Regional Trials


 Regional trials are amenable to agronomic and
agro-socioeconomic analysis. They are designed to expose
the best treatments from site-specific trials to a much
wider range of environments within a recommendation
domain. Perhaps six of the best treatments are included,
and five to ten sites can be utilized. A recommended
design is randomized complete blocks with two to four
replications per site. ANOVA, regression, or modified
stability analysis (see Chapter VII)-can be utilized.
Combined analysis with site as a source of variation can
be used in ANOVA.

Farmer-Managed Trials

 These trials provide the opportunity for the farmers
themselves to manage and evaluate the one or two most
promising treatments from regional trials. Large plots
with no replications are used. The purpose is for the
farmers to be able to compare the treatments with their
own practices, so one plot with these practices can be
included in the design. In practice, this check plot
serves the researchers more than the farmers, because the
farmers will be able to evaluate results based on their
own fields. If researchers wish to measure results of the
farmers' own practices, they can also sample from the
farmers' fields. However, agronomic and economic records
of the farmers' practices must be kept to provide the
necessary information. It is desirable to have at least
30 farmers in these trials in a recommendation domain.
Larger numbers improve the precision of the conclusion,
but smaller numbers can still provide useful information.



 The remainder of this book deals with considerations
related to on-farm trials; the different kinds of on-farm
trials are discussed in separate chapters. Stressed
throughout is the concept that each kind of trial is part
of a sequence through which technology passes as it is
being designed, evaluated, and disseminated. None of the
steps in this sequence is sufficient in and of itself, and
all, taken together, depend on other on-farm research not
covered in the book. Some of these are preliminary or