Linking the human appropriation of net primary productivity-based indicators, input cost and high nature value to the dimensions of land-use intensity across French agricultural landscapes

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Linking the human appropriation of net primary productivity-based indicators, input cost and high nature value to the dimensions of land-use intensity

across French agricultural landscapes

Claire Lorel, Christoph Plutzar, Karl-Heinz Erb, Maud Mouchet

To cite this version:

Claire Lorel, Christoph Plutzar, Karl-Heinz Erb, Maud Mouchet. Linking the human appropriation

of net primary productivity-based indicators, input cost and high nature value to the dimensions of

land-use intensity across French agricultural landscapes. Agriculture, Ecosystems and Environment,

Elsevier Masson, 2019, 283, pp.106565. �10.1016/j.agee.2019.06.004�. �hal-02904099�

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Linking the Human Appropriation of Net Primary

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Productivity-based indicators, input cost and High Nature

2

Value to the dimensions of land-use intensity across

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French agricultural landscapes

4

Authors Name:

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Claire Lorel

, Christoph Plutzar

, Karl-Heinz Erb

and Maud Mouchet

. 6

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Authors affiliation:

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aCESCO, MNHN-CNRS-SU, CP135, 57 rue Cuvier, 75005 Paris, France

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bInstitute of Social Ecology, University of Natural Resources and Life Sciences, Schottenfeldgasse 29,

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1070, Vienna, Austria

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cDivision of Conservation Biology, Vegetation Ecology and Landscape Ecology, University of Vienna,

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Rennweg 14, 1030 Vienna

13

14

Corresponding author: * 15

Authors e-mail address:

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[email protected]; [email protected]; [email protected];

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[email protected] 18

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Full postal address: Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum National 20

d'Histoire Naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, CP 135, 57 21

rue Cuvier, 75005 Paris, France

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2 / 34

23

3 / 34 - Land Use Intensity can be decomposed into three dimensions: inputs, outputs, and system- 24

level intensity of land-based production.

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- IC/ha is related to inputs, HANPPharv to the outputs, HNV and NPPeco to system-level 26

intensity.

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- HANPP seems to be more sensitive to NPPeco (i.e. HANPP values tend to be high when 28

NPPeco values are low) and thus to the system-level intensity.

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- HANPP identifies a North-South gradient of intensification, further characterized by the 30

joint use of IC/ha, HNV, HANPP, HANPPharv and NPPeco.

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- Large cereal-growing plains in the North and more heterogeneous and extensive agricultural 32

landscapes in the South.

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4 / 34

ABSTRACT

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The intensification of European land use accelerated substantially in a few decades, particularly in 37

agro-ecosystems that are facing an increasing demand for agricultural products and whose area is also 38

constrained by other uses (e.g. urbanization). Land use intensity (LUI) is characterized by the increase 39

of the agricultural outputs per land unit through management practices and/or high amount of inputs.

40

LUI is a complex and multi-dimensional issue in which each dimension needs to be considered to have 41

a better understanding of the impact of LUI on ecosystem functioning and biodiversity. Here, we 42

focused on five existing LUI indicators: the Input Cost per hectare (IC/ha), assessing the expenses in 43

inputs (fertilizers, pesticides, etc.), the High Nature Value (HNV), a scoring system of agricultural 44

areas accounting for the presence of landscape elements and practices favorable to biodiversity, and 45

three indices of the Human Appropriation of Net Primary Productivity (HANPP) framework, i.e. the 46

harvested biomass (HANPPharv), the living biomass remaining available after harvests (NPPeco) and 47

HANPP which combines harvested biomass and effects of land use conversion. First, we discussed 48

how these indicators can relate to the dimensions of LUI. Then, we tested whether HANPP, 49

HANPPharv and NPPeco were redundant with IC/ha and HNV throughout 25,758 French 50

metropolitan municipalities, using Pearson’s correlation coefficient and Linear Mixed-effects Models, 51

while accounting for climatic and landscape parameters. As expected, HANPP, NPPeco and 52

HANPPharv were highly correlated with each other, but weakly to HNV and IC/ha. HNV showed a 53

positive relationship with NPPeco but negative with HANPP and HANPPharv. The opposite findings 54

were observed with IC/ha. These three indicators seem complementary to HNV and IC/ha indicators, 55

linking farmland structural properties and inputs intensity. Finally, we showed how these indicators 56

can be linked, i.e. particular combinations of the indicator values could reveal contrasting agro- 57

ecosystems types (e.g. intensive vs extensive crop farming).

58

KEYWORDS

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Agricultural intensity, HANPP, HANPPharv, HNV, IC/ha, NPPeco.

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1. INTRODUCTION

61

Human use of lands resulted in the alteration of key ecosystem properties and changes in land 62

cover types, such as shifts from pristine forests to croplands or cities, or to the management of an 63

increasing proportion of natural environments, like managed forests (Erb et al., 2017). The demand for 64

biomass products, i.e. food, feed, fibers and bioenergy, has increased due to population growth, 65

industrialization and GDP growth (Tilman and Clark, 2014). Concomitant with the mandate to 66

conserve natural habitats for climate and biodiversity protection, these developments potentially lead 67

to conflicts between uses for a given area (e.g. farming, human settlements, environmental 68

conservation; Haberl, 2015). The dominant strategy to reduce competition for land is to raise land use 69

intensity, i.e. increasing the outputs per land unit by improved management practices or increased used 70

of inputs such as fertilizers, pesticides or energy (Erb et al., 2013).

71

Agricultural lands (i.e. croplands and grazing systems) are among the most widespread 72

ecosystems, covering circa. 47.7% of worldwide terrestrial lands in 2000 and up to 63.3% of France 73

(estimated for the year 2000 by Erb et al., 2007). Agricultural lands are usually located in productive 74

lands, with a rich soil and usually embedded in a hydrosystem, which offers also good favorable 75

conditions for high biodiversity. Agricultural expansion and intensity modify the relationships 76

between landscape and biodiversity patterns, in terms of population dynamics, taxonomic and 77

functional diversity, and resilience of ecological processes related to ecosystem services (Tscharntke et 78

al., 2012). Strategies aiming at intensifying agricultural practices can act as major driver of 79

biodiversity loss (Foley et al., 2005; 2011; German et al., 2017; Gossner et al., 2016; Kehoe et al., 80

2015) and imperils ecosystem services (Allan et al., 2015; Cardinale et al., 2012). Hence, there is an 81

increasing need for indicators suitable to evaluate land use intensity (hereafter “LUI”) and its impacts 82

on ecosystem functioning and biodiversity. In particular, there is a need for indicators linking LUI to 83

ecological concepts, for instance to estimate the amount of energy or resources removed from an 84

ecosystem in the context of LUI and no longer available for biodiversity or ecosystem functioning.

