Residual inorganic soil nitrogen in grass and maize on sandy soil

Environmental Pollution 145 (2007) 22e30 www.elsevier.com/locate/envpol

Residual inorganic soil nitrogen in grass and maize on sandy soil H.F.M. Ten Berge a,*, S.L.G.E. Burgers b, H.G. Van der Meer a, J.J. Schro¨der a, J.R. Van der Schoot c, W. Van Dijk c a

Plant Research International, Plant Sciences Group, P.O. Box 16, 6700 AA Wageningen, The Netherlands b Biometris, Plant Sciences Group, P.O. Box 16, 6700 AA Wageningen, The Netherlands c Applied Plant Research, Plant Sciences Group, P.O. Box 430, 8200 AK Lelystad, The Netherlands Received 19 September 2005; received in revised form 25 March 2006; accepted 4 April 2006

The study shows how residual inorganic soil N relates to apparent N recovery. Abstract Nitrogen (N) remaining as inorganic (‘mineral’) soil N at crop harvest (NminH) contributes to nitrate leaching. NminH data from 20 (grass) and 78 (maize) experiments were examined to identify main determinants of NminH. N-rate (A) explained 51% (grass) and 34% (maize) of the variance in NminH. Best models included in addition crop N-offtake (U ), offtake in unfertilised plots (U0), and NminH in unfertilised plots (NminH,0) and then explained up to 75% of variance. At low N-rates where apparent N recovery r keeps to its initial value rini, NminH keeps to its base level NminH,0. At N-rates that exceed the value Acrit where r drops below rini, NminH rises above NminH,0 by an amount proportional to ðrini  rÞA. About 80% of ðrini  rÞA was found as NminH, in grass as well as in maize. The fraction ð1  rini ÞA does not appear to contribute to NminH at low N-rates (A  Acrit) or at high N-rates (A > Acrit). Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Residual; Inorganic; Nitrogen; Nitrate; Surplus; Critical rate; Initial recovery; Soil nitrogen supply

1. Introduction Nitrogen (N) that remains as inorganic N (nitrate-N and ammonium-N) in the soil profile after the growing season (Nmin) can be leached during autumn and winter, and then contributes to nitrate loading of upper groundwater. Although inorganic soil N was not always found to be a good indicator for nitrate leaching (e.g., Sieling et al., 1999), other field studies have shown that it represents an important contribution to total nitrate emission (Durieux et al., 1995; Hanus and Fahnert, 1989; Strebel et al., 1989; Wiesler and Horst, 1993). Residual inorganic soil N has been adopted in some countries to gauge potential nitrate leaching, because its observation is relatively easy and inexpensive. Regions Baden-Wu¨rttemberg in

* Corresponding author. Tel.: þ31 317 475951; fax: þ31 317 423110. E-mail address: [email protected] (H.F.M. Ten Berge). 0269-7491/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2006.04.003

Germany (Anonymus, 2002) and Flanders in Belgium (De Clercq et al., 2001) have defined maximum allowable limits to Nmin, thus assigning a formal status to this indicator in their environmental regulations. Unlike nitrate in groundwater, Nmin can be observed in small experimental plots and directly after the growing season. This makes Nmin an attractive indicator for the impact of crop management practices on nitrate leaching hazard (Schro¨der et al., 1996a; Vereijken, 1997). Its response to applied N-rate has been studied in numerous field experiments. Relationships have been established between N-rate and Nmin (e.g., Hanus and Fahnert, 1989; Makowski et al., 1999; Neeteson 1990,1994,1995; Richards et al., 1995), but these appear highly variable and likely depend on weather and soil conditions, besides crop characteristics and management factors. The objectives of this study are to identify the key factors that affect Nmin and to establish and evaluate simple models for Nmin which can be used to design sound fertiliser

H.F.M. Ten Berge et al. / Environmental Pollution 145 (2007) 22e30

management. We will try to achieve this by following a balance approach for inorganic soil N, combined with regression analysis. This enables to involve variables with an agronomic meaning and dimension, which facilitates the incorporation of agronomic information while applying the models (Makowski et al., 1999). We will evaluate the results of past experiments in grass and maize, the two key crops of the Dutch dairy industry. Nitrate leaching occurs in the Netherlands mostly on sandy soils, and so most field studies have concentrated on these soils. Our analysis, too, is limited to sandy soils. In grass and maize, the end of the growing season in NW Europe coincides with the onset of the winter period when nitrate leaching occurs as a result of precipitation surplus. Nmin at harvest (denoted as NminH) would, therefore, be directly relevant to nitrate leaching in these two crops. (It must be noted that in other crops, postharvest transformations and intercropping management may modify the amount of inorganic soil N, causing sometimes large differences between NminH at harvest and Nmin at the onset of drainage (e.g., Machet et al., 1997; Makowski et al., 1999; Sieling et al., 1999)) An earlier contribution (Ten Berge et al., 2002) was limited to grass only, and focussed on NminH at low input levels. This current paper deals with the response of NminH at higher N-rates, and analyses maize as well as grass data.

