A simple regional downscaling approach for spatially distributing land use types for agricultural land

Agricultural Systems 120 (2013) 10–19

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Agricultural Systems journal homepage: www.elsevier.com/locate/agsy

A simple regional downscaling approach for spatially distributing land use types for agricultural land Dominique Gärtner a,⇑, Armin Keller a, Rainer Schulin b a b

Agroscope Reckenholz-Tänikon Research Station ART, Reckenholzstrasse 191, 8046 Zürich, Switzerland Swiss Federal Institute of Technology Zurich ETH, Institute of Terrestrial Ecosystems, Universitätstrasse 16, 8092 Zürich, Switzerland

a r t i c l e

i n f o

Article history: Received 2 May 2012 Received in revised form 29 April 2013 Accepted 30 April 2013 Available online 30 May 2013 Keywords: Agricultural land use Downscaling approach Regionalization Spatially explicit

a b s t r a c t Spatial explicit information of agricultural land use is a crucial requirement for many environmental models. In particular, for regional agro-ecosystem models that aim at agricultural management such as nutrient, soil and pest management for farming systems geo-referenced land use data are essential. In order to derive such spatial explicit agricultural land use data for Swiss agro-ecosystems, we developed a simple downscaling approach based on available geo-referenced farm census data and Swiss land use statistics. Allocation of farm census data to the raster cell of the Swiss land use statistics is achieved by using simple rules that take into account terrain attributes and the distance of agricultural land use types to the farm. The simple algorithm allocates home pastures, arable land and grassland to the farm census data. We tested the downscaling approach for the Canton Fribourg in western Switzerland for seven farm census periods (1980–2007). The agricultural land of about 770 km2 was managed by about 5900 farms in 1980 and by about 3400 farms in 2007. The modelled arable land agreed for 72% with the most recent land use statistics, while for grassland and home pasture only 41% and 37% of the grid cells were consistent. We presume that this discrepancy is partly caused by misclassification using aerial photographs in the land use statistics. Validation of the modelled agricultural land use types with recorded land use of soil monitoring sites revealed an agreement of 80% for the grassland sites and 59% for the arable sites. Hence, with the simple downscaling approach we already achieved fairly estimates of the spatial pattern of agricultural land use in the case study region. The possible application of such a downscaling approach is illustrated for the phosphorus inputs through animal manure into the agricultural soils of the Canton Fribourg. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction High livestock densities and intensive farming with large fertiliser application can lead to excessive nutrient and trace element inputs into agricultural soils. This includes trace metals (TM), especially copper and zinc, which are present at considerable concentrations in animal manure (Eckel et al., 2005; Nicholson et al., 1999; Menzi and Kessler, 1998). Accumulation of TM and nutrients in soil as well as the export of nutrients into surface and groundwater is problematic (Ogiyama et al., 2005; Qian et al., 2003; Mantovi et al., 2003; Sharpley et al., 2000), and should be identified early in order to minimise negative implications for the environment. Mass balance studies are an appropriate tool for assessing long term trends for element contents in soils (Chen et al., 2007; Öborn et al., 2003; Brouwer, 1998), locating areas within a region, where TM and nutrients slowly accumulate in topsoil and identify⇑ Corresponding author. Tel.: +41 44 37 77554; fax: +41 44 37 77201. E-mail addresses: [email protected] (D. Gärtner), armin. [email protected] (A. Keller), [email protected] (R. Schulin). 0308-521X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.agsy.2013.04.006

ing appropriate counter-measures. However, it is crucial that the spatial resolution of the flux assessment matches the resolution required by the measures in order to be effective. Many mass balance studies were conducted at field or farm scale, where land use, soil management and fertilisation are known field by field at a high temporal resolution (Bengtsson et al., 2003; Öborn et al., 2005). Others were carried out at the scale of entire regions or countries, using either balances over the whole region (Sheppard et al., 2009; Tiktak et al., 1998), or disaggregating the region into sub regions, e.g. land use systems defined by farm and crop type (Keller and Schulin, 2003; Keller et al., 2001). At regional scale, a common crucial point in modelling mass balances is to account for farming strategy at a suitable spatial resolution without averaging out the main agricultural characteristics. In this context, there is a need to have a model that links the agricultural land to the different land management practices of the farms. Recently, land use models such as RAUMIS (Weingarten, 1995), MODAM (Zander, 2001), LMK (Herrmann et al., 2003), ProLand (Kuhlmann et al., 2002), LUPOlib (Holzkämper and Seppelt, 2007) and integrated model frameworks such as SEAMLESS (van Ittersum