85

6 / 34 The concept of LUI could be analyzed in three dimensions: (i) input intensity, (ii) output intensity, 86

and (iii) associated system-level intensity of land-based production (Erb et al., 2013; Kuemmerle et al., 87

2013). “Input-output” is the classical approach and allows assessing some of the key environmental 88

impacts. Studies focusing on inputs either evaluate the fertilizer use in general (Billeter et al., 2008;

89

Dormann et al., 2007), nitrogen inputs (Kleijn et al., 2009), pesticides (Geiger et al., 2010) or more 90

integrative inputs indicator for crops and livestock (Teillard et al., 2012). Among the indicators used, 91

the Input-Cost per hectare (“IC/ha”; Teillard et al. 2012) evaluates the total input cost across French 92

agricultural landscapes as the total expenses in fertilizers, feed products, pesticides, seeds, fuel, 93

veterinary products and irrigation water. Studies focusing on outputs are rather based on food 94

production of crops or livestock (Dross et al., 2018; Teillard et al., 2016); or the yield per time and 95

unit area (Lambin et al., 2000). The studies focusing on the third dimension rather investigate changes 96

in system properties, such as landscape heterogeneity (Aggemyr and Cousins, 2012; Perović et al., 97

2015). One of the most suitable indicators currently available to address this third dimension is the 98

High Nature Value index (“HNV”; Paracchini et al., 2008), widely used in European farmland 99

analyses to classify farmlands according to their suitability for biodiversity (Aue et al., 2014;

100

Strohbach et al., 2015). This index is related to the concept of High Nature Value farmland defined by 101

Andersen et al. (2003) as “those areas in Europe where agriculture is a major (usually dominant) land 102

use and where that agriculture supports or is associated with either a high species and habitat diversity 103

or the presence of species of European conservation concern or both”. Andersen et al. (2003) 104

identified three principal types of farmlands of HNV: farmlands with a high proportion of semi-natural 105

areas (type 1), farmlands with an extensive mosaic landscapes (type 2) and farmlands hosting species 106

of conservation concern or a high biodiversity (type 3). Pointereau et al. (2007, 2010) proposed to base 107

the HNV index on three components (i.e. the diversity of crops, the level of intensity of farming 108

practices and the proportion of natural landscape elements) that are all considered to have an impact 109

on biodiversity and ecosystem functioning. Hence LUI covers a specific facet of HNV, while HNV 110

offers aspects of land use not included in common land-use intensity indicators.

111

7 / 34 However, to address the complexity between human demands, ecosystem functioning and 112

biodiversity loss, it is important to go beyond approaches reflecting only inputs or outputs and to 113

scrutinize system properties considering human-nature interactions. Such integrative perspectives can 114

be realized using concepts like the Ecological Footprint, which is related to the consumption of 115

products, or the Human Appropriation of Net Primary Productivity (“HANPP”; Haberl et al., 2007) 116

framework related to biomass production (Haberl et al., 2004b). Being based on primary production, 117

the HANPP framework is more directly linked to ecosystem functioning than the Ecological Footprint.

118

The HANPP framework encapsulates several indicators among which the HANPP index based on the 119

distinction of different NPP flows (Haberl et al., 2007; Niedertscheider et al., 2016): NPP that would 120

prevail in ecosystems in the absence of land use, NPP that is altered (often reduced) due to land use 121

change, NPP that is harvested by humans, and lastly NPP that remains in ecosystems after human 122

activities such as harvest (each NPP flow being estimated by an indicator of the HANPP framework:

123

“NPPpot”, “HANPPluc”, “HANPPharv” and “NPPeco” respectively). The HANPP framework 124

explicitly links natural with socioeconomic processes and thereby generates an integrated picture of 125

socioecological conditions in the land system (Haberl et al., 2014; Imhoff et al., 2004). HANPP 126

indicator was also proposed as a straightforward index for ecological limits to growth (Meadows et al., 127

1992, 2004; Running, 2012). Such integrative indicator is an asset for measuring LUI, but its values 128

might be difficult to interpret.

129

HNV and IC/ha have already been used to predict the diversity of a species community (HNV: Aue et 130

al., 2014; Doxa et al., 2012; IC/ha: Teillard et al., 2015) and HANPP to describe biogeochemical 131

patterns, ecosystem services, trophic ecology (Haberl et al., 2004a; Krausmann et al., 2009; Marull et 132

al., 2018; Mouchet et al., 2015). To date, no study considers these LUI dimensions together which lead 133

to a dichotomous or fragmented view of intensification (but see Herzog et al., 2006 for farmland). The 134

indicators of the HANPP framework have been discussed as part of the concept of socioecological 135

metabolism (Erb, 2012) or benchmarked against Ecological footprint (Haberl et al. 2004a). Because 136

these indicators are usually used separately, there is a need for a robust analysis that takes all three 137

dimensions into account (Erb et al., 2013; Kehoe et al., 2015; Kuemmerle et al., 2013).

138

8 / 34 If IC/ha could be easily considered as a relevant indicator of inputs, HANPPharv for outputs, NPPeco 139

and HNV for the system-level intensity, the place of the HANPP indicator and the correlations among 140

these indicators are not straightforward. It is expected that indicators of inputs and outputs should be 141

positively correlated in farmland systems. Because the concept of HNV focuses on land use 142

extensivity and structures supporting biodiversity, this indicator should be negatively correlated to the 143

indicators of inputs and outputs. As the HANPP indicator integrates several NPP flows that are related 144

to different LUI dimensions, this indicator might not be associated to one LUI dimension only. But no 145

previous work has tested these assumptions. Therefore, we propose to disentangle the degree to which 146

the indicators of a LUI dimension are redundant which each other and to estimate the correlation 147

strength between indicators of different LUI dimensions. We also aimed at figuring out how the 148

HANPP indicator relates to each LUI dimension. Finally, we examined whether the combined use of 149

several indicators allows discriminating farming systems.

