Post on 27-Apr-2023
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Title 1
Estimating the cost of different strategies for measuring farmland biodiversity: evidence from a 2
Europe-wide field evaluation1 3
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DOI 10.1016/j.ecolind.2014.04.050 5
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Authors 7
Targetti S.1, Herzog F.2, Geijzendorffer I.R.3,4,Wolfrum S.5, Arndorfer M.6, Balàzs K.7, Choisis 8
J.P.8, Dennis P.9, Eiter S.10, Fjellstad W.10, Friedel J.K.6, Jeanneret, P.2, Jongman R.H.G.4, Kainz 9
M.5, Luescher G.2, Moreno G.11, Zanetti T.12, Sarthou J.P.13, 14, Stoyanova S.15, Wiley D.2, Paoletti 10
M.G.12, Viaggi D.1 11
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Affiliations 13
1Department of Agricultural Sciences, University of Bologna, Italy 14
2 Agroscope, Institute for Sustainability Sciences ISS, Zurich, CH-8046 15
3Institut Méditerranéen de Biodiversité et d’Ecologie marine et continentale (IMBE), Aix-Marseille 16
Université, CNRS, IRD, Univ. Avignon, Technopôle Arbois-Méditerranée, Bât. Villemin – BP 80, 17
F-13545 Aix-en-Provence cedex 04, France 18
4Alterra, Wageningen UR, The Netherlands 19
5Technische Universität München, Germany 20
6Universität für Bodenkultur Wien, Austria 21
7Szent Istvan Egyetem, Hungary 22
8INRA, UMR 1201 DYNAFOR, F-31326 Castanet-Tolosan, France 23
9Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, UK 24
10Norwegian Forest and Landscape Institute, Norway 25
11Forest Research Group, University of Extremadura, Spain 26
12Department of Biology, University of Padova, Padova, Italy 27
13INRA, UMR 1248 AGIR, F-31326 Castanet-Tolosan, France 28
14Université de Toulouse, INPT-ENSAT, UMR 1248 AGIR, F-31326 Castanet-Tolosan, France 29
15Institute of Plant Genetic Resources, Bulgaria 30
1 Abbreviations: H = habitat mapping; V = vegetation parameter; B= wild bees and bumblebees parameter; S = spiders
parameter; E = earthworms parameter; Q = farm management questionnaire parameter
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Corresponding author 32
Stefano Targetti, Department of Agricultural Sciences, University of Bologna. V.le Fanin, 50, 33
40127 Bologna, Italy. 34
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1 Introduction and objectives 36
Biodiversity provides important services that enhance the environmental resource base upon which 37
agriculture depends (Swift et al., 1996; Altieri 1999; Jackson et al., 2005; Paoletti et al., 2011;). 38
Biodiversity is declining globally (Butchart et al., 2010), including in agroecosystems (Tilman et 39
al., 2001; Kleijn et al., 2009). In response to this, several international programmes and measures 40
for halting biodiversity loss have been initiated in recent years (The Aichi Biodiversity Targets 41
2011-2020 - CBD, 2010; and European Commission, 2011). The European Commission has 42
indicated the need for a limited set of standard indicators (European Commission, 2005). However, 43
positive effects of policies and adopted measures on biodiversity both at farm and landscape scales 44
are controversial and more precise evaluations of their effects are still needed (Balmford et al., 45
2005; Batáry et al., 2011; Kleijn et al., 2011). 46
Several authors have highlighted the importance and the potential economic value of biodiversity 47
monitoring (e.g. Balmford and Gaston, 1999; James et al., 1999; Juutinen and Mönkkönen, 2004). 48
Evidence points to a positive correlation between monitoring efforts and the effectiveness of 49
policies (Naidoo, et al., 2006). Still, inadequate funding is a widespread constraint that undermines 50
the effectiveness of a number of existing monitoring programmes (McDonald-Madden, et al., 51
2011). Budget limitations and the optimization of resources need to be tackled during the 52
implementation of any monitoring programme. Thus, a cost analysis should be included alongside 53
consideration of the scientific credibility of the methods and relevance to stakeholders in any 54
process concerning indicator selection. Only after such an analysis the extent to which the 55
information value justifies the cost of the monitoring programme can be evaluated (Targetti et al., 56
2012a). 57
Recent literature focuses on cost-effectiveness procedures and analyses, and several studies are 58
directly concerned with the cost assessment of monitoring activities. Nevertheless, papers 59
addressing the topic are mainly based on: a) an indirect assessment of costs, (i.e. based on an ex-60
post analysis of project costs; e.g. Qi et al., 2008; Levrel et al., 2010; de Blust et al., 2012); b) a 61
proxy estimation, such as labour effort (e.g. Carlson and Schmiegelow, 2002); c) aggregated data 62
(i.e. not per single indicator or plot e.g. Juutinen and Mönkkönen, 2004); d) expert judgement (e.g. 63
Schmeller and Henle, 2008; Laycock et al., 2009); or e) localized studies (e.g. Schreuder et al., 64
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1999; Bisevac and Majer, 2002; Franco et al., 2007; Cantarello and Newton, 2008; Gardner et al., 65
2008; Kessler et al., 2011; Sommerville et al., 2011). True empirical data from a large pool of farm 66
trials is currently lacking. In this paper, we address this issue by focusing on the costs of 67
biodiversity measurement at the farm scale since that is the scale at which the main decisions 68
regarding management practices and the adoption of policy measures are taken. 69
The objectives of this work are twofold: First, we present direct processing and analysis of 70
empirical data collected with regard to costs and effort spent on the measurement of agroecosystem 71
biodiversity, in twelve case study areas across Europe. Second, from our database we derive a cost 72
estimation for measuring biodiversity on a standardized farm. In doing so, we draw attention to the 73
cost differences between six biodiversity-related parameters and between different actors that may 74
be involved in the monitoring activities (professional agencies, farmers, volunteers). Our work takes 75
advantage of the large cost database built during the field activities of the EU-funded BioBio 76
Project (Herzog et al., 2013) and is based on a consistent methodology aimed at the analytical 77
assessment of costs. The database includes costs and efforts spent in the measurement of 78
biodiversity-related parameters on 192 farms representing more than 14,000 hectares of farmland in 79
11 European countries. 80
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1.1 State of the art 82
Even though farmland monitoring can have a wide variety of targets (e.g. economic sustainability, 83
environmental performance, etc.), all current evaluation frameworks of policy impact are based on 84
indicators (Acosta-Alba and van der Werf, 2011). A specific theoretical framework for evaluating 85