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et al., 2008) were developed to simulate land use with respect to an optimisation goal, e.g. economic efficiency of farming systems or environmental goals. The optimisation goal is mostly given by the model (Kuhlmann et al., 2002) or can explicitly be chosen, for instance in the LUPOlib-model and the framework SEAMLESS. Land use models are often used for policy support systems (van Ittersum et al., 1998; Forsman et al., 2003; van Delden et al., 2010), because they provide information regarding possible land use patterns at present as well as in future, considering scenarios for changing boundary conditions. In the context of mass balance studies, the application of land use models is expected to be beneficial, if they integrate the spatial information available in different datasets and are imbedded into modelling frameworks (Schönhart et al., 2011). But these readily available land use models do usually not account for actual farming strategies on regional scale and the spatial distribution of different farming strategies (Kuhlmann et al., 2002; Vayssières et al., 2011). Thus, they do not capture the implications of different fertilisation plans between farming systems. Land use modellers use two approaches to downscale land use data. The first approach is the use of remote sensing techniques and GIS to analyse current land use (e.g. Gao et al., 2010; Depeng et al., 2009). This is the approach used in current Swiss Land Use Statistics (LUS) of Switzerland. This approach, besides being very resource demanding, shows shortcomings, mainly if considering land uses that are quite similar and thus difficult to differentiate from remote sensing data. The accuracy of the Swiss LUS is discussed in the article. The second approach is to estimate land use based on models that distribute land use based on decision and optimisation rules, sometimes based on a farm sample or on an extensive data collecting campaign (e.g. Valbuena et al., 2010; van Delden et al., 2007). This approach needs large computational resources (and human resource if extensive data has to be collected). The advantage of the proposed downscaling approach compared to the two above mentioned is: (a) its simplicity and (b) its ability to accurately reproduce past land use patterns, given data availability for the past. It is also suited to distribute land use for the future if it is coupled with a model predicting the evolution of farms of different types. The objective of this study was to develop a regional downscaling approach for agricultural land use types that accounts for spatial differences in farm management. We tested the approach for a case study region in western Switzerland of about 770 km2 and about 5900 farms. As an example, we illustrate the possible application of the downscaling approach for the prediction of phosphorus inputs into agricultural soils by animal manure. The approach was developed for Swiss conditions, using available geo-referenced farm census data and area-wide Swiss land use statistics. Though, the principle of the downscaling approach may also be useful in other countries where database structures for land use statistics and farm census data are different.

the agricultural fields belonging to the farm cannot be derived from that dataset. The second dataset is the raster of the LUS obtained from remote sensing, that provides the utilised agricultural area (UAA) on a hectare raster basis. In a first step, the model assigns each cell of the LUS to a farm of the FC. In a second step, each cell is classified into one of three agricultural land use categories: arable land, home pasture and permanent grassland. Arable land is defined as agricultural land used in crop rotation. Home pasture is the part of permanent grassland that is regularly used by cattle livestock for grazing. It is located close to a farm, so that animals do not need to be moved very far. The rest of the permanent grassland is used for production of roughage or grazing. For the allocation of each raster cell to farms, two basic assumptions were made: (i) the average distances from farms to their land are as small as possible, and (ii) the scattering of land of a farm is reduced as much as possible. The rules applied in the model downscaling approach were based on: (a) a series of interviews with agri-

Sample n new farms

Each j = 1 … n farm

Farm has more than ai ha UAA yet not attributed?

no

yes Each i = 1 … ai grid cell allocate the i th nearest grid cell

Allocate the nearest grid cells to reach required UAA

Grid cells refering to farm j are marked

no

Each farm j holds its UAA?

yes 2. Methods and data

no

All farm samples considered?

2.1. Land allocation to farms, assignment of agricultural land use types The downscaling approach presented here exploits the features of two Swiss national databases. The first dataset is the Swiss Farm Census (FC), which consists of geo-referenced point data of the main building of a farm, including information about agricultural area (crop type and area) as well as livestock (animal type and number) that belongs to the farm. This gives us the opportunity to depict the actual location of the main building of different farms along with their farming strategy. However, the exact location of

yes

Fig. 1. Flowchart of the land attribution algorithm. Utilised Agricultural Area (UAA) from land use statistics is allocated to the different farms according to their census data minimising the distance between farm and its land. In order to reduce computer memory requirements the program processes batches of n farms. To prevent excessive spatial scattering of one farm’s land, the land is distributed in batches of ai hectares before moving to the next farm. In this study we chose n = 100 and ai = 10.

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each farm

Calculate the amount of home pasture according to cattle livestock

For the cells that belong to the farm

Assign home pasture to those cells next to the farm building

Assign arable land to those cells with minimum slope

Assign grassland to the remaining cells

no

All farms were treated?

yes

Fig. 2. Flowchart for the assignment of agricultural land use to the raster cells of each farm.

cultural experts and farmers, (b) considerations based on Swiss agricultural landscape and (c) legal boundary conditions. Given the resolution of the LUS and considering the relatively small farm sizes in Switzerland (average farm size was 17 ha in 2010), we used a raster cell size of 1 ha as basic unit. To assign a given raster cell to a farm, we used an iterative algorithm in which the farms start with no land. Proceeding farm by farm, the nearest available ai raster cells are allocated to each farm in each iteration, until the land allocated to a farm corresponds to its UAA given in the FC dataset. The allocation of land in batches of ai ha prevents an unrealistic scattering of the land. Fig. 1 delineates the land distribution algorithm. Once all land is allocated, the model assigns an agricultural land use type to each raster cell based on a set of criteria (Fig. 2). The rationale behind the classification of agricultural land use types is the huge difference in fertilisation practices among them. Home pastures get the highest rates of manure, as grazing livestock deposits its excrements directly on the fields. Moreover, farmers tend to spread manure predominantly on the fields located near to the farm. In Switzerland, grassland is predominantly fertilised with animal manure, whereas on arable land, the application of animal manure is limited to the time between harvest and soil preparation for the next crop. The acreage of home pasture of each farm is calculated from the number of cattle livestock present on

