25 Years of Agro-Ecological Research at the Research Platform Scheyern - a Comprehensive Literature Research Emphasizing Hydrology, Soil and the Nitrogen Cycle

25 years of agro-ecological research at the research platform Scheyern - a comprehensive literature research emphasizing hydrology, soil and the nitrogen cycle

Lea Rosenberger

Munich, ….

Bachelor Thesis

Environmental Engineering

Technical University Munich

Erklärung

III

Table of content

List of tables ...... VI

Table of figures ...... VIII

1. Abstract ...... 1

2. Introduction ...... 1

3. The Research Area ...... 1

3.1. Introduction to Scheyern project ...... 1

Level ...... 1

Description ...... 1

3.1.1. Project phases ...... 2

3.1.1.1. Inventory phase (1990 - 1992): ...... 2

3.1.1.2. Landscape redesign (fall/ winter, 1992/ 1993): ...... 2

3.1.1.3. Project phase (1993 – 1998): ...... 4

3.1.1.4. Project phase (1999 – 2003): ...... 4

3.1.2. Description of the two farming systems ...... 4

3.1.2.1. Organic farming system ...... 4

3.1.2.2. Integrated farming system ...... 5

3.2. Study site ...... 5

3.2.1. Geographical position ...... 5

3.2.2. Climate ...... 6

3.2.3. Geology and landscape history ...... 6

3.2.4. Hydrology ...... 7

3.2.5. Measuring stations ...... 8

4. Discussion ...... 9

4.1. Meteorological database ...... 9

4.2. Hydrology ...... 14

4.2.1. Overland runoff, infiltrated and dischargeable precipitation and soil water balance 14

4.2.2. Bypass – and matrix flows in the unsaturated zone ...... 16

4.2.2.1. Quantification of percolating water ...... 18

4.2.2.2. Infiltration after heavy rainfall events ...... 19 IV

4.2.3. Classification of outflow components ...... 21

4.2.4. Hydrological models: NASIM and BROOK 90 ...... 24

4.2.4.1. NASIM ...... 24

4.2.4.2. BROOK 90 ...... 24

4.3. Soil ...... 25

4.3.1. Erosion and surface degradation as a consequence of outflow ...... 25

4.3.2. Soil agglomeration ...... 26

4.3.3. Measuring of soil water content with Ground-Penetrating Radar (GPR) and EM38 27

4.4. Nitrogen cycle ...... 28

4.4.1. Nitrogen input ...... 28

4.4.1.1. Nitrogen concentration in rainfall ...... 28

4.4.1.2. Nitrogen fertilization ...... 29

4.4.1.2.1. Consequence of fertilizer for growth of plants ...... 29

4.4.1.2.2. Fertilization strategies and nutrient uptake for winter wheat ...... 31

4.4.2. Nitrogen emissions ...... 34

4.4.2.1. N2O release during denitrification and from soil ...... 34

4.4.3. Nitrogen concentration in soil, surface – and groundwater ...... 39

4.4.3.1. Nitrogen in surface water ...... 39

4.4.3.2. Nitrogen in soil ...... 42

4.4.3.3. Nitrogen outflow ...... 43

4.4.3.4. N2-fixation through roots and plants in soil ...... 46

4.4.3.5. Nitrogen concentration on extensive farmed pasture land ...... 49

4.4.4. Models ...... 51

4.4.4.1. Agricultural Information System (AIS): Model of dynamics of nutrient transport 51

4.4.4.2. SPASS-model: Simulation of growth, water and nitrogen uptake ...... 52

5. Outlook ...... 52

5.1. Missing data about nitrogen cycle ...... 52

5.2. Missing hydrological data ...... 52

6. Conclusion ...... 52 V

References ...... 53

List of tables Table 1: Experimental levels studied by the FAM (Schröder et al., 2008) ...... 1 Table 2: Location and soil types of the measuring funnels and their recorded acres (IP: integrated f.; OL: organic f.) (Hellmeier 2001; Schneider 2001) ...... 9 Table 3: Land use of the investigated acres (1994 - 1998) (Hellmeier 2001; Schneider 2001) . 9 Table 4: Measuring stations in the observational network in Scheyern ...... 10 Table 5: Overland runoff (with a small part of interflow) 1993 - 1998 [mm/a] (Hellmeier 2001) ...... 15 Table 6: Infiltrated and dischargeable precipitation [mm/a] (Hellmeier 2001) ...... 15 Table 7: Mean water contents [%] from 1994 to 1998 (Hellmeier 2001) ...... 16 Table 8: Relation between soil moisture tension and pore size (Schneider 2001) ...... 17 Table 9: Visual depth ranges of bypass-flows (Schneider 2001) ...... 19 Table 10: Depth range of visual bypass-flows through water content changes (Schneider 2001) ...... 19 Table 11: Balance after heavy rainfall events for 1997 and 1998 (Schneider 2001) ...... 20 Table 12: Percentage of runoff components determined with the chemical-physical and isotope method (1993 - 1995) (Loewenstern 1998) ...... 22 Table 13: Percentage of runoff components determined with the Best-fit graph method (July 1992 – October 1993) (Loewenstern 1998) ...... 23

Table 14: Percentage of runoff components determined with the AUL-Method (July 1992 - October 1993) (Loewenstern 1998) ...... 23 Table 15: Trend of overland runoff, surface degradation and dissolved P (1993-1997) (Auerswald, Schwertmann 1999) ...... 26 - Table 16: Mean NO3 concentration in precipitation at S1006 in 1993 and 1994 (Loewenstern 1998) ...... 28 Table 17: Growth staging systems for wheat (Simmons et al. 2015) ...... 32 Table 18: Fertilization modifications and profit structure (Maidl, Fischbeck 1995) ...... 32

Table 19: N2O-N losses for different crops in 1995 and 1996 (Wechselberger 2000) ...... 36 Table 20: Cumulative N2O-N losses with regard to fertilizer amount (Munch, Rackwitz 1999) ...... 36

Table 21: N-input through fertilizer and crop residues, annual N2O-emission and losses (Sehy 2004) ...... 37 Table 22: Surface waters under investigation (Beese et al. 1995) ...... 40 Table 23: Distinction of different runoff components (Seiler, Hellmeier 2002)...... 43 Table 24: Mean annual nitrate transport through runoff in organic and integrated farming (Seiler, Hellmeier 2002) ...... 44 Table 25: Nitrate concentrations in 180 cm depth in the soil solution in summer 1998 in comparison to concentrations in groundwater in summer 1994 (Seiler, Hellmeier 2002) ...... 45 Table 26: Nitrogen soil surface balance of the organic crop rotation, averaging the years 1999 VII

- 2002 (Küstermann et al. 2010) ...... 46 Table 27: Nitrogen soil surface balance of the conventional crop rotation, averaging the years 1999 - 2002 (Küstermann et al. 2010) ...... 48 Table 28: Clover-alfalfa-grass yields on two locations on division A02 (Claassen et al. 1997) ...... 49

Table 29: Variability of NO3-N and NH4-N on pasture land (Simon 1995) ...... 50

VIII

Table of figures Figure 1: Positions of the 50x50m grid nodes (small, filled circles), soil pits (large, half-filled circles) and the pond under study (Auerswald et al. 2001) ...... 2 Figure 2: Research Station Scheyern after 1992. Management systems, field numbers, grid system and long-term observation areas (Schröder et al. 2002) ...... 3 Figure 3: Land use before changing cultivation (Forschungsverband Agrarökosysteme München (FAM) 2005) ...... 4 Figure 4: Land use after changing cultivation (Forschungsverband Agrarökosysteme München (FAM) 2005) ...... 4 Figure 5: Geographical position of Scheyern Experimental Farm (Hellmeier 2001) ...... 6 Figure 6: The upslope drainage area for 12,5x12,5m blocks based on topography indicates the major flow paths (large squares) (Auerswald et al. 2001) ...... 7 Figure 7: Location of the measurement stations in the Research Area (Hellmeier 2001) ...... 8 Figure 8: Location of the measuring stations in 1995 (Kainz, Wenzel 1996) ...... 10 Figure 9: Sum of precipitation for a month at base station B01 (Hellmeier 2001) ...... 11 Figure 10: Precipitation data from 1993 - 2006 (Küstermann et al. 2010) ...... 12 Figure 11: Mean temperature and annual global radiation (1993 - 2006) (Küstermann et al. 2010) ...... 13 Figure 12: Potential Evapotranspiration for a month at base station B01(1993/1994 – 1997/1998) (Hellmeier 2001) ...... 14 Figure 13: Mean discharge (1993/1994 - 1997/1998) (Hellmeier 2001) ...... 14 Figure 14: Location of the measurement stations (Loewenstern 1998) ...... 22 Figure 15: Causes and ratios of individual measures on the reduction of soil degradation through restructuring and implementation of integrated and organic farming (Weigand et al. 1996) .. 25 Figure 16: NO3- in precipitation in 1993 at climate station S1006 (Loewenstern 1998) ...... 28 Figure 17: NO3- in precipitation in 1994 at climate station S1006 (Loewenstern 1998) ...... 29 Figure 18: Effect of soil cultivation and N-fertilization on the harvest of winter wheat (Kainz et al. 1995) ...... 30 Figure 19: Effect of soil cultivation and N-fertilization on the harvest of winter wheat (Kainz et al. 1995) ...... 30 Figure 20: Effect of soil cultivation and N-fertilization on the harvest of corn maize (Kainz et al. 1995) ...... 31 Figure 21: Effect of soil cultivation and N-fertilization on the harvest of potatoes (Kainz et al. 1995) ...... 31 Figure 22: N-withdrawals and N-balance of sand soil (Maidl, Fischbeck 1995)...... 34 Figure 23: N-withdrawals and N-balance of loam soil (Maidl, Fischbeck 1995) ...... 34 Figure 24: Creation, usage and release of N2O in soils ...... 35 Figure 25: Correlation between annual N2O-emissions and N-input for two investigated years (Sehy 2004) ...... 38 IX

Figure 26: Location of "Teufelsweiher" and its two inflows (Beese et al. 1995) ...... 39 Figure 27: Nitrate loads in “Bach West” 1993 – 1994 (Beese et al. 1995) ...... 40 Figure 28: Nitrate loads in "Teufelsweiher" 1993 - 1994 (Beese et al. 1995) ...... 41 Figure 29: N-balance of "Teufelsweiher“ July 1993 – June 1994 (Beese et al. 1995) ...... 42 Figure 30: Proportion of Nitrogen and fertilized 15N on the particle sizes in the topsoil (Lützow, Jimenez 1999) ...... 43 Figure 31: Nitrate concentrations in the effective root zone (0 - 100 cm under surface) for different crops (Seiler, Hellmeier 2002) ...... 44 Figure 32: Mean nitrogen discharge in Scheyern (Seiler, Hellmeier 2002) ...... 45

Figure 33: N-input and N2 fixation from 1993 to 2006 in the organic farm ...... 48 Figure 34: N-input and N-output from 1993 to 2006 in the conventional farm (Küstermann et al. 2010) ...... 49 1

1. Abstract

2. Introduction

3. The Research Area

3.1. Introduction to Scheyern project Scheyern experimental farm, located in the tertiary hills of southern Bavaria, Germany, is a 150 ha research farm. In 1989 the Munich Research Network on Agroecosystems (FAM) was founded and a group of scientists established guidelines for sustainable, economically and ecologically compatible management of rural landscapes. The former cloister estate Scheyern was selected to record, evaluate and discuss socio-economic and environmental impacts of land management changes. The interdisciplinary research project aimed to develop models for future agro ecological activities which are sensitive enough to quantify small changes, to assess long- term developments and to evaluate economic effects on ecosystem changes. The project was split into different structural and scale levels (Table 1).