150

We focused on metropolitan France (550,000 km², 66.9 millions of habitants), which is 151

composed of rich and diverse ecosystems due to its landscape and climatic variability (soil geology, 152

latitude, longitude, land covers, precipitation, elevation) and covers four out of six West European 153

ecoregions (Atlantic, Continental, Mediterranean and Alpine). We further focused on agricultural 154

lands because more than half of the metropolitan French territory is covered by agricultural landscapes 155

(Erb et al., 2007). Such climatic and landscape gradients result in an interesting diversity of 156

agricultural landscapes to explore the spatial variability of these LUI indicators and how their 157

combined use helps discriminating LUI contexts. These gradients are also likely to shape the spatial 158

variability of NPP and, thereby, of HANPP, HANPPharv and NPPeco. Therefore, we integrated 159

landscape and climatic parameters as potential determinants of the spatial patterns of HANPP 160

framework, and of farming systems in general, in our analyses.

161

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2. METHODS

163

2.1. HANPP FRAMEWORK 164

The Human Appropriation of Net Primary Productivity (HANPP) is an estimator of human 165

pressure on ecosystems and biodiversity (Erb et al., 2013; Haberl et al., 2004a). The quantification of 166

HANPP can be performed according to two alternative formulations: (i) the sum of the alteration of 167

NPP due to land use conversion (HANPPluc) and the amount of harvested biomass (HANPPharv), or 168

(ii) the difference between the potential NPP (NPPpot, or the amount of NPP in the absence of human 169

activities) and available biomass remaining after harvest (NPPeco) (Fig. A1 in Supplementary 170

Materials). Although the values of HANPP increases with biomass harvest in general, it may decrease 171

in some particular cases, i.e. when actual NPP (NPPact) is higher than potential NPP because of 172

intensification techniques like irrigation or fertilizer input (Haberl et al., 2001; Krausmann, 2001).

173

Conceptual considerations are discussed in Haberl et al. (2014) and methodological issues related to 174

indicators of the HANPP framework can be found in Plutzar et al. (2016). The HANPP indicator was 175

designed to be a proxy of land use intensity, but the other indicators of the framework also allows 176

assessing the spatial patterns of LUI due to activities causing land use change (HANPPharv, 177

HANPPluc), or its impact on the ecosystem (NPPeco). In this study, we focused on three indicators:

178

harvested NPP (HANPPharv), remaining NPP (NPPeco) and HANPP itself, as the difference between 179

NPPpot and NPPeco (formulation ii).

180

NPPeco is the amount of NPP remaining in ecosystems after harvest and, hence, accessible for 181

other organisms than humans. It is calculated by subtracting HANPPharv from NPPact. NPPeco 182

assesses the NPP that is potentially available for food webs, a major determinant of biodiversity 183

(Brown, 1981; Hawkins et al., 2003).

184

HANPPharv depicts energy withdrawn through harvested crops, livestock, forestry, fodder, 185

grazed grasslands, human-induced fires, and related losses of biomass (e.g. destroyed roots, unused 186

residues in croplands). It is essentially based on data provided by international agricultural and

187

10 / 34 forestry statistics (e.g. Common Agricultural Policy Regional Impact Assessment, “CAPRI”, State of 188

Europe’s Forests, “SOEF”, and FAOstat). Biomass harvest and human-induced fires accounted for 189

60% of HANPP at the global level (Krausmann et al., 2009).

190

The values of the three indicators were expressed in tons of carbon per year (tC/yr) and 191

mapped at 1km resolution at the European extent initially (Plutzar et al., 2016). In general, information 192

on the spatial patterns of relevant land cover data sets (Copernicus Land Monitoring Services, 2014;

193

Kopecky and Kahabka, 2009) were combined with data on sub-national land use activities to derive a 194

consistent framework to assess HANPP-related annual biomass flows, reflecting national harvest 195

statistics (Britz and Leip, 2009; Leip et al., 2008; Levers et al., 2014). We extracted values of these 196

three indicators for the French metropolitan territory from the dataset in Plutzar et al. (2016). The 197

distributions of values of HANPP, HANPPharv and NPPeco are given in Table A1 and Fig. A2 in 198

Supplementary Materials.

199 200

2.2. O THER LAND USE INTENSITY INDICATORS 201

We used two other indicators related to agricultural lands, i.e. agricultural intensity measured with the 202

Input Cost per hectare index (IC/ha; Teillard et al., 2012) and the potential for sustainability and 203

biodiversity conservation in farmland practices estimated with the High Nature Value (HNV;

204

Andersen et al., 2003; Paracchini et al., 2008).

205

We considered the IC/ha indicator as a proxy of the input dimension of LUI. IC/ha is the ratio of the 206

sum of different categories of inputs (fertilizers, feed, pesticides, seeds, fuel, veterinary products, and 207

irrigation water) over the total Utilized Agricultural Area (UAA) of a given farm. Depending on the 208

farm type (e.g. annual crops, permanent crops or livestock), the input categories have different impacts 209

on the land system. Nevertheless, it reflects the overall societal effort that is put into the cultivated 210

land to gain specific products. It is expressed in euros per hectare (€/ha) and is especially relevant for 211

livestock and crop productions (Teillard et al., 2012). IC/ha was computed for five production types

212

11 / 34 (industrial crops, cereals, mixed, bovine dairy, bovine meat) representing almost 80% of the French 213

agricultural areas. Production types excluded from the IC/ha calculation were small vegetable 214

cultivation lands, granivore livestock (poultry and pigs), vineyards and orchards despite their high 215

levels of inputs. Inputs like fertilizers, pesticides, veterinary products and irrigation, are expected to 216

have direct effects on biodiversity and ecosystem functioning. IC/ha also integrates inputs that are 217

expected to indicate indirect effects. For instance, higher feed inputs imply higher livestock densities 218

resulting in higher nitrogen dissipation and pollution. Although the development of machinery relying 219

on fossil fuel or biofuel has drastically reduced the animal labor demand, the use of such technologies 220

and energies also resulted in increased tillage, various pollutions (including GHG emission), land use 221

conversion to produce biofuel, etc.

222

Teillard et al. (2015) estimated IC/ha values at the Small Agricultural Region (SAR). The 223

authors calculated the averaged value of the IC/ha indicator over 2004, 2005 and 2006 to discount for 224

annual variations in price and stock. We used these averaged IC/ha values in our analysis. IC/ha 225

displayed a wide gradient of intensity ranging from 1.41 to 1080 €/ha (see Table A1 and Fig. A3a in 226

Supplementary Materials).