the farm and the minimum value of pasture per cattle livestock unit required by the Swiss directives for animal welfare (RAUSguidelines) (KIP and AGRIDEA, 2007). It is assumed that the required home pasture area is located nearest to a farm. Hence, the raster cells with the closest distance to the farm are defined as home pastures. Next, arable land is assigned to the raster cells that reveal favourable conditions for crop rotation, in our case those cells with a minimal slope. The area of arable land of each farm is determined from FC data. Non-permanent grassland is included in arable land as it is part of a crop rotation. Other criteria, such as climatic conditions, groundwater protection zones (in Switzerland, legislation restricts fertilisation and agricultural production in these zones) or soil properties could be included in the algorithm if according data is available. The remaining area is defined as permanent grassland as given in the FC data. For further illustration, Appendix A provides the pseudocode-notation of the algorithm. 2.2. Available databases 2.2.1. Swiss Farm Census The FC is carried out since 1980, in the beginning every 5 years and from 1996 on every year (Zaugg et al., 2009). The census collects detailed information about the livestock (animal type and number) and crops (type and area) produced by each farm and additional farm structure data, such as whether organic or conventional farming is practiced. Since 2000 the census database also gives the exact position of the main building of each farm, but not of the agricultural land belonging to it. Geo-referenced information is only partially available for the data sets before 2000. Unfortunately, the database records for the different time slices were not coupled. Therefore, we could not trace back the location information to the data sets before 2000. In such cases, we affiliate the centre coordinates of the corresponding community to the farms without coordinates, since the information of the community was available for all datasets. The average area of the communities in our case study region was 9.5 km2, corresponding to an average location error of 1.3 km for the farms without coordinates. 2.2.2. Swiss land use statistics Periodically, land use of Switzerland is assessed in the LUS (Humbel and Dumitrica, 2007). The data of the LUS is obtained from aerial photographs. The surface of Switzerland is divided into a regular raster of 100 by 100 m and one point is sampled per raster cell at its lower left corner (SW) to determine the cell’s land use. The LUS distinguishes 74 different types of land use, including water bodies and unproductive land such as glaciers and rock. For agricultural land, the following land uses are distinguished (categories from LUS 1992/97): regular vineyards, ‘‘Pergola’’ vineyards, extensive vines, intensive orchards, rows of fruit trees, scattered fruit trees, horticulture, favourable arable land and meadows, other arable land and meadows, farm pastures, brush meadows and farm pastures, mountain meadows, brush alpine pastures, remote and steep alpine meadows and pastures, favourable alpine pastures, rocky alpine pastures. Alpine pastures are used exclusively in summer, in most cases in cooperation between several farmers. Alpine pastures are not included in the UAA of a farm in the FC dataset, and therefore were excluded from the LUS dataset before it was imported into our model. The areas for special crops such as horticulture, orchards and vineyards are defined within the LUS. For all other agricultural land, so far no differentiation is made between arable land, permanent grassland and home pasture (aggregated category favourable arable land). However, the newest issue of the LUS (from the year 2004/09) differentiates between these three agricultural land use types. In this study, we used data from the two former LUS in

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1979/85 and 1992/97 for land allocation and assigning agricultural land use. Data from the latest LUS 2004/09, where the above mentioned land use types are distinguished, was used to check the model for plausibility.

while only 5% of the farms were larger than 50 ha. The largest farm of the canton Fribourg manages a UAA of 353.7 ha, that is about 18 times the Swiss average farm size. On the other hand, 20% of all farms manage less than 10 ha. LUS and FC show good agreement on total hectares of UAA with 75,855 ha in the FC 2007 and 75,536 ha in LUS 2004/09. The total UAA declared in the FC decreased from 77,377 ha in 1980 to 75,855 ha in 2007. This decrease was mainly due to a decrease in grassland. In the same time, the arable land increased from 27,500 ha in 1980 to 34,800 ha in the year 1990 and then remained almost constant. The decrease in grassland was associated with a decrease in animal numbers during the same period. In the early 1990s, new fertilisation regulations and ecological measures restricted livestock density and manure production (Herzog et al., 2008). As a consequence, livestock numbers decreased to some extent. For 1996, we found a surprisingly high value for the total arable land in the FC dataset. This can be attributed to new regulations implemented in that year, indicating that subsidies are linked to the FC data.

2.3. Case study of canton Fribourg The model was run and tested in a case study for the canton Fribourg, in western Switzerland. The region was chosen for following reasons: (I) It consists of a wide variety of landscapes, reaching from flat former wetland regions dominated by vegetable production in the north-eastern part, over a hilly region in the central part with a mixture of cereal production and grassland to a region dominated by dairy farming with almost exclusively grassland in the pre-alps. (II) The new LUS 2004/09 is already available for this canton. Moreover comprehensive databases on soil properties including phosphorus, organic carbon and trace metal contents are available for mass balance studies using the downscaling approach that is presented here as a basis. The canton Fribourg comprises a total area of 1671 km2 with a total UAA of approximately 770 km2 (46% of total area). This agricultural land was managed by 5899 farms in 1980. Until 2007 the number of farms had decreased to 3376. About 50% of the farms were smaller than 20 ha in 2007,

2.4. Software used The model is written in MATLAB 2007b (MathWorks, 2007). For visualisation of the maps we used MATLAB Mapping toolbox 3.1

900 800

number of farms

700 600 2 ha

500

5 ha 10 ha

400

20 ha 50 ha

300

100 ha

200

200 ha

100 0 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

number of patches

distance from farm to nearest field [m]

Fig. 3. Effect of the number of cells attributed to a farm (=batch size) in one step on the scattering of land for the batch size of 2 ha, 5 ha, 10 ha, 20 ha, 50 ha, 100 ha and 200 ha.

