Forest Genetic Trials Research Articles

A claim for a 'next generation' of multisite range-wide forest genetic trials built on the legacy of ecological genetics to anticipate responses to climate.

Hacia una nueva generación de ensayos genéticos forestales multisitio para anticipar las respuestas de las especies forestales a la crisis climáticaRealizamos un comentario sobre el trabajo de revisión presentado por Leites & Benito‐Garzón (2023) en este número sobre el valor de las redes de ensayos genéticos forestales multi‐sitio para anticipar la distribución y las respuestas de las especies forestales a la actual crisis climática. La persistencia de las masas forestales y de los servicios ecosistémicos que proporcionan necesita de un esfuerzo coordinado sin precedentes de la comunidad científica para comprender, predecir y mitigar los impactos de la crisis climática sobre los bosques templados y boreales. Para ello se necesita información precisa sobre la variación genética, plasticidad fenotípica y la variación genética en plasticidad de las especies forestales. Hacemos una llamada de atención sobre la necesidad urgente de establecer una nueva generación de ensayos genéticos forestales de procedencias replicados multisitio a lo largo del rango de distribución de las especies de interés, y enfocado en la resiliencia al cambio climático. Como científicos debemos facilitar las respuestas evolutivas a los tan rápidos que están viviendo las especies forestales. Debemos ser determinantes en proporcionar información de primera clase a los actores políticos y administradores para desarrollar una gestión forestal basada en la evidencia científica.

Ground-Penetrating Radar as phenotyping tool for characterizing intraspecific variability in root traits of a widespread conifer

Background and AimDrought is the main abiotic stress affecting Mediterranean forests. Root systems are responsible for water uptake, but intraspecific variability in tree root morphology is poorly understood mainly owing to sampling difficulties. The aim of this study was to gain knowledge on the adaptive relevance of rooting traits for a widespread pine using a non-invasive, high-throughput phenotyping technique.MethodsGround-Penetrating Radar (GPR) was used to characterize variability in coarse root features (depth, diameter and frequency) among populations of the Mediterranean conifer Pinus halepensis evaluated in a common garden. GPR records were examined in relation to aboveground growth and climate variables at origin of populations.ResultsVariability was detected for root traits among 56 range-wide populations categorized into 16 ecotypes. Root diameter decreased eastward within the Mediterranean basin. In turn, root frequency, but not depth and diameter, decreased following a northward gradient. Root traits also varied with climatic variables at origin such as the ratio of summer to annual precipitation, summer temperature or solar radiation. Particularly, root frequency increased with aridity, whereas root depth and diameter were maximum for ecotypes occupying the thermal midpoint of the species distribution range.ConclusionGPR is a high-throughput phenotyping tool that allows detection of intraspecific variation in root traits of P. halepensis and its dependencies on eco-geographic characteristics at origin, thereby informing on the adaptive relevance of root systems for the species. It is also potentially suited for inferring population divergence in resource allocation above- and belowground in forest genetic trials.

Accounting for competition in multi-environment tree genetic evaluations: a case study with hybrid pines

Key message A novel multi-environment competition individual-tree mixed model resulted in better fit, and greater individual narrow- and broad-sense heritabilities than the model without competition, notably for traits showing competition. In multi-environment tests, the proposed model would allow tree breeders to select genotypes with the best performance in both additive direct and competition breeding values, increasing forest productivity. ContextGenetic merit of trees is known to be affected and interact with local competition effects as well as changes across environmental conditions. Recent studies showed that competition genetic effects can affect the genetic variance and bias the tree breeding values, and its covariance with direct breeding values has been variable across traits.AimsThe present paper extends a mixed-model methodology to the problem of accounting for competition in a multi-environment set of forest genetic trials and exploring its impact on genetic variances as well as the multi-environment genetic correlation.MethodsThe proposed model is illustrated using data from two full-sib trials of Pinus elliottii var. elliottii × Pinus caribaea var. hondurensis F1. Dispersion parameters and (co)variance of total breeding values were estimated for diameter at breast height, total tree height, and stem straightness at age 10.ResultsFor traits showing competition effects (diameter at breast height and total tree height), the proposed multi-environment competition model gave better fit than the simpler model. Accounting for competition increased the direct additive variance, reduced the residual variances, and did not change significantly the across-site additive genetic correlation. However, for diameter at breast height, top 5% best genetic rankings showed differences.ConclusionWhen traits are strongly affected by inter-tree competition, the use of the proposed model in multi-environment analyses can efficiently identify the phenomenon with general benefits in the fitting of genetic components and open the door to select on the basis of competitiveness.

