We present an extension of the dynamic global vegetation model, Lund–Potsdam–Jena Managed Land (LPJmL), to
simulate planted forests intended for carbon (C) sequestration. We implemented three functional types to simulate plantation trees in temperate, tropical, and boreal climates. The parameters of these functional types were optimized to fit target growth curves (TGCs). These curves represent the evolution of stemwood C over time in typical productive plantations and were derived by combining field observations and LPJmL estimates for equivalent natural forests. While the calibrated model underestimates stemwood C growth rates compared to the TGCs, it represents substantial improvement over using natural forests to represent afforestation. Based on a simulation experiment in which we compared global natural forest versus global forest plantation, we found that forest plantations allow for much larger C uptake rates on the timescale of 100 years, with a maximum difference of a factor of 1.9, around 54 years. In subsequent simulations for an ambitious but realistic scenario in which 650 Mha (14 % of global managed land, 4.5 % of global land surface) are converted to forest over 85 years, we found that natural forests take up 37 PgC versus 48 PgC for forest plantations. Comparing these results to estimations of C sequestration required to achieve the 2
It is increasingly clear that the stringent climate targets of the Paris Agreement cannot be achieved without negative emissions, i.e., net removal of carbon (C) from the atmosphere later during the 21st century, to compensate for emissions in the first half of the century (Gasser et al., 2015; Rogelj et al., 2018). Of the many proposed techniques to achieve C uptake, the two options currently most discussed for large-scale implementation are bioenergy in combination with carbon capture and storage and afforestation (Williamson, 2016). Both approaches will require considerable amounts of land and thus compete with other land-use functions, for example, food production and biodiversity. While bioenergy is receiving considerable attention (van Vuuren et al., 2013), less consideration has been given to afforestation as a tool for land-based mitigation. C uptake occurs when natural vegetation is allowed to grow back on former croplands and pasture. While deliberately taking cropland or pasture out of production may involve costs, the direct management costs of natural regrowth are negligible. The carbon uptake rate of such natural regrowth, however, will usually achieve only a fraction of the potential C uptake rate at short timescales. Considerably higher C uptake rates are possible by planting forests (Paquette and Messier, 2010). Assisting regrowth by planting trees can substantially boost growth rates compared to natural forests because initial stages of primary succession (with herbaceous or shrub vegetation) are skipped and because fast-growing tree species can be selected. Moreover, trees are usually planted as saplings, cultivated under controlled conditions, which improves the chance of successful establishment compared to development from seeds (Gladstone and Thomas Ledig, 1990).
Assessing the potential of land-based approaches for climate mitigation requires reliable estimates of C sequestration rates. Process-based models, such as dynamic global vegetation models (DGVMs), are a crucial tool for providing these estimates. DGVMs simulate carbon stocks and fluxes based on mechanistic descriptions of underlying processes, such as photosynthesis and organic matter decomposition in relation to environmental conditions. However, since the focus of DGVM development has traditionally been on natural ecosystems, very few of these models have an explicit representation of planted forests. Therefore, previous modeling studies on large-scale afforestation represented afforestation as natural regrowth (Krause et al., 2017), in some cases applying corrections to account for higher growth rates (Humpenoder et al., 2014; van Minnen et al., 2008).
In this paper, we present an updated version of the Lund–Potsdam–Jena Managed Land (LPJmL) DGVM (Bondeau et al., 2007; Schaphoff et al., 2013), modified to explicitly represent afforestation. Three new plant functional types have been implemented in order to represent planted forests in temperate, tropical, and boreal regions. The parameters of these plantation types were estimated based on observations of stemwood carbon from real-world forest plantations. Using this new LPJmL version, we present a global assessment of potential carbon sequestration rates in forest plantations and compare these to rates achieved by letting forests grow back naturally.
