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Agriculture, Ecosystems and Environment 184 (2014) 158–167

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

j ourna l h omepage: www.elsev ier .com/ locate /agee

evealing N management intensity on grassland farms based onatural �15N abundance

elanie Kriszana, Jürgen Schellberga,∗, Wulf Amelungb, Thomas Gebbingc,rich M. Pötschd, Walter Kühbaucha

Institute of Crop Science and Resource Conservation (INRES), Division of Plant Production, University Bonn, Katzenburgweg 5, 53115 Bonn, GermanyInstitute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University Bonn, Nussallee 13, 53115 Bonn, GermanyChamber of Agriculture North Rhine Westfalia, Siebengebirgsstraße 200, 53229 Bonn, GermanyLFZ Raumberg-Gumpenstein, Raumberg 38, 8952 Irdning, Austria

r t i c l e i n f o

rticle history:eceived 2 April 2013eceived in revised form5 November 2013ccepted 28 November 2013vailable online 22 December 2013

eywords:15N

balanceanagementrassland

a b s t r a c t

High application rates of farmyard manure on grasslands generally results in nitrogen (N) losses and ashift in N isotope composition. The aim of this study was to elucidate to which degree the 15N signaturesof miscellaneous N pools at two different levels of management intensity may be used to reproduce the Nlevel of various grassland farms in practise, i.e. beyond the control of experimental plots. We hypothesizedthat (i) higher �15N values in soil, plant and animal samples can be found with intensified grasslandfarming and high N-input management, (ii) that the 15N signature originating from manure applicationis influenced by application technique and (iii) that it declines with increasing distance from stable tofield. To test these hypotheses, we monitored different N pools on nine different farms (dairy, suckler, beefproduction) in grassland dominated regions of North Rhine-Westphalia, Rheinland-Palatinate (Germany)and Styria (Austria). Samples were taken from 0 to 5 cm soil depth and aboveground biomass at each offive sub-sites on farm, as well as from feed components, fertilizers, and cattle product and tissue likemilk, hair, faeces and urine.

The results indicated a considerable variation in �15N values of the top soil (1.47‰ to 7.91‰) andof harvested plant material (−2.18‰ to 6.79‰). On average, �15N values of samples from high N-inputgrasslands were elevated by 2.8 delta units relative to those of low N-input grasslands. For the soiland plant samples, the �15N values were thus closely correlated with the overall N balance as well aswith stocking rate and fertilizer input (r2 = 0.71 to 0.85). Respective trends for the isotopic signaturesin milk, hair and faeces were also evident but less apparent. Furthermore, low emission application

techniques of organic fertilizer and increasing distances from the stable to the field exhibited lower�15N values of top soils and plants, but only in the low input system. We conclude that high N-input ongrassland farms systematically changes the �15N values of soils and above ground biomass and thus alsothe N signature in animal tissues. Application of N isotope technique to these N pools thus allows fortracing back intensity of fertilizer management regardless of high natural �15N variations in terrestrialenvironments.

© 2013 Elsevier B.V. All rights reserved.

. Introduction

Intensive animal husbandry frequently leads to serious imbal-nces of major nutrients on farmland (Van Keulen et al., 2000).uring the last decades, particularly increasing application rates

f N fertilizer have resulted in unwanted side effects on water andir quality. In order to lower N surpluses and improve N budgetst larger scales, farmers benefit from increasing political support

∗ Corresponding author. Tel.: +49 228 73 2881.E-mail address: j.schellberg@uni-bonn.de (J. Schellberg).

167-8809/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.agee.2013.11.028

and subsidies when practising low N-input systems. However, it isstill difficult to discover N imbalances at field and farm scale (OECD,2001). Budgeting at farm-scale is a first simple comparison of nutri-ent inputs and outputs of a system over a defined period of time(Domburg et al., 2000; Jarvis, 1993; Oenema et al., 2003). The dif-ference between N input and output is used as a measure for eithernutrient surplus or deficit and allows an easy calculation of theoverall N use efficiency (NUE) of the system (Cassman et al., 2002).

Several methods for calculating N budgets are available, namely(i) farm-gate balance, (ii) soil-surface and (iii) soil-system balance.The accuracy of these approaches is increasing in the same order(Oenema et al., 2003). However, the underlying N cycle in grassland

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M. Kriszan et al. / Agriculture, Ecosyst

ystems is more complex because of, e.g. N return from rumi-ants, N leaching and volatilization, anthropogenic N deposition,nd symbiotic N2 fixation of plants (Jarvis, 1993).

In practice, N farm-gate balances are used to assist farmersith their nutrient management, but it is also used as a regulatoryolicy instrument. With a certain instructional intent, data on farm-ate balances are published in annual farm statistics. However, theesulting budgets do not consider the various environmental inter-ctions that control N sinks and sources as well as redistribution at

spatial scale (Schnyder et al., 2010). In contrast, soil surface bal-nces record all nutrients that enter and leave the soil though stillgnoring N losses through volatilization (OECD, 2001). Only the soilystem budget allows control over either net nutrient reduction orccumulation within the system (Oenema et al., 2003). However,espective long-term observations on farmland and in laboratory,hich might allow tracking back changes in soil nutrient budget,

re missing in most instances. To retrace nutrient imbalances in theoil–plant system, especially after prolonged high N-input (HNI) orow N-input (LNI) management, it would be worthwhile to haveimple measures on farm that could be used to assess balance cal-ulation.