Table 1: Experimental levels studied by the FAM (Schröder et al., 2008)

Level Description Landscape Tertiary Hills, district of Pfaffenhofen/Ilm (400 km²), southern Bavaria, Germany Landscape Section Research Station Scheyern (150 ha) Farm Organic Farm (68 ha), integrated farming (46 ha) Field Between 2 and 7 ha on the Research Station Scheyern Plot 16 – 600 m² Model ecosystem Exposure chambers, monolith lysimeter, pure-air green house, model ponds (0,1 – 10 m²) Laboratory systems Cultures of organisms, soil aggregates, minirhizotrons, microcosms (0,01 – 0,1 m²)

To sum up the aims of the Scheyern project, the FAM specified three main hypotheses for a holistic approach (Pfadenhauer, Filser 2001; Lützow et al. 1995):

1) Sustainable land use (in contrast to exploitative) decreases the load of agro-chemicals and their metabolites on neighboring systems. 2

2) Sustainable land use (in contrast to exploitative) enlarges the diversity of plants, animals and microorganisms.

3) Sustainable land use (in contrast to exploitative) supports the economic production of high-quality food.

3.1.1. Project phases A total duration of 15 years was defined for the project, starting in 1990. Thereby it was possible to understand changes at field and farm levels, water ponds and groundwater and in the development of biodiversity. The project was divided into four phases (Schröder et al. 2008; Schröder et al. 2002; Auerswald et al. 2001):

3.1.1.1. Inventory phase (1990 - 1992): This project phase covered the cultivation of arable land with winter wheat (1991) and spring barley (1992) according to the principles of conventional farming. To uncover site effects agro-chemicals were applied moderately. The aim was to create uniform starting conditions between the various sites, so all arable fields were treated in the same manner. A grid of 600 measuring points, each 50 x 50 m, was implemented (

Figure 1).

Figure 1: Positions of the 50x50m grid nodes (small, filled circles), soil pits (large, half-filled circles) and the pond under study (Auerswald et al. 2001) 3.1.1.2. Landscape redesign (fall/ winter, 1992/ 1993): The partitions were distributed into grassland and arable land, considering aspects of nature and resource conservation. Hedges, field boundaries, forest hedges and fallow grounds were created. The aim of the redesign was to avoid fertilizer and pesticide input into water, minimize erosion, water losses and soil compaction and to make more site-specific farming possible. Furthermore, improving of the recreational function of the landscape was a goal. The landscape was divided into an organic and an integrated farm, separated by a main watershed (Figure 2). In the northern part of the research station, a 39 ha plot was divided into several experimental plots to realize detailed studies about land use changes. 3

Figure 2: Research Station Scheyern after 1992. Management systems, field numbers, grid system and long-term observation areas (Schröder et al. 2002) To increase buffer capacity, fallow strips were introduced between fields and between water and woods. Steep arable land was turned into grassland. Figure 3 and Figure 4 show the changes in land use before and after the landscape redesign when more permanent grassland and fallows were established.

Figure 3: Land use before changing cultivation (Forschungsverband Agrarökosysteme München (FAM) 2005)

Figure 4: Land use after changing cultivation (Forschungsverband Agrarökosysteme München (FAM) 2005)

3.1.1.3. Project phase (1993 – 1998): In this project phase different managing approaches of organic and integrated farming started and the northern part was set up for detailed research studies for several projects. Through measurement equipment a permanent monitoring was possible and through this, processes could be analyzed easily. The main focus of this phase was the evaluation of economic and ecological changes as a consequence of different management systems.

3.1.1.4. Project phase (1999 – 2003): The management of organic and integrated farming continued as before and the farming systems were optimized through precision farming. Models were further developed and therefore a forecasting of processes up to the landscape level was possible. This was possible through establishing of indicators for sustainable land use.

3.1.2. Description of the two farming systems

3.1.2.1. Organic farming system The organic farming system was established on a 68, 5 ha area with the goal of closed nutrient and resource cycles. The farm was managed according to Naturland and Bioland conditions, two members of the German Association for Organic Farming (AGÖL). The organic farm, mostly on low-sorption soils, was divided into four different types of uses. On 31,5 ha arable 5 land a seven-field crop rotation was cultivated with Lucerne-clover-grass-meadow, seed potatoes with mustard intercropping, winter wheat with cover crop, sunflowers (oil) with under sowing of Lucerne-clover-grass-meadow, Lucerne-clover-grass meadow as forage, winter wheat with white clover under sowing and winter rye with under sowing of Lucerne-clover- grass-meadow. Furthermore, a 95-head cattle herd for meat production was integrated. Besides this, 25 ha of grassland and 3, 5 ha set-aside land for succession was created. To achieve the ecological purpose, mineral fertilizer and pesticides were banned, external energy, damages to flora and fauna and matter exports to surface and groundwater were minimized and the nitrogen cycle in the crop rotation was optimized. Nitrogen input was realized with legumes. Also crop varieties with a broad resistance against parasites that are also competitive to weeds were selected (Schröder et al. 2008; Schröder et al. 2002; Hellmeier 2001).

3.1.2.2. Integrated farming system On 46 ha with mostly well-buffered soils the integrated farming system was established. The arable land, 30 ha, was implemented with a four-field crop rotation with winter wheat, potatoes, winter wheat and maize. Potatoes and wheat were grown as cash crops whereas maize was food for 45 bulls living in the neighboring farms. Moreover, 1, 8 ha of the remaining land was used as grassland and 8, 8 ha as fallow land. Tillage frequency and intensity was reduced to achieve a sustainable farming system. Not the maximization of harvest is the main aim of the integrated farming system but the conservation of soil fertility and protection of existing resources. Through harrowing, chiseling and wide tires, soil compaction and erosion was minimized. The soil surface was protected through cover crops and wheat and maize stubble mulch. Through this soil faunal and microbial activity was stimulated. Leaching was abated through cultivation of crop varieties with high ability to compete for nutrients, water and light and with resistance to most diseases. Pesticides were not used for prevention but only when necessary and nitrogen fertilization was only implemented equivalent to the abstraction of nitrogen through the plants (Schröder et al. 2008; Schröder et al. 2002; Hellmeier 2001).

3.2. Study site

3.2.1. Geographical position The Research Station Scheyern is located in the tertiary hills of southern Bavaria Germany, 40 km north of Munich. It is situated in the district of Pfaffenhofen/ Ilm at 445 – 498 m above sea level. Forest is located in the south and the west of the area and in the east and north are ponds which were formerly for fish breeding (Auerswald et al. 2001; Schröder et al. 2008; Hellmeier 2001). 6

Figure 5: Geographical position of Scheyern Experimental Farm (Hellmeier 2001)

3.2.2. Climate The average annual precipitation in the Scheyern catchment is 803 mm (annual mean over 30 years (Forschungsverband Agrarökosysteme München (FAM) 2008)) and the mean annual temperature is 7,4 °C. About 60% of the annual precipitation is falling between May and October partly in the form of heavy showers which can provoke erosion (Lützow et al. 1995; Schröder et al. 2008).

3.2.3. Geology and landscape history The landscape of the tertiary hills is located between the pleistone moraines of the alpine glaciers and the Danube River. The hills have coarse- and fine-grained deposits which originate from the upper sweet water molasses and are mostly covered by thin quaternary loess layers. Asymmetrical valleys shaped by uneven loess deposition, solifluction and erosion are characteristically for the landscape. The Research Station Scheyern is located between a soil of loess-loam clay ridge and a less-loam sand ridge, where the clay content varies from 90 to g 450 . The tertiary sediments, hilltops and eroded slopes are mostly of a sand-gravel-clay kg composition with a varying content of gravel. Clay lenses which are embedded in the sand are generally less than five meters thick. Approximately 85 % of the Research Station is covered by a loess-loam layer or by loess deposits thinner than two meters. Loess deposits do often not exist at ridge crests and upslope positions. Woodruff, oak and beech forests are widespread in the area. Since agriculture is popular in the tertiary hills since the younger Stone Age, reduce of forests, hedges lynchets and buffer stripes is identifiable. The tertiary hills form a third of Bavaria’s agricultural landscape and demonstrate typical problems of intensive agriculture such as compaction, erosion and overdressing, low nutrient efficiency, subsequent pollution of surface and groundwater and decrease of variability of flora and fauna. The distinct relief, 7 varied land use and spectrum of sandy, silty and clayey substrates formed a wide range of soils and site characteristics (Schröder et al. 2008; Auerswald et al. 2001).

3.2.4. Hydrology The regional groundwater level at Scheyern research station is located marginal under 450 m above sea level and flows with a medium slope of 0,45 % from south-southwest to north- northeast. The groundwater level is not reaching the local receiving stream level, so it is not connected to the surface runoff water. However, the groundwater’s surface lies above the level of the two main receiving streams “Ilm” and “Gerolsbach”. The maximum distance between the groundwater level and the ground surface is approximately 40 meters whereas it is only 2 meters in the valley areas of the Research Farm. Even though marginal hydraulic intermixture exists. With geo-electric measurements, at least two clay lenses in the soil with low permeability were found, where ponded groundwater has accumulated. These lenses, which extend to the surface of the slopes, cause surface runoff by exfiltration of groundwater during periods of heavy rainfall. In other respects the water from the underground ponds flows into the regional groundwater system or into the drainage system. Hydrologically the area is well-defined except for the south, where only a small amount of surface runoff enters the research area through the forest. The farm area is split into several catchments for surface runoff which makes it possible to study separate nutrient and material fluxes (Figure 6).

Figure 6: The upslope drainage area for 12,5x12,5m blocks based on topography indicates the major flow paths (large squares) (Auerswald et al. 2001) There are two creeks which drain the research area, one from west to east and the other one from south to north; whereas the west-east creek is not monitored since it has its main drainage area outside Scheyern farm. However, the south-north creek is almost entirely fed within the research area. It is dammed at several fish ponds, which was typical for the former land use of the monastery of Scheyern. Therefore only the uppermost (southern) pond of the south-north creek ( 8

Figure 1, page 2), which is fed by to small branches of the south-north creek, is monitored because it is not influenced by current fish-rearing activities (Auerswald et al. 2001; Hellmeier 2001).

3.2.5. Measuring stations In the Research Area measurement equipment about was installed to record data about climate, soil water, matter balance and groundwater. Figure 7 shows the location of installed stilling wells (S) which recorded the matter balance, soil temperature, matrix potentials and the soil water balance through continuous measurements with pore water samplers with a high temporal resolution. For each stilling well six measurement points in six depths up to 180 m beneath ground level were attached.

Figure 7: Location of the measurement stations in the Research Area (Hellmeier 2001) The locations were chosen to cover a wide range of topography geology, geomorphology, soil type and agriculture (Table 2). Each funnel recorded data to one or two neighbouring acres. In the following the acres are often declared with “funnel-number”-“acre”, for example 2 – 17. From 1994 to 1998 different crops on the chosen acres were evaluated (Table 3). 9

Table 2: Location and soil types of the measuring funnels and their recorded acres (IP: integrated f.; OL: organic f.) (Hellmeier 2001; Schneider 2001)

Funnel Recorded Soil type Absolute Topogr. Gradient Exp. (S) acre altitude location [%] 2 A 17, A 18 Loess loam with 467,5 Middle 6,3 ENE (IP) brown earth hillside 6 A 18 (IP) Colluvium above 454,6 Low 7,9 SE fossil, low clayey hillside brown earth with loess loam 9 A 3 (OL), Sandy molasse 495,2 Crest 6,1 NNE green with brown earth fallow 12 A 9 (OL), Sandy and clayey 481,8 Crest 7,4 NE lynchets loam with brown earth 13 A 16 (IP) Loam and 483,8 Flattenin 7,1 ENE gravelly-sandy g in molasse on clay middle with pseudogley- hillside brown earth

Table 3: Land use of the investigated acres (1994 - 1998) (Hellmeier 2001; Schneider 2001)

Acre Acre size 1994 1995 1996 1997 1998 [m²] A 3 (OL) 55 Lupine Winter Spring Grass- Potatoes wheat barley clover A 9 (OL) 40 Winter rye Grass- Potatoes Winter Sunflower clover wheat A 16 (IP) 35 Maize Winter Potatoes Winter Maize wheat wheat A 17 (IP) 1000 Summer Maize Winter Potatoes Winter wheat wheat wheat A 18 (IP) 45 Potatoes Winter Maize Winter potatoes wheat wheat

4. Discussion

4.1. Meteorological database To record meteorological data, the FAM built an observational network in the whole research area. The aim was to evaluate the spatiotemporal variability of climate data, particularly for 10 precipitation. Therefore the observational network for precipitation was fully developed. In 1995, the network contained 45 measuring stations with different configuration (Table 4). In 1996, the base stations and the precipitation measuring points (R01 – R11; RM1, RM2) were maintained whereas the other stations were not used again.