2. Materials and methods 2.1. Data sets from N-response trials We re-examined data from past experiments in the Netherlands, where responses of dry matter yield and N-offtake to N application were studied in ryegrass-dominated swards (Lolium perenne L.) and silage maize (Zea mays L.). The purpose of the original experiments was to assess economically optimal N-rates as well as to quantify nitrate leaching hazard. Irrespective of the design of the e sometimes multi-year or multi-location e trials, we define an ‘experiment’ as one trial in a given year at a given location, with either of the two crops. Each experiment comprised a series of N-rates. ‘Set’ refers to all observations made in one experiment. Grass data refer to cut grass produced for silage or drying; maize data to crops grown for whole-crop silage. All experiments were conducted on sandy soils: 20 experiments with grass and 78 with maize. Each experiment included at least four N-rates (treatments), including a zero-N treatment (no N-input applied). Depending on the experiment, N was applied in different forms, manures as well as chemical fertilisers. Manures were often injected or surface-spread cattle slurries. We normalised N-rates using equivalencies of 60 or 28 kg fertiliser-N per 100 kg of total-N in injected or surface-applied slurry, respectively (Geurink and Van der

23

Meer, 1995; Schro¨der and Stevens, 2004; Van der Meer et al., 1987; Van der Meer and Van der Putten, 1995). Crop dry matter yield and N-offtake were measured in all treatments of all experiments. Silage maize was harvested once at the end of the season, late September to mid-October. Grass was harvested in five or six cuts between early May and late October. Data refer to total season yield. The responses of dry matter yield and N-offtake to N-rate were used to quantify three parameters to be introduced later (U0, rini, Acrit). These will be used in this paper as local characteristics, to account for variation in residual inorganic soil N across experiments. The sum of nitrate-N and ammonium-N in the soil was measured at harvest at depth intervals 0e100 cm (grass) and 0e60 cm (maize) below the soil surface. NminH (kg ha1, subscript H for time of harvest) refers to these distinct depth intervals for the two crops. NminH was observed in all experiments, but only in selected treatments. A total of 237 treatments in the 20 grass experiments were sampled for NminH, and 345 treatments in the 78 maize experiments. NminH observed in zero-N treatments is referred to as NminH,0. We will use this parameter to help to explain variance in NminH across experiments. Treatments were usually replicated four times. Because it was not possible to retrieve replicate values from all original sources, treatment means were used instead of replicate plot values for all variables recorded at plot level: dry matter yield, N-offtake, and NminH. We thus ignored within-treatment variation between plots. Rainfall recorded at weather stations nearest to each experiment was used to calculate cumulative rainfall P (mm). For grass, rainfall was cumulated over the period from April 1st to October 1st. For maize, it was cumulated over the exact period between sowing and harvest, which varied between experiments. Further details on the experiments were reported by the original sources (Table 1).

2.2. Balance equations for NminH NminH is approximated by several alternative regression equations. These represent stepwise approximations towards an increasingly ‘complete’ balance equation for inorganic N. NminH,0 is taken as a base level in each of these equations. This is, most likely, equivalent to the ‘minimum value’ Rmin as used by Makowski et al. (1999). The simplest form then includes besides this base term only the rate of applied N (A, kg ha1): NminH ¼ aNminH;0 þ b½A

ð1Þ

where a and b are dimensionless regression coefficients. The second model takes the observed crop N-offtake (U, kg ha1) into account as a balance term, because N removed by the crop obviously does not contribute to NminH: NminH ¼ aNminH;0 þ b½A  U

ð2Þ

(Although parameters a and b in each model will have different magnitudes, we use the same symbols to show the correspondence between all models.) Nofftake in crop products originates not only from applied N but also from the soil itself. So, instead of subtracting the full offtake U as in Eq. (2), it would seem more appropriate to subtract only the amount that was absorbed from the dose applied, thus excluding N absorbed from the soil itself. The latter was measured in each separate experiment as N-offtake in the absence of applied N (U0, kg ha1). The equation resulting from this correction is:

Table 1 Data sets used to analyse relations between selected terms of the N-balance and inorganic soil nitrogen at harvest (NminH) Crops

