Objectives and Applications:
Objectives: 1) To determine if slash pine clones can be safely transferred throughout the species range.  2) To assess the productivity of selected clones in block-plots as opposed to row-plots.  3) To assess genotype x spacing interaction.

Applications: These tests will provide further information on the best methods to progeny test and deploy slash pine families in the CFGRP.

Materials and Methods:
There are 14 common families in tests 0-58, 59, and 60, and 20 to 23 families in each test.  Families are planted at 2 spacings, 990 trees/acre and 495 treees/acre.  Planting conditions are intended to simulate operational planting of large contiguous blocks of single families.
The four tests 0-513 to 0-516 are located in Gulfport MS, McRae GA, Marianna FL, and Bogaloosa LA, respectively.  These sites will provide an excellent geographic range with which to assess benefits or disadvantages of clone transfer.
 

Objectives and Applications:
Objectives: 1) To compare genetic assessment of slash pine in short-term, intensive culture systems and traditional, longer-term genetic tests.  2) To estimate inter-genotypic competition among slash pine progenies.  3) To evaluate genetic variation for biomass productivity in short rotations.  4) To compare progeny performance in different designs (e.g., row-plots, block-plots, mixed family blocks).

Applications: Single-tree plots are the most efficient statistical design, block-plots are probably more representative of how commercial plantings of improved genotypes will be done, row-plots are intermediate.  These tests will help quantify if design x genotype effects are of concern to us in progeny testing.
Materials and Methods:
Tests 0-509 and 0-511 examine how families perform in row-plots versus single tree plots at two spacings (2.3m x 1m, and 2.3m x 2m).  Test 0-510 utilizes the same families as 0-511, and is composed of 20 ha blocks at operational spacings.  Test 0-512 is a spacing study using Nelder's design to examine genotype by spacing interaction.
 

Objectives and Applications:
Objectives: To assess the effects of increasingly intensive cultural practices on 1) overall growth, 2) relative rankings of 4 slash pine families, 3) relative performance of slash and loblolly pine.

Applications: If slash pine families show little genotype by cultural regime interaction, 1) we can deploy slash genotypes across all types of management regimes, and 2) we may be able to shorten the progeny testing cycle by treating tests more intensively.

Materials and Methods:
The cultural treatments investigated in these studies include 1) conventional site preparation, 2) fertilization, 3) vegetation control, and 4) combined fertilization and vegetation control.  Test 0-506 is designed to investigate the effects of operational fertilization procedures on slash genotypes.  In the 4 tests, both row-plot and single-tree-plot designs were utilized.
 
 

Objectives and Applications:
Objectives:  To determine if there is genetic variability in growth model parameters for slash pine families, and to examine the effects of spacing and planting design (pure vs mixed family blocks) on growth and stand development.

Applications:  Use of growth models can help breeders make accurate projections of harvest-age genetic gain, and would also be useful in making economic decisions regarding deployment and breeding strategies.  Genetic gains have been modelled satisfactorily at the stand level by determining apparent increase in site index, however other growth model parameters may also show genetic variation.
 

Materials and Methods: In 1978 and 1979, St. Regis (now Champion) established a total of 29 progenies in six tests near Cantonment Florida (not all tests contain all families).  Three of the tests examined are split-plot designs examining pure family vs mixed family block.  Two of the tests are Nelder design spacing trials, and one test is a 10-tree row-plot design.  Height, DBH, and rust incidence were measured at 4, 6, 8, 11, and 15 or 16 years.  Analyses will examine cumulative growth, growth increments and relative growth rates. Growth models will utilize a Richards' function, and will investigate if genetic, spacing and competition differences affect growth model parameters.
 

Objectives:
1) Develop growth and yield model components for superior slash pine progenies, including:
    a. Height and DBH distribution;                                                      b. Height-Age relation, site index;
    c. Height-DBH relation, Height to Live Crown-DBH relation;       d. Survival model.
2) Estimate the effects on the parameters of the above models due to the following factors:
    a. Competition (Row plot; Mixed plot; Pure plot of one progeny; 25%/75% plot of two progenies);
    b. Progeny (Different improved progenies; Improved progenies and unimproved checklot);
    c. Density; and
    d. Site.

Applications:
     a. Quantify the effects of density, age, progeny, competition and site on family performance;
     b. Predict Height and DBH distributions, estimate the stand value;
     c. Guide decisions about value and rotation length.

Materials:
Total Height, Height to Live crown, and DBH are being collected from four growth and yield studies in
Florida (Hampton, Perry) and Mississippi (Saucier, Pearl River) where slash pine trees are 15 years -old
in 1997-1999.  Twelve or twenty-two improved slash progenies and one check lot have been planted
in these  four  studies  with  RCB  design  to  estimate  the  effect  of  different densities (narrow and
wide), competition levels (pure plot, 25%/75% plot, mix plot and row plot) on slash pine growth
performance. All plots are made up of 70 trees laid out in a 7 by 10 trees rectangle.

METHODS:
The following growth and yield  models were chosen to estimate the parameters:
1. Height and DBH distributions: Three-parameter Weibull distribution function.
2. Height-Age relation, site index: Richards function.
3. Height-Diameter, Height to Live Crown-DBH relationship:  log(H)=a+b*DBH-1
4. Survival prediction: Nt / N0 = exp(-a*tb) where t = years between two ages.

Then the above estimated model coefficients were used as dependent variables in an analysis of
variance to investigate the effects of the following different factors on the model: Competition (row plot, mixed plot, and pure plot of one progeny; 25% / 75% plot of two progenies), Progeny,
Spacing (Density), and Site.

Objectives and Applications:
Objectives: To evaluate gain in yield increases from the use of genetically improved slash pine.  A wide array of genetic entries are included in various tests: unimproved stock, seed from seed production areas, 1.0 generation orchards, 2.0 generation orchards.  Some tests maintain family identity, some use bulk see-lots.

Applications: Accurate, precise estimates of realized gain are essential for all genetic improvement programs.  They are used to justify continued expenditures, predict per-acre gains in harvest yield, and validate current progeny test procedures.

Materials and Methods:
Test design varies, but generally measurement plots have 50-60 trees.  Often plots are square (e.g., 10x10, or 8x8), and interior trees are measured.  Depending on spacing, plot size is about 1/5 acre.  There are usually a large number of replications, often 10, in order to increase statistical power.  A test with 6 types os seed-lots (e.g., unimproved, 2 seed production areas, 2 1st generation orchards, and a rogued orchard) would require approximately 12 acres.