The cultivation of hybrid poplar clones is increasing worldwide. Hundreds of hectares of plantations now occur across Europe and other continents such as North America, using tested clones and novel genotypes. Research effort aims are to develop fast growing disease- and pest-resistant clones to improve production quality and quantity. In this study the phenotypic plasticity of poplar clones was tested across environmental and temporal gradients. The growth performance of 49 hybrid poplar clones recorded between 1980 and 2021 was analysed using a mixed-effects model with climatic data as a predictor variable. Clones were aggregated into two groups according to their breeding protocol (
Poplar (
Historically, specialized poplar plantations in Northern Italy have continued to be grown due to the cultural dependence of expert knowledge and tradition. The production of poplar wood has a particular importance in the internal wood-furniture and paper sector. However, it does not cover the demand from industry, which is increasingly interested in optimizing supplies through an in-depth knowledge of the areas of growth and regional cultivation. In Italy an inventory of poplar crops has now been completed (
Spatial modelling of forest tree species niche suitability provides a useful technique to support forest management strategies and conservation planning (
The modelling framework for a sustainable and cost-efficient cultivation of polar clones in this study is inspired by the approach forest geneticists now adopt to analyse and summarize the performance of different provenances across ecological gradients (
Species Distribution Model (SDM) terminology and techniques have a clear conceptual scheme and usage. However, there is no clear boundary between Response Functions (RF), Transfer Functions (TF) and Reaction Norms (RN) since all of them deal with the variability of a measured trait across an ecological gradient. In addition, RF and TF have been often pooled across planting sites using more sophisticated techniques (
Response Function (RF): the genetic expression of a single genotype across the range of ecological distance from the climate of origin;
Transfer Function (TF): the genetic expression of a single genotype across the range of ecological distance from the climate of origin using a spatial coordinate system predictor;
Reaction Norm (RN): the pattern of phenotypic plasticity of a single genotype across a range of environments;
Universal Response Function (URF), Universal Transfer Function (UTF) and Universal Reaction Norm (URN): models where the response/reaction of different genotypes or provenances is pooled in a unique model using various mathematical techniques.
According to this hierarchical structure, the URN concept will be used for the models developed in this study, since no climate of origin can be derived for clones, no spatial predictors were used, and genotypes (
The dataset used consists of 49 poplar clones from various poplar species tested across Italy, though cultivation and production is more abundant in Northern Italy. Clones in our study cover almost all the historical breeding effort including old and experimental clones, and we grouped these into two main clusters based on characteristics that include: years of selection (starting from 1929 to present), expected production ability, pest and disease resistance. The names of these two groups were First GeNeration clones (FGN) and IMProved clones (IMP), the latter include the newest Italian MSA (
The experimental network is geographically focused on Northern Italy, mainly across the Piedmont, Lombardy, Veneto and Emilia-Romagna regions, but also including some trials in Central Italy and the Mediterranean region (
A total of 30 trials with different planting years (between 1980 and 2013) were analysed and clones were irregularly distributed across a wide range of climate (
Two-year-old cuttings were always used as starting material and established with 36 m2 of space available for each tree (spacing of 6×6 meters) with a final harvest at the age of 10 or sometimes 12. The cultivation trials followed the standard model for poplar growth with small differences due to the layout of each farm, and irrigation. Irrigation is a common practice in poplar cultivation but not all the trials were irrigated due to pedo-climatic conditions, or the lack of irrigation infrastructure. For this reason, irrigation was added into the model as a dummy variable.
