1

Quantification and prediction of biomass yield of temperate Low-Input High-1

Diversity ecosystems 2

3

Koenraad Van Meerbeek1, Jonathan Van Beek2, Lore Bellings1, Wim Aertsen1, Bart Muys1 4

and Martin Hermy1 5

6

1 Division Forest, Nature and Landscape, Department of Earth and Environmental 7

Sciences, University of Leuven, Leuven, Belgium 8

2 M3-BIORES, Geomatics Lab, Departement of Biosystems, University of Leuven, Leuven, 9

Belgium 10

11

Correspondence: Koenraad Van Meerbeek, tel. +32 16 372 189, fax +32 16 329 760, e-12

mail: koenraad.vanmeerbeek@ees.kuleuven.be 13

14

15

2

Abstract 16

Little is known about the biomass production and bioenergy potential of Low-Input High-17

Diversity (LIHD) systems in temperate non-forest conservation areas. In order to assess the 18

potential of the biomass for energetic or other purposes, accurate yield data from LIHD 19

systems are needed. We quantified the biomass yield in a wide range of semi-natural 20

systems (grasslands, marshes, tall-herb vegetation and heathlands). Our results show a 21

considerable variation in annual biomass yield ranging between 0.69 and 6.49 tDM ha-1 yr-1. 22

In addition, we provide an accurate method to determine the standing stock of harvestable 23

biomass in the field. We developed four predictive models: one multiple linear regression 24

(MLR) model and three boosted regression tree (BRT) models: (i) a vegetation model with 25

variables that are easy to measure in the field, (ii) a soil model with soil physical and 26

chemical variables and (iii) a vegsoil model with all available variables. Due to its ability 27

to fit non-linear response functions and threshold values, the boosted regression tree 28

technique outperformed the classical multiple linear regression. The vegetation model is 29

the preferred model, because it combines a good predictive performance (R²adj = 0.75 and 30

R²adjCV = 0.51) with a relatively simple application. 31

Keywords: bioenergy; boosted regression trees; LIHD; nature conservation; nature 32

reserves; predictive modelling; semi-natural areas; standing crop 33

3

Introduction 34

35

To date, the main focus of biomass production for bioenergy lies on conventional food 36

crops and lignocellulosic crops like poplar and Miscanthus [1,2]. These systems are called 37

High-Input Low-Diversity (HILD) systems because the high level of inputs such as water, 38

fertilizers, pesticides and low levels of biodiversity [3]. Bioenergy crops are usually grown 39

on fertile lands which therefore can no longer be used for food or fodder production. Food 40

and fodder crops are also increasingly used as a source for bioenergy, which forces their 41

prices upwards [4]. The resulting demand for land leads directly and indirectly to the 42

exploitation of new land for agriculture [5]. The environmental impact of these systems is 43

increasingly criticized [6,7], although the strategy of land sparing can decrease the pressure 44

of crop production on natural habitats [8]. In the search for other biomass sources, the last 45

decade Low-Input High-Diversity (LIHD) systems came into the picture [e.g. 3]. LIHD 46

systems receive little or no additional input and contain a high level of biodiversity. Recent 47

studies showed that LIHD biomass is promising for the production of bioenergy. Low-48

input biofuels potentially provide higher NEB ratios (Net Energy Balance ratio: ratio 49

between the sum of energy outputs and the sum of fossil energy inputs) compared to food-50

based biofuels [3,9]. Till recently, the emphasis of LIHD biofuel research was mostly on 51

grasslands sown on agriculturally degraded and abandoned lands [10-13]. 52

A lot of the European nature has its origin in traditional land-uses [14,15]. Because of the 53

long co-evolution of European landscapes, human activities and species, a lot of the 54

species-rich ecosystems depend on some kind of management to be preserved [16]. These 55

systems are therefore called semi-natural [14]. Because of the intensification of the land-56

use and abandonment of marginal lands, many of the semi-natural ecosystems are 57

threatened in their existence [17,18]. The key strategy to preserve these species-rich 58

4

ecosystems has been the establishment of protected areas, with an active conservation 59

management, based on the continuation of traditional agriculture practices like mowing or 60

grazing [14,15,17-19]. Many of the species-rich grasslands, heathlands and marshes in 61

conservation areas are thus being mowed to prevent succession and maintain the open 62

character [14,15,19]. Although this management produces each year a large amount of 63

biomass, nature conservation areas are commonly forgotten in biomass resource potential 64

assessments [20]. Because no fertilizers are applied and because of the late harvest date in 65

conservation areas, the forage quality is often low and therefore not used for livestock 66

production [21-23]. In some cases, the clippings originating from the management are 67

therefore even considered as a “waste product”, to which an extra cost is connected [24-68