the costs of measuring biodiversity indicators is not available in the literature. However, the main 86
cost components are provided by way of general economic cost theory and previous practical 87
attempts at assessing the costs of measuring biodiversity. 88
Economic theory tends to distinguish between the costs that are independent from the amount of a 89
monitoring/sampling effort (fixed costs) and the costs that are a function of the 90
monitoring/sampling effort (variable costs). Skalski and Robson (1992) adapted that classification 91
to costs of indicator measurement by distinguishing costs independent of the size of the survey 92
(fixed costs), costs of travelling to the research area (transportation costs) and costs related to the 93
number of study plots (variable costs). Indicators that include species also capture costs related to 94
trapping intensity, such as the purchase and maintenance of traps and the treatment and 95
identification of the specimens in each sample. 96
Practical insights regarding the data collection carried out in the framework of this study 97
highlighted further elements of complexity and the fact that the allocation of costs to a specific 98
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indicator is usually based on a number of assumptions and simplifications. Firstly, measurement 99
costs may be shared in multiple different ways amongst indicators. For example, the measurement 100
of one parameter can provide data for the computation of different indicators; there can be costs for 101
labour or equipment that are shared between different indicators. These costs can be allocated to 102
individual indicators only through an “artificial” calculation process and usually under several 103
assumptions. An even more complex issue relates to the fact that some indicator output data can 104
serve as information inputs for calculating other indicators (e.g. habitat maps for floristic and fauna 105
indicators, see below), which adds the problem of assigning shares of costs of previous indicator 106
measurement to measurement occurring for the “second stage” indicator. Secondly, from a 107
budgetary and functional perspective, a monitoring programme includes three different phases: a) 108
development and setting-up (objective setting, planning the design of the survey, administrative 109
development, and pilot study); b) regular monitoring (scientific oversight, data collection, data 110
management, analysis and reporting, quality control of data, administration; Caughlan and Oakley, 111
2001); c) review and adaptation of the design to meet monitoring goals (Lindenmayer and Likens, 112
2009). Frequently, activities directly connected to parameter measurement (i.e. pilot study and data 113
collection) are the main budget items. Other activities, like the development and setting-up phase, 114
can be considered a fixed cost with regard to the regular monitoring phase as they do not vary with 115
the intensity of the final monitoring scheme or sampling protocol (e.g. the number of samples 116
collected; Skalski and Robson, 1992). Thus, a monitoring programme frequently involves a 117
consistent temporal delay between initial fixed monetary expenses and information delivery which 118
affects cost distribution per unit of data generated. 119
Moreover, costs may depend on the specific way in which resources are mobilised to carry out the 120
monitoring programme. For example, costs may be radically different if they come from the re-121
allocation of labour from already hired public administration staff or if specialised agencies/staff are 122
hired to complete the task. In order to combine feasibility and effectiveness, growing attention has 123
been drawn to the involvement of voluntary networks and farmers in the measurement of 124
biodiversity indicators (Danielsen et al., 2005; Schmeller et al. 2012; Von Haaren et al., 2012). In 125
fact, voluntary networks are currently involved in large species monitoring frameworks (Schmeller 126
et al., 2008) like in the Farmland Bird Index which is currently employed in policy contexts (e.g. 127
the Common Monitoring and Evaluation Framework of the CAP). For instance, Levrel et al., 128
(2010) estimated that the French administration saved between €678,523 and €4,415,251 in 2007-129
2008 thanks to volunteer-based monitoring of birds and butterflies. However, such monitoring data 130
require so-called ad-hoc indicator sets (i.e. indicators that fit farmers’ or volunteers’ knowledge) 131
and need a careful coordination to prevent statistical biases (Stevens et al., 2005). On the other 132
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hand, the availability of consolidated data networks and the opportunity to promote societal 133
participation support the inclusion of “citizen science programmes” in monitoring schemes 134
(Devictor, et al., 2010; Conrad, et al., 2011; European Environment Agency, 2013a). 135
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2 Material and methods 137
2.1 Sampling methodologies of biodiversity indicators 138
The BioBio Project included 12 case study regions across Europe covering the main European farm 139
types which were located in major bio-geographical regions (Herzog, et al., 2012). Between 8 and 140
19 farms were selected in a random procedure in each case study region (overall 192 farms). 141
Biodiversity indicators were selected on the basis of available literature, and feedback from 142
stakeholder panels. Habitat mapping (H) in BioBio consisted of registering the relative location, 143
size and type of all the different habitats which were directly or indirectly influenced by the 144
management of the farmer. Habitats have a direct relation with species and are characterised by 145
environmental, biological and management factors (Bunce et al., 2013). Mapping was done 146
according to a generic mapping approach developed for Europe (Bunce, et al., 2008 and 2011). The 147
habitat indicators recorded in BioBio therefore addressed the overall composition of the farm 148
related to specific habitat types that are relevant in the agricultural context (diversity of crops, share 149
of shrub and tree habitats), and captured the amount of semi-natural habitats. 150
Areal features (at least 5 m wide and covering 400 m2) and linear features (at least 0.5 m wide and 151
30 m long) on each farm were mapped and classified in different habitat types according to primary 152
life forms, environment and management (Bunce et al., 2008). A single example of each habitat 153
type per farm was randomly selected for species sampling (the ‘plot’). Plant species (V) were 154
recorded in 10 x 10 m2 squares for areal plots and 1 x 10 m2 rectangles for linear plots. Once the list 155
of plant species was completed, the percentage of ground cover occupied by each species was 156
estimated. Wild bees and bumblebees (B) were collected three times during good weather 157