LPJmL is a global process-based model
simulating vegetation dynamics and fluxes of carbon and water in the
vegetation and soil of terrestrial ecosystems (Bondeau
et al., 2007; Schaphoff et al., 2013; Sitch et al., 2003), including
agricultural land and biomass plantations for bioenergy production
(Beringer et al., 2011). The model runs primarily on a
daily time step, except for C allocation, vegetation dynamics, and
disturbances for natural vegetation and biomass plantations, which are
resolved annually. Forcing consists of monthly climate variables (air
temperature, precipitation, cloud fraction, and number of wet days per
month) – which are interpolated to daily values (Gerten et
al., 2004) – and annual atmospheric
In all simulations for this study, the model was forced by semi-constant
monthly climate input, representative of the period 1980–2010. This
dataset was derived by repeating a cycle of detrended time series for this
period, taken from the Climate Research Unit (CRU) TS3.23 global gridded (0.5
LPJmL was extended to represent forest plantations. Specifically, a new land-use type was added, as well as three functional types to represent trees in temperate, tropical, and boreal plantations. These types – referred to as forest plantation functional types (FPFTs) – are derived from the natural PFTs (temperate broadleaved summergreen tree, tropical broadleaved evergreen tree, and boreal needleleaf evergreen tree, respectively). The occurrence of the FPFTs is subject to the same establishment and mortality rules used for natural PFTs. However, the bioclimatic limits are set such that they do not overlap; hence, co-occurrence of different FPFTs in a single grid cell is rare, occurring only when climate fluctuates near a boundary between two types.
Structurally, the implementation largely follows that of the woody bioenergy
plantations implemented in LPJmL (Beringer et al., 2011),
which in turn are based on equivalent natural PFTs. Contrary to bioenergy
trees, forest plantations are not automatically clear cut after a fixed
rotation period, but a fraction of the plantation fraction may be harvested,
specified as model input. However, for the purpose of this study, harvest
was set to zero. Forest plantation PFTs also differ from other PFTs with
regard to establishment of new trees. A fixed initial planting density (
To obtain realistic growth rates, we calibrated several FPFT-specific parameters, based on published observed growth data for forest plantations. Ideally, calibration of dynamic vegetation models should be performed using detailed observations for a given site. However, this requires a large amount of data, both for model input and to compare to model output to assess performance. While much data on growth of forest plantations have been published, the number of forest plantation sites for which calibration data as well as data for model input are available for sufficiently long time periods is not enough to derive globally applicable parameter sets. Therefore, we chose a different approach. Rather than aiming to reproduce site-level observations, we calibrated the model in order to obtain desired mean biome-level behavior for each of the three FPFTs. For every iteration in the calibration, the model was run for a selection of 100 grid cells from the spatial domain of the FPFT being calibrated. Subsequently, model output for the relevant variables was aggregated over all grid cells and compared to observed values to determine model performance.
Within the spatial domain for a given FPFT, many grid cells exist where growth is marginal due to unfavorable climate and/or soil properties. The observations used in the calibration are not representative of these locations, since forest plantations from which data have been retrieved can be assumed to represent locations where productivity is sufficient for economic profitability. Therefore, rather than choosing grid cells randomly, the selection was limited to locations for which LPJmL simulates relatively high productivity. This was done based on results from a 300-year simulation with only natural vegetation, in the same setup as the one used in the calibration (see Sect. 2.3). For each FPFT, 100 cells were selected for which the simulated stemwood C storage of the corresponding natural PFT (see Sect. 2.2) exceeds the 70th percentile over the complete domain where this PFT is dominant, i.e., has highest foliar projective cover (Fig. 1). During the calibration, LPJmL was run only for these cells, with land-use type set to forest plantations.
Location of the grid cells included in calibration simulations (100 per FPFT). The map shows simulated stemwood C (kgC m
Time series of stand-level stemwood C were collected from various sources in
the literature. We required observations in the form of time series for a
sufficiently long period to assess the growth behavior on timescales
relevant to this study – at least 50 years. Due to limited data availability
(see Sect. 2.3.1), a rigorous data-selection
procedure was not possible; hence, the observations were collected in an ad
hoc fashion. For the tropical FPFT, we used data from Brown
et al. (1986), who derived time series of stemwood biomass for several
species and species groups for tropical forest plantations. For the
temperate and boreal FPFTs, no such compilations were available; hence, we
used datasets for typical plantation species for wood production.