27]. Subsequently, little is known about the biomass and bioenergy potential of the 69

different plant communities in conservation areas. To assess this potential, we need 70

accurate data on the biomass yield of semi-natural LIHD systems. As floristic variation in 71

LIHD systems is large, estimates of standing biomass per plant community are preferable. 72

Harvesting has been the most frequently used technique for the determination of the 73

standing biomass in grasslands and shrublands [28], which is rather time-consuming and 74

therefore expensive [29]. Moreover, it is a destructive technique and therefore not suitable 75

for monitoring the standing biomass. As an alternative, different non-destructive methods 76

have been proposed based on measurements in the field, remote sensing or other 77

environmental data [e.g. 30]. These methods often need local calibration and are not 78

universally applicable. 79

80

The first objective of this study was to quantify the average biomass yield in different 81

semi-natural plant communities in nature conservation areas in the temperate climate zone 82

of Western Europe. The collected data will be used to develop empirical models of the 83

5

standing biomass in the LIHD plant communities. We hypothesized that with these models, 84

the standing stock of harvestable biomass can be predicted and monitored in the field in a 85

quick, non-destructive and reliable manner [cf. 29]. By providing accurate data on the 86

average biomass yield in a wide range of plant communities, our study delivers 87

information for future biomass potential assessments and can be considered as an essential 88

intermediate step to scale up the data to a broader, regional scale (e.g. Europe). 89

90

6

Material and methods 91

92

Study area and data collection 93

94

This study was performed in Flanders, the northern part of Belgium, Western Europe 95

(range: 50°41’14”-51°30’18” N; 2°32’43”-5°54’38” E). The region is characterized by a 96

pronounced north-south soil texture gradient of decreasing sand and increasing silt 97

fractions resulting in different ecoregions. The climate is maritime temperate, with 98

significant precipitation in all seasons, a mean annual temperature of 9.8-10.5 °C and mean 99

annual precipitation of 733-832 mm [31]. In this densely populated region [466 inhabitants 100

km-1, 32] and in many areas of temperate Europe, the key strategy for protecting the 101

remaining biodiversity has been the establishment of a network of protected areas. No 102

fertilizers are applied within the boundaries of the protected areas, but as in large parts of 103

Europe, the study area is subjected to a relatively high atmospheric nitrogen deposition 104

[33], e.g. on average 25 kg N hectare-1 in 2009 [32]. 105

106

We focused on a wide range of semi-natural plant communities in protected areas with a 107

periodic mowing regime: dry and wet grasslands, reed marshes, tall-herb vegetation and 108

heathlands. From 2009 to 2011, 135 sites in nature reserves across the different ecoregions 109

in Flanders were sampled once (Fig. 1). The vegetation surveys spanned three consecutive 110

years to incorporate the yearly variability of biomass production within the different plant 111

communities. The surveys occurred from June till mid-October, corresponding to the 112

mowing period as implemented in the field to maintain or restore biodiversity. Each site 113

was sampled just prior to the date of management on that site to give an accurate 114

estimation of the harvestable biomass. The timing of mowing is usually based on the 115

7

reproduction stage of the present characteristic and rare species (key species) and does not 116

necessarily correspond to the timing of optimal biomass yield. 117

At each site, a plot of 10x10 m was positioned in homogeneous vegetation. Therein three 118

subplots of 0.5x0.5 m were taken randomly forming a composite sample. Within each 119

subplot, a detailed vegetation survey was performed: the % cover of each plant species was 120

visually estimated. Also total cover of the vegetation, average height with or without 121

inflorescences were recorded. In each subplot, the aboveground biomass was clipped, 122

harvested and weighed to determine the fresh weight (precision 0.1 g). Afterwards, the 123

biomass was oven-dried in a Binder drying oven at a temperature of 105 °C to constant 124

weight to obtain the dry weight. Per plot, a topsoil sample (0-15 cm) was analyzed to 125

determine the following soil variables: soil texture, pH-KCl, total and Kjeldahl nitrogen, 126

total and organic carbon content and plant-available amounts of other macronutrients (P, 127

K, Mg, Ca, Na) (for more details about methods see supporting information; Online 128

