armigera commenced in 52nd SMW (last week of December) and peak trap catch was during 15th SMW with 183moths/trap/week in April. The male moth population had significant positive and very strong correlation with maximum temperature (r=0.816**) and minimum temperature (r=0.820**) of 1-lag week and significant negative correlation with morning (r=-0.774**) and evening relative humidity (r=-0.655**) of 1-and 2-lag week respectively. The repeatability of model accuracy to predict the H. armigera adult population was validated satisfactorily (R2 = 0.719, RMSE=1.66%, MBE=-0.71% and MAE=1.26%) with 2017â€“18 weather data and H. armigera trap catches.

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Development of weather based rice yellow stem borer prediction model for the Cauvery command rice areas, Karnataka, India

Cited by 5 publications

References 4 publications

Modelling the Relationship between Weather Variables and Yellow Stem Borer Population: A Count Data Modelling Approach

Modelling the Relationship between Weather Variables and Yellow Stem Borer Population: A Count Data Modelling Approach

The Effect of Planting Distance and Depth of Water Puddle to Damage Intensity by Rice Yellow Stem Borer (Scirpophaga incertulas Walker) (Lepidoptera: Crambidae) in Relation to Microclimate Change

Validation of weather based forewarning model for Helicoverpa armigera in chickpea

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