Observations for natural poplar (
Target growth curves for stemwood C, associated observations, and LPJmL output for natural vegetation in the cells selected for calibration.
Since most forest plantations are grown for timber production, they are
harvested approximately at the optimal rotation length for maximum wood
production, which is well before the trees reach maturity. Hence, growth
data for higher tree ages are scarce. Calibrating LPJmL against these
observations alone would result in excessive weight on the earlier part of
the curve, leading to unpredictable results for the later part. Therefore,
we did not use the observations directly in the LPJmL calibration but used
them to derive growth curves representing the typical growing behavior of
productive plantations for each FPFT. We refer to these as the target growth
curves (TGCs). The general structure of the TGCs is given by the
Chapman–Richards function, which is widely used to model forest growth
(e.g., Von Gadow and Hui, 1999). It defines the stemwood C (C
The parameters were estimated using Markov chain Monte Carlo (MCMC) sampling. The samples with highest posterior density, together with the variances over the marginal posterior distributions, were used in the LPJmL calibration. Further details are given in the Supplement.
Initial tests showed that parameter sets derived by calibration with the
TGCs alone result in unrealistically high values of net primary production
(NPP), leading to similarly high litter fluxes and soil carbon storage. This
was traced to a higher carbon use efficiency (CUE) – the ratio of NPP to
gross primary productivity – and a lower vegetation carbon turnover time
(
Additionally, it was found that certain parameter sets, while leading to acceptable mean results, cause simulated trees for certain cells to die-off repeatedly at regular intervals. In order to avoid this, we modified the calibration such that a penalty was added to the cost function when this occurs.
LPJmL parameters included in the calibration. “Prior mode” refers to the most probable value indicated by the prior distribution.
In the calibration, 15 parameters were estimated, separately for each FPFT
(Table 1). The calibration was performed on a
transformed scale (logit for
Similar to the parameters, all observations were transformed in the calibration (logit for CUE; log for all other observations). For the calibration simulations, LPJmL was started from zero vegetation and soil C and run for a period of 300 years, sufficient for the vegetation C to reach equilibrium with reasonable parameter values. LPJmL simulates heartwood and sapwood C pools but does not distinguish between stem, branches, and coarse roots. For the purpose of the calibration, we assumed that all heartwood and 66 % of the sapwood are located aboveground (Müller et al., 2016), and 84 % of aboveground wood is located in the stem (which is representative of mature trees; Pretzsch, 2010).
Observations and corresponding fits for the 100 included grid cells included in the calibration. Observed values correspond to the mode of the likelihood function.
After simulation, the Chapman–Richards function was fitted to the
time series of simulated stemwood C for the 100 grid cells (using non-linear
least squares) to derive FPFT-mean estimates of C
The optimal parameter set (with minimal value of C) was derived using the GENOUD algorithm (Mebane Jr. and Sekhon, 2011), which combines a genetic algorithm with a gradient search approach. This algorithm has previously been applied to calibrate LPJmL (Forkel et al., 2014). An additional description is given in Sect. S2.
After calibration, several global simulations were performed. First, in order to assess sequestration potential of afforestation, a simulation was run in the same setup as the one used for the calibration, i.e., starting with zero vegetation and soil C and with land fully allocated to forest plantations and running for 300 years so that the vegetation C pool can reach equilibrium. Additionally, a simulation with land fully allocated to natural vegetation was performed to compare natural regrowth and afforestation as land-based mitigation options.