Resource 1). The soil drainage class is derived from the soil map of the Flemish Region 129

[34]. An overview of the measured and calculated vegetation and soil variables is given in 130

Table 1. Plant species nomenclature follows Van Landuyt et al. [35]. 131

132

Vegetation classification 133

134

Vegetation relevés were classified into plant communities using PC-ORD 6 [36]. After 135

transforming the abundance data (arcsine square root), the plots were clustered using 136

hierarchical clustering with the Sørensen distance measurement and flexible beta linkage 137

(β = -0.25) as group linkage method [37]. Indicator Species Analysis (ISA) indicated the 138

optimal number of groups in the cluster analysis [38]. 139

140

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Biomass yield 141

142

Per plot, the fresh and dry weight of the harvested biomass were averaged over the three 143

subplots to get one value for each plot. In the yearly mown plant communities like 144

grasslands, the annual biomass yield is calculated as the mean of the measured standing 145

biomass of the plots within the plant community (in tons dry matter hectare-1 year-1), with 146

the standard deviation as a variance measure. In a number of plant communities the length 147

of the mowing interval (the mowing cycle) is more than one year in order to preserve a 148

particular vegetation type [39]. For example, heathlands are mown approximately every 15 149

years to let the vegetation pass all phases of its life cycle [40]. Reed and humid tall-herb 150

communities are optimally mown every 3 to 5 years [19,41]. For each of the multi-151

annually mown communities, we sampled the standing biomass at different phases of their 152

life cycle. Because of the limited time frame of the study, we used this space-for-time 153

substitution to analyze the evolution of the standing biomass during the life cycle. We 154

performed, per vegetation type, a log-linear regression between standing biomass and the 155

number of years since last mowing (Online Resource 2). The biomass yield per mowing 156

cycle (i.e. the standing biomass at the end of a mowing cycle) was then predicted based on 157

the regression function and the 95% confidence interval was used as a measure of 158

dispersion from the mean. The mean annual yield could be calculated by dividing the 159

obtained value by the number of years of the mowing cycle. In the case of abandoned and 160

unmanaged grasslands the optimal yearly management regime was not applied for several 161

years. From a conservation viewpoint a nature manager usually wants to transform these 162

degraded grasslands into more species-rich herbaceous plant communities by reinstalling 163

the yearly mowing cycle. For these communities we assumed that the optimal mowing 164

cycle of one year was reinstalled. 165

9

166

Modelling of standing biomass 167

168

In recent years, a variety of linear and non-parametric modelling techniques has been 169

developed and applied to predict ecological response variables from a wide range of 170

environmental variables. Aertsen et al. [42] compared the performance of five modelling 171

techniques for the prediction of forest site productivity in a multi-criteria analysis with 172

different model performance measures, ecological interpretability and user-friendliness as 173

criteria. From these techniques, we selected boosted regression trees (BRT), based on its 174

accuracy and ability to fit non-linear response functions and threshold values, which is 175

crucial in modelling the non-linear behavior of ecological processes [42,43]. Because user 176

friendliness and practical use in the field were important goals of the predictive model, 177

multiple linear regression (MLR) was also selected to model the standing biomass. 178

179

BRT incorporates important advantages of regression tree-based methods, including the 180

capacity of combining predictor variables of different types and distributions, the 181

automatic detection of predictor interactions and the capacity of handling missing values 182

based on ‘surrogate’ splitting [42]. The method is also invariant to monotonic 183

transformations of the predictor variables and is insensible to outliers as they are isolated 184

into a separate node and therefore have no further effect on the analysis. The boosting 185

technique is a numerical optimization algorithm that minimizes the loss function by 186

calculating a sequence of regression trees. In each step, a tree is added that best reduces the 187

loss function [44,45]. The final BRT model is a linear combination of all calculated trees 188

(hundreds to thousands). 189

10

We developed BRT models (R-code and user guide available in Online Resource 3) with 190

(i) vegetation variables that are easy to measure in the field, (ii) the soil physical and 191

chemical variables and (iii) all available variables, further referred to as the vegetation 192

model, the soil model and the vegsoil model. BRT models were constructed in R 2.15.3 193