Use of UAV photogrammetric data in forest genetic trials: measuring tree height, growth, and phenology in Norway spruce (Picea abies L. Karst.)

ABSTRACT Phenotyping of genetic resources that are used for development of new improved and adapted forest reproductive materials is costly. Therefore, high-throughput-phenotyping from a standard equipped unmanned aerial vehicle (UAV) with a RGB (red, green, blue) camera was used to develop a canopy height model (CHM) of a dense field trial of 15-year-old trees of Norway spruce. The site represents a typical situation of genetic testing on abandoned farm field land, where spacing between trees often are around 1-1.5 m. The CHM was used to estimate total height, intra-annual height growth and phenology of individual trees. The estimates were based on individual tree grid cells assigned according to the original planting grid. Intra-annual growth and phenology could not be estimated accurately, whereas total tree height to the uppermost whorl was accurately estimated provided that the trees were correctly identified. However, the apices of suppressed trees were often misidentified due to overhanging branches from more dominating neighboring trees. The method represents a cost and time efficient alternative to manual height measurements of the intermediate and dominating trees in closed stands. The accuracy is sufficiently high for most breeding purposes when total tree height is the target trait.

Phenotyping tree resistance to a bark‐chewing insect, the pine weevil Hylobius abietis

AbstractBreeding for resistance to forest pests and pathogens is emerging as a promising tool for minimising the impact of the increasing biotic threats that our forests are experiencing as a consequence of global change. Efficient phenotyping protocols of resistance are urgently needed. Here we present the results of two experiments aimed to determine whether the variation in resistance to the pine weevil Hylobius abietis, a harmful pest of European conifers, can be inferred by nondestructive bioassays using excised plant material collected in forest genetic trials. Weevil damage and amount of nonvolatile resin induced by weevil feeding were assessed in young trees and in branches of adult trees using several phenotyping procedures (bioassays using either living trees, excised plant material and cut stem twigs) on four pine species (Pinus pinaster, P. radiata, P. sylvestris and P. pinea). Half of the plants were previously induced with methyl jasmonate (MJ), a treatment that is known to affect resistance to the pine weevil. In Experiment 1, living and excised plants showed parallel results: MJ treatment significantly reduced weevil damage, and saplings responded to weevil damage locally increasing the nonvolatile resin (NVR) in the stems proportionally to the damage suffered. This response was, however, slightly lower in excised than in living saplings. On the contrary, patterns of weevil feeding on stem twigs completely departed from those observed in living and excised seedlings. Moreover, cut stem twigs were unable to respond to weevil feeding increasing NVR according to the weevil damage. In Experiment 2, assessment of weevil damage on excised branches explained around 50% of variation in damage on living branches. This relationship became much more pronounced (R2 = 0.81) when explored at the mean treatment level; branch manipulation did not alter the patterns of variation in resistance across pine species or MJ treatments. Irrespective of the assessment procedure, MJ consistently decreased weevil damage in all pine species, with larger reduction in weevil damage in stone and maritime pine than in radiata and Scots pine. Radiata pine was the most resistant while Scots pine was the most susceptible to the pine weevil. Overall, results suggest that using excised plant material is an operative alternative for phenotyping weevil resistance whenever care is taken to maintain the functionality of the excised plant material. This will allow taking advantage of multiple available conifer genetic trials to deepen the ecological genetics of resistance to the pine weevil and to screen for resistance without compromising the long‐term utility of those genetic trials.