conditions (i.e. during periods of sunshine when it was not too windy and the temperature was 158
higher than 15°C) along a predetermined 100 m transect through each plot. Spiders (S) were also 159
sampled three times on five circular areas of 35.7 cm diameter per plot using a modified leaf blower 160
with an attached net to allow a suction sample of 30-45 seconds duration to be taken. The suction 161
content was transferred to a labelled polyethylene bag and frozen for storage and later sorting and 162
identification of spider species. Earthworms (E) were sampled only once in three 30 x 30 cm metal 163
frames distributed across each plot. First, a solution of allyl isothiocyanate (0.1 g/l) was poured into 164
each frame to encourage earthworms to the surface. Subsequently, a 20-cm-deep earth-core was 165
sorted by hand. When required, plant, wild bee, spider and earthworm species collected in the plot 166
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were transported to the laboratory and identified by experts, either within the research group or by a 167
subcontracted third party (see Dennis et al., 2012 for further details). 168
Farm management information was collected by means of interviews with farmers using a 169
standardised questionnaire (Q). This included questions regarding nutrient regimes, expenditure for 170
inputs (energy, fertilizer, pesticides), farm management, stocking density and crop and animal 171
genetic diversity. Interviews were arranged face-to-face and/or by telephone. 172
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2.2 Cost of data collection 174
Data collection was organised in order to allow for an analytical assessment of recorded costs and 175
the subsequent simulation with standardised costs. During the field activities (2010) cost records 176
were gathered daily in the case study regions and then transferred and stored in a centralised 177
relational database. The database was organised in order to trace labour effort spent per farm, and 178
per biodiversity parameter measured. The records were related to labour time spent by the staff for 179
four types of activity: fieldwork, deskwork, laboratory and travel. Fieldwork activities included 180
field sampling and walking between the plots; deskwork included map digitalisation and data input; 181
laboratory work included sorting and preparation of species for taxonomic identification such as 182
insect pinning, etc.; and travel included transportation time to and from the sampled farm. In order 183
to account for surveyors sharing one car, the number of persons involved in each trip was also 184
recorded. Labour time spent by skilled and non-skilled workers was recorded separately. Costs for 185
taxonomic identification, consumables, equipment and other costs were also recorded. 186
The unitary cost of the utilisation of equipment was calculated as the cost of the equipment divided 187
by its lifetime. Vehicle costs were generally expressed per km (including fuel, road tax, car 188
insurance and vehicle depreciation) and multiplied by the actual distance travelled. Other costs 189
included accommodation and food for the fieldworkers etc. Free resources that were employed by 190
the research teams (e.g. labour time of volunteer and students, free accommodations or others) were 191
also recorded in order to include them in the cost standardisation process. 192
Costs related to reporting (e.g. analysis and calculation of indicators), programme start-up (e.g. time 193
spent for consumables or equipment purchase, the development of the programme, staff training, 194
etc.), data management, quality checks and other contingencies (such as crop damage due to 195
indicator measurements) are not included in the present analysis (see Targetti et al., 2011 for further 196
details). 197
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2.3 Cost analysis and standardisation 199
In this work, we differentiate between the recorded costs incurred by the research units for the 200
biodiversity measurement (analytical assessment) and the standardised costs estimated for a regular 201
monitoring activity (cost standardisation). In the analytical assessment, labour for fieldwork and 202
travel was calculated for each day and traced back to the single farm and parameter measured. Costs 203
and effort related to deskwork, laboratory work, taxonomic identification and other resources were 204
aggregated per case study as they could not be attributed to a specific farm. 205
In order to establish a cost reference for biodiversity measurement at the farm scale and to simulate 206
a regular monitoring measurement, we applied a cost standardisation process based on the results of 207
the analytical assessment. This standardisation aimed to normalise fieldwork effort over the three 208
main factors that contributed to the variability of measurement costs and adjustments were based on 209
the data collection exercise (Skalski and Robson, 1992; Targetti et al., 2011): a) number of plots 210
and surveyed area on each farm; b) distance between farms and research centres (labour time spent 211
in travel); and c) unitary costs of resources in different countries. In this work, we referred the 212
standardised costs to a specific farm averaged over the 192 farms sampled in the BioBio Project: 73 213
ha farm area, 8 plots, 15 habitats patches2 and 1h travel distance (hereinafter, “standardised farm”). 214
Parcels of the standardised farm were assumed to be clustered in one single polygon. In some cases 215
(e.g. Norway) farm parcels were not clustered and fieldwork was organised in order to optimise the 216
number of trips and the time spent walking. These potential biases were considered during the cost 217
data collection identifying the farms served in each trip and the number of plots measured on each 218
farm. Moreover, in specific cases where the accessibility of plots was particularly difficult or the 219
walking area largely exceeded farm areas (e.g. Wales and Bulgaria), the time spent walking during 220
fieldwork was directly recorded. 221
We assessed the standardised field labour effort spent for each single farm using the following 222
equation (adapted from Skalski and Robson, 19923): 223
[eq. 1] 224
where Lf = fieldwork per farm (person days); i= biodiversity-related parameter; A = farm area (m2); 225
p= number of plots (p refers to number of habitats for habitat mapping); v = average walking speed 226
(fixed at 4500 m h-1); j = factor translating hours in person days (fixed at 8 hours per person day); l 227
= actual labour time per plot/habitat per survey round (person days); t = travel time from research 228
2 Species measurement was performed on one plot per habitat type. Therefore, the average number of habitats and
plots were different. 3 Assuming a random distribution of sampling plots, see Skalski and Robson, 1992.
