Indian Agricultural Statistics Research Institute

About: Indian Agricultural Statistics Research Institute is a facility organization based out in New Delhi, India. It is known for research contribution in the topics: Population & Small area estimation. The organization has 454 authors who have published 870 publications receiving 7987 citations.

Weather based forecast models for diseases in mustard crop

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Predicting aphid (Lipaphis erysimi) growth in oilseed Brassica using near surface meteorological data from NOAA TOVS-a case study

Abstract: Pest infestation in crops is highly influenced by agrometeorological parameters. Weather based early warning of pest infestation is being practised using statistical and dynamic simulation models on point scale. Satellite based inputs and epidemiological models can extend the application to areas with irregular and non-existing ground observations. The present study describes the use of National Oceanic and Atmospheric Administration TIROS (Television and InfraRed Operational Satellites) Operational Vertical Sounder (TOVS) near surface air temperature at 1430 h Local Apparent Time (LAT) over India (0900 Universal Time Code) for modelling onset, build-up and peak aphid (Lipaphis erysimi) infestation on Indian Mustard (Brassica juncea) crop over Bharatpur and Kalyani, falling in semi-arid and sub-humid regions, respectively. The daily cumulative TOVS air temperature from 1 October 2001 showed high correlation (R 2: 0.99) with observed datasets. Exponential relationships were found to be the best empirical fit between TOVS cumulative air temperature (CATTOVS) and crop age at aphid onset (R 2: 0.7-0.99) and peak infestation (R 2: 0.91-0.95) for two stations representing semi-arid (Bharatpur) and sub-humid (Kalyani) agroclimatic conditions. Second order polynomial fits were found (R 2: 0.81-0.85) at both the stations between peak aphid count and CATTOVS at peak. Estimates of intermediate linear aphid build-up to peak, computed using location-specific linear growth rate (LGR) showed a higher standard error (SE) of 20% of mean at Kalyani (0-99), compared to 8% at Bharatpur (4-58). The common prediction models on linear start and peak were developed using TOVS noon time specific humidity (SPH) weighted thermal units and sowing dates. The standard error (SE) of estimated intermediate aphid-build-up became less: 4.5% of observed mean counts for pooled datasets with a common model, irrespective of sowing dates. The correlation between estimates and observations was 71%. The common model will be useful for general application in the absence of availability of local models.

A model for forecasting spot blotch disease in wheat

Abstract: Spot blotch (Bipolaris sorokiniana) in wheat is considered as one of the most important diseases in warmer areas of the world, particularly in humid South Asia. Number of lesion cm−2 leaf area, as an attribute of disease severity, was observed to be significantly influenced by temperature and duration of high relative humidity 95% or above (RH-duration). Temperature and RH-duration response of spot blotch infection index were explained based on the established infection models. Temperature response was a typically unimodal bell-shaped curve with minimum, optimum and maximum temperatures of 16°, 29° and 36°C respectively. Response of RH-duration was sigmoid asymptotic, characterized by a monotonic increase in infection index with the increase of RH-duration. Requirement of RH-duration for infection decreased with increase in temperature up to 29°C and increased afterwards till 34°C. Minimum RH-duration (m) requirement was estimated at 9 h after which lesions could be seen. The fitted equations were used to produce a surface response for temperature (T) and RH-duration (D) to explain variation in daily infection index (Y) and proposed as a model: Y = [(36-T)/7]*[(T-16)/13]1.8571*[1-(0.8114) D-m ]. During active vegetative to early reproductive phase, occurrence of favourable threshold (temperature above 16°C and RH-duration above 9 h) and accumulation of daily infection index about 1.2 was noted to match symptom appearance under field conditions. The infection model developed could be used to decide action thresholds for evolving management strategies.