227

Because the HNV concept integrates system-level elements (e.g. landscape structure, level of 228

extensivity of agricultural practices), we used a HNV indicator as a proxy of the system-level 229

intensity. Pointereau et al. (2007, 2010) developed a HNV indicator based on a scoring system 230

accounting for three components: i) the diversity of crops (related to rotation system), ii) the level 231

intensity of agricultural practices (e.g. surface of common land, fallow land, use of fertilizers), and iii) 232

the presence of natural landscape elements (e.g. hedgerows, forest edges, traditional orchards, fishing 233

ponds, wetlands). Each of the three aspects is scored from 1 to 10. The final HNV value (or score) is 234

the sum of the corresponding scores, hence HNV ranges from 0 to 30: from highly intensive 235

ecosystem (HNV=0) to very extensive agricultural lands (HNV=30) that are compatible with 236

biodiversity conservation (see Table A1 and Fig. A3b in Supplementary Materials). For instance, 237

agricultural lands characterized by a diversified rotation system together with heterogeneous land-use-

238

12 / 34 land-cover including natural elements and low use of pesticides or fertilizers will result in higher 239

values of HNV. The HNV indicator is available for the agricultural area of each municipality. Initial 240

data used can be found in Pointereau et al. (2007).

241

242

2.3. L ANDSCAPE AND CLIMATIC PARAMETERS 243

Spatial variability in NPP, land cover and the subsequent land uses, are driven by climate. So 244

we accounted for potential determinants of NPP and, therefore, HANPP, HANPPharv and NPPeco, 245

such as climatic and land use parameters (Krausmann et al., 2009; Wrbka et al., 2004). The map of 246

land-use–land-cover of the French metropolitan territory was extracted from CORINE land cover 247

raster data for 2006 at the spatial resolution of 250m (Copernicus Land Monitoring Service, 2018).

248

Variations in land uses in French landscapes were described using the proportion of semi-natural 249

elements (“Semi_pc”), urban (“Urb_pc”) and agricultural (“Agri_pc”) areas in spatial units (i.e.

250

1x1km or municipality, see section 2.4). Landscape heterogeneity was calculated as the Shannon 251

indicator of land cover diversity (SHDI), following the formula: 𝑆𝐻𝐷𝐼 = − ∑

, where p

252

is the proportion of the landscape occupied by cells of land cover i (15 land cover types in total). The 253

SHDI is sensitive to rare patch types and is the highest when land cover types are evenly distributed in 254

the landscape (SHDI=0: one dominant land use, no diversity).

255

Climatic parameters included annual mean temperature (“temp”), annual total precipitation 256

(“prec”) and elevation (“alt”). Climatic parameters over the French metropolitan territory were 257

extracted from the Worldclim database (Fick and Hijmans, 2017) at the 1x1km resolution. Basic 258

statistics of the distributions of values of landscape and climatic parameters are given in Table A1 in 259

Supplementary Materials.

260

2.4. S PATIAL HOMOGENIZATION OF THE DATASETS 261

We focused on agricultural areas because (i) agricultural lands are the dominant land use in France 262

(Fig. A4 in Supplementary Materials), with 41% of lands covered by croplands (30% of these lands

263

13 / 34 produce cereals, i.e. wheat or maize); (ii) croplands are the main land use contributor to total HANPP 264

(Plutzar et al., 2016); (iii) the LUI indicators developed so far are related to agricultural lands. To 265

focus our analysis on agricultural lands, we selected only cells with a share of agricultural land >50%

266

at both spatial resolutions (see below). It is worth noting that the distribution of HANPP, HANPPharv 267

and NPPeco in agricultural lands is representative of the distribution of HANPP values across all 268

French landscapes (Fig. A4 in Supplementary Materials).

269

The indicators and parameters used in this study were provided at different spatial resolutions:

270

climatic parameters and indicators from the HANPP framework were available at 1x1km, CORINE 271

land cover at 250x250m, HNV at the municipality level and IC/ha at the SAR level.

272

Municipality and the 1x1km resolutions address two different strategies of analysis. First, the 273

municipality unit corresponds to an administrative spatial reference unit at which policy decisions are 274

made, but municipality size diversity (n=25,758, mean=15km², from 0.04 to 767 km²) may affect the 275

response of socioeconomic and intensity processes. Then, the 1x1km resolution is a spatial reference 276

unit for statistical analysis to perform statistics on units of equal size distributed along a regular grid 277

(n=32,143, grid of 1x1km), but not necessarily of ecological or decisional relevance. HANPP, 278

HANPPharv and NPPeco and climatic parameters were averaged at the municipality resolution from 279

the original file (1x1km resolution). IC/ha, released at the SAR level, was downscaled at the 280

municipality level or the 1x1km resolution by resampling IC/ha values according to a bilinear 281

interpolation, which determines the new value of a cell based on a weighted distance average of the 282

four nearest input cell centers. Finally, the number of 250x250m cells of each CORINE land use type 283

within a given municipality or a 1x1km cell, were counted and then divided by the total number of 284

cells in the spatial unit (i.e. municipality or 1x1km cell) to get the proportions of each land use. The 285

proportions were further used to estimate SHDI (see section 2.3). Spatial extraction of values on the 286

datasets, downscaling and upscaling procedures were performed with ArcGIS 10.3 software (ESRI, 287

2014).

288

2.5. S TATISTICAL ANALYSIS

289

14 / 34 We were interested in explaining the spatial variation of HANPP, NPPeco and HANPPharv with IC/ha 290

and HNV, accounting for landscape and climatic effects as covariates, at both resolutions. We used 291

Linear Mixed-effects Models (LMM) with a Gaussian error distribution, after removing collinear 292

variables identified with Pearson’s correlation coefficients (Table A2 in Supplementary Materials).

293

Specifically, the proportion of semi-natural elements (Semi_pc) and SHDI were removed from the 294

study as they were highly correlated to the proportion of agricultural areas (Agri_pc) in our dataset 295

(Table A2 in Supplementary Materials). Covariates were scaled to allow the comparison of estimates.

296

Nonlinear effects of explanatory variables were integrated as quadratic terms in the models. The 297

interaction between IC/ha and HNV was also included because we expected that extensive agro- 298

ecosystems with natural elements should have lower levels of inputs, and vice versa (Fig. 1). Models 299

were performed accounting for SAR as a random effect at both resolutions, and also accounting for 300

municipality as a random effect at the 1x1km resolution. These random effects are expected to account 301

for spatial heterogeneity in sampling effort over the study area and for local confounding effects not 302

captured by the fixed effects. Spatial autocorrelation, tested using Moran's I autocorrelation 303

coefficient, was significantly detected in the residuals of each model, therefore a spatial autocovariate 304

structure (“pond_spatiale”) was added in each model thanks to

305

package. We estimated the coefficients of determination (R²) for fixed and random effects for each 306

LMM (Nakagawa and Schielzeth, 2013). Interaction plots were performed using the predicted values 307

of HANPP, HANPPharv, NPPeco and the levelplot function in the lattice package. All analyses were 308

conducted using R v3.0.2 software (R Core Team, 2017).