Table 4: Measuring stations in the observational network in Scheyern

Type of measuring Configuration Quantity Notation in station Figure 8 Base station Global irradiance, wind direction, 3 B01 – B03 wind speed, air temperature, air humidity, soil temperature, precipitation Micro climate Wind speed, air humidity, air 2 MK1, MK2 stations temperature, soil temperature Crop measuring Wind speed, air humidity, air 2 BK1, BK2 stations temperature, leaf moistness, leaf temperature, soil temperature, soil humidity Small station Air humidity, air temperature, leaf 1 - moistness, soil temperature Precipitation Soil temperature, precipitation 10 R01 – R11 measuring points Precipitation Strip chart recorder 2 RM1, RM2 measuring points Precipitation Storage rain gauge 25 T01 – T25 measuring points

Figure 8: Location of the measuring stations in 1995 (Kainz, Wenzel 1996) All measurements, except precipitation data, are recorded hourly. The measured values are normally generated from the average of 10- minute values except for wind speed and global irradiance. Because of quick changes those two measurements are calculated from an average of 1 - minute values. The precipitation is recorded with a dissolution of 0, 1 mm except for the 11 two base stations where the recording is for 0, 2 mm. The two strip chart recorder RM1 and RM2 note the precipitation with a mechanical recorder (Kainz, Wenzel 1996).

B01 is located at 454, 0 m above sea level in the valley cut which intersects from north to south through the area. B02 is situated at 496, 6 m above sea level on a crest. Since the two stations are exposed to different weather influences, great differences of measured values were asserted. The more representative station is B01, since it is not located in such an extreme position as B02. Long term measurements of the German Meteorological Service (DWD) from 1947 to 1993 recorded an annual mean precipitation of 805 mm. In the investigation period from 1994 to 1998 the annual mean precipitation recorded at B01 was 855 mm, 50 mm more than the long term measurements from the DWD. This can be lead back to rainfall extremes between 1994 and 1998. Precipitation events with more than 40 mm per day occurred at least once a year in the investigation period and heavy rainfalls with more than 20 mm per day happened predominantly in the summer between June and September. About 60% of the annual precipitation fell between April and September in the research period which corresponds to the long term measurements, where about 61% of the annual precipitation occurred in the summer term (Figure 9) (Hellmeier 2001; Schneider 2001).

120 108 108106 100 100 88 85 77 77 80 72 71 72 66 65 61 55 55 56 60 52 51 53 49 48 47 39

40 PRECIPITATION[MM]

0 Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec 1994 - 1998 1947 - 1993

Figure 9: Sum of precipitation for a month at base station B01 (Hellmeier 2001)

Küstermann et al. (2010) listed data about precipitation in Scheyern from 1993 to 2006 (Figure 10). The mean annual precipitation in this period was 824 mm, which is lower than the mean from 1994 to 1998. The average decreased through dry seasons from 2002 to 2006. The annual mean from April to September for the 14 investigated years is approximately 487 mm. 12

Consequently, about 60 % of the annual precipitation fell between April and September.

1200 L/M² 1015 961 966 1000 899 911 901 852 869 786 759 800 728 736 658 625 615 577 600 543 509 502 516 495 468 491 463 474 384 401 400 244

200

0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Annual precipitation [l/m²] Precipitation (April to Sept) [l/m²]

Figure 10: Precipitation data from 1993 - 2006 (Küstermann et al. 2010) The observational network also registered data of air temperature and data about annual global radiation (Figure 11) in the Research Area from 1993 to 2006. In years with a low mean temperature the measurements for the annual global radiance were also not as high as for years with high mean temperatures, for example 2003. The mean annual temperature from 1993 to 2006 is 8,4 °C and the mean from April to September is 14,1 °C. In the recorded period the mean annual global radiation is 1118 kWh/ m². 13

17 1700 16,1 15,8 1600 15 14,7 14,81500 14,1 14,1 14,3 13,7 13,8 13,7 13,8 13,4 1400

13 12,8 [KWH/M²] C]

° 12,5

[ 1305 1300 1247 11 10,8 1198 1200 1128 1111 1116 1112 1102 1094 1096 1101 1086 1100 TEMPERATURE TEMPERATURE 9 9 8,7 988 8,5 8,6 8,6 8,4 8,5 8,5 1000 971 8,1 7,8 7,7 7,5 900 7 ANNUAL GLOBAL RADIATION 6,3 800

5 700 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Mean annual temperature [°C] Mean temperature (April to Sept) [°C] Annual global radiation [kWh/m²]

Figure 11: Mean temperature and annual global radiation (1993 - 2006) (Küstermann et al. 2010) Hellmeier (2001) calculated the potential evapotranspiration (ET) over grass according to Haude (Figure 12). The mean annual potential ET was 623 mm in the investigation period. In July and August the highest evaporation with a mean of 108 mm and 106 mm was recorded, in comparison to 8 and 10 mm in December and January. With plant-specific coefficients for winter wheat, maize and sugar beets a comparison between the different land use types is possible (Figure 12). The ET for winter wheat is negligible higher than for grass, the ET for sugar beets and maize however, are significantly lower. Yet it has to be considered that sugar beets and maize are not seeded before spring, therefore the winter term is included in the calculation as fallow land. In reality, winter catch crops were seeded at all times and the land was not fallow, so the factual ET is higher. About 82 % of the potential ET for winter wheat occurred during the summer term and only about 77 % for grass, maize and sugar beets.

140

120

100

Nov Dez Jan Feb Mar Apr May June July Aug Sept Oct POTENTIAL POTENTIAL EVAPOTRANSPITATION [MM] Grass 12 8 10 21 29 61 92 95 108 106 53 28 Winter wheat 7 5 6 13 25 59 103 121 127 113 35 18 Maize 9 7 8 18 23 38 57 62 76 77 42 23 Sugar Beet 10 8 9 19 26 42 63 69 85 86 46 25 Figure 12: Potential Evapotranspiration for a month at base station B01(1993/1994 – 1997/1998) (Hellmeier 2001) The difference of precipitation and ET is defined as the mean annual discharge. In the investigation period from 1993 to 1998 the recorded annual average was 232 mm. The mean maximum was between October and December, when a discharge from 41 to 69 mm occurred. In the mean vegetation period between April and August often deficits of discharge in the water balance were recorded (Figure 13).

Oct 44 Sept 18 -18 Aug July 0 June 5 -20 May Apr 5 Mar 32 Feb 27 Jan 28 Dec 69 Nov 41 -30 -20 -10 0 10 20 30 40 50 60 70 80 DISCHARGE [MM]

Figure 13: Mean discharge (1993/1994 - 1997/1998) (Hellmeier 2001)

4.2. Hydrology

4.2.1. Overland runoff, infiltrated and dischargeable precipitation and soil water balance The mean overland runoff between 1993/ 94 and 1997/ 98 was 17 mm per year for the investigated acres A3, A9, A16, A17, A18 (Figure 2, page 3). The measured values in Table 5 15 contain the whole overland runoff and the part of interflow which is already above the level of the on-site preflooder. The highest overland runoff was registered for acre 17 with an average of 33 mm per year. A 17 is located in a medium slope position with a soil of slope-clayey loess loam. The lowest runoff was recorded at acre 3 which is located on a crest with sandy molasse (Hellmeier 2001).

Table 5: Overland runoff (with a small part of interflow) 1993 - 1998 [mm/a] (Hellmeier 2001)

A 3 A 9 A 16 A 17 A 18 1993/ 94 0,2 15 36 89 41 1994/ 95 0,2 20 10 28 14 1995/ 96 7,3 17 17 19 8,1 1996/ 97 0,1 12 2,2 17 5,2 1997/ 98 0,1 22 4,8 9,5 22 Mean 1,6 17 14 33 18

The mean infiltrated and dischargeable precipitation from 1993/ 94 to 1997/ 98 on all locations is 287 mm per year. The calculated values for the infiltrated and dischargeable precipitation depend on evapotranspiration infiltration and storage capacity. Sandy molasse, for example, shows a mean of 368 mm per year (Table 6) which can be explained through a significantly low ET, a high infiltration rate and a low storage capacity in comparison to quartiary locations with approximately 260 mm per year. The values were calculated with a method for a calculation of groundwater formation dependent on land use and soil characteristics by Renger & Strebel.

Table 6: Infiltrated and dischargeable precipitation [mm/a] (Hellmeier 2001)

Sandy Sandy/ Pseudogley- Slope-clayey Loess Colluvium molasse clayey loam brown loess loam loam earth 1993/ 94 461 338 345 287 315 303 1994/ 95 382 292 316 279 287 276 1995/ 96 324 250 268 240 247 235 1996/ 97 291 207 231 195 202 190 1997/ 98 384 292 320 291 284 272 Mean 368 276 296 258 267 255

The total runoff, compromising overland runoff and infiltrated, dischargeable precipitation, comes to a mean of 301 mm per year. The highest values occurred for sandy soils with about 370 mm per year and the lowest on the colluvium location with about 271 mm per year. 16

The soil water balance represents the temporal change of water content in the soil through adsorption, storage and release. For this, the absolute water content in the individual soil layers and also the relative water content in comparison to the field capacity is significant for the evaluation of water move in the soil. There are great differences in the water contents for different soil types from 1994 to 1998 (Table 7). On the sandy molasse acre for example there is a considerable difference between the acre and the fallow land visible. In about 100 meters depth underneath the acre there is a substantial change in the water content which can be interpreted as an existing clay lens. Underneath the fallow land obviously n clay lens exists. The soil underneath the acre of sandy molasse shows generally higher water contents than underneath the fallow land. Compared to this, the values for the sandy-clayey loam on acre and slope do not differ as much. The water content on the slope is generally higher and more equally spread over all horizons whereas the content underneath the acre increases slowly with depth (Table 7).

Table 7: Mean water contents [%] from 1994 to 1998 (Hellmeier 2001)

Depth Sandy Sandy Sandy- Sandy- Pseudog. Slope- Loess Colluv. [cm] molasse molasse clayey clayey -brown clayey loam loam loam earth loess loam Acre Fallow Acre Slope Acre Acre Acre Acre 10 27 32 28 34 27 32 32 28 20 32 29 31 34 27 32 32 31 50 41 25 35 35 31 35 34 39 90 41 27 37 41 42 39 30 43 130 25 31 42 43 41 44 36 39 180 17 20 40 42 66 43 37 44

To sum up, for all soil types except for sandy molasse, the top soil contents the least part of water. Generally, the water content strongly depends on accumulation layers and on the position on the slope. In soil parts with a strong root network there is also often a low water content recorded when there is no accumulation layer (Hellmeier 2001).

4.2.2. Bypass – and matrix flows in the unsaturated zone Subsurface water is generally differentiated between adhesive and percolate water. The part of subsurface water which is hold against the impact of gravity is called adhesive water and is subdivided into adsorption and capillary water. Subsurface water which flows downward under impact of gravity is called percolate water. Within the process of seeping, adhesive water is 17 replaced trough percolate water and also becomes to percolate water. When the percolate water leaves the unsaturated zone, it becomes part of the groundwater. The soil above the groundwater level is called capillary space and is subdivided into an open and closed part. In the closed capillary space, every capillary pore is completely filled with water and therefore a lack of oxygen and reductive conditions are present. Water in the unsaturated zone is above the groundwater level, is capillary ligated and the pressure is beneath 1 atm. The hydrological balance of a soil is essentially influenced by the pore space and the pore size distribution where coarse, medium and fine pore spaces can be found (Table 8). In the tight coarse pores and in the medium pores, it is possible to store plant accessible water, whereas water in fine pores is mostly not accessible. The usable field capacity indicates the difference between the field capacity (FK) and the permanent wilting point (PWP) and consequently the useable store capacity of a soil layer. If there is movement in the percolate water it is a consequence of a gradient in the matrix potential. For water contents below the field capacity it is significantly lower than above and therefore percolation processes are usually defined for processes above the field capacity (Schneider 2001).

