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

Figures & data

Table 1. Correlation coefficient between yearly peaks of YSB light trap catches and weather parameters (prior to peak catches) during seven years at Mandya, Karnataka

Week	Tmax	Tmin	RF	RH1	RH2	SSH
Jul-1	0.06	−0.47	0.15	−0.64	−0.69	0.05
Jul-II	−0.28	−0.098	−0.06	−0.61	−0.72	−0.36
Jul-III	−0.15	0.35	0.008	−0.66	−0.52	0.19
Jul-IV	−0.073	0.28	−0.26	−0.81	−0.45	−0.01
Aug-I	0.14	−0.07	0.40	−0.65	−0.40	−0.01
Aug-II	0.19	0.38	0.36	−0.65	−0.48	−0.14
Aug-III	0.01	−0.12	0.41	−0.72	−0.60	0.56
Aug-IV	0.01	−0.43	0.71	−0.58	−0.35	−0.13
Sep-I	−0.39	0.18	0.53	−0.002	−0.24	−0.46
Sep-II	−0.35	−0.02	0.27	−0.47	0.15	0.014
Sep-III	−0.39	0.02	−0.36	−0.61	−0.46	0.078
Sep-IV	−0.39	0.02	−0.36	−0.61	−0.46	0.078
Oct-I	−0.09	−0.20	0.13	−0.33	0.29	0.049
Oct-II	−0.01	0.03	−0.86	−0.58	−0.15	−0.059
Oct-III	−0.63	0.08	0.12	−0.70	−0.39	0.34
Oct-IV	0.22	−0.018	−0.09	−0.65	−0.31	0.57
Nov-I	−0.18	−0.62	−0.05	−0.39	−0.85	−0.10
Nov-II	0.13	−0.01	−0.01	−0.45	−0.35	0.30
Nov-III	−0.26	−0.23	−0.31	−0.74	−0.46	0.15
Nov-IV	−0.04	0.41	0.19	−0.80	−0.59	0.08
Dec-I	−0.14	−0.22	0.82	−0.68	−0.43	−0.47
Dec-II	−0.24	0.46	0.63	−0.41	0.38	−0.21
Dec-III	−0.36	0.12	0.69	−0.60	−0.10	0.089
Dec-IV	0.0001	−0.27	−0.45	−0.88	−0.25	0.40

Note: T_max (Maximum temperature °C), T_min (Minimum temperature °C), RF (Rainfall, mm), RH₁ (Morning relative humidity, %), RH₂ (Evening relative humidity, %), SSH (sunshine hours).

Table 2. Different relationships established between YSB light traps catches and meteorological parameters so as to develop a forecasting model for the pest.

Equation No.	Weather factors considered	Model equation	Model statistics
1	Tmax, Tmin, RF, RH1, RH2, SSH	$$log\text{YSB}=0.01667-0.72685\times {\text{T}}_{\text{max}}(\text{Oct}3\text{-}\text{week})-3.42\times {\text{T}}_{\text{min}}(\text{Nov}1\text{-}\text{week})+2.31\times \text{RF}(\text{Oct}2\text{-}\text{week})+11.23\times {\text{RH}}_1(\text{Nov}4\text{-}\text{week})+0.886\times {\text{RH}}_2(\text{Nov}1\text{-}\text{week})-18.49\times \text{SSH}(\text{Oct}4\text{-}\text{week})$$	(R ^2 = 0.97, p = 0.0001; RMSE = 0.68, N = 7)
2	Tmax, Tmin, RF, RH1, RH2, (SSH excluded)	$$log\text{YSB}=15.22-0.04\times {\text{T}}_{\text{max}}(\text{Oct}3\text{-}\text{week})-0.346\times {\text{T}}_{\text{min}}(\text{Nov}1\text{-}\text{week})-0.039\times \text{RF}(\text{Oct}2\text{-}\text{week})-0.136\times {\text{RH}}_1(\text{Nov}4\text{-}\text{week})+0.108\times {\text{RH}}_2(\text{Nov}1\text{-}\text{week})$$	(R ^2 = 0.99, p < 0.083; RMSE = 0.057, N = 7)
3	Tmax, Tmin, RF, RH1 (SSH and RH2 excluded)	$$log\text{YSB}=5.54+0.208\times {\text{T}}_{\text{max}}(\text{Oct}3\text{-}\text{week})-0.214\times {\text{T}}_{\text{min}}(\text{Nov}1\text{-}\text{week})-0.0145\times \text{RF}(\text{Oct}2\text{-}\text{week})-0.44\times {\text{RH}}_1(\text{Nov}4\text{-}\text{week})$$	(R ^2 = 0.98, p = 0.03; RMSE = 0.40, N = 7)
4	Tmax, Tmin, RF (SSH,RH2 and RH1 excluded)	$$log\text{YSB}=12.752-0.1502\times {\text{T}}_{\text{max}}(\text{Oct}3\text{-}\text{week})-0.256\times {\text{T}}_{\text{min}}(\text{Nov}1\text{-}\text{week})-0.0098\times \text{RF}(\text{Oct}2\text{-}\text{week})$$	(R ^2 = 0.64, p = 0.31; RMSE = 0.40, N = 7)
5	Tmax, Tmin, RH1 (SSH,RH2 and RF excluded)	$$log\text{YSB}=7.89-0.025\times {\text{T}}_{\text{max}}(\text{Oct}3\text{-}\text{week})-0.097\times {\text{T}}_{\text{min}}(\text{Nov}1\text{-}\text{week})-0.025\times \text{R}{\text{H}}_1(\text{Nov}4\text{-}\text{week})$$	(R ^2 = 0.51, p = 0.47; RMSE = 0.46, N = 7)
6	Tmax, RF, RH1 (SSH,RH2 and Tmin excluded)	$$log\text{YSB}=0.361+0.270\times {\text{T}}_{\text{max}}(\text{Oct}3\text{-}\text{week})-0.008\times \text{RF}(\text{Oct}2\text{-}\text{week})-0.054\times \text{R}{\text{H}}_1(\text{Nov}4\text{-}\text{week})$$	(R ^2 = 0.60, p = 0.30; RMSE = 0.421, N = 7)
7	Tmin, RF, RH1 (SSH,RH2 and Tmax excluded)	$$log\text{YSB}=10.631-0.223\times {\text{T}}_{\text{min}}(\text{Nov}1\text{-}\text{week})-0.0126\times \text{RF}(\text{Oct}2\text{-}\text{week})-0.032\times \text{R}{\text{H}}_1(\text{Nov}4\text{-}\text{week})$$	(R ^2 = 0.92, p = 0.030; RMSE = 0.178, N = 7)

Note: T_max (Maximum temperature °C), T_min (Minimum temperature °C), RF (Rainfall, mm), RH₁ (Morning relative humidity, %), RH₂ (Evening relative humidity, %), SSH (sunshine hours).

Table 3. Multiple linear regression between yearly peaks of YSB light trap catches and weekly mean values of weather factors (Final model) during seven years at Mandya, Karnataka

Predictors	Co-efficient	Standard error	p-value
Constant (Intercept)	10.63^**	1.1923	0.0030
Tmin (b1)	−0.22371^**	0.0532	0.0246
RF (b2)	−0.01260^**	0.0030	0.0249
RH1 (b3)	−0.03237^**	0.0085	0.0320

Note: R ² = 0.92; F = 13.21^**; p = 0.031.

Figure 1. Validation of YSB-weather model developed for Mandya, Karnataka