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Global annual average temperature – a precise modelling

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Global annual average temperature (GAAT) is regarded as a precise indicator of the warming of the globe over the centuries, and its spectre is looming large with the passage of time and with the advancement of civilization. Global warming, caused by the accumulation of greenhouse gases in the atmosphere, has become the worst environmental threat to mankind. The phase 1981 to 2012 was the most crucial phase, and the impact of global warming in that phase indeed points to a disaster if not controlled now. Work on the building of appropriate models to represent the GAAT data can be found in the literature, although the precision levels (in terms of R2 values) of such models do not exceed 0.86. In this paper, six models are developed by using different combinations of mathematical functions. The developed models are superior to existing models in terms of their precision. In fact, to generate such models, extensive simulation work has been carried out not only with respect to the types of mathematical functions, but also with respect to the choices of initial values of the coefficients involved in each model. The models developed here have attained R2 values as high as 0.896.
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On determination of ETL – a distributional approach

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Economic threshold level (ETL) is an important component in pest management and control. Usually, it is determined by the grower/technologist utilizing his experience on a crop; however, for cereals the values of these indices are available. Knowledge of ETL helps reduce crop loss (and ensure less pesticide application), and as a consequence, profit is increased. Also substantial knowledge is required on the dynamics of the pest population, in order to determine the density at which the economic injury level (EIL) may be prevented (Weersink et al. 1991). This paper is devoted to the development of an analytical method (probabilistic) for determination of ETL, which is defined as the density at which control measures should be determined to prevent an increasing pest population from reaching the economic injury level. A method to model the dynamics of the pest population is also proposed. The above method is demonstrated on a real life data set on pest (whitefly) incidence on betelvine, obtained from an experiment designed for that purpose.
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