Table 8: Relation between soil moisture tension and pore size (Schneider 2001)

Soil Equivalent Pore Subsurface Store capacity division moisture pore designation water division tension diameter sector: PF [훍m] [lg cm Ws] < 1,8 > 50 Wide coarse Fast moving Soil porosity/ store pores percolate water capacity for groundwater and accumulated water 1,8 – 2,5 50 – 10 Tight coarse Slow moving pores and plant Usable field capacity 2,5 – 4,2 10 – 0,2 Medium pores available percolate water

Permanent wilting point (PWP)

> 4,2 < 0,2 Fine pores Not plant Wilting moisture available adsorption water

4.2.2.1. Quantification of percolating water Schneider (2001) conducted six tracing experiments in the Research Area with the chemically stable isotope of water, Deuterium (2H), and three tracing experiments with fluorescence substances (eosin, pyranine, uranin). All of them are non-reactive hydrological tracers which show a representative behavior for the mean water movement. The experiments started in June 1994 and ended in October 1998 and registered tertiary gravel sands and quartiary loess loams.

All markings were implemented very close to a stilling well except those around S 14 (Figure). Kommentiert [LR1]: Put in figure Schneider page 44 The samplings of the isotopic tracer were taken with pore waters samplers for different time intervals. Since Deuterium can evaporate quite easily and bypass-flows remarkably occur in sediments with high water saturation, all percolating water tracing experiments were conducted in a time period with snowmelt or after precipitation events in the winter. The Deuterium experiment was conducted to evaluate how deep the percolate water infiltrates into the soil trough measurements of tracing substance quantity. The three experiments with fluorescence tracer were conducted in the dry valley in Scheyern (S 14, Figure). The aim was to investigate the retention time of the ground- and percolating water in this part of the Research Area. The groundwater flow velocity was estimated with radiometric drilling measurements and is approximately 0, 5 m/ d. Therefore the water will need about 600 days to pass the sampling locations which are in about 300 meters distance located from where the tracers were added. However, even 2, 5 years after the implementation no tracers could have been measured. Probably the surface near added tracers did not reach the water resources which are sampled. The percolating water with tracer supposably needs to pass a deep unsaturated zone before a lateral transport can occur.

Through the experiments with Deuterium it was investigated that the percolating water movement is inhomogeneous and that the spatial distribution of the concentrations cannot be described with the Gaussian distribution. The depth range of bypass-flows was initially evaluated through the change of 2H content (Table 9). Furthermore, visual bypass-flows which can be proven through mass transport as a consequence of water content changes (precipitation events) different depth ranges were determined (Table 10). Obviously there are differences in the measured depth ranges. In location 2 – 17, 2 – 18 and 9 – 3 the depth range of bypass-flows evaluated through the tracing experiment with Deuterium is significantly higher. In these locations, the tracers were added in the snowmelt period and therefore different hydraulic conditions dominated compared to the summer term. In 12 – 9 and 13 – 16, the measured depth ranges after heavy rainfall events in the summer are considerably higher than the measurements 19 through the tracer experiment. The concentration of the tracing fluid must have been too low in this case because the water content measurements indicated a deeper range of bypass-flows.

Table 9: Visual depth ranges of bypass-flows (Schneider 2001)

Location Visual depth range of bypass-flows Time of added tracer 2 – 17 130 cm January 1995, January 1996 2 – 18 130 cm January 1995, January 1996 9 – 3 180 cm January 1995 12 – 9 20 cm February 1997 13 – 16 50 cm February 1997

Table 10: Depth range of visual bypass-flows through water content changes (Schneider 2001)

Location Depth of bypass-flows through water content changes 2 – 17 50 cm 2 – 18 90 cm 9 – 3 90 cm 12 – 9 90 cm 13 – 16 130 cm

Moreover, the experiment indicated that Deuterium appears in all depths of the effective root space spontaneously after the marking. The estimated flow velocity of the bypass-flows is between 10 and 50 cm/ d.

4.2.2.2. Infiltration after heavy rainfall events Infiltration is the intrusion of surface water into the soil after precipitation events. The infiltration rate is defined as the volume of water that infiltrates into the soil in a unit of time and is depending on different factors. It is subdivided into the infiltration of micro- and macro pores. For the analysis of infiltration after heavy rainfall events it is essentially to evaluate movement of humidity into the soil as well as the proportion of precipitation that contributes to runoff and interflow. Therefore a balancing of the soil water in comparison to precipitation and evaporation is suitable. The difference of precipitation and evapotranspiration is calculated and is defined as climatic water balance (N-ETA). To evaluate infiltration, overland runoff and interflow, five funnels were installed on the Research Area (Figure 7, page 8) each located at different acres with different land use, soil type and topographic location (Table 2, page 9). Altogether eight arable land types were investigated. 20

In the investigation period from winter 1996 to summer 1998 two heavy rainfall events with a precipitation over 50 mm/d happened. The first of the two events started on 18.07.1997 at 4 pm and lasted for 25 hours. Measurements were recorded on the 18.7. and 19.07.1997. The second event started on 11.06.1998 for 29 hours until the 13.06.1998 but recordings are only available from 3 am to 8 am of 12.06.1998 and at location 2 – 17 and 13 – 16 because of problems with the measurement equipment. Therefore a direct comparison between the rainfall events in 1997 and 1998 is only possible for the locations 2 – 17 and 13 – 16. For the tertiary location 13 – 16 the ratio of overland runoff and interflow to the precipitation varies a lot. In July 1997 only about 6% of the precipitation became to overland runoff and interflow, whereas about 34% of the precipitation was transported as interflow in 1998 (Table 11). Responsible for these extremely different hydraulic soil conditions at the beginning of the heavy rainfall events were different crops. In 1997 where winter wheat was grown, the soil water storage needed to be filled up again before interflows occurred. The soil with grain maize in 1998 obviously did not need to backfill the soil water storage. On the tertiary crest location of funnel 9 and 12, practically no overland runoff did occur (Table 11). In the tertiary location of funnel 2 as well as in the tertiary crest location of funnel 12, lateral runoffs are expected because of the low overland runoff that has been measured. Furthermore eye-catching is the difference between location 9 – 3 and 9 – B in 1997. On the one hand, only about 15 % of the precipitation became to interflow at 9 – 3 on the other hand 70% at location 9 – B. For the fallow land a very rapid deep infiltration must be assumed. If the increase of the subsurface water column (∆θ) is after the heavy rainfall event distinctly lower than the climatic water balance (N-ETA), overland and lateral runoffs have occurred (Schneider 2001).

Table 11: Balance after heavy rainfall events for 1997 and 1998 (Schneider 2001)

Location Crop N [L] N- ∆θ [L] q0+qinter q0 [L] qN ETA [L] [%] [L] 1997 2 – 17 Potato 78 76 35 40 5,5 51 13 – 16 Winter 78 75 71 5 1,0 6 wheat 9 – 3 Lucerne- 83 79 67 13 0 15 clover- grass 9 – B Fallow 83 80 22 58 0 70 land 12 – 9 Winter 78 75 55 21 0 26 wheat 21

12 – R Lynchets 78 76 50 26 0 33 1998 2 – 17 Winter 71 62 37 25 0,1 36 wheat 13 – 16 Grain 71 65 41 24 04 34 maize N: sum of precipitation; N-ETA: sum of climatic water balance; ∆θ: maximum difference of subsurface water column; q0+qinter : overland runoff + interflow; q0: overland runoff; qN: Ratio of q0+qinter to precipitation

4.2.3. Classification of outflow components Von Loewenstern (1998) evaluated different methods from 1993 to 1995 to classify the runoff components during a precipitation event. The measurements were undertaken at two water gauges at a creek with two different catchment areas (Figure 14). The measurement location BW1 (100 % forest) is situated outside of the research area, so consequences of land use changes were only expected at BW 4 (90 % forest, 10 % agricultural land use).

Arable land Grassland 22

Figure 14: Location of the measurement stations (Loewenstern 1998) With a chemical-physical method (electrical conductibility) in combination with an isotope method, all three runoff components, overland runoff (QO), interflow (QI) and base runoff (QB), can be recorded separately with an accuracy of ± 10 % (Table 12). The overland runoff components which are recorded through electrical conductibility can be considerated as representative since approximately about 60 % of all rainfall events have been evaluated. Only about 34 % of all events could have been interpreted with the isotope method, therefore the measurements for interflow and base runoff can only be considered as an approximation.

Table 12: Percentage of runoff components determined with the chemical-physical and isotope method (1993 - 1995) (Loewenstern 1998)

Time period BW 1 BW 4

Ø QO Ø QI [%] Ø QB [%] Ø QO Ø QI [%] Ø QB [%] 23

[%] [%] Winter term 5,7 10,1 83,2 7,0 10,2 82,1 ’93-‘95 Summer term 7,9 9,4 82,7 6,4 10,5 83,7 ’93-‘94 Ø total time 6,9 9,8 82,9 6,7 10,3 83,0 period

Von Loewenstern (1998) also took measurements with two hydrograph methods (Best-fit graph and AUL-Method) which make it possible to separate direct runoff (overland runoff and interflow) and indirect runoff (base runoff). Almost 100 % of all precipitation events and dry periods were recorded, however, an analysis of individual rainfall events cannot be conducted with these methods. Measurements with the Best-fit graph method calculate the mean storage volumes of particular water spaces of runoff components (Table 13). With the AUL-Method the effective water quantity for direct and indirect runoff can be computed (Table 14).

Table 13: Percentage of runoff components determined with the Best-fit graph method (July 1992 – October 1993) (Loewenstern 1998)

Time period BW 1 BW 4

Ø QDir [%] Ø QIndir [%] Ø QDir [%] Ø QIndir [%] Winter term ’93 3,6 96,0 11,0 89,0 Summer term ’92-‘93 9,7 90,0 13,3 86,0 Ø total time period 6,7 93,0 12,2 87,6

Table 14: Percentage of runoff components determined with the AUL-Method (July 1992 - October 1993) (Loewenstern 1998)

Time period BW 1 BW 4

Ø QDir [%] Ø QIndir [%] Ø QDir [%] Ø QIndir [%] Winter term ’93 20,0 80,0 19,0 81,0 Summer term ’92-‘93 31,0 69,0 41,0 59,0 Ø total time period 26,0 74,0 29,0 71,0

A comparison between the results of the chemical-physical and isotope method and the best-fit graph method is not possible since the first method calculates the mean runoff quantity and the hydrograph method represents the mean runoff store capacity. Accordingly a comparison is only possible for trends of the direct and indirect runoff components to each other. The results of the AUL-Method however, can be compared to both of the other measurement methods. Even though the measurements were taken in different time periods, the results for the hydrologic 24 winter terms match quite well. The results for the hydrologic summer term however, do not correspond as well. The hydrograph AUL-Method apparently overestimates the direct runoffs for the summer term, given that these results are distinctly higher than the results for the other two methods (Table 12 - Table 14). An advantage of the AUL-Method is that the increase of the base runoff below the runoff peak is considered. However, for sequent rainfall events in a short period of time the base runoff graph, below the total runoff graph, has to be added discretionary. This disadvantage may be the reason for the underestimation of the base runoff in the summer term for the measurements with the AUL-Method. Furthermore, the results indicate that the differences between the land uses in BW 1 and BW 4 do not have a huge impact on the runoff components. Since the agricultural land use influences only about 10 % of the runoff catchment for BW 4, there is no significant difference between the two land uses in the catchments.

4.2.4. Hydrological models: NASIM and BROOK 90

4.2.4.1. NASIM The hydrological model NASIM calculates the individual hydrologic balance components spatially distributed for parts of a catchment area in a daily resolution. Through a higher territorial reference and with this it’s decreasing amount of available data, the soil water store is simplified presented through a conceptional backup method with only one or two horizons. The accumulation of interflow is dependent on the level of saturation of the individual soil layers whereat the supreme soil layer is the most important (Zimmermann 2002).

