STUDY ON INSECT PESTS AND THEIR NATURAL ENEMIES IN DIFFERENT CROP GROWTH STAGES AND DEVELOPMENT OF A WEATHER-BASED PEST FORECASTING MODEL FOR PIGEONPEA
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Date
2022-07-19
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University of Agricultural Sciences, Bangalore
Abstract
The experiment was conducted during Kharif, 2019 and 2020 at Zonal Agricultural Research Station, Gandhi Krsihi Vignan Kendra, University of Agricultural Sciences,Bengaluru to know the diversity, effect of different sowing dates and weather forecasting
model for major insect pests in pigeonpea. The crop was infested by forty insect pests belonging to twenty-seven families under eight orders at various phenophases. Thirtyspecies of natural enemies contributed significantly to decline in different pest
populations. In pigeonpea ecosystem, coccinellids and hoverflies were found to feed on aphids, whereas spiders preyed on mirid bugs and pod flies. Similarly, braconid and eulophid parasitoids found parasitising pod borer complex. The highest species count of
23 at flowering and pod-forming stages and highest Simpson diversity, Margalef, Shannon Weiner and Berger-Parker Index value and lowest Pielou’s index value indicated that pigeonpea ecosystem had a rich diversity of insects and natural enemies at
reproductive crop stage. Among major pests except, pod fly, early sown pigeonpea hadthe lowest populations than late sown crop. Similarly, late sowing resulted in increasedpod and seed damage, and decrease in grain yield. Relative humidity during morning
hours exhibited a significant positive correlation (r=0.103*) with moth catches of during 2019–20. Weather forecasting model developed for by using present and historical data (2015 to 2019) for 12 standard meteorological weeks (SMWs) showed coefficient of determination (R2) value ranged from 0.40 to 0.84. The model was validated through 2020 year data and average accuracy of all 12 SMWs was 75.5 per cent. However, before being applied it in farmer fields, the derived models may be further validated in
experimental fields with regard to forewarning of pest in real-time basis. Implementation of plant protection measure based on forewarning system may be useful improving yield and minimising cost of plant protection.