9000 8000 7000 6000

2 ha

5000

5 ha 10 ha

4000

20 ha

3000

50 ha

2000

100 ha 200 ha

1000 0 1

101

201

301

401

501

number of farms Fig. 4. Effect of the number of cells attributed to a farm (=Batch size) in one step on the minimal distance from the farms to their land. From a batch size of 20 ha on, the distance increases dramatically for a significant number of farms.

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Table 1 Effect of batch size ai on the number of highly scattered farms (extreme cases). Batch size

2 ha

5 ha

10 ha

20 ha

50 ha

100 ha

200 ha

Farms > 30 patches (%) Farms > 40 patches (%) Max. patches/farm

1.6 0.7 79

1.4 0.6 66

0.9 0.3 62

0.5 0.2 59

0.7 0.3 59

1.2 0.6 77

1.2 0.7 71

and ArcGIS 9 (ArcMAP version 9.3, ESRI, 2008). The calculation of the maximum slope in each raster cell was done in ArcGIS using the digital elevation model DEM25-layer for Switzerland. For comparison of the estimated spatial pattern of the agricultural land use types we used the software FRAGSTAT 4.1 (UMass Landscape Ecology, 2012). The following landscape metrics were computed: Patch density (PD), largest patch index (LPI), Total Edge (TE) and the Edge Density (ED). The PD equals the number of patches of the corre-

sponding patch type divided by total landscape area (unit: number/100 ha). The LPI equals the percentage of the total landscape area covered by the largest patch of the corresponding patch type. The TE equals the sum of the lengths (m) of all edge segments involving the corresponding patch type. The ED (unit: m/ha) equals the sum of the lengths (m) of all edge segments involving the corresponding patch type, divided by the total landscape area (ha).

3. Results and discussion 3.1. Allocation of land to farms For the allocation of land to farms, batches of ai = 10 ha raster cells were found as a good compromise to prevent on one hand that the allocation algorithm results in too many patches for one

100 90 80

% of farms

70 60 50

10% percentile

40

median

30

90% percentile

20 10 0 1

10

100

1000

10000

100000

distance to the fields [m] Fig. 5. Cumulative distribution function of the 10%, 50% and 90% percentiles of the cell distances for each of the 3376 farms.

Fig. 6. Agricultural land use modelled for the farm census year 2007.

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Fig. 7. Land use for agricultural land according to the Land Use Statistics (LUSs) 2004/09.

Table 2 Comparison of the modelled agricultural land use with the LUS 2004/09 with a set of selected landscape indices. Total area (ha)

Diff. to LUS 2004/09 (%)

Percent of landscape (%)

Number of patches (–)

Patch density (PD) (/100 ha)

Largest patch index (LPI) (%)

Total Edge (TE) (m)

Edge Density (ED) (m/ha)

Modelled Arable land Home pasture Grassland Total

34,834 6419 34,602 75,855

+15 +121 39 0.4

45.9 8.5 45.6 100.0

1513 2024 3012 6549

2.0 2.6 3.9

7.9 0.0 17.5 17.5

2772.3 1197.9 2989.9 3498.6

36.1 15.6 38.9 45.5

LUS 2004/09 Arable land Home pasture Grassland Total

40,147 14,161 21,228 75,536

53.1 18.7 28.1 100.0

1348 3397 3651 8396

1.8 4.5 4.8

4.3 0.1 3.6 4.3

2715.9 2226.5 2571.2 3756.8

36.0 29.5 34.0 49.7

farm and on the other hand that the farm land is located too far from the farm. In general, the scatter in land allocation was not very sensitive to the batch size for the majority of the farms (Fig. 3). To analyse the scattering of land, we analysed the number of patches of land of each farm. The term patch is used according to Zonneveld (1989) and describes a number of connected cells belonging to the same farm. For most farms, land was allocated in their neighbourhood across two or three patches. However, some extreme cases occurred, where farm land was scattered across more than 30 patches (Table 1). For small batch sizes in the range of 2–5 ha and for too large batch sizes >50 ha the number of farms with more than 30 patches increased remarkably. For instance, these extreme cases decreased from 54 (batch size of 2 ha) to 32 if a batch size of 10 ha was chosen. Therefore, choosing batch sizes between 10 and 50 ha were found as the optimum to reduce such extreme cases.

