Spatial analysis methods for forest genetic trials

By G. Dutkowski, João Costa e Silva, A. R. Gilmour, G. Lopez. 2002

186 citations recorded by the source corpus.

Subfields: Genetics, Management Science and Operations Research, Plant Science

View Paper: https://www.semanticscholar.org/paper/1181c32d3a016f37225dc766fff36a07e4bff4a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Hans-Peter Piepho
2. A. Büchse
3. Jens Möhring
4. A. Melchinger
5. João Costa e Silva
6. Hubert Wellendorf
7. Alexandre Aguiar
8. G. Dutkowski

In Management Science and Operations Research

1. A. Büchse
2. A. Melchinger
3. Hans-Peter Piepho
4. Jens Möhring
5. João Costa e Silva
6. Hubert Wellendorf
7. Alexandre Aguiar
8. G. Dutkowski

In Plant Science

1. Hans-Peter Piepho
2. A. Büchse
3. Jens Möhring
4. A. Melchinger
5. A. R. Gilmour
6. M. Sorrells
7. Jeffrey B. Endelman
8. Jean-Luc Jannink

Citations in the corpus

• Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials (2006, methodology): “The use of a single-stage approach, where the spatial, extraneous, and treatment effects are estimated simultaneously, should be superior, even with similar models, as it will avoid problems of confusing spatial variation with local random aggregations of good or poor families. Not surprisingly, our”
• BLUP for phenotypic selection in plant breeding and variety testing (2008, background): “There are some applications in forestry and perennial crops (White and Hodge 1989; Durel et al. 1998; Dutkowski et al. 2002; Xiang and Li 2003), but publications using BLUP with annual crops are less abundant.”