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  • First-order lateral roots  (1)
  • STATISTICS AND PROBABILITY  (1)
  • 1
    Electronic Resource
    Electronic Resource
    Springer
    New forests 9 (1995), S. 225-236 
    ISSN: 1573-5095
    Keywords: First-order lateral roots ; hardwood seedling establishment ; northern red oak ; seedling root system morphology ; nursery grading procedures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Abstract On 3 sites, 3-year performance of 1+0 northern red oak (Quercus rubra L.) seedlings was evaluated with respect to initial root system grade. Seven hundred twenty nursery-run bareroot northern red oak seedlings were graded according to numbers of large (〉1mm) first-order lateral roots and outplanted in spring 1987 on eight 90-tree plots distributed among three sites in central Iowa. Survival, height growth, and diameter growth were significantly greater for seedlings with 10 or more large first-order lateral roots than for seedlings with 4 or fewer. Seedling survival and growth were significantly and positively related to initial root grade. First-year height growth, however, was significantly and negatively correlated with initial height. Combined results for seedling survival and growth indicated that red oak seedlings with five or more large first-order lateral roots have a greater probability of success both in terms of survival and early growth than do those with four or fewer first-order lateral roots.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2019-07-13
    Description: When it is known a priori exactly to which finite dimensional manifold the probability density function gives rise to a set of samples, the parametric maximum likelihood estimation procedure leads to poor estimates and is unstable; while the nonparametric maximum likelihood procedure is undefined. A very general theory of maximum penalized likelihood estimation which should avoid many of these difficulties is presented. It is demonstrated that each reproducing kernel Hilbert space leads, in a very natural way, to a maximum penalized likelihood estimator and that a well-known class of reproducing kernel Hilbert spaces gives polynomial splines as the nonparametric maximum penalized likelihood estimates.
    Keywords: STATISTICS AND PROBABILITY
    Type: NASA-CR-144384 , REPT-275-025-016 , Ann. Meeting of the Inst. of Mathematical Statistics; Aug 15, 1974; Edmonton; Canada
    Format: application/pdf
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