According to the wide range of measurements and ages across the dataset (
In this work a sigmoid function was used to interpolate the single-clone average diameter at breast height (DBH) at age 10 for trials where this record was missing. Single-clone average DBH was the simple arithmetic mean of the measured trees and among all the possible sigmoid functions available in literature we selected the Gompertz function (
where
A reaction norm captures the phenotypic expression of a trait for a single genotype across a range of environments. For every genotype, phenotypic trait, and environmental variable, a different reaction norm can be calculated. In forest science this kind of information is generally handled by means of a mixed modelling approach where fixed effects are selected climatic variables and the site variation is a random effect. However, the use of clones did not allow us to calculate an ecological distance (
Tailored climatic data for all the trials in Italy were derived from ClimateDT (https://ibbr.cnr.it//climate-dt/), a web portal where scale-free climatic data are provided freely at global level using CRU-TS data (
In this work, we followed the model format used by several previous studies (
where
where
The AHM and bio4 describe the relationship between poplar growth and a changing climate in a Mediterranean country (
where (
The quality check on the interpolation of missing data (
To show the ecological potential of poplar clones across time and space, the spatial predictions across the whole of Italy were generated removing the irrigation effect. For the current climate (1991-2020 normal period -
When URNs were used to predict the growth and suitable area into the future an uncertain scenario was observed. While projections across RCPs and time-slices were almost in agreement with the expectations of a decrease in land suitability which was delayed for the RCP2.6 scenario compared to the RCP8.5 scenario, a regular trend across time (
The URNs we developed showed the possible effects of a changing climate on poplar clone cultivation in Italy. Overall, the improved clonal material, which include MSA material (Higher Environmental Sustainability clones), showed less sensitivity to climatic aridity and a lower effect on growth from irrigation. These results may suggest a possible reduction of cultivation inputs as well as a more stable production of wood assortments in the future than with the current standard clonal types. In addition, the resistance to biotic stress that MSA clones achieve marks them as an important candidate source of reproductive material for poplar cultivation in the future. Adding to the traditional and widely cultivated clones, such as the well-known “I-214” and here included into the FGN group, new clones selected in recent years show a predicted growth rate comparable with “I-214” and better in places. However, several issues remain unclear and must be investigated in greater depth.
In recent decades, due to projected climate change scenarios, the research on hybrid poplar clones has received a strong impulse on fast growing genotypes, drought-resistant and resistant to pathogens (
As fast-growing tree species, poplars are able to absorb high amounts of CO2 and to sequester carbon in wood used industrially for durable products with a long life-time (plywood, furniture, etc.). It has been calculated that a traditional poplar stand (as considered in this study) of 278 plants per hectare can sequester about 25 t ha-1 y-1 of CO2 (
Undoubtedly, soil physical and chemical properties such as depth and fertility are important components to consider when modelling growth. However, currently there are no high-quality, high-resolution data layers that can be used for such model improvements, thus preventing its inclusion for range-wide modelling purposes. The growth of
Our analysis investigated the whole life-span of poplar cultivation and not just the early establishment stage of tree growth, which is less common in the forestry sector (
The need for high-quality data must not shade the requirement of reliable future projections. Climatologists and atmospheric scientists are improving future scenarios with new GCM and RCM models, and climate projections based on international COP agreements. Indeed, new frameworks and new knowledge and scenarios will soon be delivered under the CMIP6 (
High quality timber assortments and wood products in general are important to society and provide long-term methods of sequestering carbon. Durable products and fast-growing trees are efficient at reducing the amount of CO2 in the air and mitigate the emissions. This is much more evident when compared to bioenergy and the use of biomass in general, where the total amount of carbon components is maintained at stable level and not sequestered for long periods. Poplars are among the most important forest tree species used in agroforestry systems and we have provided URNs to show the improvement that can be made to poplar cultivation under climate change scenarios. However, the need for future assessments is demanding, and additional efforts should be made to improve the dataset (and predictions) we have developed. Different reaction norms could be expected in the future from more extensive experimental trials, showing a more stable production of clones across the ecological gradients.
This research work was developed in the framework of WP1 and WP5 of the Horizon 2020 B4EST project “Adaptive BREEDING for productive, sustainable and resilient FORESTs under climate change”, UE Grant Agreement 773383.
Ecological distribution of the 30 trials considering the two main climatic parameters (AHM for aridity and bio4 for continentality) and abundance of tested clones across considered sites.
Temporal sampling across sites between 1980 and 2012.