309

310

15 / 34

3. RESULTS

311

3.1. C OMPLEMENTARITY BETWEEN INDICATORS FROM THE HANPP FRAMEWORK 312

AND LUI INDICATORS 313

Because results are congruent at both spatial resolutions and to ease their presentation, only 314

results at the municipality resolution will be detailed here (see Table A3 in Supplementary Materials 315

for results at the 1x1km resolution). Due to the large amount of data, all correlation coefficients among 316

indicators were significant so we won’t discuss p-values (Table 1). As expected, Pearson’s correlation 317

coefficients between HANPP, HANPPharv and NPPeco were stronger (r > 0.6, Table 1) than the other 318

pairwise correlations. While HANPP and HANPPharv were positively correlated, NPPeco showed the 319

opposite relation with the two other indicators of the HANPP framework. HNV showed a positive 320

relationship with NPPeco (r = 0.58), but negative with HANPP (r = -0.52) and, to a lesser extent, with 321

HANPPharv (r = -0.42). HANPPharv is positively correlated with IC/ha (r = 0.35). We detected weak 322

correlations between IC/ha and HANPP or NPPeco. Regardless of the sign of the coefficient, the 323

relationships between HNV and the indicators of the HANPP framework were stronger than between 324

IC/ha and these indicators (Table 1).

325

More interestingly, HNV and IC/ha were weakly correlated (r = -0.28). Indeed, many 326

agricultural municipalities in North-Western and Southern France were characterized by low HNV and 327

intermediate to high IC/ha values (yellow color gradient in Fig. 1), or by intermediate to high HNV 328

values and low IC/ha values in the Central part of France (blue color gradient in Fig. 1). There were 329

only very few municipalities in which both LUI indicators had similar values (dark grey or purple, Fig.

330 1).

331

Findings were similar at the 1x1km resolution although correlation coefficients were overall 332

lower than at the municipality scale.

333

3.2. L ANDSCAPE COMPOSITION BEST EXPLAINS SPATIAL PATTERNS OF HANPP

334

INDICATORS

335

16 / 34 Beyond the simplistic correlative approach, we were interested in identifying which, from LUI 336

indicators, landscape and climatic parameters, best explained the spatial patterns of HANPP, 337

HANPPharv and NPPeco. Final models explained non-negligible amount of variations of HANPP, 338

HANPPharv and NPPeco: 69%, 73% and 78% respectively (R²m, Table 2). The amount of explained 339

variation was lower at the 1x1km resolution (see Table A3 for further details). The proportion of 340

farmlands in the landscape was identified as the best explanatory variable of HANPP, HANPPharv 341

and NPPeco spatial patterns (Table 2), followed by the proportion of urban areas in the case of 342

HANPP and NPPeco or by temperature in the case of HANPPharv. HNV was ranked as the third best 343

explanatory variable of HANPP and NPPeco and the fourth for HANPPharv. Spatial patterns of IC/ha 344

weakly explained spatial patterns of HANPP, HANPPharv and NPPeco.

345

3.3. C OMPLEX RELATIONSHIPS BETWEEN IC/ HA , HNV, HANPP, HANPP HARV 346

AND NPP ECO 347

Both linear and nonlinear terms of HNV and, to a lower extent, IC/ha and the interaction 348

between HNV and IC/ha were significant predictors, suggesting complex and not only monotonous 349

relationships between the indicators of the HANPP framework and HNV and IC/ha. HNV had a 350

significant nonlinear effect, negative on HANPP and HANPPharv spatial patterns, but positive on 351

NPPeco spatial pattern. Although both linear and nonlinear terms were significant, HNV estimates 352

(between -12.81 and 14.28) were higher than HNV² estimates (between -2.73 and 2.48) for each 353

indicator of the HANPP framework, suggesting the dominance of the linear effect (Table 2). Similarly 354

to HNV, the linear term of IC/ha explained a greater amount of variance than the nonlinear term.

355

Comparing the estimates of both LUI indicators, HNV captured more explained variance of each 356

indicator of the HANPP framework than IC/ha. The interaction term between HNV and IC/ha was 357

significant only for HANPPharv (-4.89). HANPP predicted values were higher in agricultural 358

municipalities with low HNV values and intermediate to low values of IC/ha (Fig. 2a), which 359

appeared to be the case of most municipalities (Fig. 1). Values of NPPeco were predicted to be 360

maximized for high values of both LUI indicators (i.e. above 600 €/ha for IC/ha and 15 for HNV, Fig.

361

2b). Below a HNV value of 15, NPPeco decreased, regardless of IC/ha values. As expected,

362

17 / 34 municipalities with high IC/ha and low HNV had a higher level of HANPPharv (Fig. 2c). At high 363

IC/ha levels, increasing HNV values correlated with lower values of HANPPharv (up to 300 tC/yr).

364

HANPPharv predicted values seemed to increase too for low levels of IC/ha and high levels of HNV 365

(Fig. 2c).

366

18 / 34

4. DISCUSSION

367

We aimed at linking HANPP, HANPPharv, NPPeco, HNV and IC/ha to the three dimensions of LUI.

368

We first examined the degree of redundancy between HNV and NPPeco, and the complementarity 369

among indicators related to different LUI dimensions. In particular, we examined the complementarity 370

of the HANPP indicator with IC/ha, HANPPharv and NPPeco/HNV because the link between this 371

indicator and LUI dimensions was not straightforward. We also examined the sensitivity of the spatial 372

patterns of the indicators of the HANPP framework to landscape composition and climatic variables.

373

Based on these findings, we finally discuss how the combined use of these indicators can inform on 374

LUI contexts.

