At Faridkot, the peak occurrence recorded in 37th and 39th weeks between 2005-06 and 2009-10, whereas it was recorded in 30th and 37th weeks between 2010-11 and 2018-19. At Hisar, the peak occurrence recorded in 28th and 30th weeks between 2005-06 and 2009-10, whereas it was recorded in 32nd and 37th weeks between 2010-11 and 2018-19. At Sriganganagar, the peak occurrence recorded in 31th and 36th weeks between 2005-06 and 2009-10, whereas it was recorded in 29th and 33th weeks between 2010-11 and 2018-19.
Statistical Model (log-Linear) developed for the whitefly population (dependent variable) against the weather parameters (independent Variables) Maximum Temperature, Minimum Temperature, Rain Fall, Morning Relative Humidity, Evening Relative Humidity. The model attempted for three different scenarios – whitefly scenario based on the same week, previous week and week before previous week climatic factor.
The model found that in Faridkot, minimum temperature had larger influence on whitefly build-up. Statistically, 1% increase in minimum temperature in previous week or week before previous week had high influence on whitefly build-up to the tune of 500% during the period 2010-11 to 2017-18. On the other hand, equally, Maximum temperature had negative influence on whitefly build-up. 1% increase in maximum temperature in same week or previous week or week before previous week had high influence on whitefly reduction ranging from 444 to 545% during the period 2010-11 to 2017-18.Stability analysis of G. arboreum cotton yield under different agronomical practices
The prime purpose of this study was to explore yield stability of long linted G. arboreum cotton cultivars based on stability statistics, with specific aim at studying cultivar behaviour regarding yield of cotton under different agro-ecological conditions. Seven G. arboreum cotton cultivars were used under two spacings (60 x 15 cms and 60 x 30 cms.) on two soil types (inceptisol and vertisol) and two dates of sowing (early and late) in a replicated field trial. Both parametric and non-parametric stability statistics used for the study.
Stability analyses of both parametric, as well as non-parametric, were done for seed cotton yield data collected from eight agronomical situations. Among the genotypes tested, PA-812 was identified as a stable and robust genotype with good seed cotton yield under varied agronomical situations, besides, possessing very good fiber quality.Decision support systems for cotton pest management – a mobile app
The study aimed to develop an interactive decision support system to assist the cotton farmers to make appropriate cotton pest management strategies with the help of illustrative and voice module without any literacy and language boundary. The contents and knowledges in the system have been used from output of various research projects undertaken by cotton entomologists over the period for Indian condition and scrupulously analysed and incorporated.
The initial screen has the option for symptoms of damages from flower, boll, stem, leaf. If the user selects any of the options the system takes to the multiple image screen where the different symptoms of damages by different pest for the selected part of the plant. If the user selects any one the multiple images screen, the system takes to another window, which contains related images of the select image for clarity. This process under taken by the system internally using artificial intelligence tools. If the user selects any one of the images in the screen, the system identifies the exact pest. The system has options classification, identification, damage symptoms, and life history of the identified pest, along with audio option of the description mentioned. This option is handy for the illiterates to know the contents.
The system also has management option, in the management screen has the options for Chemical control, biological control, and cultural and mechanical control. If the user selects the chemical control option, the system check for Economic Threshold Level (ETL) of the pest infestation and based on the user input the system decides further course of action. All the recommended chemicals are listed with commercial brand names. If the user selects brand button for particular chemical, the system takes to next window it contains insecticide/pesticide name, active ingredient, formulation, waiting period, available commercial brand in the market along with approximate rate.