Statistical relationship between the measured diameter at breast height (DBH) at age 10 (
Expected diameter at breast height (in cm) of poplar clones at age 10 without irrigation under the current 30-years normal climate (1991-2020). The experimental sites used for modelling the group are shown as black dots and the statistical extrapolation outside the investigated ecological domain is shown as shaded area.
Predicted difference in diameter at breast height (in cm) at age 10 between poplar FGN and IMP clones across Italy. Positive values indicate that IMP clones are more productive than FGN clones and
Predicted DBH (cm) at age 10 for the two groups of poplar clones in 2040s under RCP8.5 using the variant01 (up) and variant21 (down). The experimental sites used for modelling the group are shown as black dots and the statistical extrapolation outside the investigated ecological domain is shown as shaded area.
List of trials included in this study and main average climatic parameters occurred over the growing period of clones (10 years). The variables bio1 and bio12 represent the mean annual temperature (°C) and the mean total annual precipitation (mm), respectively, while AHM (annual heat moisture index, °C m-1), bio4 (temperature seasonality, sd °C·100) and MSP (mean summer precipitation, mm) are the variables we used for modelling.
Site name | Establ.(year) | LongE | LatN | Elev(m) | bio1 | bio12 | AHM | bio4 | MSP |
---|---|---|---|---|---|---|---|---|---|
Azienda Cesurni | 1990 | 12.72377 | 41.94512 | 53 | 16.4 | 681.8 | 38.8 | 649.7 | 207.6 |
Azienda Ovile | 1990 | 12.3556 | 41.90792 | 43 | 16.5 | 708.8 | 37.4 | 635.7 | 188.7 |
Azienda Scottine | 1990 | 9.48700 | 45.06267 | 55 | 14.3 | 814.0 | 29.8 | 752.8 | 326.0 |
Azienda Volpares | 1985 | 13.09427 | 45.79843 | 7 | 13.9 | 1176.2 | 20.3 | 737.6 | 511.7 |
Brisighella | 1982 | 11.76317 | 44.21714 | 106 | 14.1 | 730.1 | 33.0 | 745.5 | 280.8 |
Carpaneta | 2007 | 10.88056 | 45.17917 | 24 | 14.9 | 732.8 | 34.0 | 761.9 | 317.9 |
Castiglion d Pescaia | 1986 | 10.94816 | 42.82812 | 52 | 15.9 | 574.3 | 45.1 | 622.5 | 177.5 |
Cavagnolo | 2012 | 8.08833 | 45.16861 | 153 | 14.7 | 730.7 | 33.8 | 757.5 | 298.9 |
Cavarzere | 2007 | 11.97333 | 45.14 | 4 | 15.2 | 688.8 | 36.6 | 753.8 | 298.5 |
Ceva | 2000 | 8.0002 | 44.38667 | 445 | 13.0 | 602.3 | 38.2 | 691.2 | 229.6 |
Dronero | 2000 | 7.38583 | 44.45472 | 605 | 11.9 | 645.0 | 33.9 | 708.2 | 289.0 |
Feltre | 1982 | 11.93074 | 46.03544 | 338 | 11.5 | 930.5 | 23.1 | 742.3 | 464.4 |
Frassinello | 2004 | 8.40611 | 45.04611 | 142 | 14.3 | 634.8 | 38.2 | 758.3 | 264.0 |