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centre to the farm (person days); d = days needed to complete the field survey round; k = number of 229
surveys required per farm (1 for H, V and E, 3 for B and S)4. Equation [1] was employed for the 230
estimation of labour time spent in transport from the research centre to the farm and walking to the 231
plots. This allowed for the assessment of the standardised field-labour required for the measurement 232
of one habitat in the habitat mapping and one plot in the species biodiversity parameters. 233
The costs of the sampling activity per farm were then assessed as: 234
F= (Lf + Ld) *C + T+ R [eq. 2] 235
where F= cost per farm (€ farm-1); Ld = deskwork (person days); C = person days cost (€); T = cost 236
of taxonomic identification of bees, spiders and earthworms; R = other resource costs (equipment, 237
consumables, vehicles, etc.). 238
Since the BioBio activities should be referred to as a “pilot study” with considerably higher labour 239
effort requirements than a regular monitoring phase (Caughlan and Oakley, 2001), the recorded 240
labour efforts were adapted in order to fit a more realistic estimation of costs for a regular data 241
collection activity. The envisaged reduction rates were specifically devised by the leaders of the 242
field activities and were based on the potential optimisation of the sampling design and synergies 243
between indicators groups (see Appendix A; Geijzendorffer et al., 2012 and Jongman et al., 2012 244
for further details). 245
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2.4 Staff scenarios 247
Given the trends and existing monitoring efforts in Europe, we present results for three hypothetical 248
scenarios in which different actors may be involved in the monitoring activities: Strategy A is based 249
on the employment of professional agencies (e.g. by calls for tender). Strategy B directly involves 250
farmers in the field data collection, whilst deskwork and taxonomic identification is provided by 251
professional agencies as in strategy A. In strategy C, fauna data collection is performed by 252
volunteer networks while habitat mapping and vegetation sampling are performed by paid 253
professionals as in strategy A. 254
Strategy A is considered to be the general approach of public bodies that invite private agencies to 255
tender for the collection of information of interest. This approach is currently applied, for instance, 256
in LUCAS (Eurostat, 2013) where in situ monitoring is performed by private companies. In order to 257
consider the differences in labour costs across Europe, the costs of professional agencies in EU-27 258
countries were based on available information and assessed according to the correction factors 259
proposed in the Council Regulation –EC- No 1239/2010 (see Appendix B for data sources and 260
procedure of calculation). The same procedure was applied to the cost estimation of strategy B (see 261
4 For the farm questionnaire the function is simplified to L= l+2t
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below). The rationale of strategies B and C lies in the growing interest in so-called “citizen science 262
programmes” that seek wider societal involvement in conservation (Devictor, et al., 2010; Conrad, 263
et al., 2011). In strategy B, information is provided directly by farmers under non-mandatory 264
contracts. This strategy involves “payment by results”, in which land managers are incentivised to 265
use their private knowledge to optimise actions for environmental conservation and claim support 266
for service provision from society (Oppermann, 2003; Gibbons et al., 2011). In this strategy, travel 267
costs are not included and an average person day cost of a farmer is hypothesised as a reasonable 268
incentive. Strategy C is related to the current data collection of several fauna monitoring schemes in 269
Europe (Schmeller et al., 2008) and the growing interest within European agencies, such as the 270
European Environment Agency, to incorporate “bottom-up” information in addition to scientifically 271
“top down” approaches or as a standalone source of knowledge (European Environment Agency, 272
2013a and 2013b). In this scenario, volunteers only receive reimbursement for consumables, vehicle 273
and equipment costs. 274
Standardised costs per farm for equipment, consumables, taxonomy and other costs were assessed 275
as an average of the costs incurred by the research units in the 11 European countries5 involved in 276
the BioBio Project. 277
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3 Results 279
3.1 Analysis of research costs 280
The costs incurred by the research teams during the field trials were on average around € 4,000 per 281
farm (Table 1). Yet the large differences in unitary costs across the different countries (e.g. the cost 282
of researchers ranged between 6 € h-1 and 83 € h-1) led to substantial variability between the case 283
studies (range € 673 and € 8346 per farm; see Appendix C). As expected, labour (fieldwork, 284
deskwork and taxonomic identification) was the highest share of costs (about 90%). In particular, 285
fieldwork activities alone (i.e. labour time spent in travel, walking from one plot to the next and the 286
actual plot measurement) accounted for more than half of total costs. 287
Variability of data among the 192 sampled farms was less evident for the measures related to the 288
labour time spent by the research teams on the measurements (Figure 1). In particular, estimation of 289
actual fieldwork (i.e. fieldwork after travel and inter-plot walking) contributed to reduce 290
consistently the variability of effort requirements among the cases. The higher variability observed 291
for the lab and deskwork activities can be justified by the availability of aggregated results only (i.e. 292
at case study level, not per farm). In particular, spiders required considerable effort – but of large 293
5 The exchange rates from Norwegian Krone, British Pound, Swiss Franc and Hungarian Forint to Euro were: € = .92
NOK, € = . £, € = . CHF and € = ,818.25 HUF. All costs were related to 2010 (average published ECB
Exchange Rate values for year 2010).
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variability - for laboratory work. This is related to the effort spent in sorting the captured specimens, 294
which was particularly time-consuming, and directly related to the characteristics of the sample 295
(e.g. number of species and individuals to be sorted, litter and other material present). Furthermore, 296
different strategies for the sorting of spider species were used in the different case study regions, 297
which is typical for the pilot phase of an investigation and contributed to higher variability. 298
Labour time spent in travel and time to access the plots was relevant for the “field-measured” 299
parameters (Table 2). It ranged between 4.23 and more than 7 hours per farm on average for H, and 300
the species indicator groups, whereas the farm questionnaire required the lowest travel time (Max 3 301
hours per farm6). Labour spent for travel is mainly related to the number of people involved and the 302
number of trips needed to collect the data for the biodiversity indicators. Even though the 303
measurement protocol was not strict concerning the number of persons to be included in the 304
fieldwork, the research units ended up converging with respect to an “optimal “organisation of 305
personnel: habitat mapping and vegetation were usually sampled during the same trip by a field 306
team composed of 2 persons (the number of persons ranged between 1 and 4). The staff involved in 307
the field activities for the other parameters ranged on average between 1 (wild bee and bumblebees, 308
farm questionnaire) and 3 (earthworms). The percentage of skilled workers used in the various case 309
studies was similar as well. Namely, the farm questionnaire, and the wild bee and bumblebee 310
parameters required about 80% of skilled workers, whereas involvement of persons without specific 311
skills was higher for the earthworm sampling. 312
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3.2 Results of standardised costs 314
On average, the full set of parameters required a considerable amount of labour time for completing 315
the measurement of the standardised farm (14.3 person days - Table 3. See Appendix D for the 316
estimated person day requirements in farms characterised by different numbers of plots and 317
habitats). Nevertheless, consistent differences in labour time requirements were identified between 318
the biodiversity parameters. In general, fauna indicators were the most time consuming (almost 10 319
person days on average plus time needed for taxonomic identification). Of these, earthworms were 320
the most time consuming group of indicators (around 4.8 person days), whereas the farm 321
questionnaires required the lowest effort (about one person day per farm). Habitat mapping, 322
vegetation and bee recording required a similar labour effort (between 1.3 and 1.6 person days per 323
farm). The latter, however, required a higher number of journeys (because of the 3 repetitions) and 324
an extra-cost to be added for species identification subcontracts. Spiders were the most time-325
