Adaptation trials of Atriplex and Maireana species and their response to saline waterlogged conditions in Pakistan

Utilization of salt affected wasteland by growing forage shrubs has enormous economic and environmental implication for developing countries like Pakistan, where approximately 6.3 million ha of the land is salt affected. Considering the importance of Atriplex and Maireana species, research has been conducted using their different species on the salt affected soils of Faisalabad. Most of Atriplex and Maireana species survived under the environmental conditions of Faisalabad and gave the good yield in the form of forage. Some of these species are woody and can be used for fuel purposes. Sixteen genotypes of Atriplex and Maireana were tested for their tolerance to waterlogging in order to identify halophytic fodder shrubs suitable for growth on secondary salt-affected and waterlogged farmland. The physiological and morphological responses of the species tested were typical of species with a generally poor tolerance to waterlogging. Despite this, some species (eg A. Amnicola) were surprisingly resistant, surviving up to five months of waterlogging at moderate salinity and high evapotranspirational demand. The most resistant species, A amnicola maintained higher transpiration rates, leaf water potentials and shoot extension rates than most other species during five weeks of waterlogging, and a return to control levels more quickly than other species after plots were drained. Although little morphological adaptation to waterlogged conditions was detected, a shallow and extensive lateral root system and the formation of many short aerenchymatous adventitious roots from procumbent branches appeared to advantage A. Amnicola in an environment highly heterogeneous in salinity and low in oxygen concentration. Shallow fibrous rooted species were quickly killed by waterlogging, although the procumbent branches of some individuals survived as clones if they developed adventitious roots.

Design of clinical pharmacology trials

1. There are a variety of methods that could be used to increase the efficiency of the design of experiments. However, it is only recently that such methods have been considered in the design of clinical pharmacology trials. 2. Two such methods, termed data-dependent (e.g. simulation) and data-independent (e.g. analytical evaluation of the information in a particular design), are becoming increasingly used as efficient methods for designing clinical trials. These two design methods have tended to be viewed as competitive, although a complementary role in design is proposed here. 3. The impetus for the use of these two methods has been the need for a more fully integrated approach to the drug development process that specifically allows for sequential development (i.e. where the results of early phase studies influence later-phase studies). 4. The present article briefly presents the background and theory that underpins both the data-dependent and -independent methods with the use of illustrative examples from the literature. In addition, the potential advantages and disadvantages of each method are discussed.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.