Gabiano | 2003 | 8.20361 | 45.17389 | 143 | 14.2 | 661.6 | 36.5 | 779.0 | 284.9 |
Grassaga | 1987 | 12.59043 | 45.67981 | 1 | 14.1 | 1059.0 | 22.8 | 719.4 | 471.7 |
Istrana | 1984 | 12.08753 | 45.68105 | 37 | 13.8 | 935.6 | 25.4 | 744.5 | 430.9 |
Lorenzana | 1991 | 10.52623 | 43.53259 | 41 | 15.6 | 722.7 | 35.4 | 648.8 | 235.5 |
Marsicovetere | 1983 | 15.84118 | 40.34179 | 612 | 13.0 | 536.4 | 43.0 | 635.7 | 143.7 |
Mezzi | 1997 | 8.5125 | 45.13722 | 105 | 14.4 | 700.4 | 34.8 | 756.8 | 302.9 |
Mezzi-Baldo | 2013 | 8.50639 | 45.14056 | 106 | 15.1 | 701.3 | 35.8 | 761.1 | 261.4 |
Migliarino | 1991 | 10.30831 | 43.75409 | 1 | 15.8 | 778.1 | 33.1 | 660.6 | 241.1 |
MolinellaMarmorta | 1993 | 11.72237 | 44.61177 | 4 | 15.1 | 639.9 | 39.2 | 744.1 | 267.8 |
Mombello | 2006 | 8.28972 | 45.115 | 146 | 14.3 | 643.6 | 37.8 | 752.9 | 274.3 |
Monticello d’Alba | 1992 | 7.93583 | 44.71778 | 267 | 13.6 | 693.1 | 34.1 | 700.3 | 343.9 |
Piopp.Pisano | 2004 | 8.49944 | 45.09167 | 121 | 14.4 | 697.1 | 35.0 | 760.1 | 279.1 |
PoggioUgolino | 1993 | 11.29399 | 43.69585 | 205 | 14.5 | 746.4 | 32.8 | 701.5 | 254.1 |
Rosignano M.mo | 2000 | 8.42111 | 45.06694 | 151 | 14.0 | 692.9 | 34.6 | 760.5 | 295.1 |
San Basilio A.P. | 1979 | 12.18473 | 44.95184 | 1 | 14.2 | 619.0 | 39.1 | 750.8 | 280.4 |
Sant’Agata | 2004 | 11.20417 | 44.64694 | 17 | 15.1 | 669.4 | 37.5 | 766.5 | 249.3 |
Trino | 2003 | 8.30833 | 45.185 | 131 | 14.2 | 637.4 | 38.0 | 781.4 | 275.6 |
Results of the model fitting. FGN model: R2 = 0.858, Fx = 0.823, Rn = 0.034; IMP model: R2 = 0.843, Fx = 0.815, Rn = 0.029.
Predictor | FGN model | IMP model | ||||||
---|---|---|---|---|---|---|---|---|
Estimate | Std.Error | Pr(>|t|) | Sig. | Estimate | Std.Error | Pr(>|t|) | Sig. | |
Intercept | 1778 | 301.4 | 6.81E-07 | *** | 1859 | 320.9 | 1.33E-07 | *** |
AHM | -11.170 | 7.655 | 0.152876 | - | -8.061 | 8.299 | 0.334252 | - |
I(AHM2) | -0.162 | 0.042 | 0.000463 | *** | -0.147 | 0.048 | 0.00318 | ** |
bio4 | -4.451 | 0.572 | 1.71E-09 | *** | -4.996 | 0.549 | 5.39E-14 | *** |
I(bio42) | 0.002 | 0.000 | <0.000001 | *** | 0.003 | 0.000 | 6.03E-15 | *** |
MSP | -2.322 | 1.299 | 0.082127 | - | -1.971 | 0.854 | 0.023575 | * |
WATER | 4.755 | 0.857 | <0.000001 | *** | 2.242 | 0.614 | 0.000477 | *** |
AHM:bio4 | 0.035 | 0.010 | 0.001235 | ** | 0.028 | 0.008 | 0.000401 | *** |
AHM:MSP | 0.063 | 0.035 | 0.075775 | . | 0.065 | 0.027 | 0.018987 | * |
bio4:MSP | 0.004 | 0.002 | 0.045837 | * | 0.003 | 0.001 | 0.006201 | ** |
AHM:bio4:MSP | -9.896E-05 | 0.000048 | 0.046503 | * | -9.795E-05 | 0.00004 | 0.007153 | ** |
Tab. S1 - Full list of clones used in this study and distribution across groups.