SMS scnews item created by Rafal Kulik at Wed 31 Oct 2007 1631
Type: Seminar
Distribution: World
Expiry: 9 Nov 2007
Calendar1: 9 Nov 2007 1400-1500
CalLoc1: Carslaw 373
Auth: rkuli(.ststaff;2434.3001)@p818.pc.maths.usyd.edu.au

# Statistics Seminar: Bishop -- How can we perform experiments in real landscapes?



How can we perform experiments in real landscapes?

Tom Bishop
(University of Sydney)

Friday, 9 November, 2.00pm

Carslaw 373

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Environmental variability is a major problem for the design and the analysis of
experiments performed in the field.  In agronomic experiments, where design-based
statistics are used, the traditional approach is to use blocking and randomization.
Part of the variation due to the environment is attributed to variation between blocks,
which reduces the residual variance and improves the sensitivity with which we can
detect treatment contrasts.  A major assumption of this approach is that block effect,
treatment effect and residual variation are all additive effects.  In other words the
treatment effect is uniform across the study area.  In a heterogeneous landscape this is
less plausible.  In the past agronomic experiments have been performed in carefully
chosen fields where the environmental, i.e.  soil variation, is relatively homogenous.

Increasingly, as agronomy moves beyond fertilizer trials, we are interested in other
types of experiments which must be located in heterogeneous landscapes if they are to
address real problems, e.g.  the effect of different land uses on carbon sequestration
rates, possibly across multiple fields or catchments.  As we increase our spatial extent
it is likely that more heterogeneity will be encountered, and the assumption of
whether a particular contrast is significant on average over the experimental area.
Increasingly we would like to manage at fine spatial resolutions, responding to
environmental variability, rather than based on averages across areas.  An example of
this is the increasing interest in site-specific crop management.  Clearly there is a
need for new approaches for the design and analysis of experiments.

In this talk I will
present a geostatistical approach for analysing experiments which enables the prediction
of treatment responses or contrasts at points or regular blocks across an experiment, or
averages over the whole experiment.  The methodology will be illustrated by 2 case
studies.

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