Stable isotopic approaches are increasingly used to study thempact of environmental change and agricultural practices oncosystems (Dittert et al., 1998; Kerley and Jarvis, 1996). The nat-ral abundance of the stable isotope of 15N (�15N) provides insight

nto the main N transformation processes in the N-cycle of agricul-ural farms (Högberg, 1997; Robinson, 2001). This is attributed tohe isotopic discrimination of heavy against light isotopes in bio-hemical processes due to their mass differences. Every processesulting in N losses thus discriminates against the heavier isotope5N. This causes a depletion of 15N in products that are lost to thenvironment (NH3, N2, N2O, NO3

−) and an enrichment of 15N in theesidual substrate (NH4

+, organic N).Long-term forest fertilization trials showed elevated �15N iso-

ope ratios in needles, grass and soil samples, mainly due to thereater losses of the lighter 14N isotope through volatilization ofmmonia (NH3), leakage of NO3

− and denitrification (Högbergnd Johannisson, 1993; Johannisson and Högberg, 1994; Templert al., 2007). These observations have been confirmed by inves-igations on cropland (Meints et al., 1975) and grassland (Frankt al., 2004; Watzka et al., 2006; Wrage et al., 2011). The degreef 15N enrichment depends on type and amount of fertilizersed (Kriszan et al., 2009). Among them, organic amendments,uch as animal manure, slurry or compost are enriched in 15NKreitler, 1979; Wassenaar, 1995; Yoneyama, 1996), and so areoil and plants that receive these fertilizers (Watzka et al., 2006;uan et al., 2012). In contrast, N in chemical–synthetic fertilizersriginates from atmospheric N synthesized in Haber–Bosch pro-edure. This is why the �15N signature of soil and plant materialypically remains depleted or unaffected when these fertilizersre applied: their �15N values consistently range between −2‰nd +2‰. From these findings one can conclude that stable iso-ope analyses of 15N can serve as a suitable indicator that allowsistinguishing between conventionally and organically agricul-ural management, due to the exclusive use of farm manure inrganic farming systems (Bateman et al., 2007; Flores et al., 2007;chmidt et al., 2005). Thus, �15N should also be a suitable indica-or for reconstructing the intensity of N management on practicearms.

It has been shown that the N signature of animal products, suchs milk, is also influenced by the �15N values of the feed with differ-nt degrees of fractionation during digestion (DeNiro and Epstein,

981; Knobbe et al., 2006; Kornexl et al., 1997). Samples like blood,at and liver memorize the diet for weeks to months (Yanagi et al.,012). Urine also provides conclusions on diets (Knobbe et al., 2006;ponheimer et al., 2003).

d Environment 184 (2014) 158–167 159

In an earlier study, Schwertl et al. (2005) found that �15N inanimal hair is suitable to detect differences in N management andN fluxes on farms and that the N signatures nicely integrates thedietary information across longer time scales due to low metabolicactivity of the tissue. In this study, �15N was well correlated withstocking rate and N farm gate balance, which, if beyond a tolerablelevel, caused volatilization of excess N thereby leading to discrim-ination against the heavier N isotope. Based on these findings,similar studies published by Watzka et al. (2006) and our exper-imentation on changes in N isotopic signature on grassland plots(Kriszan et al., 2009), we learned that the �15N natural abundancecan generally be used as an assessment indicator of NUE. One majoradvantage of the approach is that the sampling of milk, hair, urine,faeces and slurry and the preparation for stable isotopic analysisis simple, i.e. these materials could therefore additionally help totrace back the history of the underlying N management.

The objective of this study was, therefore, to relate �15N isotoperatios of several N pools of grassland farms to the underlying N man-agement of the latter, and to test whether the isotopic signature ofN pools reflects long-term N losses. Compared with previous workcited above we widened the scope from the individual animal tothe farm in that we compared two levels of N intensity, integratedseveral pools and low and high emission N application techniques,and sampled the soil at different distances from the farmyard. Thisallowed us to integrate soil properties, application technique anddistance to the farm into the evaluation of former N managementintensity.

We hypothesized (i) that we find higher �15N values in soil, plantand animal samples on intensively managed farms and especiallywith high N-input, (ii) that the slurry application technique influ-ences the 15N signature in the above mentioned samples and (iii)that a spatial variation in �15N exists on farm such that it declineswith increasing distance from stable to field.

2. Materials and methods

2.1. Study area

The study area was located between 50◦13′N to 51◦27′Nand 6◦12′E to 8◦51′E in North Rhine Westphalia and Rhineland-Palatinate, Germany, and included sites between 260 and 520 mabove sea level. Additionally, one site in Styria, Austria, was cho-sen (47◦29′N and 14◦09′E, 900–1100 m above sea level). Long-termmean annual air temperatures ranged between 7 and 10 ◦C. Long-term mean annual precipitation varied between 830 and 1200 mm.Soils were predominantly of sandy loamy texture and comprisedmainly Cambisols and Stagnosols (WRB, 2006) with pH values ran-ging from 4.6 to 7.0 (data provided by farmers).