Using unmanned aerial vehicle‐based multispectral, RGB and thermal imagery for phenotyping of forest genetic trials: A case study inPinus halepensis

The assessment of genetic differentiation in functional traits is fundamental towards understanding the adaptive characteristics of forest species. While traditional phenotyping techniques are costly and time‐consuming, remote sensing data derived from cameras mounted on unmanned aerial vehicles (UAVs) provide potentially valid high‐throughput information for assessing morphophysiological differences among tree populations. In this work, we test for genetic variation in vegetation indices (VIs) and canopy temperature among populations ofPinus halepensisas proxies for canopy architecture, leaf area, photosynthetic pigments, photosynthetic efficiency and water use. The interpopulation associations between vegetation properties and above‐ground growth (stem volume) were also assessed. Three flights (July 2016, November 2016 and May 2017) were performed in a genetic trial consisting of 56 populations covering a large part of the species range. Multispectral (visible and near infrared wavelengths), RGB (red, green, blue) and thermal images were used to estimate canopy temperature and vegetation cover (VC) and derive several VIs. Differences among populations emerged consistently across flights for VC and VIs related to leaf area, indicating genetic divergence in crown architecture. Population differences in indices related to photosynthetic pigments emerged only in May 2017 and were probably related to a contrasting phenology of needle development. Conversely, the low population differentiation for the same indices in July 2016 and November 2016 suggested weak interpopulation variation in the photosynthetic machinery of mature needles ofP. halepensis. Population differences in canopy temperature found in July 2016 were indicative of variation in stomatal regulation under drought stress. Stem volume correlated with indices related to leaf area (positively) and with canopy temperature (negatively), indicating a strong influence of canopy properties and stomatal conductance on above‐ground growth at the population level. Specifically, a combination of VIs and canopy temperature accounted for about 60% of population variability in stem volume of adult trees. This is the first study to propose UAV remote sensing as an effective tool for screening genetic variation in morphophysiological traits of adult forest trees.

Spatial analysis increases efficiency of progeny testing of Chinese fir

We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height (DBH) and tree height data had significant spatial auto-correlations among rows and columns. Adding a first-order separable autoregressive term more effectively modelled the spatial variation than did the incomplete block (IB) model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis relative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3–5%. Fitting global trend and post-blocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites.

Estimation of genetic parameters for height using spatial analysis in Tsuga heterophylla full-sibling family trials in British Columbia

Abstract Non-spatial and spatial analyses were carried out to study the effects on genetic parameters in ten-year height growth data across two series of 10 large second-generation full-sib progeny trials of western hemlock [Tsuga heterophylla (Raf.) Sarg.] in British Columbia. To account for different and complex patterns of environmental heterogeneity, spatial single trial analyses were conducted using an individual-tree mixed model with a two-dimensional smoothing surface with tensor product of B-spline bases. The spatial single trial analysis, in all cases, showed sizeable lower Deviance Information Criterion values relative to the non-spatial analysis. Also, fitting a surface displayed a consistent reduction in the posterior mean as well as a decrease in the standard deviations of error variance, no appreciable changes in the additive variance, an increase of individual narrow-sense heritability, and accuracy of breeding values. The tensor product of cubic basis functions of B-spline based on a mixed model framework does provide a useful new alternative to model different and complex patterns of spatial variability within sites in forest genetic trials. Individual narrow-sense heritabilities estimates from the spatial single trial analyses were low (average of 0.06), but typical of this species. Estimated dominance relative to additive variances were unstable across sites (from 0.00 to 1.59). The implications of these estimations will be discussed with respect to the western hemlock genetic improvement program in British Columbia.

A novel individual-tree mixed model to account for competition and environmental heterogeneity: a Bayesian approach