375 376

4.1. R ELATIONSHIPS BETWEEN INDICATORS OF THE HANPP FRAMEWORK 377

AND LUI DIMENSIONS 378

HANPP, HANPPharv and NPPeco appear to be strongly correlated with each other. This finding 379

was expected as HANPP calculation relies either on NPPeco or HANPPharv. The correlation is 380

stronger between HANPP and NPPeco than between HANPP and HANPPharv. This suggests that the 381

spatial variability of HANPP values is primarily driven by the amount of NPP available in the system:

382

higher levels of HANPP are spatially congruent with low values of NPPeco and, conversely, high 383

amounts of NPP available (i.e. high values of NPPeco) spatially match low values of HANPP. This 384

finding might result from a long agricultural history. Major land use changes (e.g. forest to farmland) 385

in France date back to the early 20

century and arose as a result of the successive interventions of 386

French governments to satisfy the increasing demand for agricultural products by means of 387

mechanization and increasing intensification, together with the emergence of the Common 388

Agricultural Policy (Ruttan, 2002; van Zanden, 1991). As a consequence, the vast majority of the 389

French metropolitan territory is covered by agricultural and urbanized lands. Therefore, this 390

correlation between HANPP and NPPeco suggests that current patterns of HANPP across French 391

agricultural landscapes (and in particular low and high values of HANPP) are more sensitive to the

392

19 / 34 lack of or, on the contrary, to the presence of high amounts of energy available after harvest than to the 393

energy regularly lost through the annual harvesting regime. The correlation between HANPP and 394

HANPPharv is also strong; suggesting that the amount of NPP removed from the ecosystem drives 395

HANPP to a certain extent, depending on the amount of potential NPP.

396

The study of the spatial covariation between HANPP, HANPPharv and NPPeco values and HNV 397

and IC/ha values highlighted that correlations were stronger between the three indicators related to 398

HANPP and HNV, than with IC/ha. The highest values of NPPeco and the lowest values of HANPP 399

are observed for HNV values around 15-22.5. Higher values of HNV are associated with landscapes 400

including semi-natural elements, like shrubs, trees, hedgerows, but also a diversity of crops. That type 401

of heterogeneous agricultural landscapes is essential to support biodiversity (Pointereau et al., 2010), 402

and is one strategy to improve productivity while limiting soil depletion (Carvalheiro et al., 2011;

403

Davis et al., 2012) and fertilizer use. Besides, since these natural elements are not harvested, they most 404

likely contribute to maintain a certain level of NPPeco in ecosystems, especially after the harvest 405

season. This would explain that some systems show intermediate levels of HANPPharv for high levels 406

of HNV and low levels IC/ha. Considering the links between HANPP, NPPeco and HNV, the HANPP 407

indicator seems more sensitive to the third dimension of LUI, i.e. the system properties (consistent 408

with Erb et al. 2013). Even HANPPharv, that is used as an indicator of outputs, appears sensitive to 409

the system properties, to a certain extent. Indeed, HANPPharv, as the other indicators of the HANPP 410

framework, is sensitive to the amount of NPP in the system and to (semi-)natural elements providing 411

it. It is important to note that NPPeco value should decrease if the level of intensity in the system 412

properties increases, while HANPP and HANPPharv should increase. As expected, the highest levels 413

of HANPPharv are spatially congruent with high levels of IC/ha (intensive systems) and low levels of 414

HNV.

415 416

4.2. T HE PREDOMINANT LANDSCAPE EFFECT

417

20 / 34 The links between the indicators of the HANPP framework, landscape parameters and HNV 418

suggests that landscape composition (i.e. presence/absence of natural elements) has a significant 419

effect on the spatial patterns of HANPP, HANPPharv and NPPeco. Considering that HANPP, 420

HANPPharv and NPPeco depend on NPP, we could expect that climate might affect their values 421

through its effect on NPP (Melillo et al., 1993). In our study, climatic variables seemed to have a far 422

weaker influence on HANPP, HANPPharv and NPPeco than landscape composition, with the 423

exception of temperature for HANPPharv (consistent with findings from Niedertscheider and Erb, 424

2014 on land use change in Italy). The influence of landscape composition on the indicators of the 425

HANPP framework, in particular the proportion of farmlands and/or urban areas within agricultural 426

municipalities, has already been demonstrated by Wrbka et al. (2004). The link between the 427

indicators of the HANPP framework and landscape composition is partly driven by the use of 428

CORINE typology as an input dataset to map and downscale these indicators. Therefore, two spatial 429

units with the same CORINE class are more likely to have similar values of these indicators. To limit 430

that bias, landscape parameters were included in our analyses, so we chose not to put forward the 431

strong influence of land use composition to avoid a circular reasoning (see also Plutzar et al., 2016).

432

Beyond, this methodological caveat, we cannot exclude that the competition for space between 433

urbanized areas, (semi-)natural areas and agricultural lands influence the amount of energy available 434

and strengthen the landscape effect, at least by constraining the surface available for primary 435

producers (cultivated or natural). This would be congruent with Wrbka et al. (2004) who showed 436

that, even in industrialized countries, spatial patterns of HANPP can be explained, to some extent, by 437

landform indicators such as elevation, slope, terrain ruggedness, commonly used as landscape 438

naturalness indicators. This finding suggests that natural preconditions are still setting limits to 439

economically viable land use.

440

However, downscaling the analysis at a finer scale to limit the landscape effect might reinforce 441

the bias due to the use of CORINE. Besides, our findings suggest that landscape, climatic and LUI 442

indicators better explain the spatial variability of HANPP, HANPPharv and NPPeco at the 443

municipality scale than at the 1x1km resolution. In our study, the municipality scale corresponds to

444

21 / 34 an intermediate spatial scale among the spatial resolution of our datasets and has the advantage to be 445

an operational spatial unit for landscape management and the implementation of environmental 446

policies.

447 448

4.3. R ELATING THE SPATIAL CO - VARIATION OF LUI INDICATORS TO 449

PROPERTIES OF AGRICULTURAL LANDSCAPES 450

The results of our analyses show that HANPPharv appears to be the most sensitive to spatial 451

covariations between IC/ha and HNV. Logically, intensive management allows high biomass harvest, 452

hence high HANPPharv values are associated with high IC/ha values. But intensive management is 453

hardly compatible with the maintenance of natural landscape elements supporting biodiversity 454

conservation, resulting in low HNV values. Overall, we observed that below an HNV value of circa.

455

15 and for a mean IC/ha value of circa. 540€/ha, HANPP values are high, i.e. up to ~400 tC/yr (low in 456

the case of NPPeco). This result is coherent with Andersen et al. (2003) and Pointereau et al. (2010) 457

who found that the threshold value of HNV above which a landscape has a high nature value, is 458

around 15.

459

The most intensive agro-ecosystems (monoculture with high agricultural inputs) are essentially located 460

in Northern France and the extended Parisian basin (wide fields of cereal crops in Beauce and 461

Champagne regions), or in Brittany (poultry, dairy cows, pig farming), as identified by the high 462

HANPPharv values and low NPPeco values in these agricultural landscapes. In general, high inputs (in 463

intensive croplands) are not compatible with a high degree of naturalness. However, we found that 464

some specific French regions could combine intermediate to high levels of both IC/ha and HNV (Fig.

