Miscanthus Clones Display Large Variation in Floral Biology and Different Environmental Sensitivities Useful for Breeding

Arnoult, Stéphanie; Quillet, Marie-Christine; Brancourt‐Hulmel, Maryse

doi:10.1007/s12155-013-9381-9

Cited by 4 publications

(2 citation statements)

References 17 publications

(35 reference statements)

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“…Furthermore, knowledge about the flowering period for both the sexual and the apomictic components in interspecific hybridization is an essential condition for planning controlled crosses and achieving success in obtaining hybrids. The effects of genotype x environment interaction in the expression of flowering are well known in grasses , Arnout et al 2014, and this interaction may result in the different expression of traits per local. This can be exploited in practice, if necessary, by carrying out crosses between asynchronous elite individuals.…”

Section: Rm Simeão Et Almentioning

confidence: 99%

Flowering traits in tetraploid Brachiaria ruziziensis breeding

Simeão

Silva

Valle

2016

Crop Breed. Appl. Biotechnol.

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Section: Rm Simeão Et Almentioning

confidence: 99%

Flowering traits in tetraploid Brachiaria ruziziensis breeding

Simeão

Silva

Valle

2016

Crop Breed. Appl. Biotechnol.

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“…Arnoult et al [21] designed a two-factor experiment with four macro-environmental treatments, defined by all combinations of two daylengths and two temperature regimes, each assigned to one glasshouse. This is almost identical to the design shown in Fig.…”

Section: Guidelines For Glasshouse and Growth Chamber Researchmentioning

confidence: 99%

Replication Concepts for Bioenergy Research Experiments

2015

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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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