6 Farm questionnaires were performed by telephone in Norway and hence required no travel.
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consuming indicator group for lab and deskwork activities only, whereas earthworms required the 326
highest labour time investment per farm for the fieldwork. 327
In Table 4, the estimated costs for the assessment of biodiversity of the standardised farm are 328
presented according to the three different strategies. On average, the assessment of biodiversity 329
would cost more than € 8,000 in strategy A (range € 4700 - € 9100 in EU 27 countries), with more 330
than 50% being spent on fieldwork activities. The possibility of employing farmers for the 331
fieldwork –strategy B- would allow for a 46% cost reduction in comparison to strategy A (range € 332
3200 - € 5800 in EU 27 countries). In this scenario, deskwork would absorb the greatest portion of 333
the available budget (around 78% of total costs). The volunteer-based approach – strategy C- would 334
clearly be the least expensive strategy allowing for a 77% cost reduction compared with strategy A 335
(range € 1600 - € 3100 in EU 27 countries). In this scenario, deskwork would be the most expensive 336
activity, absorbing more than 90% of the costs. Since sampling of fauna indicators is performed by 337
volunteers, the vegetation indicator group would be most costly budget item in strategy C. 338
339
4 Discussion 340
Our results revealed that labour is the critical resource for field-based indicator measurements. The 341
effort required to carry out the measurements on farms within each case study region varied greatly, 342
in particular for fieldwork activities. Similar variability was noted by other studies focusing on the 343
time requirements of measurement protocols (e.g. de Blust, et al., 2012). Labour effort variability 344
across different case studies was associated with the inherent organisational issues of the research 345
teams, ease of access to farms and plots, differences in farming types, farm size and habitat 346
diversity. The normalisation process allowed for a consistent reduction in the variability. This 347
enabled a more meaningful comparison of costs per farm referring to a standardised number of 348
plots/habitats, farm areas and travel distance. The high variability observed, however, underscores 349
the need to base cost estimations on a large number of observations in various conditions, and not 350
on generalised observations based on very few field teams or contexts. 351
The estimation of effort for biodiversity monitoring on a standardised farm allowed us to report the 352
costs of a monitoring set-up for a clearly defined object, defined by area, number of plots and 353
habitats. Nevertheless, a straightforward application of our results could lead to an 354
oversimplification of the problem. The literature on monitoring activities reports a wide range of 355
costs and effort for the measurement of different indicators. For instance, Levrel et al., (2010) 356
presented the effort spent in the measurement of 4 parameters (common bird census, bird ringing, 357
garden butterfly observation and common butterfly census) ranging from 7 h to 44 h per site (ratio 358
1:6). Our results were comparatively similar, ranging between 1 person day for questionnaires to 359
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almost 5 person days for earthworm recording on the standardised farm (ratio 1:5). At the same 360
time, different objectives involve different protocol requirements for the measurement of the same 361
parameters along with notable labour time differences: for instance, Schmeller and Henle (2008) 362
reported an average 17.6 person days per site for high precision biodiversity surveys of plant 363
species, while Bisevac and Majer (2002) considered 0.67 person days per site to be sufficient for 364
surveying vegetation in restored areas. Even if sampling protocols and aims are similar, large 365
differences in effort can occur when sampling different environments. Time requirements for 366
habitat mapping in the European Biodiversity Observation Network Project (EBONE) were 367
considerably higher than in the farm scale BioBio Project (requirements for field mapping of 100 368
ha: between 4.1 and 5.2 person days in EBONE reported in de Blust et al., 2012 vs. 1.3 person 369
days7 in BioBio). Very likely, that difference was largely related to the more detailed list of habitat 370
categories required for the semi-natural environments targeted by EBONE in comparison to the 371
BioBio farmland areas. 372
The question arises whether the more costly indicators yield more (valuable) information than the 373
less costly ones. Amongst the six parameters, the more expensive ones include the direct 374
measurement of species and habitat diversity, whilst the questionnaire-based indicators only yielded 375
information on drivers (farm management practices), which are an indirect measure of biodiversity. 376
Whilst in the BioBio Project, correlations between farm management and biodiversity status 377
indicators were observed in individual case study regions, those correlations were not consistent 378
across study regions or farm types (Jeanneret et al. 2012). Therefore, the questionnaire- based 379
indicators did not generally allow for inferences for species or habitat diversity. 380
Information delivered by indicators could also be assessed with regard to its usefulness for different 381
end-users. For instance, a multicriteria evaluation was performed in order to assess the usefulness of 382
the BioBio indicator set. The survey was built upon three Stakeholder Advisory Board (SAB) 383
meetings over the course of the BioBio project and allowed for the comparison of the indicator 384
groups according to a list of ten criteria that was produced by the stakeholders. By means of a 385
multicriteria hierarchical procedure, the members of the SAB ranked the criteria and the parameters. 386
The results allowed for the evaluation of each measured parameter using a relative “usefulness” 387
scale which could be readily related to the cost of measurement. The pilot survey did not reveal any 388
linear relation between the costs of the measurement and the usefulness of the information (Targetti 389
et al., 2012b)8. Similar results concerning non-linearity between time requirements and information 390
of indicators are also reported e.g. in Gardner et al. (2008). If the costs and effectiveness of the 391
7 In order to compare with the EBONE Project, effort requirements for the mapping of 100 ha areas are estimated
before standardisation. 8 Further information are available upon request to the authors.