In contrast to the number of patches the distance of the allocated land to the farm was quite sensitive to the batch size. In terms of the cell located nearest to the farm (minimal distance) we observed that this distance measure increased sharply with batch sizes larger than 10 ha (Fig. 4). In such cases the downscaling approach led to implausible results, where the nearest allocated land for more than 100 farms was >1 km remote. Consequently, the choice of the batch size is a quite sensitive model parameter that has to be evaluated thoroughly for each case study region. The average distance from the farm buildings to the allocated cells of a farm was less than 3 km for 95% of the farms (Fig. 5). For almost all farms the UAA was in average attributed nearer than 5 km, for 90% of the farms within a radius of 3.1 km and for about half of the farms within a radius of 1 km. Only very few patches (<1%) where allocated to farms with distances longer than 5 km. These figures are realistic in view of the relatively small-scaled

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Table 3 Paired comparison for each raster cell between modelled land management (farm census 2007 and land use statistics (LUS) 1992/97) and LUS 2004/09 (in% of the modelled area). LUS 2004/09

Modelled Grassland Arable land Home pasture

Grassland (%)

Arable land (%)

Home pasture (%)

40.9 12.4 32.6

39.5 75.7 30.5

19.6 11.9 36.8

agricultural structure in Switzerland, in particular in pre-alpine regions (Herzog et al., 2008). Besides, we observed some border effects in the north-eastern part of the case study area, where some raster cells could not be allocated to any farm. We suspect that a considerable fraction in this area is owned by farms outside the case study region. The nonassigned cells were marked accordingly and excluded from the dataset before further analyses were carried out. Moreover, some data gaps in farm coordinates led to implausible results. In the data set FC 1980 and FC 1985 most of the farm coordinates were lacking and were approximated by the centre coordinates of the associated community where the farms belongs to. As a consequence various farms competed in the same area in the land allocation process. This led to geometrical untypical land patches, e.g. land patches arranged in a circle around the community centre. Therefore we conclude that the exact geo-referenced coordinates of the farms are a major requirement of the proposed algorithm. To evaluate the model results, we compared the modelled agricultural land use types with: (a) the latest LUS 2004/09 and (b) with the recorded land use of 184 soil monitoring sites of the cantonal soil monitoring network.

3.2. Comparison of modelled agricultural land use and the latest LUS 2004/09 By comparing modelled agricultural land use based on the dataset FC 2007 and LUS 1992/97 with the latest LUS 2004/09 we ensure that the modelled land use is independent from the recorded LUS of the year 2004/09. The temporal change of the agricultural area between the LUS 1992/97 and LUS 2004/09 dataset was less than 1000 ha, and thus negligible for the evaluation. The spatial pattern was well reproduced by the land use distribution model (Figs. 6 and 7). The modelled agricultural land use agreed with the LUS 2004/09 for 72% of the arable land, 41% of the grassland and 37% of the home pasture. While the coarse pattern of the modelled agricultural land use is determined by the FC 2007 dataset, the fine scale pattern within a few square kilometres, i.e. approximately within a community, is mainly model-dependent. Figs. 6 and 7 show clearly that the allocation of the modelled arable land at the finer scale agrees well with the LUS 2004/09 dataset. This can be seen particularly well in the centre of the canton, where the ratio between arable and grassland is approximately one to one. In the lower (northern) part of the canton, the amount of arable land is so high that it is difficult to see any pattern, because virtually all land is classified as arable. In such areas, modelled home pastures are located around farms. This was also confirmed by the LUS 2004/09 dataset. In the pre-alpine region, i.e. the southern part of the canton, the land is predominantly grassland. Also in these areas, the modelled allocation of arable land was in good agreement with the LUS 2004/09 dataset. Comparison of the spatial patterns between both maps by spatial metrics and indices provided a more detailed characterisation of spatial similarities and distinctions. The modelled number of patches for arable land is 12% larger than for the LUS 2004/09,

Table 4 Cell by cell comparison of land management along the studied period. The modelled land uses of a given farm census period were compared to the modelled land uses of the following farm census period. All areas are in hectares (equivalent to number of cells). The underlying LUS is LUS 1979/85 for the years 1980, 1985 and 1990 and LUS 1992/97 for the years 1996, 2000, 2005 and 2007. The bold values need to be highlighted because they show the number of cells that are assigned to the same land use type in following census years.

Grassland Arable land Home pasture Undistributed Grassland Arable land Home pasture Undistributed Grassland Arable land Home pasture Undistributed Grassland Arable land Home pasture Undistributed Grassland Arable land Home pasture Undistributed Grassland Arable land Home pasture Undistributed

Grassland

Arable land

Home pasture

Undistributed

Total

Years 1980

1985 28,761 5322 4008 64

8825 19,571 2212 654

4026 1782 1865 25

193 806 6 663

41,805 27,481 8091 1406

Years 1985

1990 30,278 4430 1548 48

6367 25,580 963 209

1257 645 5187 0

253 607 0 1411

38,155 31,262 7698 1668

Years 1990

1996 28,368 4946 1276 355

6563 27,195 989 800

1325 784 4823 0

48 194 1 1116

36,304 33,119 7089 2271

Years 1996

2000 27,266 5766 1142 153

5290 28,170 746 533

1082 678 4664 0

10 32 0 633

33,648 34,646 6552 1319

Years 2000

2005 28,826 4966 985 38

4948 29,286 665 77

966 588 4823 0

17 85 1 560

34,757 34,925 6474 675

Years 2005

2007 30,455 3607 537 3

3677 30,765 353 39

557 375 5487 0

126 229 0 621

34,815 34,976 6377 663

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Fig. 8. Simple regional P surface balance for the year 2007 based on modelled land use with the downscaling approach and average crop uptake as published in the Swiss guidelines for fertilisation (Flisch et al., 2009).

