WEATHER-BASED REGRESSION ANALYSIS FOR PREDICTION AND VALIDATION OF GROUNDNUT LEAF MINER (APROAEREMA MODICELLA) INCIDENCE UNDER ARID ALFISOLS - P , RADHIKA (The Journal of Research ANGRAU, 2026)
A study was conducted during 2020 to 2022 under arid alfisols at the Agricultural Research Station, Ananthapuramu, Andhra Pradesh.A Regression analysis of Groundnut Leaf Miner (GLM ) moth catches with weather parameters revealed a significant positive correlation with Relative Humidity-I ( RH-I) at 5 days lag (0.55*) and 6 days lag (0.59*) and Rainfall (RF) at 8 days lag (0.51*) and negative correlation with Wind velocity (Wv) (-0.51*).The leaf damage showed significant and positive correlation with Maximum Temperature (Tmax) (0.67*), Mean Temperature (Tmean) (0.53*),Evaporation (Evp) (0.72*) at 6 days lag and negative correlation with Relative Humidity-II (RH-II)(-0.68*) and Wv(-0.5*) at 5 days lag, and RH-I (-0.5*),Rf(-0.65*) at 6 days lag. RH-II at (-0.58*) at 7 days lag and 8 days lag RH-II (-0.55*) and Sun shine hours (Ssh) ( 0.69*).No.of Live larvae/plant recorded significant positive correlation with Tmean(0.5*) and Rf(0.67*) at 9 days lag. and significant negative correlation with Rainfall (-0.5*) at 6 days lag and Wv(-0.79*) at 7 days lag and Minimum Temperature (Tmin) (-0.50*), Tmean(-0.55*) at 8 days lag and Tmax(-0.62*), Tmin(-0.65*), Tmean(-0.74*) and RH-I (-0.5*) at 9 days lag. The study showed that mean temperature was the strongest positive predictor of groundnut leafminer incidence, while higher maximum and minimum temperatures suppressed the pest. Morning humidity slightly favoured infestation, but higher afternoon humidity reduced it. These results highlight temperature and humidity as key weather drivers of leafminer outbreaks. With an R² of 0.97, the model explained most of the variation in pest incidence, though independent validation is needed to confirm its reliability.