13
measurement are not related, a consistent cost/effort analysis should be included in order to 392
optimise monitoring programmes. 393
Flexibility afforded by the possibility of using low-skilled (and thus low cost) workers to complete 394
the field activities should also be considered. For instance, the average percentage of skilled persons 395
involved in spider sampling in the BioBio research activities could be considerably reduced. The 396
laborious laboratory work for sorting spiders from suction samples could also be assigned to non-397
skilled staff. This would lead to a significant simplification of logistics e.g. limited need for skilled 398
workers. Spider identification, on the other hand, requires specialised skills that could be scarce in 399
some countries. 400
Our reference of costs to the standardised farm allowed us to assess a reliable range of percentage 401
cost savings in the three scenarios based on a consistent methodology and a large data-set. For a 402
routine application in a monitoring scheme, the employment of professional agencies is the most 403
expensive option (even though some saving could be expected from the call for tender 404
mechanisms). Strategy A is most likely to assure the highest level of data quality because of the 405
technical and scientific expertise of the staff and because of the expertise that would be built over 406
the duration of the study. Nevertheless, the high cost of this approach could hinder the future 407
application of widespread biodiversity monitoring programmes at farm level, and the consideration 408
of less expensive options could be a conditio sine qua non that deserves specific consideration. 409
Considerable savings could be obtained if fieldwork measurements were carried out by farmers 410
themselves, for whom we considered an average person day cost as a reasonable incentive (Strategy 411
B). Travel time would be lower and farmers would also develop expertise and provide continuity. 412
Yet, the participation of farmers in the field activities introduces several shortcomings. Very likely, 413
habitat and vegetation surveys require botanical skills beyond the level of expertise that the average 414
farmer is willing to acquire. Farmers could take responsibility for the field sampling of spiders, bees 415
and earthworms after an appropriate training period but such sampling will overlap with periods of 416
high workloads for farmers (e.g. in spring). Moreover, to reduce biases, farmers would need to be 417
supervised by trained staff to make sure that sampling protocols are correctly and consistently 418
applied. Some of the labour cost savings would therefore be lost by increased training and 419
transaction costs (including data quality and control costs), which are not logistically easy to 420
provide for the large number of people involved. From a broader perspective, Strategy B could also 421
provide additional advantages, such as: endorsement of a bottom-up approach, information readily 422
available for farmers on their environmental performance, awareness raising and the incentive to 423
constitute environmentally-friendly brands and the possible implementation of a payment-for-424
results approach (Von Haaren, et al., 2012; Kelemen et al., 2013). Those advantages need to be 425
14
balanced against the potential of a less representative farm sample: i.e. the “monitoring effect” 426
which shows that the monitored farms increase their biodiversity performance more rapidly than the 427
farms which are not involved in the monitoring. 428
Strategy C (citizen science approach with volunteers) would allow for a substantial reduction of 429
costs but would require mechanisms to involve volunteer-networks and methods for maintaining 430
adequate quality of data (Schmeller et al., 2012; Holt et al., 2013). Similarly to Strategy B, the 431
involvement of volunteers would generate additional benefits such as societal inclusion and 432
commitment and mainstreaming of information on environmental performance of the farming 433
sector. Consistent money savings in Strategy C may also be realised because of the possibility of 434
recruiting the existing large pool of volunteer specialists who currently outnumber professionals for 435
some taxa (Schmeller et al., 2008; Levrel et al., 2010). As for Strategy B, this approach would need 436
to be framed by careful training and quality control activities. A major possible drawback consists 437
in potential restrictions for the sampling design (the selection of farms to be monitored). The 438
number of volunteers and interested laypersons is likely to be higher near urban areas, whereas in 439
more remote rural communities – which also need to be adequately sampled – it may be more 440
difficult to find sufficient volunteers. There will also be differences between countries due to 441
differences in size and distribution of volunteer networks (Van Swaay et al, 2008). A further 442
restriction is related to the actual biodiversity indicators. Volunteers tend to be attracted by 443
charismatic species (groups) such as birds and butterflies and by rare species often found in nature 444
reserves. It may be more difficult to motivate them to record earthworms and spiders on less 445
biodiverse farmland. 446
Even though the involvement of farmers and volunteers in monitoring activities could lead to 447
substantial savings, this opportunity requires a careful analysis of: a) the mechanisms needed to 448
incentivise the participation of farmers and volunteers; b) the methods needed to assure the required 449
level of data quality (Schmeller et al., 2012); c) the development of protocols that require lower 450
technical expertise if farmers are involved (e.g. Oppermann, 2003; Von Haaren, et al. 2012); and d) 451
the reliability of volunteer specialists for repeated periods of species identification. 452
453
5 Conclusions 454
Biodiversity monitoring is generally considered to be a relatively costly endeavour. Yet, systematic 455
information about the actual costs of different options and indicators for biodiversity monitoring are 456
not readily available. In addition, with regard to public decision making about biodiversity 457
monitoring, these costs would need to be balanced against the costs deriving from not being able to 458
measure to a higher detail the state and trends of biodiversity, thus to derive the costs incurred due 459
15
to biodiversity loss and the cost of not knowing the effects on biodiversity of different options 460
promoted by public policies. 461
In this paper, we focus on the former area of investigation (cost side). Our results confirm the 462
expectation that the costs for farmland biodiversity monitoring in Europe vary considerably 463
