Design and optimisation of a large-area process-based model for annual crops

The formulation of a new process-based crop model, the general large-area model (GLAM) for annual crops is presented. The model has been designed to operate on spatial scales commensurate with those of global and regional climate models. It aims to simulate the impact of climate on crop yield. Procedures for model parameter determination and optimisation are described, and demonstrated for the prediction of groundnut (i.e. peanut; Arachis hypogaea L.) yields across India for the period 1966-1989. Optimal parameters (e.g. extinction coefficient, transpiration efficiency, rate of change of harvest index) were stable over space and time, provided the estimate of the yield technology trend was based on the full 24-year period. The model has two location-specific parameters, the planting date, and the yield gap parameter. The latter varies spatially and is determined by calibration. The optimal value varies slightly when different input data are used. The model was tested using a historical data set on a 2.5degrees x 2.5degrees grid to simulate yields. Three sites are examined in detail-grid cells from Gujarat in the west, Andhra Pradesh towards the south, and Uttar Pradesh in the north. Agreement between observed and modelled yield was variable, with correlation coefficients of 0.74, 0.42 and 0, respectively. Skill was highest where the climate signal was greatest, and correlations were comparable to or greater than correlations with seasonal mean rainfall. Yields from all 35 cells were aggregated to simulate all-India yield. The correlation coefficient between observed and simulated yields was 0.76, and the root mean square error was 8.4% of the mean yield. The model can be easily extended to any annual crop for the investigation of the impacts of climate variability (or change) on crop yield over large areas. (C) 2004 Elsevier B.V. All rights reserved.

Design and formative evaluation of an information kiosk on cattle health for landless cattle owners

This paper describes and analyses the experience of designing, installing and evaluating a farmer-usable touch screen information kiosk on cattle health in a veterinary institution in Pondicherry. The contents of the kiosk were prepared based on identified demands for information on cattle health, arrived at through various stakeholders meetings. Information on these cattle diseases and conditions affecting the livelihoods of the poor was provided through graphics, text and audio back-up, keeping in mind the needs of landless and illiterate poor cattle owners. A methodology for kiosk evaluation based on the feedback obtained from kiosk facilitator, critical group reflection and individual users was formulated. The formative evaluation reveals the potential strength this ICT has in transferring information to the cattle owners in a service delivery centre. Such information is vital in preventing diseases and helps cattle owners to present and treat their animals at an early stage of disease condition. This in turn helps prevent direct and indirect losses to the cattle owners. The study reveals how an information kiosk installed at a government institution as a freely accessible source of information to all farmers irrespective of their class and caste can help in transfer of information among poor cattle owners, provided periodic updating, interactivity and communication variability are taken care of. Being in the veterinary centre, the kiosk helps stimulate dialogue, and facilitates demand of services based on the information provided by the kiosk screens.

Statistical design and analysis of pharmacogenetic trials

Pharmacogenetic trials investigate the effect of genotype on treatment response. When there are two or more treatment groups and two or more genetic groups, investigation of gene-treatment interactions is of key interest. However, calculation of the power to detect such interactions is complicated because this depends not only on the treatment effect size within each genetic group, but also on the number of genetic groups, the size of each genetic group, and the type of genetic effect that is both present and tested for. The scale chosen to measure the magnitude of an interaction can also be problematic, especially for the binary case. Elston et al. proposed a test for detecting the presence of gene-treatment interactions for binary responses, and gave appropriate power calculations. This paper shows how the same approach can also be used for normally distributed responses. We also propose a method for analysing and performing sample size calculations based on a generalized linear model (GLM) approach. The power of the Elston et al. and GLM approaches are compared for the binary and normal case using several illustrative examples. While more sensitive to errors in model specification than the Elston et al. approach, the GLM approach is much more flexible and in many cases more powerful. Copyright © 2005 John Wiley & Sons, Ltd.

Bayesian design and conduct of phase II single-arm clinical trials with binary outcomes: a tutorial

The aim of phase II single-arm clinical trials of a new drug is to determine whether it has sufficient promising activity to warrant its further development. For the last several years Bayesian statistical methods have been proposed and used. Bayesian approaches are ideal for earlier phase trials as they take into account information that accrues during a trial. Predictive probabilities are then updated and so become more accurate as the trial progresses. Suitable priors can act as pseudo samples, which make small sample clinical trials more informative. Thus patients have better chances to receive better treatments. The goal of this paper is to provide a tutorial for statisticians who use Bayesian methods for the first time or investigators who have some statistical background. In addition, real data from three clinical trials are presented as examples to illustrate how to conduct a Bayesian approach for phase II single-arm clinical trials with binary outcomes.

Design and analysis of climate model experiments for the efficient estimation of anthropogenic signals

Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings. The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.

Design and theoretical study of nickel catalysts for syndiotactic polyolefins

A nickel catalyst was modeled with ligand L-2, [ NH = CH-CH = CH-O](-), which should have potential use as a syndiotactic polyolefin catalyst, and the reaction mechanism was studied by theoretical calculations using the density functional method at the B3LYP/ LANL2MB level. The mechanism involves the formation of the intermediate [(NiLMe)-Me-2](+), in which the metal occuples a T-shaped geometry. - This intermediate has two possible structures with the methyl group trans either to the oxygen or to the nitrogen atom of L-2. The results show that both structures can lead to the desired product via similar reaction paths, A and B. Thus, the polymerization could be considered as taking place either with the alkyl group occupying the position trans to the Ni-O or trans to the Ni-N bond in the catalyst. The polymerization process thus favors the catalysis of syndiotactic polyolefins. The syndiotactic synthesis effects could also be enhanced by variations in the ligand substituents. From energy considerations, we can conclude that it is more favorable for the methyl group to occupy the trans-O position to form a complex than to occupy the trans-N position. From bond length considerations, it is also more favoured for ethene to occupy the trans-O position than to occupy the trans-N position.