Forcasting major pests of rice using weather variables

Abstract

Rice is the staple food crop of majority of the global population and production of rice has been affected by various factors. Insect pest attack in rice causes significant yield loss in rice. Forecasting models can be developed to determine the incidence of the pest using various weather parameters and it is a significant step in helping the farmers to mitigate the loss of crop due to insects. The forecasting of two major insect pests of rice namely Yellow stem borer (YSB) and Brown plant hopper (BPH) has been taken up in this study. Population count data of YSB and BPH from light trap catches installed at Regional Agricultural Research Station (RARS), Pattambi, Kerala was utilised for the study. The objectives of the study were to establish the relationship of YSB and BPH population with the weather variables which includes maximum temperature (TMAX), minimum temperature (TMIN), morning relative humidity (RH I), evening relative humidity (RH II), rainfall (RF) and sunshine hours (SSH); to develop suitable forecasting models for YSB and BPH using weather variables; and to study the dynamics of these pests under pre and post flood conditions. Two period of peak incidence of the YSB population was revealed from the analysis of their distribution during the study period ranging from 1997 to 2023. These periods of peak incidence were 10th to 16th standard meteorological week (SMW) and 38th to 48th SMW. Relationship of pest population with weather variables was studied using correlation analysis. The peak week corresponding to the 12th SMW had significant negative correlation with TMAX, TMIN, RH I, RH II and SSH, whereas count of YSB for the 43rd SMW had an association with TMIN, RH I and RH II. The weekly distribution of BPH showed one prominent peak incidence corresponding to the 39th to 44th SMW and it was found that minimum temperature, morning and evening relative humidity, rainfall, sunshine hours were the weather variables that had significant correlation with the BPH population. Multiple linear regression model for to predict the incidence of YSB at 12th week of peak incidence, had weather variables TMAX, TMIN, RH I and SSH as explanatory variables and the model yielded an adjusted R2 of 81%. The interaction of temperature and relative humidity was found to be significant in case of composite regression models. Multinomial logistic regression models developed for determining the pest infestation status, in terms of low, medium and high incidence of the YSB population had an accuracy of 92% and 72% for the 12th and 43rd peak weeks respectively. In case of BPH population, multiple linear regression model to predict BPH incidence during 39th – 44th SMW yielded a model with adjusted R2 value of 39% with weather variables TMIN, RH I and RH II. For 42nd SMW, model with variables TMIN, RH I and RH II yielded an R2 value of 45%. In case of BPH also, interaction of temperature and relative humidity was found to be significant in the fitted composite regression models, with a greater model R2 in comparison to that of multiple linear regression model. An accuracy of 66% and 64% was obtained for the model fitted using multinomial logistic regression analysis during peak period and peak week respectively. Autoregressive Integrated Moving Average (ARIMA) model, ARIMA with exogenous variables (ARIMAX) and Integer valued generalized autoregressive conditional heteroscedasticity with exogenous variables (INGARCHX) models were employed for the time series analysis of the pest data. Three different trend periods were identified for both YSB and BPH population. In the case of YSB population during first period, ARIMAX (2,1,1) was the best fit model with MAPE value of 11.29%. For second period, ARIMAX (2,1,1) with MAPE of 11.36% and for the third period, ARIMAX (2,0,1) with a MAPE of 15.02% were the best fitted models. In the case of BPH, ARIMAX (2,0,2) was the best fit model for first period, ARIMAX (2,1,2) and ARIMAX (1,1,1) for the second and third period respectively. INGARCHX models fitted for YSB and BPH population were of poor fit, as these models did not have any significant weather variables in the model, with only model parameters turning out to be significant. Further, pre and post flood analysis of pest dynamics with weather variables revealed that there is a shift in the peak period of incidence for both YSB and BPH and further there is an overall decline in both YSB and BPH population for post flood period. The individual and joint effect of the weather variables were determined using the multiple linear regression analysis and composite regression analysis respectively, wherein composite regression provided better model accuracy in comparison to multiple linear regression analysis. Multinomial logistic regression analysis helped to determine the epidemic status of YSB and BPH at different peak periods of incidence. These epidemic status can be used as a warning alert for the farmers on the level of incidence of these pests. Time series analysis of the pest population count revealed that ARIMAX models performed better than INGARCHX models in predicting population of YSB and BPH and it also shed light into the various trend breaks observed in the pest population count for both YSB and BPH across the time period studied.