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  • Oxford University Press  (2)
  • Blackwell Science Ltd  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Science Ltd
    Grass and forage science 60 (2005), S. 0 
    ISSN: 1365-2494
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: The use of imaging spectroscopy to predict the herbage mass of dry matter (DM), DM content of herbage and crude fibre, ash, total sugars and mineral (N, P, K, S, Ca, Mg, Mn, Zn and Fe) concentrations was evaluated. The experimental system used measured reflectance between 404 and 1650 nm at high spatial (0·28–1·45 mm2) and spectral resolution. Data from two experiments with Lolium perenne L. mini-swards were used where the degree of sward damage or N-fertilizer application varied. Regression models were calibrated and validated and the potential reduction in prediction error with multiple observations was estimated. The mean prediction errors for DM mass, DM content and N, total sugars, ash and crude fibre concentrations were 235–268 kg ha−1, 9·6–16·8 g kg−1, 2·4–3·4 g kg DM−1, 16·2–27·7 g kg DM−1, 5·8–6·5 g kg DM−1 and 8·4–10·4 g kg DM−1 respectively. The predictions for concentrations of P, K, S and Mg allowed identification of deficiency levels, in contrast to the concentrations of Na, Zn, Mn and Ca which could not be predicted with adequate precision. Prediction errors of DM mass may be maximally reduced to 95–142 kg ha−1 with 25 replicate measurements per field. It is concluded that imaging spectroscopy can provide an accurate means for assessment of DM mass of standing grass herbage. Predictions of macronutrient content and feeding value were satisfactory. The methodology requires further evaluation under field conditions.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2012-09-01
    Description: In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experimental design. Several methods have recently become available for analyzing such data while utilizing the underlying design. We review these methods by putting them in a unifying and general framework to facilitate understanding the (dis-)similarities between the methods. The biological question dictates which method to use and the framework allows for building new methods to accommodate a range of such biological questions. The framework is built on well known fixed-effect ANOVA models and subsequent dimension reduction. We present the framework both in matrix algebra as well as in more insightful geometrical terms. We show the workings of the different special cases of our framework with a real-life metabolomics example from nutritional research and a gene-expression example from the field of virology.
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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  • 3
    Publication Date: 2011-12-23
    Print ISSN: 1467-5463
    Electronic ISSN: 1477-4054
    Topics: Biology , Computer Science
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