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Crop Variety Trials Data Management and Analysis von Yan, Weikai (eBook)

  • Erscheinungsdatum: 11.03.2014
  • Verlag: Wiley-Blackwell
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Crop Variety Trials

Variety trials are an essential step in crop breeding and production. These trials are a significant investment in time and resources and inform numerous decisions from cultivar development to end-use. Crop Variety Trials: Methods and Analysis is a practical volume that provides valuable theoretical foundations as well as a guide to step-by-step implementation of effective trial methods and analysis in determining the best varieties and cultivars. Crop Variety Trials is divided into two sections. The first section provides the reader with a sound theoretical framework of variety evaluation and trial analysis. Chapters provide insights into the theories of quantitative genetics and principles of analyzing data. The second section of the book gives the reader with a practical step-by-step guide to accurately analyzing crop variety trial data. Combined these sections provide the reader with fuller understanding of the nature of variety trials, their objectives, and user-friendly database and statistical tools that will enable them to produce accurate analysis of data.

Produktinformationen

    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 360
    Erscheinungsdatum: 11.03.2014
    Sprache: Englisch
    ISBN: 9781118688564
    Verlag: Wiley-Blackwell
    Größe: 50588 kBytes
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Crop Variety Trials

Preface

Crop variety trials are the most valued and best-funded research among applied agricultural researches. Regardless of the economic developmental level and the budget situation, crop variety trials are conducted every year in every region for every major crop of the region. Breeders rely on variety trials to select superior breeding lines to release as new cultivars; farmers rely on variety trials to choose suitable crop cultivars to grow in their farms. Processors rely on variety trials to decide where and of which cultivars to source their grains or other crop products to process.

The direct outcome from crop variety trials is data; the ultimate outcome from crop variety trials is information on the target region, the test locations, and the genotypes, thereby correct decisions can be made on the genotypes for the target region. Data analysis is the process to extract useful information and draw conclusions from the data.

Data analysis is the process to extract useful information and draw conclusions from the data.

Data analyses performed by most researchers conducting variety trials are quite simple, in spite of numerous new and advanced methods advocated by statisticians. In most variety trial systems, the annual report of variety trials is limited to the following aspects: (1) Genotype-by-trait two-way tables for each trial (location), with summary statistics for each trait, such as trial mean, standard error, and least significant difference. (2) Genotype-by-location two-way tables for each trait in absolute values. (3) Genotype-by-location two-way tables for each trait in values relative to the trial mean or to a check. Presenting relative values is one step forward, which serves as a means to remove the environmental main effects and facilitates data summary across trials. (4) Genotypic means across all locations and/or locations within subregions. This is another step forward as this gives genotypic values for the region or subregions, thereby any genotype-by-location interactions across the whole region or a subregion are removed. Genotypic values for a trait can then be used to rank the genotypes, which become the basis for selecting genotypes and recommending cultivars. (5) In addition to genotypic means for the current year, some reports also include genotypic means across recent 2–5 years, when applicable. Genotypic ranking based on data from multiyears is certainly more credible as any genotype-by-year interaction and genotype-by-location-by-year interactions would be removed.

Primitive as it may appear, these simple data summary and analyses are quite effective, as evidenced by the continuous progress in cultivar development and crop production in various crops worldwide. However, the analyses may be improved by asking a few questions. First, when summarizing across all test locations, it is assumed that there are no repeatable genotype-by-location interactions (GL) within the target region represented by these locations. Is this true? When summarizing across locations within subregions, it is assumed that there are repeatable genotype-by-subregion interactions and there are no repeatable GL within subregions. Are these true? If the answer to any of these questions is "no" or "not sure," then the data summary system may be suboptimal and should be improved. The process to answer these questions is "mega-environment analysis." Second, the genotypic means across locations and years are calculated under the assumption that all test locations are equally representative of the target mega-environment and equally informative about the genotypes. Are these true? If the answer to any of these is "no" or "not sure," then the system may be also suboptimal and needs to be improved. The process to answer these questions is "test location evaluation." Third, two genotypes ranked

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