[46], using the functions developed by Elith et al. [45], as an extension of the ‘gbm’ 194

package [47]. The models were fitted with the gbm.step function and a Gaussian response 195

type (aimed at minimizing squared error). The number of nodes in a tree was set to 3, as 196

recommended by Elith et al. [45] for small datasets and most effective settings for learning 197

rate (0.001-0.005) and bag fraction (0.75) were found by repeated trial-and-error. Models 198

were simplified by reducing the number of explanatory variables with the gbm.simplify 199

function. The simplification process was run within a 10-fold cross-validation procedure, 200

progressively simplifying the model. The average cross-validation error was used to decide 201

the number of variables that can be removed from the original model without affecting 202

predictive performance [45]. The predicted contribution of the predictor variables is a 203

measure of the predictor’s importance within the model and can be calculated as the 204

proportional contribution to the total error reduction. Predictor effects are visualized in 205

partial dependence plots and indicate how much the response is affected by a certain 206

predictor value, after accounting for the average effects of all other variables in the model. 207

[43,45]. Most important pairwise interactions were explored using the gbm.interactions 208

function. 209

The MLR model was constructed in R using the stepAIC function of the MASS-package 210

for selecting the most important predictor variables from the suite of vegetation variables 211

[48]. Like the vegetation BRT model, only variables that are easy to measure in the field 212

were included. All MLR and BRT models had an acceptable level of multicollinearity 213

between predictor variables (i.e. Pearson correlations between predictors <0.7, individual 214

11

variance inflation factor (VIF) <5 and median VIF <3). Model fit and predictive 215

performance after 10-fold cross-validation were quantified by the coefficient of 216

determination (R²), the adjusted R² (R²adj), the root mean squared error (RMSE), the 217

relative RMSE (rRMSE = RMSE / mean response) and the AIC (Akaike information 218

criterion) [42,49]. Cross-validation was preferred to split-sample techniques because it 219

offers the possibility to use all the data for fitting and validating the model [50]. 220

Particularly in small data sets, cross-validation is proven to be more robust and reliable 221

[51-53]. 222

223

12

Results 224

225

The cluster analysis was successful in capturing well-known phytosociological patterns 226

present in the data (Fig. 2). Based on the indicator species analysis, we chose to cut the 227

dendrogram at two points to obtain a classification with two hierarchical levels (dashed 228

and dotted lines, Fig. 2). This allowed us to select the appropriate grouping level for each 229

analysis. The first level contains 7 groups, namely reed marsh, tall-herb vegetation, wet 230

grasslans, mesotrophic grassland, dune slack, low productive grassland on sandy soils and 231

heathland. This level was used as a variable in the predictive modelling. In the second 232

level, we obtained a finer division into 12 plant communities, of which significant 233

indicator species and corresponding EUNIS (European Nature Information System) [54] 234

habitat types are given as supporting information (Online Resource 4). Per group we 235

calculated the mean annual yield and the standing biomass at the end of the mowing cycle. 236

The distribution of the samples over the different plant communities is shown in Table 2. 237

Within the yearly mown plant communities, tall-sedge dominated wetlands and the 238

abandoned and unmanaged grasslands had the highest annual yield (6.07-6.49 tDM ha-1 yr-1, 239

Table 2). The low productive grasslands had the lowest (2.59-2.69 tDM ha-1 yr-1). Wet and 240

dry heathlands had an average yield per mowing cycle of 10.35-15.28 tDM ha-1 and an 241

annual yield of 0.69-1.02 tDM ha-1 yr-1 (Table 2). 242

243

The vegsoil BRT model had the highest performance on the fitted data as well as after 244

cross-validation (R²adj for fit and cross validation of 0.81 and 0.55, Table 3). The height of 245

the vegetation (without inflorescences) and the number of years since last mowing 246

contributed most to the model (41.2% and 27.7%, Fig. 3). The cover of graminoid species 247

(without reed) was also a strong predictor of the standing biomass, together with the plant 248

13

community (level 1 grouping variable) and the total carbon content of the soil. The 249

vegetation model contained the same variables as the vegsoil model, except for the total 250

carbon content of the soil (Fig. 4). While only incorporating easy-to-measure variables, it 251

almost achieved an equally high performance as the vegsoil model (R²adj cross-validation 252

= 0.51). The soil model performed considerably less well (Table 3). It scored the worst of 253

the three BRT models on all model performance indicators (R², R²adj, RMSE, rRMSE and 254