and smaller for grassland (17%) and home pasture (40%) (Table 2). The median PD for arable land was around 2 patches per 100 ha for both maps. Home pasture allocated by the LUS 2004/ 09 revealed a higher PD than the modelled land use type map indicating a finer spatial pattern of home pasture. The LPI shows that 17.5% of the landscape was comprised by the largest patch for modelled grassland, while the LUS 2004/09 revealed only a LPI of 3.6%. For the modelled arable land the LPI was about double as for the LUS 2004/09. Hence, the modelled land use estimates in some areas larger patches than recorded by the LUS 2004/09. For home pastures the ED of the LUS 2004/09 was about twice as high as for the modelled home pastures indicating larger spatial heterogeneity of home pastures for the LUS 2004/09. Independent of the fairly good agreement in the spatial pattern of the modelled agricultural land use and the LUS 2004/09 dataset, the comparison clearly revealed discrepancies in the total area of the agricultural land use (Table 2). As a consequence, this discrepancy was also found in the paired comparison for each cell (Table 3). While 40% of the modelled grassland was classified as arable land in the LUS 2004/09 dataset, only 12% of the modelled arable land was classified as grassland. Only 17% of the land classified as home pasture in the LUS 2004/09 dataset was also classified as home pasture by the downscaling approach. We presume that this discrepancy in agricultural land use classification was primarily caused from misclassification of aerial photographs for the LUS 2004/09 dataset. Some arable crops are very difficult to distinguish from permanent grassland by visual inspection. In addition, it is difficult to discriminate between artificial meadows in a crop rotation sequence and permanent grassland or home pastures on the base of aerial photographs (BFS, 2004). Since the FC data are provided di-

rectly by the farmers and are also the basis for independent farm audits in the context of receiving agricultural subsidies, we believe that the FC data are more reliable than the LUS 2004/09 dataset. 3.3. Model evaluation using the dataset of the cantonal soil monitoring network In order to evaluate our model and the LUS 2004/09, we compared the recorded land use of the 184 arable and grassland soil monitoring sites with both. In total, the land use of 80% of the sites was allocated correctly by the LUS 2004/09. The presented downscaling approach predicted the land use correctly for 68% of the points. If we compare the different land use types, the LUS behaves better for the estimation of arable land (84% correct versus 59% with the downscaling approach), but the downscaling approach achieves better results for the grassland (80% versus 75% with the LUS). This result show that: (a) even the LUS with its resource demanding methodology is not able to reproduce agricultural land use correctly, and (b) that the downscaling approach is able to reproduce land use with an accuracy that might be sufficient for many modelling purposes. These results show that the accuracy of the LUS and the downscaling approach are comparable and justify the use of the downscaling approach for periods when the LUS is not available. In order to test whether the model results can be improved using additional information, we tested the model performance for the 184 validation points using available indication on the usability of areas for arable land. We used following criteria: Maps of agricultural suitability (based on climate and a rough 1:200,000 soil map), an erosion risk map, and the nature conservation areas. With

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these criteria we could improve the classification of two points, which is only a very limited improvement. Unfortunately, more appropriate soil maps are not available yet for canton Fribourg. This information would lead to further improvement of our approach. 3.4. Change of agricultural land use type over time In order to check for model stability and consistency, we compared model results for the different FC periods. In the simulations, there was a considerable change in the agricultural land use allocation in the period from 1980 to 1985 (31% of grassland, 26% of arable land and 77% of home pasture, see Table 4). These changes resulted mainly from different data gaps for the farm coordinates. This effect is especially pronounced for home pasture, since they are allocated next to the farm buildings. According to the model, land use changes decreased to 13% for grassland, 12% for arable land and 14% in home pasture for the period between 2005 and 2007 (Table 4). The good agreement of the modelled agricultural land use between the years 2005 and 2007 shows that if the exact locations of the farms are known, the model gives realistic values for land use changes. In the light of changing farming strategies and economic boundary conditions an agricultural land use change of about 10% within some years is interpreted as the normal range of possible changes. 3.5. Application for regional nutrient balance To demonstrate a possible application of the model, we used the results to calculate a simple nutrient balance in canton Fribourg for the year 2007. The simple P balance was calculated using the per animal excretion for the different livestock types and the crop uptake for the different crops published in the Swiss guidelines for the fertilisation of arable and fodder crops (GRUDAF) (Flisch et al., 2009). The total amount of manure produced in each farm was distributed to the corresponding agricultural land proportionally to the crop needs given by (Flisch et al., 2009). Hence, the P surface balance shows where P surpluses resulting from manure at farm level exists (Fig. 8). The regional map of the P balance shows a area in the centre of the canton where there is a surplus of manure compared to crop needs, indicating that there large amounts of manure P are available in this area. 4. Conclusions With a limited number of simple assumptions and rules, as well as a good dataset (geo-referenced FC data and LUS), we were able to model realistic land use for agricultural land with high spatial resolution and with a fairly good accuracy. Given the high spatial resolution and the differentiation between individual farms, it is possible to capture most of the spatial variation of farming systems and their arable land and grassland. Hence, spatial explicit implications of different fertilisation plans between farming systems and agricultural land use types can be accounted for in regional model approaches. This downscaling approach can be used for many regional environmental models, for instance models that deal with nutrient, pesticide and trace element inputs into agricultural soils. Furthermore, we found some discrepancy in agricultural land use classification in the land use statistics LUS 2004/09. We presume that this was primarily caused from misclassification of aerial photographs. In this context, the downscaling approach might be useful to cross-validate such land use statistics with farm census data. The resolution of the model can be adjusted to different requirements depending on data availability and the structure of farms in the target region. The availability of detailed geo-referenced data in sufficient quality is probably the limiting factor for the applicability of the model to other areas.