Negative correlation caused by competition among individuals and positive spatial correlation due to environmental heterogeneity may lead to biases in estimating genetic parameters and predicting breeding values (BVs) from forest genetic trials. Former models dealing with competition and environmental heterogeneity did not account for the additive relationships among trees or for the full spatial covariance. This paper extends an individual-tree mixed model with direct additive genetic, genetic, and environmental competition effects, by incorporating a two-dimensional smoothing surface to account for complex patterns of environmental heterogeneity (competition + spatial model (CSM)). We illustrate the proposed model using simulated and real data from a loblolly pine progeny trial. The CSM was compared with three reduced individual-tree mixed models using a real dataset, while simulations comprised only CSM versus true-parameters comparisons. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. Simulation results showed that the CSM yielded posterior mean estimates of variance components with slight or negligible biases in the studied scenarios, except for the permanent environment variance. The worst performance of the simulated CSM was under a scenario with weak competition effects and small-scale environmental heterogeneity. When analyzing real data, the CSM yielded a lower value of the deviance information criterion than the reduced models. Moreover, although correlations between predicted BVs calculated from CSM and from a standard model with block effects and direct genetic effects only were high, the ranking among the top 5 % ranked individuals showed differences which indicated that the two models will have quite different genotype selections for the next cycle of breeding.

A high-throughput X-ray-based method for measurements of relative wood density from unprepared increment cores from Picea abies

We have described a high-throughput method for ring width and relative wood density analysis of unprepared increment cores based on conventional computer tomography scanners (CT scanners) used in health care. The method was developed for analysis of 5-mm increment cores collected in forest genetic and tree breeding trials. For these studies, the relative differences between individuals are more important than the exact value. The method is highly efficient in terms of quantitative data collection from large number of samples. There are, however, limitations in terms of resolution and accuracy on very small volumes of wood that must be considered when using the method to measure individual growth rings or increment cores thinner than 5 mm. However, in studies comprising large number of samples, the use of cost-efficient CT scanning on the major part of the material, combined with more laborious high-resolution scanning on sub-samples can be a good combination for efficient determination of radial variation in growth rate and wood density.

Accounting for competition in genetic analysis, with particular emphasis on forest genetic trials

Available experimental evidence suggests that there are genetic differences in the abilities of trees to compete for resources, in addition to non-genetic differences due to micro-site variation. The use of indirect genetic effects within the framework of linear mixed model methodology has been proposed for estimating genetic parameters and responses to selection in the presence of genetic competition. In this context, an individual’s total breeding value reflects the effects of its direct breeding value on its own phenotype and its competitive breeding value on the phenotype of its neighbours. The present study used simulated data to investigate the relevance of accounting for competitive effects at the genetic and non-genetic levels in terms of the estimation of (co)variance components and selection response. Different experimental designs that resulted in different genetic relatedness levels within a neighbourhood and survival were other key issues examined. Variances estimated for additive genetic and residual effects tended to be biased under models that ignored genetic competition. Models that fitted competition at the genetic level only also resulted in biased (co)variance estimates for direct additive, competitive additive and residual effects. The ability to detect the correct model was reduced when relatedness within a neighbourhood was very low and survival decreased. Selection responses changed considerably between selecting on breeding value estimates from a model ignoring genetic competition and total breeding estimates using the correct model. Our results suggest that considering a genetic basis to competitive ability will be important to optimise selection programmes for genetic improvement of tree species.

Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials

The gain in accuracy of breeding values with the use of single trial spatial analysis is well known in forestry. However, spatial analyses methodology for single forest genetic trials must be adapted for use with combined analyses of forest genetic trials across sites. This paper extends a methodology for spatial analysis of single forest genetic trial to a multi-environment trial (MET) setting. A two-stage spatial MET approach using an individual-tree model with additive and full-sib family genetic effects was developed. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. The procedure is illustrated using height growth data at age 10 from eight large Tsuga heterophylla (Raf.) Sarg. second-generation full-sib progeny trials from two series established across seven sites in British Columbia (Canada) and on one in Washington (USA). The proposed multi-environment spatial mixed model displayed a consistent reduction of the posterior mean and an increase in the precision of error variances $$ \left( {\sigma _{e}^{2}} \right) $$ than the model with “sets in replicates” or incomplete block alpha designs. Also, the multi-environment spatial model provided an average increase in the posterior means of the narrow- and broad-sense individual-tree heritabilities (h N 2 and h B 2 , respectively). No consistent changes were observed in the posterior means of additive genetic correlations (r Ajj′). Although computationally demanding, all dispersion parameters were successfully estimated from the proposed multi-environment spatial individual-tree model using Bayesian techniques via Gibbs sampling. The proposed two-stage spatial MET approach produced better results than the commonly used nonspatial MET analysis.

