Replication Concepts for Bioenergy Research Experiments

Abstract

While there are some large and fundamental differences among disciplines related to the conversion of biomass to bioenergy, all scientific endeavors involve the use of biological feedstocks. As such, nearly every scientific experiment conducted in this area, regardless of the specific discipline, is subject to random variation, some of which is unpredictable and unidentifiable (i.e., pure random variation such as variation among plots in an experiment, individuals within a plot, or laboratory samples within an experimental unit) while some is predictable and identifiable (repeatable variation, such as spatial or temporal patterns within an experimental field, a glasshouse or growth chamber, or among laboratory containers). Identifying the scale and sources of this variation relative to the specific hypotheses of interest is a critical component of designing good experiments that generate meaningful and believable hypothesis tests and inference statements. Many bioenergy feedstock experiments are replicated at an incorrect scale, typically by sampling feedstocks to estimate laboratory error or by completely ignoring the errors associated with growing feedstocks in an agricultural area at a field or farmland (micro-or macro-region) scale. As such, actual random errors inherent in experimental materials are frequently underestimated, with unrealistically low standard errors of statistical parameters (e.g., means), leading to improper inferences. The examples and guidelines set forth in this paper and many of the references cited are intended to form the general policy and guidelines for replication of bioenergy feedstock experiments to be published in BioEnergy Research.

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