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recommendation domain is a unique combination of these environmental characteristics and evaluation criteria.
Recommendation domains, then, are one or more subsets of a research domain which
target for homoceneitv of 1) natural and farmer-created biophysical environments, and 2) farmers' evaluation criteria for the technology being evaluated. Also modified from previous thinking that recommendation domains pertained to whole farms (Byerlee, et al., 1982), or cropping or farming systems (Hildebrand, 1981) are that these logically can refer as well to individual fields on a farm, or even different locations in the same field. The most important concept is to consider recommendation domains as environments whose biophysical and socioeconomic characteristics can be identified.
The nature of on-farm research in recommendation domains is validation, to confirm
answers as to 1) how each alternative (treatment) will respond, and 2) where each alternative is best, as well as to refine the characterization of the recommendation domains and farmer evaluation criteria. At this stage, the number of treatments in on-farm trials is limited. Extension personnel can play an increasingly important role by expanding coverage for evaluation and enhancing exposure (diffusion) of the technology.
Diffusion domains
Diffusion domains are informal interpersonal communication networks through which newly acquired knowledge of agricultural technology normally flows. Knowledge of these networks is important in helping research and extension personnel lc-ate on-farm trials to target for communication.
The challenge of diagnosis and identification of these several domains is complicated by how information is collected, analyzed, and evaluated from on-farm trials. We need to clearly identify where research results can be applied, how broad the recommendations can be, and for whom these new technologies are appropriate. To be credible for farmers as well as rigorous from a statistical point of view, results from on-farm research must be analyzed and evaluated according to valid statistical methods.
ANALYTIC VERSUS ENUMERATIVE STATISTICAL METHODS
Over the past several decades, procedures for the design and analysis of experiments have been developed and utilized very effectively in agricultural research. Many of these procedures have become so institutionalized that it is easy to lose sight of the fact .that they are only specific applications of statistical theory to specific experimental conditions namely, those of the agricultural experiment station.
Are the requirements of on-farm trials; identical to those of experiment station trials? There is no good reason to expect they should be. In fact, on-farm trials differ from their on-station counterparts in two very significant ways: 1) the objectives are typically quite different; and 2) the variability of the data in an on-farm trial is typically more complex and must be addressed with greater sophistication than is normally required for an on-station trial.
How do the objectives of on-farm trials differ from on-station research? How does this in turn affect decisions regarding appropriate statistical methodology? Although probably not obvious to agricultural researchers, on-farm trials have many statistical similarities to quality improvement experimentation in. manufacturing.. Deming (1953. 1 9751, the statistician whose contributions to