between countries and indicator types. The ratio of costs for measuring the BioBio indicator set was 464
1:12 between the lowest and the highest cost case studies. This evidence hampers the setting up a 465
common basket of biodiversity indicators because the cost-effectiveness of the programme will 466
inevitably diverge across EU countries. Nevertheless, the difficult implementation of a common 467
EU-wide monitoring program also depends from the different characteristics of farmland areas and 468
farming types, coupled to a difficulty in running a wide monitoring program9. 469
Our work was based on a consistent assessment methodology and involved a large and 470
comprehensive data set with selected habitat, management and species indicators. It could be a 471
plausible reference for the assessment of farm-scale monitoring of biodiversity. However, these 472
results can hardly be extrapolated to other types of indicators (e.g. other taxa, other indicators for 473
genetic diversity such as molecular-biological analysis, etc.), which would require specific cost 474
references or, at least, a careful consideration of the operational differences between the 475
measurement protocols. Similarly, specific features of the farms to be monitored should be 476
included. For instance, a farm consisting of a large number of scattered parcels would need more 477
journeys and walking time in comparison to clustered farms. 478
The paper also highlights the fact that the feasibility and coverage of a biodiversity monitoring 479
system is also dependent to the institutional arrangements that could be put in place. If a fixed 480
budget was available for farmland biodiversity monitoring (e.g. a percentage of the Common 481
Agricultural Policy –CAP- expenditure), our findings point to a potential 46% increase in the 482
coverage of a farm-scale biodiversity monitoring with Strategy B (farmer involvement) and a 77% 483
increase with Strategy C (volunteer involvement) in comparison with monitoring activities 484
subcontracted to private agencies only (Strategy A). Very likely, these opportunities come at a cost: 485
the quality of data and the statistical design should be carefully considered and adequate logistics 486
for the contribution of farmers and volunteers would be necessary. On the other hand, decisions 487
concerning the involvement of different actors are not only related to costs but also to “secondary” 488
additional returns. Namely, societal participation and information, increasing responsibility for 489
farmers’ environmental performance, education, etc. are aspects that might motivate farmers and 490
volunteers to be involved. In a policy context, these additional advantages could be of significant 491
value. Where labour costs are not a relevant limiting factor (e.g. in developing countries), the focus 492
9 But see http://www.bfn.de/0315_hnv+M52087573ab0.html for a succesful example of HNV monitoring
16
of the participative approach might be much more on education and information aspects than on 493
cost reduction. On the other hand, in policy contexts in which payments may be attached to 494
different levels of biodiversity provisions (such as in the context of the CAP or the implementation 495
of Payments for Ecosystem Services), such arrangement should also be balanced with the potential 496
emergence of distortions and acceptability issues. 497
As fieldwork was the most expensive activity, we focused on scenarios mainly based on different 498
strategies for field-data collection. This approach is also consistent with current approaches that 499
seek the involvement of local actors for field data collection. However, the possibility to involve 500
volunteers would allow also for cost reductions in species identification (laboratory work). This 501
opportunity was not included in strategy B. Nevertheless, Strategy B and C would involve extra-502
costs such as traning and data quality checks that are not included in this work. 503
A consistent assessment of the costs of biodiversity measurement is essential in order to minimize 504
the costs for a given level of information needed. This could be a first step towards the 505
improvement of knowledge-based decisions concerning biodiversity trends and cause-effect 506
mechanisms. The next major step that is envisaged in this paper, but still largely unexplored, is the 507
assessment of potential benefits of the additional information available through better monitoring. 508
In contexts heavily affected by public policies (i.e. the CAP in the EU), this primarily involves the 509
assessment of differential effects of better designed policies resulting from an improved knowledge 510
base for biodiversity. The potential for pursuing greater investment in biodiversity monitoring is 511
largely connected to the perception of these benefits, which makes this field a clear priority for 512
further investigation. 513
514
Acknowledgements 515
Part of the research was granted by the EU-FP7 Project BioBio, Indicators for biodiversity in 516
organic and low-input farming systems, contract No KBBE 227161. www.biobio-indicator.org. The 517
authors gratefully acknowledge the work of the field teams, the collaboration of the farmers that 518
allowed trampling of their plots and the valuable comments of two anonymous referees. This work 519
does not necessarily reflect the view of the European Union and in no way anticipates the 520
Commission’s future policy in this area. 521
522
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Targetti, S., Viaggi, D., Cuming, D., 2011. Analysis of Costs and Efforts of BioBio Indicators of 697
Biodiversity. BioBio Project deliverable D3.3. 698
Targetti, S., Viaggi, D., Cuming, D., Sarthou, J.P., Choisis, J.P., 2012a. Assessing the costs of 699
measuring biodiversity: Methodological and empirical issues. Food Economics, 1-8. 700
Targetti, S., Viaggi, D., Pointereau, P., Herzog, F., 2012b. Report on the SAB questionnaire. 701
Available at: www.biobio-indicator.org Accessed on: March 2013. 702
Tilman, D., Fargione, J., Wolff, B., D’Antonio, C., Dobson, A., Howarth, R., Schindler, D., 703
Schlesinger, W.H., Simberloff, D., Swackhamer, D., 2001. Forecasting agriculturally driven global 704
environmental change. Science, 292, 281-284. 705
Van Swaay, C.A.M., Nowicki, P., Settele, J., Van Strien, A.J., 2008. Butterfly monitoring in 706
Europe: methods, applications and perspectives. Biodiversity Conservation, 17, 3455-3469. 707
Von Haaren, C., Kempa, D., Vogel, K., Rüter, S., 2012. Assessing biodiversity on the farm scale as 708
basis for ecosystem service payments. Journal of Environmental Management, 113, 40–50.Acosta- 709
710
711
22
Tables 712
Table 1 Cost of resources spent per farm during the research activities in the 12 case studies and 713
cost share of the resources on total costs (standard error of the mean in brackets). 714