Appendix A

Input: A: set of raster cells A ¼ fAi gM1 i¼0 B: set of farms  N1 B ¼ Bj j¼0

a: hectares of home pasture per livestock unit Attributes: coord(Ai): gives the coordinates of raster cell Ai slope(Ai): gives the slope of aster cell Ai coord(Bj): gives the coordinates of farm area(Bj): gives the total UAA of farm Bj cattle(Bj): gives the number of cattle livestock units of farm Bj arable(Bj): gives the area of arable land of farm Bj Output: Fi: allocation of the ith raster cell to the farm Bj ‘‘owner farm’’ Ui: Land use of the ith raster cell (Ui 2 {home pasture, arable land, grassland}) Pseudocode:

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References Bengtsson, H., Öborn, I., Jonsson, S., Nilsson, I., Andersson, A., 2003. Field balances of some mineral nutrients and trace elements in organic and conventional diary farming – a case study at Öjebyn, Sweden. Eur. J. Agron. 20, 101–116. BFS, 2004. Ackerland, ackerfähige Böden: GIS-Auswertung auf Basis von digitalen Polygon- und Punktdaten. Swiss Federal Statistical Office, Berne, Switzerland, 17. Brouwer, M., 1998. Nitrogen balances at farm level as a tool to monitor effects of agri-environmental policy. Nutr. Cycl. Agroecosyst. 52, 303–308. Chen, W., Chang, A.C., Wu, L., 2007. Assessing long-term environmental risks of trace elements in phosphate fertilizers. Ecotoxicol. Environ. Saf. 67, 48–58. Depeng, Y., Jiping, W., Yongbing, L., Xiaoli, Z., Hailong, L., Jie, W., 2009. Ecologically based landscape pattern optimization in northwest of Beijing. Geogr. Sci. 19, 359–372. Eckel, H., Roth, U., Döhler, H., Nicholson, F., Unwin, R., 2005. Assessment and reduction of heavy metal trace metal input into agro-ecosystems. Final report of EU-concerted action. AROMIS. KTBL-Schrift 432, vol. 3. Kuratorium für Technik und Bauwesen in der Landwirtschaft, Darmstadt, Germany, pp. 7843–2176. Flisch, R., Sinaj, S., Charles, R., Richner, W., 2009. GRUDAF 2009 – Grundlagen für die Düngung im Acker- und Futterbau. Agrarforschung 16, 1–97. Forsman, A., Grimvall, A., Scholtes, J., Wittgren, H.B., 2003. Generic structures of decision support systems for evaluation of policy measures to reduce catchment-scale nitrogen fluxes. Phys. Chem. Earth 28, 589–598. Gao, Q., Kang, M., Xu, H., Jiang, Y., Yang, J., 2010. Optimization of land use structure and spatial pattern for the semi-arid loess hilly-gully region in China. Catena 81, 196–202. Herrmann, S., Dabbert, S., Schwarz-von Raumer, H.-G., 2003. Threshold values for nature protection areas as indicators for bio-diversity – a regional evaluation of economic and ecological consequences. Agric. Ecosyst. Environ. 98, 493–506. Herzog, F., Prasuhn, V., Spiess, E., Richner, W., 2008. Environmental crosscompliance mitigates nitrogen and phosphorus pollution from Swiss agriculture. Environ. Sci. Policy 11, 655–668. Holzkämper, A., Seppelt, R., 2007. A generic tool for optimising land-use patterns and landscape structures. Environ. Modell. Softw. 22, 1801–1804. Humbel, R., Dumitrica, R., 2007. Arealstatistik nach Nomenklatur 1992. Swiss Federal Administration. Bundesamt für Statistik (BFS). Keller, A., Schulin, R., 2003. Phosphorus and heavy metal balances of agroecosystems and their uncertainty sources. Nutr. Cycl. Agroecosyst. 66, 271–284. Keller, A., von Steiger, B., van der Zee, S.E.A.T.M., Schulin, R., 2001. A stochastic empirical model for heavy-metal balances in agroecosystems. J. Environ. Qual. 30, 1976–1989. KIP and AGRIDEA. 2007. Kontrollhandbuch Ökologischer Leistungsnachweis(ÖLN), Besonders tierfreundliche Haltungssysteme (BTS), Regelmässiger Auslauf von Nutztieren (RAUS). KIP/AGRIDEA Lindau, [email protected]. Kuhlmann, F., Möller, D., Weinmann, B., 2002. Modellierung der Landnutzung. Regionshöfe oder Raster-Landwirtschaft. Berichte über Landwirtschaft, 351– 392. Mantovi, P., Bonazzi, G., Maestri, E., Marmiroli, N., 2003. Accumulation of copper and zinc from liquid manure in agricultural soils and crop plants. Plant Soil 250, 249–257. Menzi, H., Kessler, J., 1998. Heavy metal content of manure in Switzerland. In: Martinez, J. (Ed.), Proc. of the FAO-Network on Recycling Agricultural, Municipal and Industrial Residues in Agriculture (RAMIRAN 98), Rennes, France, pp. 495– 506. Nicholson, F.A., Chambers, B.J., Williams, J.R., Unwin, R.J., 1999. Heavy-metal contents of livestock feeds and animal manures in England and Wales. Bioresour. Technol. 70, 23–31. Öborn, I., Edwards, A.C., Witter, E., Oenema, O., Ivarsson, K., 2003. Element balances as a tool for sustainable nutrient management: a critical appraisal of their