465 1):

466

i) High levels of HNV and IC/ha in the Loire and Seine estuaries which are mainly composed 467

of swamp and meadows for, pastures, grass mowing, cows grazing for milk and meat production 468

(Perrot et al., 2013). High inputs may be essentially associated to veterinary products rather than

469

22 / 34 fertilizers or pesticides, and a significant proportion of pasturelands, thus explaining high levels of 470

NPPeco and low levels of HANPP and HANPPharv in these two regions.

471

ii) Intermediate levels of HNV and IC/ha in Central France and Normandy corresponding to 472

extensive production systems, with heterogeneous cropping systems characterized by middle size 473

fields, hedgerows, pastures (crop-livestock farming systems). These mosaic landscapes are also 474

favorable to NPPeco because extensive production provides the double benefit of allowing high 475

primary productivity but also a non-negligible amount of available NPP remaining in the ecosystem 476

after harvest. The remaining NPP will support biodiversity and, in particular, pollinators and pests 477

predators that are beneficial to agricultural systems.

478

We also encountered agricultural lands with low values of IC/ha (low inputs) and HNV (intensive 479

practices and few natural elements) in the regions surrounding the Parisian basin, Bordeaux and 480

Bourgogne. In these regions, high levels of NPPeco are associated to intermediate to high levels of 481

HANPPharv, and negative HANPPluc values, suggesting that the actual NPP is higher than NPPpot 482

(NPP expected in the absence of human land use and under current climatic conditions). Negative 483

HANPPluc values are expected when arid or forested lands are converted to productive agricultural 484

lands. In the aforementioned regions, forests were converted mainly to vegetable cultivation and 485

vineyards. Despite the presence of natural elements in these types of agricultures, they are considered 486

as very intensive because of the strong use of pesticides, resulting in low values of HNV.

487

It is worth noting that this North-South gradient is roughly depicted by the spatial patterns of the 488

indicators of the HANPP framework: high levels of HANPP due to large cereal-growing plains (i.e.

489

wheat, maize) in the North, less human appropriation and more NPP remaining in more heterogeneous 490

agricultural landscapes including pastures, crop-livestock farming systems, vineyards, forestry, in the 491

South (below the Loire river).

492

493

23 / 34

5. CONCLUSION

494

Published LUI indicators are rarely available and tested over a wide diversity of farming 495

systems (Ruiz-Martinez et al., 2015) or large climatic gradients (but see Billeter et al., 2008). The 496

indicators of the HANPP framework have the advantage to be available on the global scale, 497

permitting large scale analysis on two complementary trajectories: carbon material flows driven by 498

human consumption and the spatially-explicit agricultural intensity indicators on ecosystem and 499

biodiversity (Haberl et al., 2004a; 2005). The indicators of the HANPP framework also have the 500

asset to cover other land use types than agriculture. The current expansion of forests throughout 501

Europe challenges our understanding of how forest expansion counteracts or contributes to LUI (e.g.

502

increasing LUI on the shrinking croplands). Based on temporal trends of HANPP in Europe, findings 503

from Krausmann et al. (2012) and Gingrich et al. (2015) suggest an overall HANPP decline related 504

to forest expansion. Therefore, the indicators of HANPP framework are very relevant to assess 505

material and energy flows across various land uses and long-term land use trajectories.

506

Our results showed that indicators from the HANPP framework, HNV and IC/ha are 507

complementary with each other. The joint use of HNV and IC/ha makes it possible to address two 508

complementary aspects of LUI: the impact of the practices and the landscape configuration on one 509

side, and the inputs, on the other side. HANPP framework addresses another aspect of LUI: energy in 510

the system. NPPeco and, to a lesser extent, HANPP, seems more sensitive to the system-level intensity 511

of land-based production than to inputs. Spatial variations of HANPPharv seem also sensitive to 512

specific combinations of HNV and IC/ha. Even though HANPP framework integrates both the impact 513

of management practices on naturalness in agricultural lands and inputs, these indicators cannot 514

entirely replace HNV and IC/ha in an agricultural context. Although French agro-ecosystems present a 515

wide diversity of agricultural landscapes, from wooded pasturelands to intensive croplands, a 516

significant proportion of French metropolitan landscapes is characterized by mosaics of land uses and 517

land covers, allowing, in some cases, strongly intensive and harvested agricultural lands to keep a

518

24 / 34 significant degree of naturalness, and so to conciliate human food supply and biodiversity to some 519

extent.

520

521

25 / 34 Acknowledgments

522

We would like to warmly thank Muriel Tichit and Felix Teillard for sharing IC/ha index data and 523

Philippe Pointereau for sharing HNV index data. We warmly thank Helmut Haberl for these helpful 524

advices. We acknowledge the availability of Worldclim dataset supported by “Feed the Future” to the 525

“Geospatial and Farming Systems Consortium” of the Sustainable Intensification Innovation Lab.

526

Funding: This study was supported by a grant from Region Ile-de-France within the DIM-ASTREA 527

program.

528

Conflict of Interest: The authors declare that they have no conflict of interest.

529

530

26 / 34

REFERENCES

531

Aggemyr, E., Cousins, S.A.O., 2012. Landscape structure and land use history influence changes in 532

island plant composition after 100 years. J. Biogeogr. 39, 1645–1656.

533

Allan, E., Manning, P., Alt, F., Binkenstein, J., Blaser, S., Blüthgen, N., Böhm, S., Grassein, F., 534

Hölzel, N., Klaus, V.H., Kleinebecker, T., Morris, E.K., Oelmann, Y., Prati, D., Renner, S.C., 535

Rillig, M.C., Schaefer, M., Schloter, M., Schmitt, B., Schöning, I., Schrumpf, M., Solly, E., 536

Sorkau, E., Steckel, J., Steffen-Dewenter, I., Stempfhuber, B., Tschapka, M., Weiner, C.N., 537

Weisser, W.W., Werner, M., Westphal, C., Wilcke, W., Fischer, M., 2015. Land use 538

intensification alters ecosystem multifunctionality via loss of biodiversity and changes to 539

functional composition. Ecol. Lett. 18, 834–843.

540

Andersen, E., Baldock, D., Bennett, H., Beaufoy, G., Bignal, E., Brouwer, F., Elbersen, B., Eiden, G., 541

Godeschalk, F., Jones, G., McCracken, D., Nieuwenhuizen, W., van Eupen, M., Hennekens, S., 542

Zervas, G., 2003. Developing a High Nature Value Farming area indicator. Internal Report for 543

the European Environment Agency, IEEP.

