The Experts below are selected from a list of 58248 Experts worldwide ranked by ideXlab platform
Wang Jian - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Morphological Structure of Nature Yellow Silkworm Silk Fiber With Other Silk Fibers
Science of Sericulture, 2008Co-Authors: Wang JianAbstract:To promote the development and application of multicolor silk,the morphological structure of the nature yellow silkworm silk from a silkworm variety,selected by cross Breeding Method,was investigated by SEM and compared with the normal white silkworm silk,the golden wild silkworm silk from Cambodia,and the Antheraea pernyi silk,respectively.Results shown that the morphological structure of the nature yellow silkworm silk fiber was triangle,more close to the normal white silkworm silk fiber,the golden wild silkworm silk fiber also was close to triangle,but the Antheraea pernyi silk fiber was close to oval.The gloss of the nature yellow silkworm silk was better than the normal white silkworm silk and the Antheraea pernyi silk.The ionetching experiment results showed that tissue structure of the nature yellow silk was tighter than that of the normal white silkworm silk.
Ma Hong-ying - One of the best experts on this subject based on the ideXlab platform.
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Effect of Different Seedling Raising Methods on Seedling Growth of Eustoma grandiflorum
Tianjin Agricultural Sciences, 2012Co-Authors: Ma Hong-yingAbstract:The experimental comparison of different seedling raising Methods on seedling growth of Eustoma grandiflorum was done. The results showed that floating Breeding Method and a special fertilizer for tobacco seedlings (N∶P ∶K=18∶9 ∶18) could shorten Breeding period and improve seedling quality.
A. Klein - One of the best experts on this subject based on the ideXlab platform.
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Genomic selection programs for yield stability in pea
2016Co-Authors: A. Klein, Vincent Richer, J.f. Herbommez, Paul Declerck, M. Floriot, Jean-philippe Pichon, H. Duborjal, M. Leveugle, Emilie Vieille, Karen BoucherotAbstract:Genomic selection is a Breeding Method that uses increasingly abundant genomic information and statistical modelling to select superior genotypes based on genomic estimated Breeding values (GEBV). The aims of the PeaMUST work-package1 are: 1- to build a genomic selection prediction equation for yield stability inl ow-input cropping systems, 2- to implement a genomic selection program and, 3- to evaluate the genetic progress obtained after one and two genomic selection cycles. A classical GEBV Breeding scheme was defined.
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Towards genome-wide Breeding for yield stability in spring pea
2014Co-Authors: A. Klein, J.f. Herbommez, Jean-philippe Pichon, H. Duborjal, Nadim Tayeh, Mathieu Siol, Jorge Duarte, Hervé Houtin, Norbert Blanc, Jean-marc ValdriniAbstract:Field pea (Pisum sativum L.) is an attractive crop for human and livestock nutrition and an important contributor to low-input farming systems. Multiple environmental challenges face field pea production and penalize yield regularity. The work-package 1 of the French National ANR project PeaMUST aims at identifying efficient gene combinations for yield stability in low-input cropping systems through genomic selection. Genomic selection is a new Breeding Method that uses increasingly abundant genomic information and statistical modelling to select superior genotypes based on genomic estimated Breeding values (GEBVs). The main goals are: 1- to build a genomic selection prediction equation for yield stability in low-input cropping systems, 2- to implement a genomic selection program and, 3- to evaluate the genetic progress obtained after one and two genomic selection cycles.
Eugenia Kalnay - One of the best experts on this subject based on the ideXlab platform.
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Ensemble Forecasting at NCEP and the Breeding Method
Monthly Weather Review, 1997Co-Authors: Zoltan Toth, Eugenia KalnayAbstract:The Breeding Method has been used to generate perturbations for ensemble forecasting at the National Centers for Environmental Prediction (formerly known as the National Meteorological Center) since December 1992. At that time a single Breeding cycle with a pair of bred forecasts was implemented. In March 1994, the ensemble was expanded to seven independent Breeding cycles on the Cray C90 supercomputer, and the forecasts were extended to 16 days. This provides 17 independent global forecasts valid for two weeks every day. For efficient ensemble forecasting, the initial perturbations to the control analysis should adequately sample the space of possible analysis errors. It is shown that the analysis cycle is like a Breeding cycle: it acts as a nonlinear perturbation model upon the evolution of the real atmosphere. The perturbation (i.e., the analysis error), carried forward in the first-guess forecasts, is ‘‘scaled down’’ at regular intervals by the use of observations. Because of this, growing errors associated with the evolving state of the atmosphere develop within the analysis cycle and dominate subsequent forecast error growth. The Breeding Method simulates the development of growing errors in the analysis cycle. A difference field between two nonlinear forecasts is carried forward (and scaled down at regular intervals) upon the evolving atmospheric analysis fields. By construction, the bred vectors are superpositions of the leading local (timedependent) Lyapunov vectors (LLVs) of the atmosphere. An important property is that all random perturbations assume the structure of the leading LLVs after a transient period, which for large-scale atmospheric processes is about 3 days. When several independent Breeding cycles are performed, the phases and amplitudes of individual (and regional) leading LLVs are random, which ensures quasi-orthogonality among the global bred vectors from independent Breeding cycles. Experimental runs with a 10-member ensemble (five independent Breeding cycles) show that the ensemble mean is superior to an optimally smoothed control and to randomly generated ensemble forecasts, and compares favorably with the medium-range double horizontal resolution control. Moreover, a potentially useful relationship between ensemble spread and forecast error is also found both in the spatial and time domain. The improvement in skill of 0.04‐0.11 in pattern anomaly correlation for forecasts at and beyond 7 days, together with the potential for estimation of the skill, indicate that this system is a useful operational forecast tool. The two Methods used so far to produce operational ensemble forecasts—that is, Breeding and the adjoint (or ‘‘optimal perturbations’’) technique applied at the European Centre for Medium-Range Weather Forecasts—have several significant differences, but they both attempt to estimate the subspace of fast growing perturbations. The bred vectors provide estimates of fastest sustainable growth and thus represent probable growing analysis errors. The optimal perturbations, on the other hand, estimate vectors with fastest transient growth in the future. A practical difference between the two Methods for ensemble forecasting is that Breeding is simpler and less expensive than the adjoint technique.
Xiao Guang-hui - One of the best experts on this subject based on the ideXlab platform.
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Watermelon Molecular Breeding and Utilization of Bottle Gourd's Resistance to Fusarium Wilt in Disease Resistant Breeding of Watermelon
Hunan Agricultural Sciences, 2002Co-Authors: Xiao Guang-huiAbstract:The principle, Methods, and technical characteristics of the 2 watermelon molecular Breeding technologies of exogenous DNA direct introduction and gene engineering technology, and the recent advances were reviewed. watermelon molecular Breeding Method of bottle gourd's resistance to fusarium wilt introduction into watermelon, variations in the characters of introduced offsprings, the results of resistant to milt materials and resistant hybrid combinations Breeding, and the recent breakthrough advances on watermelon resistant to wilt disease Breeding were reviewed in detail in this paper.