/pdf/southern-pine-seed-sources-1ynqhtud5a.pdf

Southern Pine Seed Sources

The selection of an appropriate seed source is critical for successful southern pine plantations. Guides for selection of seed sources are presented for loblolly, slash, longleaf. Virginia, shortleaf, and sand pines. Separate recommendations are given for areas where fusiform-rust hazard is high. KEUtVORDS: Planting zones, provenance tests, geographic variation, Pinars taeda, Pinus elliottiz, Pznus paE?~stris, Pinus ui?y iniana , Pinus echinnta, Pinus clausa.

/pdf/a-guide-to-southern-pine-seed-sources-22h8mkl2jj.pdf

A Guide to Southern Pine Seed Sources

A genetic conservation and breeding program for shortleaf pine (Pinus echinata Mill.) was initiated in the 1960s by the Mark Twain National Forest in Missouri. Superior trees were selected from natural stands throughout the Forest. Fifty of the top-ranked superior trees were grafted into a first generation seed orchard at the Ouachita National Forest in central Arkansas. Major seed collections from the clonal seed orchard were made in 1981, 1983, 1986 and 2003. Thirteen open pollinated progeny tests were established in the early to mid-1980s to evaluate orchard parents and obtain data to rogue the orchard. About half of these tests were lost or severely damaged by severe heat and drought in 1980 and 1983. A control pollinated progeny test was established in 2002 to further evaluate parents in the seed orchard, and to develop a second generation seed orchard. Progeny test results suggested that genetic variation exists in shortleaf pine, and genetic gain is predicted to be significant. Improvement of shortleaf pine by the Ouachita and Ozark National Forests in Arkansas began in the early 1960s with the selection of superior shortleaf pine trees on the Ouachita and Ozark National Forests. Fifty superior trees were selected from each of the following geographic sources: East Ouachita, West Ouachita, and Ozark. Scions were collected from these superior trees and grafted to root stock at the orchard. Once established, these grafts were outplanted into their respective blocks of the Ouachita seed orchard. Eighty-four progeny tests were established from controlled pollinated crosses in breeding population one of the Region 8 shortleaf pine breeding program. Several of these progeny tests were lost for a variety of reasons, including fire, animal damage, and unfavorable weather conditions. However, data from over 60 valid full-sib progeny tests were used to choose selections to be established in the second generation orchard. Challenges and opportunities of the shortleaf pine tree improvement programs on the three National Forests are discussed.

https://www.nrs.fs.fed.us/pubs/gtr/gtr_p-15 papers/11studyvin-p-15.pdf

Genetic improvement of shortleaf pine on the Mark Twain, Ouachita, and Ozark National Forests

Planning for regenerating the southern pines should include an informed allocation of species and the appropriate seed sources within each species choice. Examples are given demonstrating successes and failures associated with allocation. Southern pine tree- improvement methodology and seed-orchard establishment and management are presented. Geographic variation in growth and disease resistance is discussed, and seed-source recommendations are given in detail for loblolly pine and for other species where pertinent. To aid in species and seed-source allocation, a key (appendix) is provided that incorporates biologic as well as management considerations.

Species variation, allocation, and tree improvement.

Genetic and phenotypic correlations for height were estimated for shortleaf pine (Pinus echinata Mill.) in Missouri from a single progeny test comprising 44 half-sibling families assessed at 3, 5, 7, 10, 17 and 25 years of age. The age-age genetic correlations for height ranged from 0.68 to 0.99, and phenotypic correlations from 0.28 to 0.84. Age-age phenotypic correlations for height had a strong linear relationship with logarithm of age ratio (R2 = 0.89) but age-age genetic correlations had a weak linear relationship with logarithm of age ratio (R2 = 0.27). Early selection efficiency for height was examined using the ratio of gain per year between indirect early selection and direct selection at age 25. When flowering age was assumed to be 10 years, optimum selection age was predicted to be 10 using either the genetic or the phenotypic linear model. When flowering age was assumed to be 3 years, optimum selection age was predicted to be 3 and 8 years based on the genetic and phenotypic linear models, respectively. The phenotypic linear model underestimated genetic gain at all ages, particularly at young ages.

Optimum selection age for height in shortleaf pine.

Tree improvement programs of shortleaf pine (Pinus echinata Mill.) have developed over the past 25 years to the point that virtually all demand for planting stock is met with genetically improved trees. About 22,600 acres of improved stock are planted each year. Although shortleaf has the largest geographic range of any southern pine, it is not being promoted in reforestation programs as much as alternate species, presumably because of slower growth. The largest shortleaf reforestation and tree improvement programs are on the National Forests with the bulk of the program in Arkansas on the Ouachita and Ozark National Forests. Within a few years National Forests will have second generation orchards established.

/pdf/trends-in-shortleaf-pine-tree-improvement-1mvpj82qlg.pdf

Trends in shortleaf pine tree improvement

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Robert N. Kitchens

Author Tools

Chat about Author

Papers

Trends in shortleaf pine tree improvement