Number of experiments

Number of treatments with NminH observations

Years

Source

Silage maize

78

345

1975e1999

Anonymus (1999), Schro¨der (1987a,b, 1990, 1998, 1999), Schro¨der and De la Lande Cremer (1989), Schro¨der and Ten Holte (1993), Schro¨der et al. (1985, 1992, 1993, 1996b, 1998), Van der Schans et al. (1995), Van der Schoot (2000), Van Dijk (1996, 1997a,b, 1998), Van Dijk et al. (1995, 1996)

Cut grass

20

237

1980e1998

Fonck (1982a,b, 1986a,b,c), Hofstede (1995a,b), Hofstede et al. (1995), Snijders et al. (1987), Wadman and Sluijsmans (1992), Wouters et al. (1992)

H.F.M. Ten Berge et al. / Environmental Pollution 145 (2007) 22e30

24 NminH ¼ aNminH;0 þ b½A  ðU  U0 Þ

ð3Þ

For the next model we need to invoke the concept of apparent N recovery (ANR) which is the fraction of applied N that is apparently recovered in the harvested crop products. ‘Apparent’ refers to the presumption that N-uptake derived from the soil is not affected by N-input. ANR is here noted as r, and is defined as rhðU  U0 Þ=A; 0  r  1. This r is affected by crop, soil and weather conditions as these affect U and U0, but also by N-rate A itself. The effect of N-rate on r is usually absent at low N-rates and so r remains constant in this range. We refer to this constant ‘initial’ value as rini. If N-rate surpasses a certain threshold, r drops below rini. This threshold is denoted as Acrit, the critical N-rate. Parameters rini and Acrit were assessed per experiment from the response curves of dry matter yield and N-offtake. The complement of initial recovery can be viewed as a single sink term, ð1  rini ÞA, representing all N that was inaccessible for crop uptake because it was either lost during the growing season (volatilisation, denitrification, leaching and runoff) or was immobilised in crop residues, soil organic matter, or microbial biomass. If this amount does not contribute to NminH, it must be subtracted from the right hand side of Eq. (3) which then transforms to: NminH ¼ aNminH;0 þ b½rini A  ðU  U0 Þ

ð4Þ

The above definition for r implies that for subcritical N-rates e where r equals rini e the term ½rini A  ðU  U0 Þ is equal to zero; and that this term equals ðrini  rÞA for N-rates that exceed Acrit. So according to the balance approximation proposed in Eq. (4), NminH accumulates only at N-rates high enough to induce a reduction of r. This concept is illustrated in Fig. 1. Eqs. (2)e(4) include observed N-offtake and this limits their applicability in forecasting NminH, because N-offtake itself depends on N-rate. Introduction of a relation for U(A) (Appendix) removes this drawback and so allows to generalise Eq. (4) into:   NminH ¼ aNminH;0 þ b mðA  Acrit Þ2 ð5Þ where m is a fixed crop coefficient with values 0.924  103 ha kg1 for grass and 1.750  103 ha kg1 for maize (Appendix). In Eq. (5), local information on U0 and rini is replaced by local (experiment-specific) values of Acrit. The relation between these parameters is outlined in the Appendix. Regression coefficient b and crop coefficient m (fixed) were kept separate to facilitate comparison between the balance expressions given in Eqs. (1)e(5). The form of Eq. (5) is comparable to that of Eq. (12) in Makowski et al. (1999), where their Xr would be equivalent to our Acrit. The latter, however, is defined independent of NminH and could be assessed beforehand from agronomic information (Appendix), whereas Xr was a regression parameter based on NminH observations. Net mineralization of N obviously contributes to NminH. Nevertheless, it could not be addressed explicitly in our models because no measured values

A (1 -

crop residues plus true losses

N-output (kg N/ha)

A(

ini -

)

slope 1:1

N-offtake slope

U0 Acrit

N rate

2.3. Regression analysis After merging the data from all experiments into a single combined data set per crop, the 20 models were evaluated for each of the two crops by linear regression. We thus aimed to explain the variation in NminH between individual treatments across all experiments, using information referring to treatmentlevel (A, U ) as well as information at experiment-level. Each separate experiment was characterised by one corresponding value of NminH,0, U0, Acrit, and rini, respectively. Parameters Acrit and rini were assessed per experiment from the observed local U(A) response, by numerical optimisation according to Kirkpatrick et al. (1983). No information on NminH was used in that procedure, and so it is justified to use these parameters in explaining the variance of NminH, as we do. (See Appendix for the U(A) function chosen.) Higher percentages of variance accounted for R2adj could obviously be achieved by allowing separate regression coefficients for A, U, U0, Acrit, and rini, instead of constructing compound regressor variates while maintaining two regression coefficients, as we do. Our purpose, however, is to understand NminH as the outcome of a balance for inorganic N and this, we believe, is better served with the regressors as chosen.