544

Aue, B., Diekötter, T., Gottschalk, T.K., Wolters, V., Hotes, S., 2014. How High Nature Value (HNV) 545

farmland is related to bird diversity in agro-ecosystems – Towards a versatile tool for 546

biodiversity monitoring and conservation planning. Agric. Ecosyst. Environ. 194, 58–64.

547

Billeter, R., Liira, J., Bailey, D., Bugter, R., Arens, P., Augenstein, I., Aviron, S., Baudry, J., Bukacek, 548

R., Burel, F., Cerny, M., De Blust, G., De Cock, R., Diekötter, T., Dietz, H., Dirksen, J., 549

Dormann, C., Durka, W., Frenzel, M., Hamersky, R., Hendrickx, F., Herzog, F., Klotz, S., 550

Koolstra, B., Lausch, A., Le Coeur, D., Maelfait, J.P., Opdam, P., Roubalova, M., Schermann, 551

A., Schermann, N., Schmidt, T., Schweiger, O., Smulders, M.J.M., Speelmans, M., Simova, P., 552

Verboom, J., Van Wingerden, W.K.R.E., Zobel, M., Edwards, P.J., 2008. Indicators for 553

biodiversity in agricultural landscapes : a pan-European study. J. Appl. Ecol. 45, 141–150.

554

Britz, W., Leip, A., 2009. Development of marginal emission factors for N losses from agricultural 555

soils with the DNDC–CAPRI metamodel. Agric. Ecosyst. Environ. 133, 267–279.

556

Brown, J.H., 1981. Two decades of Homage to Santa Rosalia: Toward a general theory of diversity.

557

Am. Zool. 21, 877-888.

558

Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Narwani, A., Mace, 559

G.M., Tilman, D., A.Wardle, D., Kinzig, A.P., Daily, G.C., Loreau, M., Grace, J.B., 560

Larigauderie, A., Srivastava, D.S., Naeem, S., 2012. Corrigendum: Biodiversity loss and its 561

impact on humanity. Nature 489, 326–326.

562

Carvalheiro, L.G., Veldtman, R., Shenkute, A.G., Tesfay, G.B., Pirk, C.W.W., Donaldson, J.S., 563

Nicolson, S.W., 2011. Natural and within-farmland biodiversity enhances crop productivity.

564

Ecol. Lett. 14, 251–259.

565

Copernicus Land Monitoring Service, 2014. European Environment Agency (EEA), European Union.

566

Copernicus Land Monitoring Service, 2018. Corine Land Cover 2006, v.18.5.1. URL 567

https://land.copernicus.eu/pan-european/corine-land-cover/clc-2006?tab=download (accessed

568

27 / 34 8.15.18).

569

Davis, A.S., Hill, J.D., Chase, C.A., Johanns, A.M., Liebman, M., 2012. Increasing Cropping System 570

Diversity Balances Productivity, Profitability and Environmental Health. PLoS One 7, e47149.

571

Dormann, C.F., Schweiger, O., Augenstein, I., Bailey, D., Billeter, R., de Blust, G., DeFilippi, R., 572

Frenzel, M., Hendrickx, F., Herzog, F., Klotz, S., Liira, J., Maelfait, J.-P., Schmidt, T., 573

Speelmans, M., van Wingerden, W.K.R.E., Zobel, M., 2007. Effects of landscape structure and 574

land-use intensity on similarity of plant and animal communities. Glob. Ecol. Biogeogr. 16, 774–

575

787.

576

Doxa, A., Paracchini, M.L., Pointereau, P., Devictor, V., Jiguet, F., 2012. Preventing biotic 577

homogenization of farmland bird communities: The role of High Nature Value farmland. Agric.

578

Ecosyst. Environ. 148, 83–88.

579

Dross, C., Princé, K., Jiguet, F., Tichit, M., 2018. Contrasting bird communities along production 580

gradients of crops and livestock in French farmlands. Agric. Ecosyst. Environ. 253, 55–61.

581

Erb, K.-H., 2012. How a socio-ecological metabolism approach can help to advance our understanding 582

of changes in land-use intensity. Ecol. Econ. 76, 8–14.

583

Erb, K.-H., Gaube, V., Krausmann, F., Plutzar, C., Bondeau, A., Haberl, H., 2007. A comprehensive 584

global 5 min resolution land-use data set for the year 2000 consistent with national census data, J.

585

Land Use Sci. 2, 191–224.

586

Erb, K.-H., Haberl, H., Jepsen, M.R., Kuemmerle, T., Lindner, M., Müller, D., Verburg, P.H., 587

Reenberg, A., 2013. A conceptual framework for analysing and measuring land-use intensity.

588

Curr. Opin. Environ. Sustain. 5, 464–470.

589

Erb, K.-H., Luyssaert, S., Meyfroidt, P., Pongratz, J., Don, A., Kloster, S., Kuemmerle, T., Fetzel, T., 590

Fuchs, R., Herold, M., Haberl, H., Jones, C.D., Marín-Spiotta, E., McCallum, I., Robertson, E., 591

Seufert, V., Fritz, S., Valade, A., Wiltshire, A., Dolman, A.J., 2017. Land management: data 592

availability and process understanding for global change studies. Glob. Chang. Biol. 23, 512–

593

533.

594

ESRI, 2014. ArcGIS Desktop: Release 10.3. Redlands, CA Environ. Syst. Res. Institute.

595

Fick, S.E., Hijmans, R.J.R.J., 2017. WorldClim 2: new 1‐km spatial resolution climate surfaces for 596

global land areas. Int. J. Climatol. 37, 4302–4315.

597

Foley, J. a., Ramankutty, N., Brauman, K. a., Cassidy, E.S., Gerber, J.S., Johnston, M., Mueller, N.D., 598

O’Connell, C., Ray, D.K., West, P.C., Balzer, C., Bennett, E.M., Carpenter, S.R., Hill, J., 599

Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D., Zaks, D.P.M., 600

O’Connell, C., Ray, D.K., West, P.C., Balzer, C., Bennett, E.M., Carpenter, S.R., Hill, J., 601

Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D., Zaks, D.P.M., 602

2011. Solutions for a cultivated planet. Nature 478, 337–342.

603

Foley, J.A., Defries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., 604

Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., 605

Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., 2005. Global 606

consequences of land use. Science 309, 570–4.

607