4.2.4.2. BROOK 90 The site model BROOK 90 is a lumped-parameter model and calculates with a mean set of parameters for the considered area. It can be used for small homogenous catchment areas as well as for individual locations. The BROOK90 model subdivides the subsurface water flow into a matrix - and a macro pore flow. The matrix flow is simulated with the RICHARDS- equitation for up to ten soil layers. The macro pore flow however is represented as a quick bypass-flow in different soil depths. Depending on saturation and gradient, fast and slow lateral flows which generate the interflow are simulated in the individual soil layers. Thus the runoff generation in the soil is significantly more precisely presented in comparison to the NASIM model (Zimmermann 2002). 25

4.3. Soil

4.3.1. Erosion and surface degradation as a consequence of outflow Through overland runoff and especially during individual heavy rainfall events dissolved and particulate substrates are transported into surface waters. Since the intensification of agriculture provokes an increase of erosion and excess of fertilization, the surface degradation into surface waters becomes more important. Particularly in sloping locations with missing or fragmentary land cover, significant soil erosion through water combined with capping can occur. The precipitation cannot infiltrate anymore due to capping and therefore overland runoff increases which leads to surface degradation. Through a consequent preserving soil cultivation in combination with catch crop cultivation and mulch seeds, soil erosion can be diminished effectually (Wechselberger 2000). A successful reduce of surface degradation for the organic as well as for the integrated farming system could have been realized mostly through changed field division (size, form) and area rededication (Figure 15) (Weigand et al. 1996).

18 Changed field division Before: 16,1 t/ha*a 16 7% Area rededication 14 Changed crop rotation 31% 12 Reduced soil tillage 10 17% 8

6 Before: 5,1 t/ha*a 17% 4 45% 36%

2 36%

0 11% Integrated Farming Organic Farming

After: 1,7 t/ha*a After: 1,6 t/ha*a

Figure 15: Causes and ratios of individual measures on the reduction of soil degradation through restructuring and implementation of integrated and organic farming (Weigand et al. 1996) In 1993 the FAM built up a measuring network to monitor continuously the overland runoff and surface degradation on about 45 % of the agricultural area in the research area. The investigations aimed to evaluate the dependency of matter transport through overland runoff on the agricultural land use and the methods to reduce the matter transport. In 1997 the overland runoff was one-tenth of the runoff in 1993 and also the surface degradation decreased to a twentyfold compared to 1993 (Table 15). 26

Table 15: Trend of overland runoff, surface degradation and dissolved P (1993-1997) (Auerswald, Schwertmann 1999)

Year Precipitation Runoff Surface degradation Dissolved P (PO4- [mm/a] [mm/a] [kg/ ha*a] P) [kg/ ha] 1993 948 30 1700 0,14 1994 863 32 170 0,10 1995 766 9 24 0,04 1996 711 11 76 0,14 1997 655 3 11 0,01 1998 918 15 248 0,07

It seems that the restructuring to a resource-saving and sustainable agriculture shows good consequences. 1998 has been the year with the highest annual precipitation since 1993 and also with more rainfall than the mean 30-year precipitation. The surface degradation stayed despite of high and disadvantageous distributed rainfalls in 1998 significantly below the degradation in 1993 and earlier. Within this precipitation in 1998, the trend of decreasing annual precipitation since 1993 has been broken up and so the first time the success of the change to a resource- saving and sustainable agriculture could be assessed. Also the decline of overland runoff is an achievement of the conversion to a soil protecting land use. However, the discharge of phosphate did not change considerably. Due to its high concentrations in the overland runoff it poses a risk for surface waters but through good buffer properties of the soil the phosphate concentrations do not decrease even for long lasting overland runoff. It could not have been determined in this investigation project how the phosphate concentrations can be influenced effectively (Auerswald, Schwertmann 1999).

4.3.2. Soil agglomeration Main reason for agglomeration of agricultural soils is the mechanical pressure through machines, devices and transport vehicles. High wheel loads increase the force of wheel pressures and also overrun frequency, wheel slip, and speed and soil moisture pose factors for rising agglomeration. Basically there is no coherence between the size of field divisions and soil agglomeration but as a general tendency agglomerations occur rather on small divisions than on bigger ones because of a higher turning point rate and consequently a higher overrun frequency. Otherwise are bigger and heavier machines on bigger divisions more common. Soil agglomerations can cause a worse fertilizer processing and/ or less harvest and therefore economical losses. The FAM designed the research area in 1992 with regard to prevention of soil agglomeration. The field divisions were sectored in terms of erosion vulnerability and agglomeration sensibility. Field paths were set up to build field sizes suitable for transport and machine capacities. Furthermore, fieldwork is limited to time periods with optimal navigability 27 dependent on soil moisture (Wechselberger 2000).

4.3.3. Measuring of soil water content with Ground-Penetrating Radar (GPR) and EM38 For an implementation of a resource-saving and sustainable land use a knowledge of the variation of soil type and soil moisture is necessary. To cultivate an agricultural area site- specific, georeferenced maps with these soil properties with a resolution in the meter range are required. With Ground-Penetrating Radar (GPR) it is generally possible to measure the water content in the root zone of the soil. To create the model numerical simulated calculations for the spread of electromagnetic fields for several vertical water content distributions were implemented. The development of a measurement method for georeferenced determination of the electrical conductivity ECa of a soil was based on the electromangetical measurement device

EM38. With this it was investigated how far the ECa can give information about the clay and water content.

Ground-Penetrating Radar (GPR) The numerical simulated calculations were conducted with the program REFLEX. Based on 2- D layer models a simulation of electromangetical wave propagation was set up with a finite difference method. It was investigated that in a medium similar to a two-layer model mean or integral speeds and water contents cannot be determined. The measured speeds of electromangetical waves represent the electric characteristics of either the first or the second layer but never of both layers together. It depends on the spatial distribution of the electrical characteristics and on the frequency which of the speeds can be recorded. In any case multi- frequency GPR-measurements are necessary to make an explicit statement about the spatial especially about the vertical variation of the soil water content.

EM38 The georeferenced measurement method can plot the mean electrical conductivity up to 1,5 m soil depth with a lateral resolution of 5 m quickly (10 – 100 ha/d). In the research area the electric conductivity is most of all dependent on clay content, water content and soil temperature. The clay content in the investigated soils varied between 3 – 60 %. Measurements in the spring and summer show that the crop influences the electrical conductivity of the soil through its specific water use. However, during measurements in the range of the field capacity about 92 % of laboratory samples showed a relation between the ECa and the clay content. A transfer function was developed that can estimate the clay content dependently on the electrical conductivity. For a correction of the soil temperature an equation was set up to adjust the influence of the soil temperature on the ECa. In order to evaluate the water content in the soil a 28 mapping of the ECa under field capacity is necessary because the variability of the clay content needs to be investigated before. With this a georeferenced map of soil characteristics is available and can be used as a base for site-specific farming (Stanjek et al. 1999).

4.4. Nitrogen cycle

4.4.1. Nitrogen input

4.4.1.1. Nitrogen concentration in rainfall From January 1993 to May 1995 von Loewenstern (1998) recorded the solution concentrations of nitrate in the precipitation in Scheyern at climate station S1006 (Figure 14, 4.2.3). The mean - concentrations of NO3 in the precipitation in Scheyern are generally quite low (Table 16) and the concentrations in the summer term (ST) are generally slightly lower than in the winter term - (WT). However, individual rainfall events contain a significantly increased NO3 concentration, for example in May 1993 with 15, 80 mg/l (Figure 16) or in April 1994 with 13, 94 mg/l (Figure 17).

- Table 16: Mean NO3 concentration in precipitation at S1006 in 1993 and 1994 (Loewenstern 1998)

Ø ST 1993 Ø WT 1993 Ø ST 1994 Ø WT 1994 Ø 1993 - 1994 - NO3 [mg/l] 3,86 3,75 3,24 2,37 3,31

- NO3 in precipitation in 1993 [mg/l] 18 16 14 12 10 8 6 4 2

31.01.1993 22.03.1993 11.05.1993 30.06.1993 19.08.1993 08.10.1993 27.11.1993 16.01.1994 12.12.1992 Figure 16: NO3- in precipitation in 1993 at climate station S1006 (Loewenstern 1998)

- NO3 in precipitation in 1994 [mg/l] 16

27.11.1993 16.01.1994 07.03.1994 26.04.1994 15.06.1994 04.08.1994 23.09.1994 12.11.1994 01.01.1995 20.02.1995

Figure 17: NO3- in precipitation in 1994 at climate station S1006 (Loewenstern 1998)

4.4.1.2. Nitrogen fertilization

4.4.1.2.1. Consequence of fertilizer for growth of plants To evaluate the effect of N-fertilizer on the growth of plants, an experiment in the integrated farming part of the research area was conducted. Kainz et al. (1995) analyzed the effects of soil cultivation, regulation of wild herbs and fertilization in the Scheyern catchment in 1994. In total potatoes, winter wheat, corn and winter wheat (crop rotation) where investigated with regard to three different soil cultivations, three N-fertilizations and three Fungicides. Each experiment was repeated three times. For the growth of winter wheat it was an advantage if harvest remains from the previous crop granary corn was available. This can increase the utilization of available nitrogen and can be used for the following winter wheat (Figure 18). Winter wheat following after potatoes generally produces less output than winter wheat with previous crop granary corn (Figure 19). However, soil cultivated by plough generates the highest produce of winter wheat (previous crop potatoes) whereas this applies for soil without any processing for winter wheat (previous crop corn granary) (Figure 18 and Figure 19). 30

T/ HA 10,0 Winterwheat previous crop: granary corn 8,0

6,0

4,0

2,0

0,0 no processing rototiller plough 90 kg N/ha 135 kg N/ha 180 kg N/ha

Figure 18: Effect of soil cultivation and N-fertilization on the harvest of winter wheat (Kainz et al. 1995)

10,00 T/ HA Winterwheat previous crop: potatoes 8,00

6,00

4,00

2,00

0,00 no processing rototiller plough

90 kg N/ha 135 kg N/ha 180 kg N/ha

Figure 19: Effect of soil cultivation and N-fertilization on the harvest of winter wheat (Kainz et al. 1995)

For the cultivation of corn maize is significant that the available nitrogen is utilized better than from winter wheat since the output is substantially higher (at least 20 t/ha higher). The best results can be achieved with soil cultivation by rototiller but the variability of output quantity is relatively low (Figure 20). Potatoes also generate the highest amount of harvest without any soil processing similar to winter what with previous crop granary corn. Nitrogen fertilizer seems to support the growth of potatoes intensely even if the applied amount of fertilizer is lower than on the other crops (Figure 21).

However, the application of N-fertilizer increases the growth of all three crops even though the efficiency of the used fertilizer decreases. Especially for corn maize the efficiency declines significantly. 31

T/ HA corn maize 15 previous crop: winter wheat

0 no processing rototiller plough 65 kg N/ha 105 kg N/ha 135 kg N/ha

Figure 20: Effect of soil cultivation and N-fertilization on the harvest of corn maize (Kainz et al. 1995)

T/ HA potato 60 previous crop: winter wheat 50

0 no processing rototiller plough 50 kg N/ha 100 kg N/ha 150 kg N/ha

Figure 21: Effect of soil cultivation and N-fertilization on the harvest of potatoes (Kainz et al. 1995)

Since these experimental results were only recorded in 1994, more data needs to be evaluated to form a long-term study. Nevertheless the efficiency of N-fertilizer seems to decrease with the amount of applied fertilizer yet no data about the growth without fertilizer is available so a comparison between a small amount of applied fertilizer and no fertilization is also not possible.

4.4.1.2.2. Fertilization strategies and nutrient uptake for winter wheat Fertilization strategies are essentially for nitrogen saving and increase of harvest but often developed strategies only apply for an individual soil type. In practice this is often problematically since the estimation of nitrogen need is time-consuming and complex. Therefore the development of fertilization strategies for an uncomplicated and quick estimation of used fertilizer is reasonable.