AIC). Incorporated predictors were total carbon content of the soil (30%), plant-available 255

potassium (27.3%), pH-KCl (24.9%) and plant-available sodium (17.8%, Fig. 5). Most 256

important interactions between predictor variables as detected by the BRT technique are 257

visualized in Online Resource 5. 258

The final MLR model was a log linear model with height without inflorescences (Hwi), 259

years since last mowing (years SLM), date of harvest and sum of cover as predictors (eq. 1, 260

see Table 1 for the description of the variables). The MLR model performed well on the 261

goodness-of-fit (R²adj = 0.56), but had a low predictive performance (R²adj cross-262

validation = 0.23). Still, it scored better than the soil model on all performance indicators. 263

264

log10(standing biomass) = -0.31933 + 0.57284 * log(Hwi) + 0.00141 * sum of cover – 0.00105 265

* date + 0.77781 * HL * log(years SLM) - 0.13809 * HL * log(Hwi) 266

+ 0.23090 * THV * log(years SLM) (eq. 1) 267

where HL = 1 for heathlands and 0 for other plant communities 268

THV = 1 for tall-herb vegetation and 0 for other plant communities 269

270

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Discussion 271

272

Biomass yield 273

274

The results show a considerable variation in annual biomass yield of temperate semi-275

natural LIHD systems ranging between 0.69 and 6.49 tDM ha-1 yr-1 (Table 2). The highest 276

annual yield was found in abandoned and unmanaged grasslands on rich soils. For 277

communities with a multi-annual mowing cycle, the annual biomass yield is calculated as 278

the biomass yield per mowing cycle divided by the length of the mowing cycle. Because 279

the seasonal decomposition of plant material is not taken into account, the values will 280

deviate from the net primary productivity [cf. 55]. The biomass yield per mowing cycle is 281

the biomass that can really be harvested and varies between 2.59 tDM ha-1 for dry acid and 282

neutral grasslands with Agrostis (MC = 1 year) and 15.28 tDM ha-1 for sub-Atlantic dry 283

heathlands (MC = 15 years). 284

Other research on LIHD systems reported comparable yields. In the USA, researchers 285

found 3.49-4.75 tDM ha-1 yr-1 on lands that are set aside under the Conservation Reserve 286

Program [CRP, 13] and an average of 3.7 tDM ha-1 yr-1 on degraded land were multispecies 287

mixtures were sown [3]. In Germany, six extensively managed grasslands had a yield 288

between 2.0 and 4.5 tDM ha-1 yr-1 [56]. As expected, the biomass production is substantially 289

lower compared to most HILD crops (e.g. 15 tDM ha-1 yr-1 for an intensively managed 290

grassland and 22 tDM ha-1 yr-1 for maize) [57], but LIHD biofuels provide a higher net 291

energy balance, as they require a lower amount of inputs [3]. 292

293

Little research has been done on the long-term effects of biomass removal on biomass 294

yield in LIHD ecosystems. Nutrients are removed during harvest and because no fertilizers 295

15

are applied in the conservation areas, it can lead to a depletion of nutrients in the system. 296

Bakker [58] reported an amount of 15-150 kg nitrogen per hectare removed by hay in 297

semi-natural plant communities. But the study area is subjected to a relatively high 298

atmospheric nitrogen deposition [32]. Bobbink et al. [59] found an increased biomass 299

production in different semi-natural ecosystems with N inputs (15-40 kg N ha-1) that 300

equaled the rates of the atmospheric deposition in the study area. Also, the effects of 301

biomass removal differ between communities and depend on the soil conditions and the 302

history of the management [58,60]. So, overall it is difficult to assess the long-term effects 303

on biomass yield. To incorporate some of this variability, we sampled sites with different 304

histories of biomass removal. Some sites were mown for a longer time without 305

fertilization, other sites were just recently subjected to conservation management. The 306

long-term effects are thus partly reflected in the variance of the mean annual yield. 307

308

Predictive models of standing biomass 309

310

We developed four predictive models of the standing biomass: one multiple linear 311

regression (MLR) model and three boosted regression tree (BRT) models. The vegsoil 312

model outperformed the other models, both in terms of goodness-of-fit and validation (R², 313

RMSE, rRMSE). Even when model complexity was taken into account, it remained the 314

best model (R²adj, AIC). However, it had higher values of the relative RMSE, which could 315

be caused by measurement errors, sampling bias or limitations in field data collection [43]. 316

The vegetation height without inflorescences and the number of years since last mowing 317

were the most important predictors of the vegsoil model. This is not surprising because the 318

height is a volume indicator and the number of years is an indicator of biomass 319

accumulation. The cover of graminoid species, the plant community (level 1) and the total 320