2.2. Farms selection

With the support of local agricultural services, nine typicallivestock farms in the above-named regions were selected. Farmsdenoted as A, B, C, E, G, H and I were typical grassland farms witha proportion of grassland of more than 90%, whereas farm D cul-tivated arable crops on approximately 16% of its total farm land.Merely, the bull fattening farm (F) had a higher proportion of arableland (71%). Based on the production system we separated thesefarms into five conventional (A, B, C, E, F) and four organic farms(D, G, H, I). On four farms (F, G, H, I), some fields were managed fol-

lowing the nature conservation program (NPA—nature protectionby agreement) funded by the government. Fertilizer application isgenerally not permitted on these NPA fields. Farm (I) was charac-terized by a very low N input, a low milk production rate and a more

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60 M. Kriszan et al. / Agriculture, Ecosyst

ntegrated and self-contained system due to its exposed location inhe Austrian alpine region.

.3. Farm data acquisition

All farm data and samples were collected twice in 2007, beforeApril/May) and after the grazing season (October/November). Onarm (I), samples were taken only once (July). In addition, weecorded data on farm management characteristics and nutrientookkeeping from interviews and stated questionnaires (Table 1).anagement system on the above listed farms has not been

hanged at least during the past 10 years before sampling, and sohort term effects of system disequilibria on isotopic compositionf samples as discussed in Schwertl et al. (2005) can be excluded.

Farms were allocated to two different types, low emissionLE) and high emission (HE), according to the slurry applicationechniques they have been using. LE included band spreading, trail-ng shoe and slurry injection, whereas HE included techniques ofroadcast spreading and uniform covering of slurry across the grassrop (Misselbrook et al., 2002). Questionnaires to farmers revealedhat slurry was usually applied four times per year. Accordingly,

aximum and minimum age of the slurry was two to four months,espectively.

Calculation of nutrient balances were conducted according toethods and data detailed in the European Community regula-

ion (91/676/EWG, European commission 12, December 1991)ransferred into the ministerial legal order “Verordnung überie Anwendung von Düngemitteln, Bodenhilfsstoffen, Kultur-ubstraten und Pflanzenhilfsmitteln nach den Grundsätzen deruten fachlichen Praxis beim Düngen (Düngeverordnung—DüV)”.armers obtain and keep these data to comply with Cross Com-

liance regulations (European Commission no. 1122/2009 ando. 73/2009) and control their efficient fertilizer management. Aetailed description and an example spreadsheet calculation arevailable at the Chamber of Agriculture, North Rhine-Westphalia

able 1roduction characteristics of the studied grassland farms in 2007.

Individual farm

High N-input Low N-in

A B C D

Production type Dairy cows Dairy cows Sucklercows

Dairy cow

Production systemcharacteristic

Con1 Con Con Org2

NPA3 fields (no. of plots) No No No No

Cattle keeping/feeding Zerograzing4

Zerograzing

Zerograzing

Summergrazing

Slurry applicationtechnique [LE/HE]5,6

LE HE HE HE

Total milk production[t farm−1 a−1]

2600 1450 n.a.7 0350

Milk yield [kg cow−1 a−1] 8700 9800 n.a. 5800

Stocking rate [LU ha−1]8 3.7 2.1 4 0.9

N-input/output balance9

[kg N ha−1 a−1]196 24 102 −111

Precipitation [mm yr−1] 850 1150 1150 900

Average yearlytemperature [◦C]

9.7 7.1 7.8 8.5

Altitude [m a.s.l.] 250 450 350 250–350

1 Conventional farming.2 Organic farming.3 Nature protection agreement (no additional use of fertilizer N).4 Year-round indoor housing of animals.5 LE—low emission.6 HE—high emission.7 Not available.8 Livestock unit (=500 kg live weight).9 N-balance was calculated using detailed information provided by the chamber of agr

d Environment 184 (2014) 158–167

(http://www.lwk.nrw.de/landwirtschaft/ackerbau/duengung/naehrstoffvergleich/index.htm, last visit 15 November 2013, inGerman). The N balances were calculated based on the dataprovided by the North Rhine-Westphalia (NRW) Chamber ofAgriculture and on N management data provided by the individualfarmers.

The N-inputs comprised applied mineral and organic N fer-tilizer, which reached 450 kg N ha−1 a−1 on the HNI farms ongrassland, 12% to 48% of which were applied as calcium ammo-nium sulphate. On LNI farms, 100% of fertilizer N was derived fromanimal excreta. The N input via N2 fixation was determined foreach field by multiplying the percentage dry matter contribution(DM %) of legumes in the sward by annual N2 fixation capac-ity [3 kg N ha−1 a−1] (Pötsch, 1998). Across all farms, the swardsconsisted of 0% to 40% legumes, in most cases approximately10–15%. In addition to N fertilizer application and symbiotic fix-ation of atmospheric N, we took into account an estimated amountof 20 kg N ha−1 a−1 of deposited NH3 (Schwertl et al., 2005; Watzkaet al., 2006).

The N-exports were mainly in the form of biomass production,selling of organic N fertilizer and N losses during storage and appli-cation. According to the above mentioned EU regulations, N balancecalculation included a constant annual N loss rate of 30% of total Napplied.