Experiments concerning the improvement of nitrogen fertilization for winter wheat in terms of economic and ecological aims were conducted in land parcels in two different field divisions (brown earth from loess loam and brown earth from tertiary sand) with different soil conditions. 32

In total nine fertilization modifications were tested with four repeats. The fertilization modifications differ in the amount of added nitrogen in different growth periods (Table 17). The different fertilization modifications lead for both locations to a nearly same gradation of harvest but on sand the outcome was about 10 % lower than on the loam soil (Table 18). However, the stocking density is 355 ears/ m² on an average for sand in contrast to 492 ears/ m² in the other location. The higher rate of seeds per ear for winter wheat plants growing on sand yet cannot compensate the lower output through the lower stocking density. To attain the particular maximum yield, the amount and scheduling of the first implementation of fertilizer was decisive and not the absolute amount of fertilizer. Fertilizer that was applied at the beginning of vegetation up to 40 kg N/ ha and in total up to 140 kg N/ ha generated a significantly increasing output (Modification 1 – 4, Table 18) for both locations. The addition more fertilizer at the beginning of vegetation (modification 6 and 9) did increase the harvest on loam soil in direction whereas on sandy soil no change was recognizable. Also higher fertilizer amount added in the vegetation periods EC 32 and EC 49 did not boost the yield on sandy soil (modification 4 and 9). With modification 4 the approximately same amount of harvest was generated as with modification 7 for both soils. Unlike for loess loam modification 7 with a splitted addition of fertilizer performed better on the tertiary sand than modification 8 with a retarded addition. Modification 5 however, did not bring success on one of the locations (Table 18).

Table 17: Growth staging systems for wheat (Simmons et al. 2015)

Code Description VB Germination EC 25 Tillering: Main shoot plus 5 tillers EC 32 Stem elongation: 2nd node detectable EC 49 Boot: first awns visible

Table 18: Fertilization modifications and profit structure (Maidl, Fischbeck 1995)

Sand Fertilization (kg N/ha) Biomass Corn Ears/ Seeds/ Modif. VB EC25 EC32 EC49 Total [t/ha] [t/ha] m² ear 1 0 0 0 0 0 10,58 4,92 299 36,1 2 25 25 25 75 14,91 7,28 341 42,6 3 35 35 40 110 16,56 8,14 339 47,7 4 40 50 50 140 18,01 9,08 350 50,4 5 40 50 50 140 16,62 8,09 372 43,7 6 70 50 50 170 18,83 9,10 376 47,1 7 40 30 50 50 170 18,14 8,84 345 48,8 33

8 70 50 50 170 19,41 8,58 408 43,2 9 70 70 60 200 18,09 8,78 366 46,3 Mean 16,79 8,09 355 45,1

Loam 1 0 0 0 0 0 13,3 5,84 345 33,3 2 25 25 25 75 18,19 8,48 434 38,7 3 35 35 40 110 19,05 8,80 446 39,6 4 40 50 50 140 20,51 9,65 497 39,9 5 40 50 50 140 20,58 8,84 502 35,8 6 70 50 50 170 22,53 9,99 546 37,4 7 40 30 50 50 170 21,56 9,52 542 36,7 8 70 50 50 170 21,84 9,74 541 39,7 9 70 70 60 200 23,01 10,12 571 38,3 Mean 20,06 9,00 492 37,7

As nitrogen is added through the fertilizer into the soil in different modifications and amounts it can be investigated how much the plants can take up. For both sand and loam soils the nitrogen withdrawal through seeds of the winter wheat and straw which serves as soil coverage was higher than the nitrogen take was put in through the fertilizer (Figure 22 and Figure 23). The difference of the nitrogen balance decreased with rising addition of fertilizer. Only for modification 9 on sandy soil the nitrogen balance was compensated (Figure 22). With a higher amount of added nitrogen the withdrawal through straw also increased, on sand about 11 – 14 % of nitrogen was absorbed by straw and on loam about 16 – 20 %. In total the nitrogen balances on loam and sand do not differ significantly for modification 1 to 4 whereas the N-balance is about 20 % lower on loam for modifications 5 to 9. 34

KG N/HA 300 Sand 250

200

150

100

-50

-100 1 2 3 4 5 6 7 8 9 N-withdrawal through seed N-withdrawal through straw N-balance (fertilization-N-withdrawal-seed)

Figure 22: N-withdrawals and N-balance of sand soil (Maidl, Fischbeck 1995)

KG N/HA 300 Loam 250

200

150

100

-50

-100 1 2 3 4 5 6 7 8 9 N-withdrawal through seed N-withdrawal through straw N-balance (fertilization-N-withdrawal-seed)

Figure 23: N-withdrawals and N-balance of loam soil (Maidl, Fischbeck 1995) The experimental results concerning fertilization strategies for winter wheat on two different soil types lead to the conclusion that varying amounts and spread of nitrogen fertilizer are not necessary to achieve high outputs and an optimal fertilizer utilization. On both locations similar amounts of harvest and nitrogen withdrawals through the seeds were realized and the withdrawal nearly always exceeded the amount of fertilized nitrogen (Maidl, Fischbeck 1995).

4.4.2. Nitrogen emissions

4.4.2.1. N2O release during denitrification and from soil Nitrous oxide (N2O) results in high amounts through the microbial processes of denitrification and nitrification. The rate of denitrification is dependent on the store of easily soluble nitrogen 35 compounds in the soil and is therefore influenced by additions of mineral fertilizers. Especially nitrate is involved in the microbial process of denitrification and therefore its rate defines considerably the potential overall loss of nitrogen. Nitrous oxide is an obligatory intermediate product of the denitrification process and is formed as well as consumed. Furthermore, NH4+ is nitrified mostly into NO3-. N2O can arise here of NO2- is used as an electron acceptor and is reduced to N2O and N2 (Figure 24). So N2O emissions also occur within the use of organic fertilizers. Through storage of slurry and solid manure and also in soils N2O-N losses emerge in anaerobic conditions. The loss of N2O-N is furthermore dependent on temperature, frost- and tau-cycles, and rate of microbial degradable matter in the soil and availability of aerobe conditions.

Atmosphere Soil surface

N2O Gas phase

N2O Liquid phase

+ Denitrification NH4 NO− + 2H+ + 10 [H] → N + 6H O Nitrification 3 2 2

+ − + NH + O2 → 1,5 NO + 2H + H2O 4 2 − − − NO3 NO2 + 0,5 O2 → NO3

Figure 24: Creation, usage and release of N2O in soils About 80 % of all anthropogenic nitrous oxide emissions can be lead back to agricultural activities and more than 50 % of this descend from soils. The increase of these emissions from soils in the last decades can be traced back particularly to a rise of nitrogen input. Only the input through fertilizers increased from 2 Tg N/a (= 2 Mio. t) in 1930 to 77 Tg N/a in 1990. Compared to this is the natural nitrogen fixation about 80 to 130 Tg N/a. The increase is mainly problematic since the applied nitrogen cannot be totally absorbed by the plants and is therefore 36 lost through erosion, gas emissions, overland runoff and volatilization and poses a risk for the environment. Since more than 50 % of N2O-N emissions are released during winter and Kommentiert [LR2]: Grafik Tonhöhen S.239 einfügen fertilization, strategies to reduce N losses should be developed (Sehy 2004; Wechselberger 2000; Hantschel, Stenger 2001).

Wechselberger (2000) published N2O-losses for three different crops measured in 1995 and 1995 in Scheyern. For potatoes the highest rates of losses were recorded, the losses for corn maize and winter wheat are considerably lower (Table 19). Significant is the higher percentage of loss for the lower nitrogen input. However, the N2O-N loss in kg/ ha is clearly higher expect for winter wheat. The nitrogen losses do not change significantly whereas the N-input is doubling.

Table 19: N2O-N losses for different crops in 1995 and 1996 (Wechselberger 2000)

Crop N-input [kg/ ha] N2O-N losses [kg/ ha] N2O-N losses in % of N-input Potato 50 8,00 15,3 150 16,03 10,5 Corn maize 65 1,77 2,2 135 2,74 1,8 Winter wheat 90 3,01 4,7 180 3,56 2,5

Other measurements about the N2O-N emissions were taken by Munch, Rackwitz (1999) in 1995 and 1996 also with regard to the added amount of nitrogen fertilizer. The emissions from areas with wheat are significantly lower despite of a higher amount of nitrogen input (Table 20). Mentioned reasons for this are the splitted addition of fertilizer as required for wheat which barely leaded to an increase of nitrogen in the topsoil and on the other hand did the one-time application of nitrogen on potatoes cause a considerable rise of nitrate in the topsoil. In the traffic lanes on the field the N2O emissions were also higher because of the strongly compressed soil which is not as good rooted as the rest of the soil.

Table 20: Cumulative N2O-N losses with regard to fertilizer amount (Munch, Rackwitz 1999)

Crop N-input [kg N/ha] N2O-N/ N-input [%] 1995 1995 Potato, without traffic lane 50 9,3 10,6 Potato, with traffic lane 50 n.a. 15,3 37

Potato, without traffic lane 150 3,1 5,5 Potato, with traffic lane 150 n.a. 10,5 Wheat 90 1,3 4,7 Wheat 180 1,2 2,5 Maize 65 n.a. 2,2 Maize 130 n.a. 1,8

Sehy (2004) also evaluated data about nitrogen input and following N2O-emissions. The measurements were taken separately for different land uses and different types of fertilizer. Furthermore, the induction of nitrogen from crop residues is considered (Table 21). The annual emissions of N2O varied between 0, 6 and 2, 9 % of all nitrogen that was put in. The lowest emissions were recorded for Lucerne-clover-grass (LCG) combinations where no fertilizer was used because of a high rate of crop residues and for maize with a low rate of mineral fertilizer. Sandy soil in the organic farming system emitted more N2O-N than loamy soil in the same farming system with the same input of nitrogen. This observation can be compared with 4.4.1.2.2 (page 31) where, however in the integrated farming system, nitrogen uptake trough winter wheat is evaluated. Winter wheat withdraws here less nitrogen from sandy soil than from loamy soil and therefore the N-balance is lower for loamy soil. If N2O-N emissions occur this is only possible for positive N-balances.

Table 21: N-input through fertilizer and crop residues, annual N2O-emission and losses (Sehy 2004)

Main crop N-input [kg/ ha] N2O-N N2O-N/ fertilizer Crop residues total [kg/ N-input Main Catch (ha*a)] [%] crop crop HP Maize 175 (M) 55 63 293 8,9 2,87 HC Maize 150 (M) 55 63 268 7,8 2,72 LP Maize 125 (M) 55 63 243 2,8 0,95 LC Maize 150 (M) 55 63 268 3,7 1,19 OL Lucerne- - 69 - 69 1,5 1,46 clover- grass (LCG) OS LCG - 86 - 86 1,0 0,58 HP Winter 195 (M), 35 262 7,2 2,56 wheat 32 (G) HC Winter 170 (M), 35 237 6,4 2,49 wheat 32 (G) 38

LP Winter 170 (M), 35 237 6,3 2,45 wheat 32 (G) LC Winter 170 (M), 35 237 5,0 1,90 wheat 32(G) OL Winter 42 (G), 35 127 2,3 1,42 wheat 50 (S) OS Winter 42 (G), 35 127 3,3 2,20 wheat 50 (S)

(M): mineral fertilizer; (G): liquid manure; (S): farmyard manure

HP: high yield area, conventional farming; HP: high yield area, precision farming; LC: low yield area, conventional farming; LP: low yield area, precision farming; OL: organic farming, loamy soil; OS: organic farming, sandy soil

The relation between N-input and the annual N2O-emissions are analyzed graphically by Sehy (2004) (Figure 25). Obviously there is a positive correlation between the two factors that can be described by the function y = −0,89 + 0,0272x. The square of the ordinate is for y = −0,89 which can be interpretated as the netto N2O adsorption on non-fertilized areas.

Figure 25: Correlation between annual N2O- emissions and N-input for two investigated years (Sehy 2004) However, this value was not measured for non- fertilized areas in Scheyern and therefore it is rather a term for uncertainty factors such as

N-input [kg N/ ha] weather, location and land use. The calculated emission factor of 2, 72 % for this dataset is significantly higher than 1, 25 % that were calculated from the IPCC (Intergovernmental Panel on Climate Change) in 1997. Both calculations go back to determined flow rates and so site- specific factors may be the reason for a high emission rate. Sehy (2004) mentions several reasons for the variability of N2O-emission rates. High emissions in Scheyern can be supported by soil characteristics (fine, silty structure), land use measures such as organic fertilizer and use of main and catch crops and weather conditions which promote denitrification. Also reduced soil cultivation in the integrated farming can be a reason for a high level of emissions. In total, about 48 % of annual N2O-emissions are independent from the N-fertilization in the vegetation period and occur during the winter (December – February) during frost-tau cycles. The 39 statistical correlation between nitrate- and water content and the flow rates of N2O indicate a higher N2O-production through denitrification. Hence, cultivation methods such as the use of catch crops which can conserve the nitrate in the soil during the winter months can contribute to a reduction of N2O emissions outside of the vegetation period and can therefore decrease the annual N2O emissions. About 71 % of the annual N2O-N losses can be explained with the mean annual nitrate content of the topsoil whereas the nitrate fertilizer amount is not significantly correlated with the N2O flows. With regard to the N-fertilization about 1,2 – 15,3 % of the applied nitrogen was emitted as N2O-N (Munch, Rackwitz 1999).