Second, we applied the model for an ambitious scenario of large-scale
afforestation, assuming that from 2015 onwards approximately 14 % of
global managed land is (corresponding to 650 Mha or 4.5 % of global land
surface) gradually replaced by forest plantations over the course of 85 years. This afforestation area is in line with the average land area used
for land-based mitigation (both bio-energy and afforestation) in 1.5
Figure 2 depicts the stemwood C observations for
the three FPFTs, LPJmL simulations for the corresponding natural PFTs, and
the TGCs resulting from the fitting procedure. The
values of the maximum stemwood C (C
The tropical FPFT has substantially higher C
The parameter estimates resulting from the calibration are shown in
Fig. 3, together with the range of the prior
distributions. Most estimates are within interquartile range of the priors,
but for several parameters the calibration resulted in relatively strong
changes, in particular
Prior distributions and estimated values of the FPFT parameters estimated in the calibration. The box plots indicate the 5th and 95th percentiles (whiskers), the median (red line), and 25th and 75th percentiles (box) or the priors. The final parameter estimate is indicated by the asterisk (
The ranges of the observed variables are depicted in
Fig. 4, together with the LPJmL predictions for
the calibration grid cells, based on the optimized parameter sets. The
parameter C
Ranges of the observations used in the calibration and LPJmL estimates after calibration. The box plots indicate the 5th and 95th percentiles (whiskers), the median (red line), and 25th and 75th percentiles (box) of the likelihood function. The fitted value is indicated by the asterisk (
Predicted stemwood C for 100 calibration grid cells of each FPFT based on the optimal parameter sets. Note the different scales of the
Figure 6 depicts the predicted spatial distribution
of forest plantation functional types for a global simulation experiment
with land fully allocated to forest plantations. The total area for the
temperate, tropical, and boreal plantation types is 2472 Mha (10
Spatial distribution of the different forest plantation functional types resulting from the bioclimatic limits. In marginal regions, no trees are simulated, but grass may be present.
The global vegetation C stock over time is depicted in Fig. 7a (see also Fig. S2). Tropical plantations contribute most to C storage due to their larger area and higher productivity. Comparison with the simulation where all land is allocated to natural vegetation shows considerably faster C uptake for forest plantations (Fig. 7b), with a maximum difference of 308 PgC (193 %) after 54 years. After 300 years, global vegetation C is 102 PgC higher (112 %) for afforestation simulation. Soil and litter C storage is also proportionally higher for forest plantations. Note that the soil and litter C uptake rate is extremely high due to the fact that the simulation was started with zero C. In reality, soil C will already be present before land-use change and uptake will be much slower, possibly even negative, depending on previous land use.
Global total ecosystem C over time for simulations with global forest plantations or global natural vegetation.
The potential for C uptake is illustrated by the mean annual increment (MAI) of vegetation C since the start of plantation (Fig. 8). There are remarkable differences between the two simulations. After an initial similar increase, MAI sharply drops after approximately 10 years for natural regrowth, while for afforestation MAI keeps rising until approximately 30 years. The behavior for natural regrowth can be explained by vegetation succession, leading to a shift from grasses to trees. This succession does not occur for forest plantations, where trees start growing immediately, resulting in a substantially higher MAI in the early part of the simulation. From spatial differences in MAI after 50 years, it is evident that tropical regions contribute most to this difference.
Mean ecosystem sequestration rate (mean annual increment, MAI), determined as total C storage divided by time since the start of LPJmL simulations with only forest plantations or only natural vegetation.
Figure 9 depicts results of the global simulation
scenarios with gradual increase in forest, applying either afforestation or
natural regrowth. Since changes in C storage – particularly for the
soil – result also from land-use changes before 2015, we focus on the difference in global C stocks compared to the baseline
simulation with constant land use from 2015. Until 2015, all three
simulations have very similar results, but small differences arise from the
stochastic generation of daily precipitation. Gradual afforestation of 650 Mha of land between 2015 and 2100 results in 19, 48, and 75 PgC additional C storage by 2065, 2100, and 2150, respectively, versus 16, 37, and 61 PgC for natural regrowth. Most of the difference between the two simulations is due to vegetation C, but from 2100 the difference for soil C grows and would
ultimately dominate, had the simulation been continued after 2150. Global
C sequestration rate peaks between 2090 and 2100 at approximately 0.91 and
0.68 PgC yr
Results of the simulations for gradual afforestation and natural regrowth. Both graphs show differences relative to the baseline simulation with constant land use.