We are well aware that obtained N balance data for farms maynot compare with experimental data derived from controlled envi-ronment experiments in terms of precision. It should be noted thatthe low extremes sampled here comprise de-intensified farms thatexport and loose more N than they import, i.e. the current N bal-ance may have turned into negative numbers. Further, although aregular budget of about −100 kg N ha−1 yr−1 as calculated on some

farms seems unlikely and may deserve further investigation, it stillreflects the long-term average of nutrient management on lowinput farms and also allowed for robust relationships with N isotopedata in this study.

put

E F G H I

s Dairy cows Bullfattening

Dairy cows Sucklercows

Dairy cows

Con Con Org Org Org

No Yes (1) Yes (1) Yes (2) Yes (1)Summergrazing

Summergrazing

Half-daygrazing

Summergrazing

Summergrazing

LE HE LE LE HE

0190 n.a. 0905 n.a. 0051

5100 n.a. 7500 n.a. 46001.2 0.5 1.4 1 1−51 −130 −110 −102 −99

1200 800–1000 730 800 11008 7.8 7.5 6.9 6.8

420 300–400 540 475 1000

iculture and on additional information given by the farmers.

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.4. Preparation of samples

On each farm, plant samples were taken from five sub-sites onither five or seven (farm H) selected meadows or pastures pro-iding the bulk forage production of the farm, leading to a sampleize of n = 25 per farm. The fields differed considerably in their dis-ance to the farmyard ranging from 50 to 5500 m. We created threeistance classes—close (average distance = 100 m), intermediateaverage distance = 900 m) and far (average distance = 2200 m). Oneooled bulk soil sample from at least 10 randomized replicates wasaken at 0–5 cm soil depth from each of the five fields in questionn = 5 per farm). Soil samples were sieved to <2 mm and dried for8 h at 100 ◦C. At each sub-site, five samples of plant biomass wereut with scissors 1 cm above ground. Pooled samples of animal feed,uch as grass silage and hay (not for farm B and H) were takenrom each farm. All plant biomass and feed samples were ovenried for 48 h at 60 ◦C. Thereafter, feed samples and soil materialas ground in a ball mill (Retsch, Germany) to obtain homogenous

amples prior to 15N analysis. Hair samples were collected fromhree cows (Holstein Friesian, Simmenthal and Limousin) per farmxcept for farm F and H. A tuft of tail hair (5–10 cm) was cut withcissors as close as possible to the skin (root of hair). Hair sam-les were taken from dairy cows that were older than 12 monthsnd were kept on the respective farm most of their lifetime. Theseamples were prepared according to Schwertl et al. (2005). Con-aminations of hair with faeces were removed by ultra-sonicationith de-ionised water. After drying for 48 h at 40 ◦C, samples were

oaked into a 2:1 (v/v) mixture of methanol/chloroform solution toemove surplus fat and then dried again under the same conditions.hereafter, an aliquot was ground in the freezer mill (Freezer Mill780; Spex Industries, Edison, NJ, USA) prior to �15N abundanceeasurements.Samples of urine (not for farm C and F) and faeces were collected

rom at least three cows per farm. One pooled sample of cattle slurryas taken and, for farm D, E, H and I, a bulk sample of farmyardanure was collected at each sampling period. To avoid N losses

hrough volatilization, samples were immediately acidified with2SO4 to pH 4 to 5. Additionally, on the dairy farms (A, B, D, E, G,

) one pooled sample of milk was taken. Subsequently, all samplesere freeze dried and ground in a freezer mill (Freezer Mill 6780;

pex Industries, Edison, NJ, USA).

.5. Isotope ratio measurements

The dried samples were weighed into tin capsules and com-usted in an elemental analyser (NA 1110; Carlo Erba, Milan, Italy)

nterfaced (Con FLo III, Finnigan MAT, Bremen, Germany) to ansotope ratio mass spectrometer (Delta Plus, Finnigan MAT). N

able 2ean isotopic signatures of various N pools [denoted in ‰] on the study farms (A–I). Valu

Pool type Pool description Individual farm

HNI LNI

A B C D

I Fresh grass 4.84 ± 1.40 4.28 ± 1.20 5.98 ± 1.46 1.90 ± 1.2Silage 4.51 4.00 6.05 2.19

Hay 3.51 n.a. 6.96 -1.13

II Top soil (0–5 cm) 7.35 ± 0.48 6.06 ± 0.47 7.03 ± 0.36 4.42 ± 0.8III Hair 6.44 ± 1.04 6.27 ± 0.48 8.35 ± 0.12 4.93 ± 0.2

Milk 6.37 ± 3.38 7.00 ± 2.17 n.a 5.52

IV Faeces 4.58 ± 0.34 5.08 ± 0.58 6.08 ± 0.39 3.55 ± 0.8Urine −0.25 ± 0.56 −0.08 ± 0.83 n.a. −1,13 ± 0Slurry 11.19 8.15 13.78 8.17

Manure n.a. n.a. n.a. 1.69

.a—not available; HNI—high N input; LNI—low N input.

d Environment 184 (2014) 158–167 161

isotopic data are represented as �15N (‰) relative to the air Nstandard:

ıR [0%] =[(

Rsample

Rstandard

)− 1

](1)

where R is the ratio of 14N/15N. All samples where measured againstlaboratory working standard gases, which were previously cali-brated against IAEA secondary standard (IAEA no. 3.). Precision ofisotope analyses was on average 0.2 [‰].