4.4.3. Nitrogen concentration in soil, surface – and groundwater

4.4.3.1. Nitrogen in surface water Beese et al. (1995) investigated the matter balance of small surface waters and their change through agricultural and landscape shaping measures. For this purpose the input, transformation and remain of matters before and after the extensification was evaluated qualitatively and quantitative for an eutrophic pond, “Teufelsweiher” and its two inflows “Bach West” and “Bach Ost” (Figure 26 and Table 22).

Figure 26: Location of "Teufelsweiher" and its two inflows (Beese et al. 1995)

After the landscape redesign the arable land in the catchment area of the two creeks was part of the integrated farming system. In the north-east of creek “Bach West” a permanent fallow land was created that extends from the forest about 150 m in direction to the pond. For the quantification of matter flows four measurement stations were installed at the in- and outflows 40 of the pond (Figure 26). At W 73 and W 17 an estimation of matter flow from the agricultural land is possible and at the stations W 73, W 79 and W 21 measurements about the matter loads from and into the pond are made.

Table 22: Surface waters under investigation (Beese et al. 1995)

Teufelsweiher Surface 3155 m² Depth 0,3 – 1,3 m Volume Ca. 3000 m³ Bach West Mean runoff 3,8 l/s Mean runoff (dry period) 2 l/s Bach Ost Mean runoff 1,5 l/s Mean runoff (dry period) 0,1 l/s During dry periods the nitrate concentrations in the base runoff in “Bach West” broadly correspond between W 17 and W 73. The mean runoff and therefore also the material loads, however, did approximately double on the 330 m long distance from W 17 to W 73 (Figure 27). For the other creek, “Bach Ost”, the mean annual nitrate load was similar. For heavy rainfall events a distinction between leaching (nitrate, chloride, sodium, sulphate, magnesium and calcium) and erosion parameters (phosphate and potassium) is necessary. Leaching parameters are transported from surrounding areas in a solved form into the receiving waters. Heavy precipitation solves particulate matters and rinses them into surface waters through surface degradation. The concentration of leaching parameters did decrease about 20 – 50 % in the investigated creeks but the material loads rose with slightly increased runoff. For example in July 1993 the precipitation was about 200 mm and in April 1994 about 170 mm (Figure 27). However, the concentration of erosion parameters rose about three to five times in comparison to the runoff in dry periods.

100 [KG] 90 80 70 60 50 40 30 20 10 0 1993 Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr May 1994 Jun NO3-N in W17 NO3-N in W73

Figure 27: Nitrate loads in “Bach West” 1993 – 1994 (Beese et al. 1995) 41

In the investigation period from 1993 – 1994 the measured nitrate concentrations in the creeks (annual mean W 73; W 79: 5,5 mg/l) did almost always surpass the concentrations in the pond (annual mean 3,0 mg/l NO3-N) except for the two heavy rainfall events in July 1993 and April 1994. The distinct seasonal course of the reduction of nitrate in the pond (Figure 28) can be lead back to sedimentation, nutrient uptake through plants and increased microbial productivity for higher temperatures in the summer, which lead to a decline of approximately 60 %. In the winter the nitrogen reduction goes back to 20 % after the death of macrophytes.

140 [KG] [%] 1,2

120 1 100 0,8 80 0,6 60 0,4 40

20 0,2

0 0 1993 Jul Aug Sept Oct Nov Dec Jan Feb Mar Apr May 1994 Jun

NO3-N inflow NO3-N outflow Ratio (outflow/ inflow) [%]

Figure 28: Nitrate loads in "Teufelsweiher" 1993 - 1994 (Beese et al. 1995) Between July 1993 and June 1994 the pond “Teufelsweiher” stored about one third of the NO3- O that was put in through both creeks and the atmosphere (Figure 29). The main part of the stored N is fixed through sedimentation. The sediment increased in the investigation period about 3 cm. The missing part in the N-balance can be ascribed to N2- emissions into the atmosphere through denitrification. 42

[KG] 1000 7 900 800 700 100 600 670 500 400 300 665 200 244 100 12 0 150 N-input N-output N-storage NO3-N-input through "Bach Ost" NO3-N-input through "Bach West" Atmospherical N-deposition NO3-N-output through pond drain N-release into atmosphere N-sedimentation Total N of macrophyt-biomass in August

Figure 29: N-balance of "Teufelsweiher“ July 1993 – June 1994 (Beese et al. 1995) According to Beese et al. (1995) is no significant difference between the matter flows of 1992 and 1992 (before change of land use) and 1993 and 1994 recognizable. The change of agricultural land use can therefore not meliorate the rates of matter transport into surface areas in the short term. Furthermore it needs to be considered that a major part of matter transport into the creeks is through overland runoff. The amount of matter flows through erosion is dependent on the development and density of plants on the arable land.

4.4.3.2. Nitrogen in soil Lützow, Jimenez (1999) investigated the chronological sequence of nitrogen in the soil depended on its particle size. The fertilizer 15N was applied on day 0 of the experiment and recordings were made on day 0, 7, 272 and 630. The total sum of N in the soil did not change significantly in its distribution on the individual particle sizes (Figure 30, left) in the investigated period of time. The mean proportions of nitrogen were found in particles with a size of 2 – 63 μm and 0, 1 – 2 μm. Regarding the fertilized 15N only in one week the distribution changed substantially. The proportion of coarse sand decreased about 2/3 on day 7 in comparison to day 0 (Figure 30, right). This measurement corresponds with the amount of microbial immobilization of 15N. The observed redistribution of 15N from the coarse sand in smaller particle sizes proceeded in the following time of the investigation period. 43

total N fertilized 15N 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0 7 272 630 0 7 272 630 DAYS AFTER FERTILIZATION

> 250 μm (CS) 63 - 250 μm (FS) 2 - 63 μm (SL) 0,1 - 2 μm (C) < 0,1 μm (L) CS: corase sand; FS: fine sand; SL: silt; C: clay; L: soluble fraction

Figure 30: Proportion of Nitrogen and fertilized 15N on the particle sizes in the topsoil (Lützow, Jimenez 1999) 4.4.3.3. Nitrogen outflow Depending on its flow path runoffs transport individual amounts of solved matters from stones and humus cover (natural matters) or from substances for plant nutrition and protection (foreign matters). The export of natural and foreign matters in high amounts influences the water quality and can cause unproductive income losses for the farmer. To evaluate the importance of the pollution of ground – and surface waters through agrochemicals several transport mechanisms for substance export were investigated considering the different farming systems. Depending on the morphology and the water absorption capacity of a landscape there are maximum three runoff components (Table 23) which are recorded through two different methods: the hydrograph analysis separates between fast (overland runoff, interflow) and slow flowing water amounts (groundwater formation) whereas the chemical-isotopically method differentiates between high (interflow, groundwater formation) and low chemical charged water amounts (overland runoff). Between 1992 and 2000 the measurements recorded an annual mean of 6 % overland runoff, 24 % interflow and 70 % groundwater formation for Scheyern.

Table 23: Distinction of different runoff components (Seiler, Hellmeier 2002)

Runoff component Flow velocity Overland runoff >> 10 m/ day Is not flowing in the underground Interflow > 1 m/ day Flows in the underground Groundwater Few meters/ day Vertical flow in the underground formation 44

The measurements were taken in five pits in the research area where the bottom and groundwater were analysed continuously in several depths through suction cups and drainages. In the effective root zone (0 – 100 cm under surface) the nitrate concentrations vary through fertilization, mineralization, plant absorption and leaching through runoff components in the vegetation period. This variability is not common in the deeper soil areas. The highest concentrations of nitrate were measured in the effective root zone underneath potatoes (“Kartoffel”), maize (“Mais”), sunflower and spring barley (Figure 31). All of them were seeded in spring.

Figure 31: Nitrate concentrations in the effective root zone (0 - 100 cm under surface) for different crops (Seiler, Hellmeier 2002) The low and predominantly organic input of nitrogen in the organic farming system does not lead to a noteworthy decrease of matter load into the groundwater in comparison to the integrated farming system (Table 24). However, the mainly strong and mineral fertilization in the integrated farming has an effect on the matter transport through interflow. The amount of transported nitrate here is approximately 3, 5 times higher than in the organic land use.

Table 24: Mean annual nitrate transport through runoff in organic and integrated farming (Seiler, Hellmeier 2002)

Nitrogen [kg/ (ha*a)] 45

Organic farming Integrated farming Overland runoff 1,4 1,7 Interflow 3,9 14 Groundwater formation 10 11 Total 16 27

The mean load of nitrate for both organic and integrated farming together is dominated by the transport into the groundwater followed by interflow (Figure 32).

Nitrogen

groundwater formation interflow overland runoff 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Figure 32: Mean nitrogen discharge in Scheyern (Seiler, Hellmeier 2002) In 1994, the nitrogen concentration in the drainage and groundwater was approximately 75 – 85 mg/ l. In 1998 the nitrate concentrations have decreased about the half. The nitrate concentrations in the percolate water directly underneath the effective root zone on the hillside however, did diminish more. At the end of the investigation period the concentrations in the loess loam soils was higher (27 mg/l) than in the sandy soils (10 mg/l) and about 4 mg/l in the pseudogley soil (Table 25). The concentrations decreased up to 97 % (extensified slope area) and at least 18 % (Table 25). Merely on the foot of the slope (colluvium) the concentrations were a bit higher in 1998 than in 1994 but in this location all lateral flows from superior heights flow together so it has to be considered as a special characteristic.

Table 25: Nitrate concentrations in 180 cm depth in the soil solution in summer 1998 in comparison to concentrations in groundwater in summer 1994 (Seiler, Hellmeier 2002)

Nitrate Difference Decrease concentration [mg/ l] [mg/ l] [mg/l] Summer Summer ‘94 ‘98 Sandy molasse Fallow 1,5 0,4 -1 74 Sandy-clayey loam Slope 50 1 -49 97 Sandy molasse Organic f. 21 10 -11 51 Sandy-clayey loam Organic f. 35 13 -21 43 Pseudogley Integrated f. 24 4 -20 82 Hillside gley Loess Integrated f. 36 30 -6 18 loam Loess loam Integrated f. 36 27 -9 24 Colluvium Integrated f. 12 15 +3 46

With regard to the concentrations in the groundwater a decline of at least 50 % of the previous concentrations is expected due to the measurements from 1998 (Table 25). Since the flow velocity of the percolate water is very low and the distance between the groundwater level and the surface is 4 – 24 m, the measurements from 1998 can only show a trend for the consequences of the land use change. With this knowledge a substantial aim can be obtained: a simultaneous intensive agricultural production and also a protection of adjacent aquatic systems. The investigations show that the interflow contributes through its export of matters to a protection of the groundwater. But this way of export leads to shock loads into surface waters through precipitation events. With this, unproductive losses of agrochemicals occur which can be reduced by an application of in dry periods and more often in smaller amounts (Seiler, Hellmeier 2002).

4.4.3.4. N2-fixation through roots and plants in soil Nitrogen input is one of the most important yield limiting factors in crop production yet it also has a huge relevance for the environment. The investigation about nitrogen utilization by crop production is not complete and primarily under unfavorable weather conditions great amount of added nitrogen to the soil cannot be used by the plants. These quantities are either stored in the soil organic nitrogen stock or are lost by gas emissions or leaching. Küstermann et al. (2010) evaluated the nitrogen surface balance from 1999 to 2002 (Table 26). The experiment was splitted into an organic and an integrated farming part and allowed a comparison because the same crops (wheat, potatoes) and crop rotation (soil surface balance) was used. The organic farming system consisted of a 7-year crop rotation with two fields of grass-clover-alfalfa (GCA) (28, 6 %). The crops present a huge difference in their nitrogen outputs even though each hectare of arable land received about 70 kg N as farmyard manure (FYM) and 9 kg N as slurry according to the stock density of 1,40 LSU/ ha (Table 26). For example did potatoes, cereals and sunflowers only reach about 15 – 30 % of the nitrogen output of GCA. The total mean N output in the organic crop rotation most of all consequences from the GCA. Furthermore, GCA was the only crop mix that resulted high measurements for N2- fixation. Table 26 also shows recordings for the grassland which was used both as meadow and pasture land.