Compared to the prior distributions – which are largely based on values for
corresponding natural PFTs – the calibration resulted in a substantial shift
for several parameters. We will discuss the more notable changes. First,
The parameter
The maximum leaf-to-root mass ratio, lr
For the tropical FPFT,
The calibration resulted in good fits with respect to most observations, with the exception of the growth rate parameter of the target growth curves. Despite substantial improvement compared to the corresponding natural PFTs, this parameter is underestimated for all three FPFTs. As a result, predicted initial C uptake rates are lower than implied by the stemwood C observations, possibly underestimating the potential efficacy of forest plantations for climate mitigation.
As discussed in Sect. 2.3.2, we incorporated data
into the calibration to constrain NPP-to-GPP ratio and vegetation C turnover
time to values similar to those of the corresponding natural PFTs. Earlier
calibrations, in which these constraints were not included, yielded a
substantially better fit to the growth rate but with unrealistically high litter
fluxes, which points to a trade-off between the fit to these observations.
From a mass-balance perspective, this result is explicable: fast growth
requires high NPP, which will result in high litter fluxes once vegetation
reaches equilibrium biomass. This is exacerbated by the fact that we
constrained maximum stemwood C (C
Despite the underestimated growth rates, our results show C can be
sequestered substantially faster by forest plantations compared to natural
regrowth (Fig. 7), particularly in the first 50 years following land conversion. The largest potential for plantations lies
in tropical regions, which is not surprising, given that the maximum biomass
of tropical FPFTs is more than twice as high compared to the temperate and
boreal FPFTs. In addition to faster C sequestration, LPJmL also predicts a
12 % higher equilibrium global vegetation C pool for forest plantations,
despite the fact that the FPFTs were calibrated to produce a value of C
Combined soil and litter C is also higher for forest plantations after 300 years (Fig. 7b), but its proportion to total ecosystem C (60 %) is globally almost identical to that of the natural vegetation, due to the constraints on NPP-to-GPP ratio and vegetation C turnover time included in the calibration (see Sect. 2.3.2). It is difficult to compare these results to observations for real-world plantations since studies on this topic have generally compared natural forests to tree plantations for production of wood or other products, where the effects of harvest and other management on soil C are likely considerable (Guo and Gifford, 2002; van Straaten et al., 2015). Such effects are not relevant for plantations intended for C sequestration.
According to our projections, gradual conversion of 650 Mha managed land to
natural forest between 2015 and 2100 results in additional C uptake of 16 and 37 PgC by 2065 and 2100, respectively. If these lands are converted to
forest plantations, the estimated C uptake is 19 and 48 PgC, i.e., 19 % and 30 % higher. These should be seen as conservative estimates, in view of the underestimated growth rates resulting from the calibration. To put these numbers into perspective, we compare them to results of Gasser et al. (2015), who estimated the negative emissions needed to
limit global warming to 2
The results of the simulations for transient afforestation and natural regrowth compare well to results of previous studies on potential C sequestration rates of forest plantations and natural regrowth. For example, using the IMAGE integrated assessment model, van Minnen et al. (2008) performed a simulation experiment based on the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario where 831 Mha of agricultural land is converted to permanent forest plantations between 2000 and 2100, taking into consideration land demand for food production and other uses. They estimated an additional 93 PgC can be sequestered but mostly after 2050, when land becomes gradually available due to decreasing population and increasing agricultural efficiency.
Humpenoder et al. (2014) presented a much more ambitious afforestation scenario, in which 2773 Mha of land is converted to forest plantations. The authors used maximum C storage for natural vegetation predicted by LPJmL but corrected sequestration rates using stylized growth curves for plantations in different climate regions. They estimate an additional C uptake of 192 PgC after 80 years. Roughly converting our estimate to the same land area yields a similar result (205 PgC). This similarity is not surprising, given that we used the same model, and our FPFTs were calibrated to produce the same maximum biomass as the natural PFT equivalents.
Potential sequestration rates by natural regrowth were studied by Krause et al. (2017), using the dynamic global vegetation model LPJ-GUESS. In two scenarios, derived by IMAGE and the agricultural land-use model MAgPIE, 1119 and 914 Mha were converted to natural lands, resulting in a predicted additional C uptake of 76 and 55 PgC, respectively, between 2000 and 2099. This compares well with our estimates for natural regrowth.