2.6. Statistical analysis

All results were evaluated using analysis of variance (ANOVA)with the software package SPSS v. 20.0 for windows software (SPSS,Chicago, US), except for comparisons of �15N values of variousN pools, top soils and plant biomass in relation to the N fertil-izer application and application technique (without NPA). ANOVAwas followed by a multiple range test (Scheffé post hoc test). Ifhomogeneity of variance was missing, Dunnet’ T3 was calculated.For each sample, the 95% confidence intervals on the mean werecomputed based on the sample mean and sample standard devia-tion. Hierarchical clustering of �15N values of soil and biomass ofall farms was done with SPSS software 20.0 (IBM® SPSS® Statis-tics). Between groups linkage method was applied, and squaredEuclidean distance was selected as measurement.

3. Results

3.1. Isotope signatures of N pools and the impact of field Nbalances on ı15N values

Pooled �15N values of samples taken in late spring did not differsignificantly from those sampled in autumn 2007 (P ≥ 0.05). Hence,the values of both periods were merged and were further on con-sidered as mean values where possible (Table 2). Large variationsin �15N occurred among the different N pools. This is most evi-dent from the �15N values observed for urine and slurry, whichdiffered by almost 17‰. Urine was constantly the pool that wasmost depleted in 15N (−2.94‰). Its �15N values remained negativeeven on farms which applied highest amounts of total N fertilizer.As expected, the highest �15N values were detected in the slurry(+13.78‰).

Based on their �15N values, the N pools could be divided into

four groups, soil, feed, animal products and animal excreta (Fig. 1,Table 2). Except for the latter, isotopic signatures did not differ sig-nificantly among groups (Table 2). Moreover, there was a significantdifference between farms managed at low and high N fertilizer

es include the standard error across both sampling periods (autumn and spring).

E F G H I

1.73 ± 1.00 1.37 ± 0.90 1.86 ± 1.69 1.12 ± 1.76 1.55 ± 1.031.32 1.54 0.95 2.08 2.800.42 1.09 2.94 n.a. 0,79

4 3.95 ± 0.41 4.66 ± 0.52 3.80 ± 0.85 3.93 ± 1.02 4.79 ± 0.509 4.27 ± 0.61 n.a. 4.27 ± 0.18 4.47 ± 0.12 4.63 ± 0.14

3.68 ± 0.12 n.a. 4.72 n.a. 4,321 2.55 ± 0.52 2.43 ± 1.37 3.23 ± 0.72 1.58 ± 038 2.09.92 −1.68 ± 0.75 n.a. −1.11 ± 0.55 −1.89 ± 0.96 −2.94 ± 0.77

9.86 5.35 10.61 8.88 6.876.29 n.a. n.a. 4.75 4.34

162 M. Kriszan et al. / Agriculture, Ecosystems and Environment 184 (2014) 158–167

F livestock unit ha−1) and HNI management (≥1.4 livestock unit ha−1). Error bars representt

rrtlo

NtaaN(

tmaafAf0fimeN

oN+btNuatpss

3v

w

Fig. 2. Hierarchical dendrogram of �15N values of top soils and biomass obtained

ig. 1. Mean �15N values of various N pools on farms under LNI management (<1.4

he 95% confidence interval of the means.

egime. On HNI farms, all N pools were enriched in 15N and theespective �15N values were significantly elevated by 1.46 (urine)o 4.09 (hay) delta units (P < 0.05) relative to those of the LNI grass-and farms (Fig. 1, Table 2). Hence, differences in N fertilizer regimebviously affected the �15N signature of all N pools on farm.

For a better illustration of the �15N pattern across the different pools, we performed a cluster analysis (Fig. 2) which subdivides

he preselected nine farms into three classes, HNI farms, LNI farms,nd nature protection farms (NPA acreages). The latter were char-cterized by zero fertilizer application and therefore all measured

pools showed very low �15N values relative to the other fieldsFig. 2).

Field N balances were calculated and outlined in Table 1. Onhe input side, organic N was derived from animals, N output was

ostly composed of harvested biomass as fresh grass, silage or haynd to a minor degree of N losses during fertilizer storage andpplication. Imports of chemical-synthetic N fertilizer were onlyound in the HNI farms and ranged from 55 to 120 kg N ha−1 a−1.ll LNI farms solely applied organic fertilizer, namely slurry and

armyard manure. The application of N fertilizers thus varied from (NPA) up to 450 kg N ha−1 a−1. N from NH3 deposition and N2xed by legumes were not included in the calculation. Feed wasostly imported as cereals and concentrates. At field scale, N-

xports consisted of N removed with biomass, selling of organic fertilizer and N losses during storage and application.