Average € farm-1 Share of total costs (%)
Fieldwork and travel 2017 (450) 52
Deskwork 754 (174) 19
Taxonomy 712 (140) 18
Transport (vehicle costs) 211 (52) 6
Consumables 69 (19) 2
Other costs* 85 (27) 2
Equipment 35 (9) 1
Total 3883
*Other costs include field accommodation, food allowances and incentives for farmers. 715
716
717
Table 2 Average labour time spent in travel and in walking to the plots, number of persons and 718
share of highly skilled persons involved in the research activities (standard error of the mean in 719
brackets; n = 192 for habitat mapping and vegetation, 189 for wild bees and bumblebees, 190 for 720
spiders, 182 for earthworms and 12 for questionnaires). 721
Habitat mapping
Vegetation
Wild bees and bumblebees
Spiders Earthworms
Questionnaire
Travel + transfers to plots [h farm-1
5.2 (0.30)
4.2 (0.35) 5.6 (0.31)
6.9 (0.48)
7.5 (0.50) 1.8 (0.44)
Persons involved in the field staff [Number]
1* (0.06)
1* (0.04)
1 (0.07) 2 (0.14) 3 (0.33) 1 (0.00)
Highly skilled persons in the field staff [%]
69 (2.66)
68 (2.41)
79 (2.54)
71 (2.58)
58 (3.00) 80 (9.79)
*very often, habitat mapping and vegetation surveys were performed together 722
723
724
Table 3 Standardised person days spent per farm in the measurement of biodiversity (data are 725
referred to the standardised farm: No of plots = 8, No of habitats = 15, area = 73 ha, travel distance 726
23
= 1 h). Time spent for taxonomic identification is included for vegetation, whereas it was 727
subcontracted for wild bees and bumblebees, spiders and earthworms and is not included in this 728
table (see Table 4). 729
H V B S E Q
Fieldwork 0.52 1.12 0.70 0.60 2.85 0.32
Lab and deskwork 0.86 0.51 0.56 1.58 0.94 0.37
Travel + transfers to plots 0.50 1.50 1.00 0.38
Total 14.3*
* Assuming travel synergies between habitat mapping and vegetation indicator groups and between 730
wild bees and bumblebees and spiders indicator groups 731
732
Table 4 Estimated costs of biodiversity measurement in EU-27 for the standardised farm in Euro. 733
Source: Levrel et al., (2010) and own data collection. Elaborations according to the Council 734
Regulation (EC) No 1239/2010 correction factors. Strategy A: all activities performed by 735
professionals; strategy B: fieldwork for vegetation and species indicators by paid farmers, deskwork 736
and laboratory by professionals; strategy C: species collection and identification by volunteers with 737
travels and consumables refunds, other desk and lab activities, habitat mapping and vegetation by 738
professionals. 739
H V B S E Q Total
Strategy A (professional agency-based) 894 1006 1438 1993 2332 513 8175
Strategy B (farmer-based fieldwork) 548 493 600 1196 1291 251 4380
Strategy C (volunteer-based species
sampling) 894 1006 66 88 107 513 2674
740
741
24
Figures 742
Figure 1 Average labour time spent for the measurement of the indicator groups during the research 743
activities. Bars over columns show the standard errors of the mean. Number of cases is 192 for 744
habitat mapping and vegetation, 189 for wild bees and bumblebees, 190 for spiders, 182 for 745
earthworms and 12 for farm questionnaires. Lab and deskwork data is aggregated at case study level 746
for all indicator groups (n=12). 747
*plot size: 100 m2 areal; 10 m2 linear 748
**actual fieldwork is fieldwork after time spent in travelling to the study area and estimated time 749
spent walking from plot to plot (see eq. 1) 750
751
752
753
25
Appendix 754
Appendix A. Reduction rates and explanations applied to labour efforts recorded for the activities of 755
the BioBio Project. 756
Parameter/activity Reduction rate (%)
Explanations
Habitat mapping 50 The regular monitoring phase will benefit from the availability of trained staff. The utilisation of field computer and the utilisation of open-source GIS software will contribute to cut costs. Synergies with the vegetation data collection are possible.
Vegetation 20 The reduction rate is mainly related to the possibility to employ ad-hoc species lists
Wild bees and bumblebees
- No cost reductions are envisaged.
Spiders 30 Very likely that the number of sub-samples can be reduced; possibilities to employ other machinery and methods (e.g. for spider sorting) is envisaged.
Earthworms - No cost reductions are envisaged. Questionnaire 30 Regular monitoring will benefit from a standardized
questionnaire in order to reduce the data input effort. A reduction in the number of questions is also envisaged.
Taxonomy 15 Taxonomy costs could be reduced by optimisation of staff resources (e.g. internal resources); availability of reference collections; electronic keys, bar-coding.
Consumables and equipment
20 Regular monitoring should benefit from economies of scale (e.g. bulk contracts)
757
Appendix B. Person day costs in the EU-27 Member States in Euro. Source: personal 758
communication from 4 private agencies in Italy and Germany, data presented in Levrel et al., 759
(2010), the current Italian national contract for managers in agriculture and own elaborations 760
according to the Council Regulation (EC) No 1239/2010 correction factors. 761
Private agency (skilled worker) Private agency (not- skilled worker) Farmer
Austria 567 344 180 Belgium 534 324 169 Bulgaria 335 203 106 Cyprus 447 271 142 Czech Republic 450 273 142 Denmark 716 435 227 Estonia 404 245 128 Finland 638 387 202 France 620 376 196 Germany 506 307 160 Greece 506 307 160 Hungary 423 257 134 Ireland 583 354 184 Italy 569 346 180 Latvia 397 241 126
26
Lithuania 387 235 123 Luxembourg 534 324 169 Malta 439 267 139 Netherlands 556 338 176 Poland 412 250 130 Portugal 454 276 144 Romania 366 222 116 Slovak Rep. 427 259 135 Slovenia 478 291 151 Spain 522 317 165 Sweden 633 385 201 UK 718 436 227 762
Appendix C Average costs of resources spent per farm in the 12 case studies during the field trials 763
in Euro 764
Fieldwork Deskwork Taxonomy Consumables Equipment Other costs Transport Total Austria 1949 1097 423 44 32 20 481 4045 France 5297 549 1479 155 40 233 595 8346 Germany 3221 1010 464 167 35 166 137 5198 Bulgaria 383 30 163 6 2 29 61 673 Hungary 745 88 416 38 8 3 75 1373 Norway 4197 361 1329 53 30 175 303 6448 Switzerland 2039 848 828 14 110 249 344 4432 Wales 1063 1072 536 72 25 0 107 2874 Spain (dehesas)
1544 986 975 186 25 78 146 3939
Spain (olive groves)
793 197 488 58 9 24 69 1637
Netherlands 2551 2220 1402 50 76 0 99 6397 Italy 428 598 44 32 26 46 113 1288 765
Appendix D Trend of person day requirements for the measurement of the six indicator groups: 766
Estimated person days to complete the measurement with different sampling intensity. The 767
standardised farm is the “average” farm from the study trials (15 habitats, 8 plots, 73 ha, 1 h travel 768
distance). Patches of the standardised farm are assumed to be clustered. 769