16

carbon content of the soil contributed each about 10% to the model. The high standing 321

biomass at low cover percentage of graminoids can be explained by the fact that graminoid 322

cover is low in heathlands and tall-herb vegetation which have high standing biomass. 323

Unexpected is the absence of soil drainage class as a predictor in the vegsoil model. The 324

effect of this variable is probably already largely incorporated in the plant community 325

variable. 326

The vegetation model is the same as the vegsoil model, except for the variable total carbon 327

content of the soil and consequently does not lose much of its predictive power (R²adj for 328

fit and cross validation of 0.75 and 0.51). It is the most cost-effective model to predict the 329

standing biomass in the field, because it only includes variables that are easy to measure on 330

the field. The predictive performance of the soil model was considerably less. The high 331

contribution of the total carbon content and soil acidity (pH-KCl) to the model was not 332

unexpected. Both variables also show a strong interaction with each other. Low carbon 333

content or high soil acidity result in lower biomass productivity. Plant-available potassium 334

and sodium completed the model. The unexpected absence of the nitrogen content in the 335

model is due to the high correlation between carbon and nitrogen content of the soil (r = 336

0.78, P<0.001). 337

The log-linear multiple regression model incorporated log(height), sum of cover and 338

log(years since last mowing) for heathland and tall-herb vegetation. Also the date of 339

harvest had a significant influence on standing biomass. At the end of the growing season, 340

standing biomass is reduced by death of plant parts and allocation of nutrients to 341

belowground parts. The final model had a reasonably good fit of the data (R²adj = 0.56), 342

but a less predictive performance (R²adj cross-validation= 0.23). It still performed better 343

than the soil model. 344

345

17

With an R²adj ranging from 0.52 to 0.81, our models perform relatively good compared to 346

other biomass yield studies [cf. 43,61]. But inevitably, our models still result in prediction 347

errors and could undoubtedly be improved by collecting more field data. Our models are 348

calibrated and validated for the region of Flanders. Based on similar climatic conditions, 349

soil types and plant communities in adjacent regions (e.g. lowlands of N-France, the UK, 350

the Netherlands and Germany), our models may have a broader geographic applicability. 351

The boosted regression trees technique has several advantages that make it especially 352

suitable for ecological research [42,45]. The major drawback of the BRT models is the 353

absence of a model function on paper that can be directly used in practice, as provided by 354

the MLR technique. The latter has the advantage of being very straightforward, user-355

friendly and easy to use in the field. But due to the simplicity of the technique, the model 356

compromised on its predictive performance and did not succeed very well in predicting 357

real-world ecological processes [cf. 45]. Overall, the vegetation model is the preferred 358

model, because of the combination of a good predictive performance and a relatively 359

simple application. 360

18

Conclusion 361

362

The studied semi-natural LIHD systems in temperate conservation areas had a considerable 363

variation in annual biomass yield ranging between 0.69 and 6.49 tDM ha-1 yr-1. Unlike 364

HILD systems, the biomass yield of these LIHD systems does not conflict with other 365

ecosystem services like biodiversity conservation, erosion control, carbon sequestration 366

and clean water production [62]. Management practices in conservation areas can be 367

compatible with fauna by delayed mowing or leaving uncut areas as a refuge [63-65]. The 368

conversion of biomass from conservation areas to bioenergy or materials is an opportunity 369

to reconcile biodiversity goals and energy policies and is also an incentive to preserve the 370

protection on the land [63]. 371

Given the vast areas of LIHD systems (e.g. ca. 6.8 million ha of the 13.8 million ha of CRP 372

land in the USA [66] or 385-473 million ha of abandoned agricultural land for the world 373

[10]) the potential of LIHD biomass is enormous. However, LIHD biomass is often widely 374

scattered and discontinuously available [56,67], making that biomass transport and 375

handling often is a great challenge and a limiting factor to the profitability of bioenergy 376

systems [20]. Network optimization and supply chain management are increasingly used 377

for the development of efficient bioenergy networks [68]. Accurate data and monitoring of 378

biomass availability in LIHD systems are essential for a correct dimensioning and location 379

of conversion plants and to allow the integration of LIHD biomass into the biomass-to-380

bioenergy supply chain. With predictive models and data on biomass production in a wide 381

range of semi-natural plant communities in conservation areas, we provide detailed 382

information to fill existing gaps in knowledge. Our research also paves the way for 383

biomass potential assessments and upscaling of the data to a broader, regional scale (e.g. 384