According to the N balance provided by the NRW—Chamberf Agriculture and additional notes received from the farmers,

input–output balances varied between −130 (farm F) and196 kg N ha−1 a−1 (farm A) (Table 1). The plotting of these Nalances against �15N values of top soils indicated a general quan-itative relationship between both. The higher the excess in the

balance was, the more positive were the respective �15N val-es of top soil (Fig. 3). Although similar close relationships werelso found for the �15N values of plant biomass, no such clear rela-ionships were found for the N balance against the remaining Nools (hair r2 = 0.61; hay: r2 = 0.59; urine: r2 = 0.54; faeces: r2 = 0.40;lurry: r2 = 0.27; milk: r2 = 0.24 and manure: r2 = 0.03; raw data nothown).

.2. Impact of stocking rate and total milk production on ı15N

alues

Since mean �15N values of various N pools correlated stronglyith N intensity on farm, we presumed that they could also be

from the fields on all studied farms. The fields were grouped into high N input(HNI = ≥1.4 LU ha−1 a−1; farms A, B, C, in Table 1), low N input (LNI = ≤1.4 LU ha−1 a−1;farms D, E, F, G, H, I in Table 1) and NPA (nature protection by agreement where nofertilizers were applied).

M. Kriszan et al. / Agriculture, Ecosystems an

N balance [kg ha-1 a-1]

-200 -100 0 100 200

δδ15N

[‰]

0

1

2

3

4

5

6

7

8topsoils plant biomass

y=0.0114 x+5.405r²=0.849

y=0.0144 x+3.153r²=0.765

Fig. 3. Mean �15N values of top soils and plant biomass in relation to N balance, cal-culated as the difference of total N input (fertilizer, atmospheric deposition andsymbiotic N2 fixation) to total N output (biomass production, selling of organicN fertilizer and N losses during storage and application). Both sampling periods(April/May and October/November in 2007) are pictured separately, except for farmI for which only one sampling was carried out (July 2007). Fig. 4: Mean �15N val-ues of various N pools, grouped in different N-intensity systems (grassland farmsbased on low or high N-input management) as related to the stocking rate. Sampleswere taken twice in April/May and October/November in 2007, except for farm I, forwhich only one sampling was carried out in July 2007.

Fig. 4. Mean �15N values [in ‰] of various N pools, grouped into different N-intensity

stocking rate.

d Environment 184 (2014) 158–167 163

used to differentiate between other characteristics of low and highN-input management, such as stocking rate (in livestock units,LU ha−1) or total milk production on farm (t a−1). In Fig. 4, the �15Nsignatures of miscellaneous N pools are plotted against the aver-age stocking rate on farm. The highest correlations were observedwith soil, biomass, silage, hay, and hair. Hence, particularly thoseN pools that integrate information over longer periods of timelike soil and hair were very suitable to reflect the preceding ani-mal husbandry practices. Since the �15N of biomass was stronglycorrelated with the �15N of soil, there was also a strong relation-ship between the �15N of biomass and the stocking rate on farm.The same trend and correlation was observed for silage and hay.Hence, the �15N of biomass (including grass silage and hay) servedas an indicator for stocking rate that was as good as the �15Nsignature of long-term N pools for revealing total N balance. Incontrast, the N pools that appear on farm for comparatively short-term periods of time, like excreta and milk, performed much lowerr2-values. Comparable results were found when plotting the �15Nsignatures against milk production (Fig. 5). High stocking rates andhigh total milk production were accompanied by high N inputs andthus reflected a positive N balance. HNI farms produced consid-erable N surplus (24 to 196 kg N ha−1 a−1), whereas all LNI farmsincurred N deficits (−130 to −51 kg N ha−1 a−1). Hence, correlationsof the �15N signature of N pools to the stocking rate contributed totheir overall relationship to N balance as shown before in Fig. 3.

As a consequence of high N losses and 15N enrichment in soil andplant material (Fig. 6), high N input also increased �15N in cattlehair.

levels (grassland farms based on low or high N-input management) as related to

164 M. Kriszan et al. / Agriculture, Ecosystems and Environment 184 (2014) 158–167

F ity levm

3f

N

Fasfic

ig. 5. Mean �15N values [in ‰] of various N pools, grouped into different N-intensilk production.

.3. ı15N values of plant biomass and soil as influenced by Nertilizer application

Higher productivity on farm frequently goes along with higher input. According to earlier findings (Schwertl et al., 2005) we

ig. 6. Mean �15N values of top soils and plant biomass in relation to total organicnd mineral N fertilizer input. On NPA fields, no fertilizers were applied at all. Eachampling data point refers to the mean value of five samples taken on each of theve probed fields of the respective farm, except for farm H where eight fields wereonsidered.

els (grassland farms based on low or high N-input management) as related to total

hypothesized that the isotopic signature of the �15N pools is gen-erally influenced by the amount of applied N. We also assumedthat application technique as well as the distance between farm-yard and pastures/meadows had an effect on N signature, becauseusually animal excreta and fertilizer application – for several rea-sons – concentrate on paddocks close to the cow house. This shouldhold true especially for those N pools that integrate over a longerresidence time, like the top soil layer. When we related the amountof fertilizer input to the �15N values of top soils and plant biomass,we could confirm a significant positive relationship between both(Fig. 6; top soils r2 = 0.81, plant biomass r2 = 0.71). The correlationbetween the �15N values of top soils and plant biomass on theone hand and fertilizer input on the other hand was lower whenestimated amounts of N fixed by legumes were included into thecalculation (plant biomass r2 = 0.64; top soil r2 = 0.77; raw data notshown). Mean �15N values of plant biomass were lower than thoseof top soil (Fig. 6), because plants discriminated against the heavier15N isotope by approximately 2.5 delta units, on average (Watzkaet al., 2006; Kriszan et al., 2009; Wrage et al., 2011). In NPA farms,even negative �15N values were recorded (Fig. 5).