Two-dimensional penalized splines via Gibbs sampling to account for spatial variability in forest genetic trials with small amount of information available

Abstract Spatial environmental heterogeneity are well known characteristics of field forest genetic trials, even in small experiments (<1ha) established under seemingly uniform conditions and intensive site management. In such trials, it is commonly assumed that any simple type of experimental field design based on randomization theory, as a completely randomized design (CRD), should account for any of the minor site variability. However, most published results indicate that in these types of trials harbor a large component of the spatial variation which commonly resides in the error term. Here we applied a two-dimensional smoothed surface in an individual-tree mixed model, using tensor product of linear, quadratic and cubic B-spline bases with different and equal number of knots for rows and columns, to account for the environmental spatial variability in two relatively small (i.e., 576 m2 and 5,705 m2) forest genetic trials, with large multiple-tree contiguous plot configurations. In general, models accounting for site variability with a two-dimensional surface displayed a lower value of the deviance information criterion than the classical RCD. Linear B-spline bases may yield a reasonable description of the environmental variability, when a relatively small amount of information available. The mixed models fitting a smoothed surface resulted in a reduction in the posterior means of the error variance (σ2 e), an increase in the posterior means of the additive genetic variance (σ2 a) and heritability (h 2 HT), and an increase of 16.05% and 46.03% (for parents) or 11.86% and 44.68% (for offspring) in the accuracy of breeding values, respectively in the two experiments.

Bayesian estimation of a surface to account for a spatial trend using penalized splines in an individual-tree mixed model

Unaccounted for spatial variability leads to bias in estimating genetic parameters and predicting breeding values from forest genetic trials. Previous attempts to account for large-scale continuous spatial variation employed spatial coordinates in the direction of the rows (or columns). In this research, we use an individual-tree mixed model and the tensor product of B-spline bases with a proper covariance structure for the random knot effects to account for spatial variability. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. The procedure is illustrated with data from a progeny trial of Eucalyptus globulus subsp. globulus Labill. Four different models were used in the sequel. The first model included block effects and the three other models included a surface on a grid of either 8 × 8, 12 × 12, or 18 × 18 knots. The three models with B-splines displayed a sizeable lower value of the deviance information criterion than the model with blocks. Also, the mixed models fitting a surface displayed a consistent reduction in the posterior mean of σ2e, an increase in the posterior means of σ2A and h2DBH, and an increase of 66% (for parents) or 60% (for offspring) in the accuracy of breeding values.

Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials

Spatial analysis of progeny trial data improved predicted genetic responses by more than 10% for around 20 of the 216 variables tested, although, in general, the gains were more modest. The spatial method partitions the residual variance into an independent component and a two-dimensional spatially autocorrelated component and is fitted using REML. The largest improvements in likelihood were for height. Traits that exhibit little spatial structure (stem counts, form, and branching) did not respond as often. The spatial component represented up to 50% of the total residual variance, usually subsuming design-based blocking effects. The autocorrelation tended to be high for growth, indicating a smooth environmental surface, it tended to be small for measures of health, indicating patchiness, and otherwise the autocorrelation was intermediate. Negative autocorrelations, indicating competition, were present in only 10% of diameter measurements for the largest diameter square planted trials, and between nearest trees with rectangular planting at smaller diameters. Bimodal likelihood surfaces indicate that competition may be present, but not dominant, in other cases. Modelling of extraneous effects yielded extra genetic gain only in a few trials with severely asymmetric autocorrelations. Block analysis of resolvable incomplete-block or row–column designs was better than randomized complete-block analysis, but spatial analysis was even better.