Europe). 385

19

Acknowledgements 386

We would like to thank Natuurpunt vzw and the Agency of Nature and Forestry (ANB) of 387

the Flemish government for the access to the nature reserves and Andreas Demey for the 388

permission to sample his permanent plots. The comments and suggestions of the 389

anonymous reviewers were greatly acknowledged. Research funded by a Ph.D. grant of the 390

Agency for Innovation by Science and Technology (IWT). 391

392

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25

Tables 568

569

Table 1 List of measured and calculated soil and vegetation variables 570

Soil variables

Soil texture Code

Clay fraction Soil fraction < 2 µm (%)

Loam fraction Soil fraction 2-50 µm (%)

Sand fraction Soil fraction > 50 µm (%)

Soil drainage Soil drainage condition (classes)

pH-KCl Soil acidity (KCl)

Phosporus Plant-available phosphorus (mg/100 g air dry soil)

Potassium Plant-available potassium (mg/100 g air dry soil)

Magnesium Plant-available magnesium (mg/100 g air dry soil)

Calcium Plant-available calcium (mg/100 g air dry soil)

Sodium Plant-available sodium (mg/100 g air dry soil)

Kjeldahl nitrogen Total Kjeldahl nitrogen (mg/100 g air dry soil)

Total carbon Total carbon content (% of air dried soil)

Vegetation variables

Years SLM Years since last mowing

Date Date of sampling (number of the day: 1-365)

Species diversity (S) Number of species per plot

Simpson diversity (D) 1/∑𝑝𝑖2

𝑛

𝑖

Simpson evenness (ED) D/S

Shannon diversity (H) −∑ 𝑝𝑖ln 𝑝𝑖𝑛

𝑖

Shannon evenness (EH) H/ln S

Dry weight Standing biomass (ton dry matter/ha)

Cover of graminoids Sum of cover of graminoid species without reed (%)

Cover of reed Sum of cover of reed (%)

Cover of legumes Sum of cover of leguminous species (%)

Cover of non-leguminous forbs Sum of cover of non-leguminous species (%)

Cover of woody species Sum of cover of woody species (%)

Sum cover (Csum) Sum of cover of all species (%)

Total cover (Ctot) Total cover of vegetation per plot (%)

Height without inflorescences (Hwi) Height of the vegetation without inflorescences (cm)

Total height (Htot) Maximum height of vegetation (cm)

Volume (Hwi*Csum) Volume (Height without inflorescences *Sum cover) (m³/m²)

Volume (Htot*Csum) Volume (Total height*Sum cover) (m³/m²)

Volume (Hwi*Ctot) Volume (Height without inflorescences*Total cover) (m³/m²)

Volume (Htot*Ctot) Volume (Total height*Total cover) (m³/m²)

571

26

Table 2 The two-level classification of the plant communities with the optimal mowing cycle, the mowing period, biomass yield per 572

mowing cycle (tDM ha-1 mowing cycle-1) and annual biomass yield (tDM ha-1 year-1). For yearly mown communities, the standard deviation is the 573

variance measure, for multiannual mown communities the 95% confidence interval of the regression is used (see Online Resource 2). The 574

mowing cycle (MC) is the length of the mowing interval (in years). Detailed information on the level 2 classification plant communities is given 575

in Online Resource 4. 576

Level 1 classification Level 2 classification Mowing

cycle

Mowing

period

Biomass yield

per mowing cycle

(tDM ha-1 MC-1)

Annual biomass

yield

(tDM ha-1 yr-1)

N

(plots)

Reed marsh Reed marsh 5 yr July-Aug 15.15 (11.64-19.72) 3.03 (2.33-3.94) 10

Tall-herb communities Humid tall-herb communities 3 yr July-Aug 6.11 (3.98-9.36) 2.04 (1.33-3.12) 13