In our implementation of planted forests, the diversity of plantation tree species is reduced to three functional types with fixed properties. While the functional diversity of plantation tree species is not as vast as that of natural forests – especially in the context of C sequestration – the predictions would likely improve from implementation of additional FPFTs, particularly for the tropical biome. The model currently predicts a relatively large C storage for dry tropical zones compared to natural regrowth, which may not be fully realistic, given water limitations. Addition of a dry tropical FPFT would allow for a more accurate assessment of C sequestration in these regions.
This study does not consider the effects of climate change and
We also did not consider possible management options that may improve C uptake rates. In particular, regular thinning can result in substantially higher C uptake rates (van Minnen et al., 2008). The model supports harvesting, but this feature was not used in this study. However, continual thinning would result in export of nutrients from the ecosystem, which would ultimately slow down growth rates, unless plantations are fertilized. Thus, representing regular harvest in LPJmL would also require representation of nutrient limitation.
Evaluation of afforestation and natural regrowth as strategies for climate change mitigation involves a range of considerations other than carbon sequestration. First, converting agricultural land to forest involves a number of costs. For both natural and planted forests, this includes price for acquiring land, while specifically for the latter costs related to establishing and maintaining the plantation are relevant (e.g., land preparation, planting of seedlings). The costs per unit C sequestered will rise with increasing area of (planted) forest, mainly due to competition for land (Doelman et al., 2019a).
Second, the positive effects of carbon uptake of changing land cover to forest can be offset due biophysical changes in the surface energy budget, related to changes in albedo, evapotranspiration, and surface roughness (Perugini et al., 2017). This may result in a net warming effect, regionally, and possibly globally, depending on the extent of land-cover change.
Third, the reduction of cropland and pasture might also have a negative
impact on food security due to increased competition for land
(Hasegawa et al., 2018). In order to maintain food production
for the growing population, strong intensification of the agricultural
sector would be required. Locally, this will result in a range of negative
effects on the environment, due to higher application of fertilizers and
plant protection products, as well as water extraction for irrigation
(Smith et al., 2013). Furthermore, in terms of climate
change mitigation, agricultural intensification will likely partially offset
the benefits of afforestation and regrowth, e.g., due to higher
Finally, biodiversity is a particularly important aspect to consider, given that plantation forests have usually substantially lower species richness than primary or secondary forests (Barlow et al., 2007). A more balanced solution may be a compromise between biodiversity and C sequestration by establishing a mixture of native and plantation species, or plantation forest with a native undergrowth (Barlow et al., 2007; Bremer and Farley, 2010).
To our knowledge, the extension of LPJmL presented here represents the first model of forest plantations for C sequestration as part of a DGVM for global-scale applications. Although calibration of the model still resulted in underestimated growth rates compared to observations of stemwood C, this represents an improvement over previous approaches. According to our simulations, conversion of 650 Mha of land to forest over 85 years results in an additional C uptake of 48 PgC for forest plantation versus 37 PgC for natural regrowth, with greatest potential in the tropics. We conclude that large-scale afforestation can offer a substantial contribution to C uptake, particularly on a timescale of approximately 50–100 years. Evaluating afforestation as a strategy for climate change mitigation requires consideration of all relevant aspects in a comprehensive assessment. Our model can contribute to such an evaluation by providing improved estimates of C uptake rates.
The source code of the LPJmL version with forest plantations and the model output of the global simulations will be made available in a public data repository at a later date. Until then they will be sent upon request to the corresponding author.
The supplement related to this article is available online at:
ES, DvV, and JD initiated the project and contributed to discussions during all stages. PB and MB implemented the modifications to the LPJmL model to represent forest plantations. MB implemented the calibration algorithm and ran the calibrations and global simulations. JD provided the input data needed for global simulations. CM and SS provided expert advice on the workings of the LPJmL model and the interpretation of its results. MB drafted the paper, and all authors contributed to the final paper.
The authors declare that they have no conflict of interest.
We gratefully acknowledge Matthias Forkel for advice on using the GENOUD algorithm to calibrate LPJmL.
This paper was edited by Govindasamy Bala and reviewed by Jagmohan Sharma and one anonymous referee.