19

merits and limitations within an agronomic and environmental context. Eur. J. Agron. 20, 211–225. Öborn, I., Modin-Edman, A.-K., Bengtsson, H., Gustafson, G.M., Salomon, E., Nilsson, S.I., Holmqvist, J., Sverdrup, H., 2005. A systems approach to assess farm-scale nutrient and trace element dynamics: a case study at the Öjebyn Diary Farm. Ambio 34, 301–310. Ogiyama, S., Sakamoto, K., Suzuki, H., Ushido, S., Anzai, T., Inubushi, K., 2005. Accumulation of zinc and copper in an arable field after animal manure application. Soil Sci. Plant Nutr. 51, 801–808. Qian, P., Schoenau, J.J., Wu, T., Mooleki, S.P., 2003. Copper and zinc amounts and distribution in soil as influenced by application of animal manure in eastcentral Saskatchewan. Can. J. Soil Sci. 83, 197–202. Schönhart, M., Schauppenlehner, Th., Schmid, E., Muhar, A., 2011. Integration of biophysical and economic models to analyze management intensity and landscape structure effects at farm and landscape level. Agric. Syst. 104, 122–134. Sharpley, A., Foy, B., Withers, P., 2000. Practical and innovative measures for the control of agricultural phosphorus losses to water: an overview. J. Environ. Qual. 29, 1–9. Sheppard, S.C., Grant, C.A., Sheppard, M.I., de Jong, R., Long, J., 2009. Risk indicator for agricultural inputs of trace elements to Canadian soils. J. Environ. Qual. 38, 919–932. Tiktak, A., Alkemade, R., van Grinsven, H., Makaske, K., 1998. Modelling cadmium accumulation on a regional scale in the Netherlands. Nutr. Cycl. Agroecosyst. 50, 209–222. UMass Landscape Ecology. 2012. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Version 4.1. URL: (visited 16.11.2012). Valbuena, D., Verburg, P.H., Veldkamp, A., Bregt, A.K., Ligtenberg, A., 2010. Effects of farmers’ decision on the landscape structure of a Dutch rural region: an agentbased approach. Landscape Urban Plan. 97, 98–110. van Delden, H., Luja, P., Engelen, G., 2007. Integration of multi-scale dynamic spatial models of socio-economic and physical processes for river basin management. Environ. Modell. Softw. 22, 223–238. Van Delden, H., Stuczynski, T., Ciaian, P., Paracchini, M.L., Hurkens, J., Lopatka, A., Shi, Y., Gomez Prieto, O., Calvo, S., van Vliet, J., Vanhout, R., 2010. Integrated assessment of agricultural policies with dynamic land use change modelling. Ecol. Model. 221, 2153–2166. van Ittersum, M.K., Rabbinge, R., van Latesteijn, H.C., 1998. Exploratory land use studies and their role in strategic policy making. Agric. Syst. 58, 309–330. van Ittersum, M.K., Ewert, F., Heckelei, T., Wery, J., Alkan Olsson, J., Andersen, E., Bezlepkina, I., Brouwer, F., Donatelli, M., Flichman, G., Olsson, L., Rizzoli, A.E., van der Wal, T., Wien, J.E., Wolf, J., 2008. Integrated assessment of agricultural systems – a component-based framework for the European Union (SEAMLESS). Agric. Syst. 96, 150–165. Vayssières, J., Vigne, M., Alary, V., Lecomte, Ph., 2011. Integrated participatory modelling of actual farms to support policy making on sustainable intensification. Agric. Syst. 104, 146–161. Weingarten, P., 1995. Das Regionalisierte Agrar- und Umweltinformationssystem für die Bundesrepublik Deutschland (RAUMIS). Berichte über Landwirtschaft 73, 272–302. Zander, P., 2001. Interdisciplinary modelling of agricultural land use: MODAM – multi objective decision support tool for agroecosystem management. In: Helming, K. (Ed.), Multidisciplinary Approaches to Soil Conservation Strategies, vol. 47, ZALF Bericht, pp. 155–160. Zaugg, H.-U., Bohnenblust, D., Humbel, R., 2009. Eidgenössische Betriebszählungen 1996, 2000, 2005 im ersten Wirtschaftssektor. Swiss federal administration. Bundesamt für Statistik (BFS). Zonneveld, I.S., 1989. The land unit – a fundamental concept in landscape ecology applications. Landsc. Ecol. 3, 76–80.