3.4. Spatial patterns and N application technique

Frequently, fertilizers are not evenly distributed on farmsmainly because larger amounts of fertilizer are applied near the

M. Kriszan et al. / Agriculture, Ecosystems and Environment 184 (2014) 158–167 165

Fig. 7. Mean �15N values of (a) top soils and (b) plant biomass of HNI farms (grey column) and LNI farms (white column) at field sites at different distances to the farmyard.V distai

stfiitcnchtt

apdcalw

4

re

F(

alues with the same letters are not significantly different with respect to the threenterval of the means.

tables. Such an irregular spatial distribution is as important for fer-ilizer N use efficiency as is the application technique. To accountor such phenomena, we grouped our biomass and soil samplesnto three distance categories, i.e. close (average distance = 100 m),ntermediate (average distance = 900 m), and far (average dis-ance = 2200 m) away from the centre of major farming activity, i.e.owshed, barnyard, slurry container and food store. On HNI farms,o significant difference between the three distance categoriesould be determined. In contrast, LNI farms showed significantlyigher �15N values in samples taken close to the farms than in thoseaken further away, and this was found for both the top soils andhe plant biomass (Fig. 7a and b).

Surprisingly, the fertilizer application technique did not leave precise mark concerning the �15N values of top soils andlant biomass (Fig. 8a and b). Accordingly there were only smallifferences within low-emission and high-emission fertilizer appli-ation, with higher �15N values in the top soils and high emissionpplication technique on LNI farms. With increasing amount of Nosses during spreading, the �15N of top soils increased significantly

ithin the LNI farms.

. Discussion

In this study, isotopic signatures of biomass and soil closelyeflected the underlying N management on farms. Main differ-nces between the isotopic signatures of various N pools were

ig. 8. Mean �15N values of (a) top soils and (b) plant biomass of HNI farms (grey columlow and high emission application).

nce categories (one-way ANOVA, P < 0.05). Error bars represent the 95% confidence

affected by increasing input of N from organic manure, causing Nvolatilization and 15N enrichment. In contrast, mineral N derivedfrom atmospheric di-nitrogen through Haber–Bosch technologyexhibited lowest �15N values. Hence, most experiments provide�15N values close to that of atmospheric N after application ofchemical-synthetic N-fertilizers like calcium ammonium nitrate(Kriszan et al., 2009; Shearer et al., 1974; Wassenaar, 1995), withsmaller effects on �15N than after slurry application.

Organic fertilizer like cattle slurry or farmyard manure areconstantly enriched in 15N, due to fractionation processes dur-ing digestion of forage and manure storage (Choi et al., 2002;Kerley and Jarvis, 1996), ranging from 6‰ to 13‰ in earlier reports(Bol et al., 2005; Choi et al., 2002; Glaser et al., 2001; Wassenaar,1995; Watzka et al., 2006). In our investigation, the �15N isotopicsignatures of all sampled organic fertilizers were in the same mag-nitude as reported elsewhere (Bol et al., 2005; Choi et al., 2002;Wassenaar, 1995). The processes accounting for high �15N frac-tionation rates mainly comprise ammonia volatilization (0–29‰),nitrification (0–35‰) and denitrification (0–33‰) (Handley andRaven, 1992; Högberg, 1997), which all permanently occur onfarms.

With higher N input to the system, the �15N values of both

biomass and soil increased significantly, as was found already ear-lier in controlled experiments (e.g. Bol et al., 2005; Choi et al., 2003;Kriszan et al., 2009; Yoneyama, 1996,). The amount and type of fer-tilizer influenced the N losses and thus the �15N values (Kriszan

n) and LNI farms (white column) related to the N fertilizer application techniques

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66 M. Kriszan et al. / Agriculture, Ecosyst

t al., 2009; Watzka et al., 2006). There was even a positive linearelationship between the intensity of N application in grasslandarms and the �15N values of plant biomass and top soils (Fig. 6),uggesting that similar processes accounted for the N losses andhanges in farm �15N values across all sites. Only the scale of thesehanges varied due to different degree of N losses. In this respect,NI farms could be clearly separated from LNI and NPA farms in

he cluster analysis.