Abandoned and unmanaged grasslands 1 yr June 6.49 ± 1.69 6.49 ± 1.69 10

Wet grasslands Calthion grasslands 1 yr June-July 4.56 ± 1.34 4.56 ± 1.34 17

Tall-sedge dominated grasslands 1 yr June-July 6.07 ± 2.65 6.07 ± 2.65 4

Mesotrophic grasslands Mesotrophic grasslands with R. repens

and T. repens

1 yr June-July 3.69 ± 1.34 3.69 ± 1.34 16

Mesotrophic graslands with A. elatius 1 yr June-July 4.60 ± 1.89 4.60 ± 1.89 13

Dune slacks Wet and moist dune slacks 1 yr Sept-Oct 2.94 ± 1.72 2.94 ± 1.72 5

Low productive

grasslands on sandy soils

Dry acid and neutral grasslands with

Agrostis

1 yr Aug-Sept 2.59 ± 0.95 2.59 ± 0.95 9

Dry and wet low productive

grasslands

1 yr Aug-Sept 2.69 ± 0.69 2.69 ± 0.69 17

Heathlands Sub-Atlantic dry heaths 15 yr Aug-Sept 15.28 (10.56-22.13) 1.02 (0.70-1.48) 11

Temperate northern wet heaths 15 yr Aug-Sept 10.35 (9.20-11.64) 0.69 (0.61-0.78) 6

577

27

Table 3 Model performance indicators of the constructed multiple linear regression 578

and boosted regression tree models: coefficient of determination (R²), adjusted R² (R²adj), 579

root mean squared error (RMSE in tDM ha-1), relative RMSE (rRMSE, in %) and AIC 580

(Akaike information criterion) for model fit and for 10-fold cross-validation 581

582

Model Fit Cross-validation

R² R²adj RMSE rRMSE AIC

R² R²adj RMSE rRMSE AIC

BRT Vegsoil model 0.82 0.81 1.50 28.87 68.31

0.56 0.55 2.30 44.28 129.85

BRT Vegetation

model 0.76 0.75 1.72 33.15 86.19

0.53 0.51 2.39 46.13 133.76

BRT Soil model 0.54 0.52 2.46 47.42 137.72

0.22 0.19 3.07 59.07 169.36

MLR model 0.60 0.56 2.27 42.65 129.02 0.30 0.23 3.05 57.38 171.47

583

28

Figures 584

585

586

Fig. 1 Location of the sampled sites in Flanders, the northern part of Belgium 587

588

29

589

Fig. 2 Hierarchical classification of vegetation relevés into plant communities with two 590

cluster levels (dashed line: level 1 with seven clusters – dotted line: level two with twelve 591

clusters). First dichotomy splits off the low productive (LP) plant communities on sandy 592

soils from the other communities (OC) 593

594

30

595

Fig. 3 Partial dependence plots of the response on the predictor variables in the vegsoil 596

boosted regression tree model (Plant communities: level 1 plant community). The plots 597

indicate how much the response is affected by a certain predictor value, after accounting 598

for the average effects of all other variables in the model. Predicted functions are centered 599

around the mean standing biomass (tDM ha-1). The relative contribution of each predictor is 600

reported between brackets. Hash marks along the top of the plots show the distribution of 601

the sample plots along that variable, in deciles 602

603

31

604

Fig. 4 Partial dependence plots of the response on the predictor variables in the vegetation 605

boosted regression tree model (Plant communities: level 1 plant community). The plots 606

indicate how much the response is affected by a certain predictor value, after accounting 607

for the average effects of all other variables in the model. Predicted functions are centered 608

around the mean standing biomass (tDM ha-1). The relative contribution of each predictor is 609

reported between brackets. Hash marks along the top of the plots show the distribution of 610

the sample plots along that variable, in deciles 611

612

32

613

Fig. 5 Partial dependence plots of the response on the predictor variables in the soil 614

boosted regression tree model. The plots indicate how much the response is affected by a 615

certain predictor value, after accounting for the average effects of all other variables in the 616

model. Predicted functions are centered around the mean standing biomass (tDM ha-1). The 617

relative contribution of each predictor is reported between brackets. Hash marks along the 618

top of the plots show the distribution of the sample plots along that variable, in deciles 619

620

33

Supplementary material 621

622

Online Resource 1 Description of the methods used in the soil samples analysis to 623

determine the different soil variables 624

625

Online Resource 2 Log-linear regression between standing biomass (tDM ha-1) and years 626

since last mowing for multi-annually mown plant communities 627

628

Online Resource 3 R-code and manual for the prediction of standing biomass on new 629

data with the three developed boosted regression tree models 630

631

Online Resource 4 Description of the 12 plant communities (level 2 classification) with 632

their corresponding EUNIS habitat type and significant indicator species from the Indicator 633

Species Analysis 634

635

Online Resource 5 Two-dimensional partial dependence plots representing the strongest 636

interactions in the three different boosted regression tree models 637

638