Climate change is likely to affect agriculture and forestry globally. In this dissertation, we present methodologies to predict the effects of climate change on the dominant commercial pine species in the southeastern US making use of provenance tests data. The first chapter develops height growth prediction models using classical and penalized regression methods. The second chapter introduces a Bayesian spatial approach for modeling the relative performance of seed sources, which is used to develop a seed deployment tool. The third chapter presents methodology for the analysis of cloned progeny tests using multi-environmental trials data.
First, we present a statistical model to predict the effects of climate change on the height growth of loblolly pine (Pinus taeda L.) families using provenance test data. Provenance tests are a common tool in forestry designed to identify superior genotypes for planting at specific locations. The trials are usually replicated experiments established with seed from parent trees collected from different regions and grown at several locations. The geographic and climatic differences between the seed source and test site locations can be used to make predictions about the performance of provenances under different environmental conditions. Ordinary least squares, ridge regression, and LASSO regression were used to develop height growth prediction models. The models were tested using a hypothetical future climate scenario with 5% decrease in precipitation and 0.5°C increase in maximum and minimum temperatures, relative to historical average values. Under this scenario, local families from the coastal plains of Georgia, Florida, and South Carolina showed the highest performance relative to the current climate in their native environments. As these seed sources were moved to colder northern and inland regions from their origin we observed decline in their height growth. Similarly, the climatic change scenario suggested that performance of northern seed sources decline significantly when they were moved to more southern warmer regions.
The second chapter presents a Bayesian spatial approach for modeling the expected relative performance of seed sources in terms of climate variables associated with the location of the origin of seed and the planting site. The proposed modeling technique accounts for the spatial dependence in the data and provides a flexible means to estimate effects associated with the origin and planting site locations. The statistical model was used to develop a quantitative tool for seed deployment aimed to identify the location of superior performing seed sources that could be suitable for a specific planting site under a specific climate scenario. Cross-validation results indicate that the proposed spatial models provide superior predictive ability compared to multiple linear regressions.
In the last chapter, a cloned progeny test of loblolly pine is analyzed to identify superior genotypes using multi-environmental trials data. The genetic material consisted of 51 crosses from 21 parents. Each cross had about 45 full-sib progeny resulting in a total of 2362 progeny that were cloned and tested in seven sites. Height, diameter, stem straightness, and fusiform rust incidence were assessed four years after planting. Genetic merits of clones were predicted for tree height using linear mixed models. Various covariance structures were employed to account for the heterogeneity in the data. Genotype-by-environment (G×E) interactions were assessed, and clusters of models were identified for making rankings based on genetic merit. The factor analytic formulation was parsimonious, informative, and provided a good approximation to the unstructured variance model. The environments exhibited relatively high pair-wise genetic correlation values, suggesting that G×E should not be a concern for the population under study. The clone means were reasonably highly repeatable suggesting that selection from tests of cloned progeny could be more efficient than from traditional seedling tests.
|Advisor:||Reich, Brian J., Isik, Fikret|
|School:||North Carolina State University|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 78/08(E), Dissertation Abstracts International|
|Subjects:||Genetics, Climate Change, Statistics, Plant sciences|
|Keywords:||Climate change, Growth prediction, Seed sources, Tree growth|
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