.1. Stocking rates

High stocking rates, a major indicator for high N-input grass-and farms, may lead to high accumulation of organic fertilizer NSchwertl et al., 2005). The higher the stocking rate, the higher is theisk of N losses, presumably through NH3 volatilization (Högberg,997). Thus, HNI farms with a stocking rate above 1.4 LU ha−1 wereharacterized by higher amounts of organic N per area, resulting inncreased �15N of plant biomass and top soils and increasing lossesf N from slurry or farmyard manure. According to our findings,he �15N signatures of different farm N pools are thus plausiblendicators of the efficiency of N management, some of which areong-term signals. For example, in the present experiment, the iso-ope N signatures of cattle responded to N balance as induced bytocking rate. The conversion of ingested plant material enrichedn 15N increased the N isotope ratio of cattle hair. The importancef �15N in cattle hair has been confirmed as a long-term signal of

management in a previous study by Schwertl et al. (2005), how-ver, with different quantitative response of � 15N to N surplus onarm. Obviously, variations in �15N in animal diet as induced bynimal feed derived from external sources (commercial feed con-entrates), as well as variable field conditions under which NH3olatilization and discrimination against the heavier 15N isotopeccurs, contributed to the variations in 15N enrichment in plantsnd subsequent shifts in �15N in cattle hair and N balance to �15Nelationships as shown in Figs. 4–6.

The LNI farms did not cause N surplus, however, especially onastures notable N losses have been unavoidable, as grazing ani-als usually excrete 75–90% of feed intake on spots (Ball et al.,

979). The amount of N returned to the pasture in these spots isaturally high (Wachendorf et al., 2005) and not evenly distributed.igh N losses due to nitrate leaching or volatilization take place par-

icularly under these urine and dung patches (Anger et al., 2003).onsequently, in this study also LNI farms exhibited positive �15Nalues though to a smaller extent than the HNI farms.

.2. Spatial distribution of fertilizer application

In all of the investigated grassland farms we found the highest15N values in top soils and plant biomass on pastures and grass-

and located close to the farmyards. Top soils and plant biomass ofelds that were located further away from the farms showed lower15N values (Fig. 7a and b). This situation is plausible, because largeristances from the farmyard to the field imply higher costs for thearmers, due to higher expenditure of time and fuel consumption.s a result, fields at a distance from the farmyard are frequentlyeglected with respect to fertilizer application, resulting also in

ower overall N losses and less raised �15N values.Even several field plots of the LNI farms, mainly the pas-

ures/meadows close to the farmyard, did not exhibit the expectedow �15N values. This was probably caused by high local stock-ng rates and long grazing periods especially on the paddocks inhe vicinity of the barn. Moreover, these plots were also used

or the first cut. That way they likely received more organic fer-ilizer than meadows located more far away from the farm, orocated at higher altitude with less favourable conditions for ani-

al husbandry. The observations on LNI farms thus confirmed our

d Environment 184 (2014) 158–167

hypothesis that farmers tend to fertilize grassland most intensivelywhen it is located in the vicinity of their stables, and so N load gen-erally tends to decrease with increasing distance from that place,whereas no such clear differentiation could be made for the HNIfarms, since animals were kept indoors the whole year round.

In general, NPA acreages that did not receive any fertilizer due togovernmental requirements were located at the greatest distanceto the farmhouses. Indeed, we found the lowest 15N signatures intop soils and plant biomass of these NPA plots (Fig. 6).

Our data clearly show that for revealing the N status on practisefarms it is crucial to account not only for the N intensity of grasslandmanagement but also for the distribution of fertilizer applicationwithin the farm area.

4.3. Fertilizer application technique

Effects of the fertilizer technique on soil �15N values were onlyindicated for the LNI but not for the HNI farms (Fig. 8a and b). How-ever, compared with the spatial distribution of soil and plant �15Nvalues, these effects were rather small. On the one hand these find-ings may indicate that major N losses occurred also from soil afterapplication of the fertilizer, irrespectively of the application tech-nique. On the other hand, these findings may also indicate thatthe use of modern application techniques affected soil �15N val-ues over a shorter period of time than accounted for by the effectsof the amount and type of fertilizer N input and the grazing sys-tem. Hence, elucidating the rates of soil and plant �15N changesnow warrant further attention.

5. Conclusions

This study supports the hypothesis that high N surplus in grass-land farms over extended periods of time can be traced back byelevated �15N values of soil and biomass, and, to a smaller extent,also by the �15N values of the hair of animals. The higher the �15Nvalue, the lower was the overall farm’s N balance. Among the dif-ferent parameters and N pools tested, we found particular strongcorrelations between the N balances and the �15N values of topsoils and plant biomass and furthermore between the stocking ratesand the �15N values of top soils, plant biomass, silage, hay andhair. All these N pools integrate over several months to seasons.In other words, the longer the timeframe of 15N accumulation, thehigher was the accuracy of using its �15N value as an indicatorfor N use efficiency on a total farm’s scale. Similarly, the longerthe N compounds are recycled in the system, the stronger is the Nisotope signature influenced by discrimination processes and thebetter is the information that can be depicted from these isotopicmeasurements about N input and output ratios. Especially whenfarming practice is not well recorded, screening for �15N valuesin soil, plants, silage and hair may thus help identifying good Nmanagement practice.

Acknowledgement

The authors are grateful to the German Science Foundation(DFG) for the financial support of this investigation (project num-ber 74533). We are indebted to Rudolf Schäufele at the GrasslandScience Laboratory of the Technical University at Munich for analy-ses of isotopic composition and helpful discussions and comments

during the early experimental phases of the project. The techni-cal assistance of the laboratory staff at the Crop Science Group atINRES University of Bonn, especially Mrs. Petra Weber, is gratefullyacknowledged.

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