Table 26: Nitrogen soil surface balance of the organic crop rotation, averaging the years 1999 - 2002 (Küstermann et al. 2010)

Crop N input Yield N N N + catch N2 FYM Slurry [Mg output surplus utiliza crop fixation [kg [kg DM/ha [kg [kg tion [kg N/ha] N/ha] N/ha] ] N/ha] N/ha] [kg 47

N/ha] GCA 261 11,8 304 -27 1,10 Potatoes 186 4,9 66 136 0,33 +under sown mustard Winter 99 18 2,4 59 74 0,44 wheat Sunflower 1,6 46 60 0,34 31 + 31 +under sown GCA GCA 236 10,9 283 -31 1,12 Winter 26 23 3,1 82 -17 1,26 wheat Winter rye 95 17 2,9 64 64 0,79 +2,9 + 76 -26 +under 50 sown GCA Crop 83 70 9 6,9 140 38 0,77 rotation Grassland 33 10 95 6,2 136 18 0,88 Agricultural 60 43 48 6,6 138 29 0,82 area

For the organic farm the stock density increased from 0, 52 LSU/ ha in 1993/ 1994 to 1, 40 LSU/ ha in 2001/ 2002 which is the highest density allowed by the German guidelines for organic farming. From 2003 the stock numbers were reduced again. Therefore a differentiation between a phase of intensification until 2001/ 2002 and a phase of extensification after 2003/ 2004 has to be made for the organic farm (Figure 33).The changes had significant effects on the nitrogen balance. By 2001/ 2002 a peak of N2-fixation was registered since the nitrogen cycle was intensified by then. Also the highest N output in products (143 kg/ (ha*a)) was recorded within the peak of stock density. In the subsequent period of extensification the nitrogen output declined to 55 kg/ (ha*a) and the N2-fixation to 48 kg/ (ha*a). 48

KG N/ (HA*A) 200 intensification extensification 1,6 173 183 180 1,4 1,38 160 1,4 1,2 147 143 146 140 137 130 120 124 1 108 0,91 110 0,86 112 100 97 0,8 85 84 80 0,52 0,62 80 0,6 60 63 64 68 54 55 48 0,4 40 20 0,2 0,1 0 0 93/'94 95/'96 97/'98 99/'00 01/'02 03/'04 05/'06 N-input [kg N/(ha*a)] N2-fixation [kg N/(ha*a)] N-output [kg N/(ha*a)] livestock density [LSU/ha]

Figure 33: N-input and N2 fixation from 1993 to 2006 in the organic farm A completely different situation was given by the conventional crop rotation were no legumes were included and the main nitrogen input came from mineral nitrogen with about 145 kg/(ha*a) (Table 27). The yield in the conventional crop rotation was about 23 % higher than in the organic crop rotation and the nitrogen output was about 8 % higher. For winter wheat and potatoes, both grown in both crop rotations, were wide deviations between the yields recorded. Whereas the organic farming system is based on nitrogen cycling it the conventional system just designed for passing through of nitrogen. The highest use intensity was registered in 1997/ 1998 were the nitrogen input reached a peak (Figure 34). After this, the nitrogen input was decreased to diminish the nitrogen surpluses. With this, the output increased continuously through cash crops with rising yields and higher productivity.

Table 27: Nitrogen soil surface balance of the conventional crop rotation, averaging the years 1999 - 2002 (Küstermann et al. 2010)

Crop N input [kg N/ha] Yield N output N surplus N + catch Mineral N Slurry [Mg [kg N/ha] [kg N/ha] utilization crop DM/ha] [kg N/ha] Potatoes 90 8,9 128 -22 1,02 + mustard 20 Winter 160 46 5,9 144 78 0,65 wheat Maize 130 58 13,8 178 26 0,79 + mustard 20 20 Winter 160 28 5,5 149 55 0,73 wheat 49

Crop 145 33 8,5 150 44 0,79 rotation

250 KG N/ (HA*A) 225 208 200 187 180 170 176 173 155 160 156 150 146 130 120 100 118

0 93/'94 95/'96 97/'98 99/'00 01/'02 03/'04 05/'06

N-input [kg N/(ha*a)] N-output [kg N/(ha*a)]

Figure 34: N-input and N-output from 1993 to 2006 in the conventional farm (Küstermann et al. 2010) Claassen et al. (1997) evaluated data about the N2-fixation of GCA in two different locations. Yield and N2-fixation on loamy soil did exceed the measurements on sandy soil about 20 – 30 % (Table 28).

Table 28: Clover-alfalfa-grass yields on two locations on division A02 (Claassen et al. 1997)

Sand Loam Harvest TS- N- Legume- N2- TS- N- Legume- N2- yield yield N fixation yield yield N fixatio n 03.11.93 242 6,3 3,9 2,9 271 7,7 4,7 4,6 31.05.94 623 13,6 10,7 9,3 845 18,8 15,6 15,6 12.07.94 464 12,1 10,2 10,5 516 13,5 11,6 12,0 27.09.94 459 12,1 9,1 9,8 500 12,9 10,7 11,1 Total 1788 44,1 33,9 32,5 2131 52,8 42,6 43,3

4.4.3.5. Nitrogen concentration on extensive farmed pasture land On extensive farmed pasture land the nitrogen concentrations and distribution is different in comparison to meadow land through loads of excrements and urine. There is a high spatiotemporal variability of nitrogen concentrations and the risk of high nitrogen losses on intensive farmed pasture land is twice as high as for meadow land. The nitrogen load of excrements is nearly constant with about 0, 8 g per 100 g and the load through urine is strongly dependent on the animal food. The density of excrements is strongly dependent on the slope. 50

With a slope of 15 % the density decreases with a rising slope. The growth of vegetation was limited through nitrogen on the whole pasture land except for positions with urine (König 2000; Schnyder 1998).

Simon (1995) recorded data about the variability of excrements on pasture land on 0,63 ha with

126 measurement points. The variability of minimum nitrogen values (NMin) is significantly high with up to 294 % (Table 29). The measurements for nitrate spread wider (50 – 294 %) than those for ammonium with 23 – 102 %. In the first five weeks after the 1st grazing (31.03.93) the variability and absolute measures of nitrate increase in all depths whereas there is only a rising variability of ammonium for a short term and only in the top soil. After the 2nd grazing the measurement show clear trends: the variability of nitrate only increases in the subsoil. However, the absolute values and the variability of ammonium is rising in the first two weeks after grazing in all horizons. These trends can be interpreted trough a high precipitation in the whole 2nd grazing period and hot-dry weather conditions in the 1st period.

Table 29: Variability of NO3-N and NH4-N on pasture land (Simon 1995)

Depth Date NO3-N [cm] M S CV [%] Min [kg/ha] Max [kg/ha] 10 31.03.93 4,2 2,6 62 0,7 10 27.05.93 3,4 3,9 114 0,3 15 24.06.93 4,9 10,7 220 0 50 07.08.93 2,4 1,2 52 0,7 5 21.09.93 2,0 1,1 54 0,6 4 15.12.93 3,8 2,4 63 0,7 12 30 31.03.93 5,3 2,9 56 1,5 12 27.05.93 2,4 2,9 119 0 12 24.06.93 17,7 51,9 294 0 212 07.08.93 2,4 1,2 50 0 5 21.09.93 1,4 0,9 61 0 4 15.12.93 5,4 3,0 55 1,6 14 60 31.03.93 4,8 3,8 80 2,3 19 27.05.93 2,5 3,9 156 0 18 24.06.93 6,8 18,0 266 0 83 07.08.93 2,8 3,6 130 0 14 21.09.93 0,8 1,0 129 0 4 15.12.93 6,4 6,3 97 1,7 31

Depth Date NH4-N [cm] M S CV [%] Min [kg/ha] Max [kg/ha] 51

10 31.03.93 0,7 0,6 76 0,3 2 27.05.93 7,2 6,9 96 1,0 31 24.06.93 3,9 1,2 30 1,0 6 07.08.93 11,8 9,9 84 5,3 45 21.09.93 4,5 1,1 23 2,9 7 15.12.93 6,8 2,9 43 2,6 13 30 31.03.93 1,2 1,1 93 0,7 6 27.05.93 3,8 2,0 52 2,0 10 24.06.93 4,0 4,0 102 1,8 21 07.08.93 7,3 5,1 70 1,6 27 21.09.93 3,0 0,8 25 1,1 4 15.12.93 8,4 3,4 40 3,4 15 60 31.03.93 1,4 0,6 43 0,7 3 27.05.93 2,6 0,9 36 1,3 5 24.06.93 2,7 0,7 27 1,2 4 07.08.93 8,0 6,2 78 1,3 33 21.09.93 4,3 4,2 97 0 17 15.12.93 8,7 2,7 31 5,2 16

M: mean value; S: standard variance; CV: Variability coefficient (S/M)

1st grazing 2nd grazing

Figure shows the spatiotemporal development of the mineral nitrogen on positions with urine. Kommentiert [LR3]: Put in figure page 84, Jabresbericht 1994 After about 24 hours high values for ammonium can be recorded and after about one week the mean part of nitrate is transformed into NH4-N. After about 2 weeks after the application the ammonium values decrease significantly which can be explained through processes of immobilization, denitrification, nitrification and transport. At the same time the nitrate values rise and after in total eleven weeks neither ammonium or nitrate can be found in the soil (figure) (Simon 1995).

4.4.4. Models

4.4.4.1. Agricultural Information System (AIS): Model of dynamics of nutrient transport The AIS is divided into two main components: Handling of spatial data with a Geographical Information System (GIS) and integrated analysis and simulations on a second information processing level. The aim is to establish a model for simulation of spatial and seasonal dynamics in matter transport and nutrient cycling for ecosystems and on the landscape level. 52

4.4.4.2. SPASS-model: Simulation of growth, water and nitrogen uptake The parametrized model SPASS (Soil – Plant – Atmosphere Systems Simulation) can simulate nitrogen uptake, biomass growth and water uptake for regular situations when no extreme conditions or damage such as pest or disease occur. It is possible to re-parametrize the model for different crops and it was constructed on the basis of common processes across plant species. The processes are grouped into three categories: soil, plant and microclimatic processes (Gayler et al. 2002; Wang, Engel 2002). Wang, Engel (2002) executed a SPASS experiment for winter wheat crop in Scheyern on two fields (“Kehrfeld” and “Oberes Hohlfeld”) in the year 1990/ 91. The simulation started on the harvest date of the previous crop. On Kehrfeld, winter barley was harvested on 17 July 1990 and on 27 September 1990 white rape at Oberes Hohlfeld. The soil mineral nitrogen and soil water content were assessed according to the previous crops. The model simulated the total nitrogen uptake, nitrogen concentration in the grains and grain weight very precisely as well as the water and nitrogen content in the soil. SPASS can calculate values for biomass growth, water uptake and nitrogen uptake of crops with an error of around ± 10 % in comparison to values measured under normal conditions. Gayler et al. (2002) implemented a SPASS experiment in 1996 at the Research Station Scheyern for two potato varieties (“Christa” and “Agria”). Actual measurements from different years on the Research Station were compared with the calculated values of the model with regard to yields and nitrogen uptake. The results of the simulation show that the SPASS model can present the effect of individual nitrogen fertilizer additions on potato growth and nitrogen uptake. Furthermore, the model could predict the differences between the two potato varieties. Summarizing the SPASS model can simulate for winter wheat as well as for potatoes. However, the model was not tested for other crops yet and further model applications to other crop species and at other site need to be carried out. Otherwise it is not possible to establish SPASS as a prognosis tool for nitrogen uptake and crop growth under individual fertilization strategies and environmental conditions.

5. Outlook

5.1. Missing data about nitrogen cycle

5.2. Missing hydrological data

6. Conclusion

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