Iterative kriging for removing spatial autocorrelation in analysis of forest genetic trials

Conventional analysis of spatially correlated data in inadequately blocked field genetic trials may give erroneous results that would seriously affect breeding decisions. Forest genetic trials are commonly very large and strongly heterogeneous, so adjustments for micro-environmental heterogeneity become indispensable. This study explores the use of geostatistics to account for the spatial autocorrelation in four Pinus pinaster Ait. progeny trials established on hilly and irregular terrains with a randomized complete block design and large blocks. Data of five different traits assessed at age 8 were adjusted using an iterative method based on semivariograms and kriging, and the effects on estimates of variance components, heritability, and family effects were evaluated in relation to conventional analysis. Almost all studied traits showed nonrandom spatial structures. Therefore, after the adjustments for spatial autocorrelation, the block and family × block variance components, which were extremely high in the conventional analysis, almost disappeared. The reduction of the interaction variance was recovered by the family variance component, resulting in higher heritability estimates. The removal of the spatial autocorrelation also affected the estimation of family effects, resulting in important changes in family ranks after the spatial adjustments. Comparison among families was also greatly improved due to higher accuracy of the family effect estimations. The analysis improvement was larger for growth traits, which showed the strongest spatial heterogeneity, but was also evident for other traits such as straightness or number of whorls. The present paper demonstrates how spatial autocorrelation can drastically affect the analysis of forest genetic trials with large blocks. The iterative kriging procedure presented in this paper is a promising tool to account for this spatial heterogeneity.

Spatial analysis methods for forest genetic trials

Spatial analysis, using separable autoregressive processes of residuals, is increasingly used in agricultural variety yield trial analysis. Interpretation of the sample variogram has become a tool for the detection of global trend and "extraneous" variation aligned with trial rows and columns. We applied this methodology to five selected forest genetic trials using an individual tree additive genetic model. We compared the base design model with post-blocking, a first-order autoregressive model of residuals (AR1), that model with an independent error term (AR1η), a combined base and autoregressive model, an autoregressive model only within replicates and an autoregressive model applied at the plot level. Post-blocking gave substantial improvements in log-likelihood over the base model, but the AR1η model was even better. The independent error term was necessary with the individual tree additive genetic model to avoid substantial positive bias in estimates of additive genetic variance in the AR1 model and blurred patterns of variation. With the combined model, the design effects were eliminated, or their significance was greatly reduced. Applying the AR1η model to individual trees was better than applying it at the plot level or applying it on a replicate-by-replicate basis. The relative improvements achieved in genetic response to selection did not exceed 6%. Examination of the spatial distribution of the residuals and the variogram of the residuals allowed the identification of the spatial patterns present. While additional significant terms could be fitted to model some of the spatial patterns and stationary variograms were attained in some instances, this resulted in only marginal increases in genetic gain. Use of a combined model is recommended to enable improved analysis of experimental data.

Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual

This study explores the use of a mixed linear model including spatially correlated residuals in addition to the traditional randomized complete block (RCB) analysis in 12 forest genetic trials. The analysis of early height data from progeny and clonal tests of three species (Picea sitchensis (Bong.) Carr., Pinus pinaster Ait., and Pinus radiata D. Don) showed that there was significant spatial variation in all trials. Adding a basic first-order separable autoregressive error term more effectively modelled the spatial variation than the RCB model and greatly reduced the block and plot variances. There was no evidence that extended spatial modelling was required. The spatial analysis greatly improved the accuracy of genetic value estimation in some trials and was accompanied by large changes in rank of the genetic entries and by greater gains in selection relative to the RCB analysis.

Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual

Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual

Incomplete block designs for genetic testing: statistical efficiencies of estimating family means

A half-sib progeny trial with single-tree plots on one test site of various environmental variations is simulated to examine the relative statistical efficiencies of randomized incomplete block designs (ICBs) over their corresponding randomized complete block design (RCB) in estimating family means. The ICBs with full constraint (FC), half constraint (HC), and no constraint (NC) over replicates and an alpha design (Alpha) are considered. A geostatistical spatial model is employed, which allows specifications of patchy and systematic environmental variations across the test site and estimations of block and error components with SAS PROC MIXED. The simulation shows that Alpha is the most efficient ICB in the 21 scenarios of patchy and systematic site variations, followed by NC and HC, and these ICBs (except FC) are generally more efficient than RCB in terms of the average variance of a family mean contrast. The gains in statistical efficiencies with implementations of Alpha and NC (rather than RCB) in forest genetic testing can be high, depending largely on the nature (and level) of spatial environmental variation. Practical implications of these simulated results for forest genetic trials are discussed.