2.4. On the magnitude of a and b For an ideal and perfect model, the expected values for regression parameters a and b are both one. In other cases, they differ from one. For b > 1, the balance approximation in square brackets (Eqs. (1)e(5)) underestimates, the actual response of NminH. For b < 1, the reverse is true which indicates that some of the mineral N was lost by a process not accounted for. The fitted values of the regression parameters may thus help to validate the assumptions made in deriving the models. One must be aware, however, that compensation between the parameters may occur which then requires further inspection.

3. Results

slope ini

ini)

were available. Variation in net mineralization between experiments is, however, addressed through NminH,0 (see Appendix). Eqs. (1)e(5) define our Models II.1eII.5, respectively (Series II). In addition, a series of simplified models (Series I, Models I.1eI.5) was defined by replacing the term aNminH,0 by a constant (regression coefficient c), thus ignoring variation in NminH,0 between sets (experiments). Thus a total of 10 regression models are compared. It can be argued that models given by Eqs. (1) and (2) are too coarse approximations to a true balance and therefore should be excluded from this comparison. We did include them, nevertheless, for their practical significance: N-management legislation in most European countries imposes limits on either N-rate (Eq. (1)), N-surplus (Eq. (2)) or NminH.

A (kg N/ha)

Fig. 1. Schematic representation of Eq. (4). The solid line represents N-offtake in response to N-rate. For a ¼ 1 and b ¼ 1, the increment of Nmin,H over NminH,0 would be equal to (rini  r)A.

Mean, minimum and maximum values of NminH,0, U0, Acrit, rini and P are presented in Table 2. The parameter estimates and R2adj obtained for the various models are given in Tables 3 and 4 (grass and maize). In grass, N-rate alone accounted for 51% of variance in NminH. R2adj increased with increasing model complexity (Table 3). Adoption of parameters NminH,0 and U0 increased R2adj by 13% points (Model II.3 versus I.2). Introduction of parameter rini brought further improvement by 10% points (Models I.3 and II.3 versus I.2 and II.2). These improvements suggest that the respective models are stepwise better approximations for NminH, while the same number of regression coefficients (two) is maintained. In maize, N-rate alone accounted for only 34% of the variance in NminH, while the surplus model A  U (Model I.2) performed even worse accounting for not more than 13%. Parameters NminH,0 and U0 were more important than

H.F.M. Ten Berge et al. / Environmental Pollution 145 (2007) 22e30

growing season up to the last harvest, when NminH is measured. Mineralised N is readily absorbed by the sward and so it will not accumulate in the soil. N-absorption in maize, in contrast, virtually stops after tasseling (Pollmer et al., 1979). In the Netherlands it takes approximately 60 days from tasseling to harvest. N mineralised during this period accumulates in the soil and so NminH,0 determines NminH to a large extent, irrespective of the N-rate applied. Sources of variation in N-mineralization, particularly on maize land, include crop rotation and past applications of extreme slurry doses. The term in square brackets in each of Eqs. (1)e(5) is meant to express the response of NminH to N-input, that is, the increase in NminH above NminH,0. Values of b smaller than 1 indicate that the term in square brackets by itself overestimates this response. For both crops, irrespective of whether NminH,0 is included or not, coefficient b increases as we move from the simplest to more complex models, indicating that the more complex models are indeed improvements. The observation that b remains below 1 in Models II.4 and II.5 seems to justify the earlier assumption (derivation in Eqs. (4) and (5)) that the fraction (1  rini) of applied N does not contribute to NminH. So, first the amount (1  rini)A must be subtracted from the non-absorbed N-input (1  r)A. The remaining difference (rini  r)A then is an approximation for the increase of NminH above NminH,0. Still, this gives an overestimation of NminH, as b-values remain smaller than 1 (Models II.4 and II.5, both crops). With b in these models ranging between 0.726 and 0.840, apparently 16e27% (namely, the fraction (1  b)) of (rini  r)A does not contribute to NminH. We do not know the fate of this missing N, but presume that losses must be attributed to the same processes that made (1  rini)A unavailable for crop uptake. The observation b < 1 implies that for A > Acrit, a larger fraction than (1  rini)A is lost. This indicates that at A > Acrit competition for N by crop uptake is reduced in favor of other ‘sinks’. While b is smaller than 1, coefficient a often exceeds 1 significantly (Tables 3 and 4). We wanted to confirm that b-values smaller than 1 were not the result of numerical compensation between a and b in the regression analysis. If they were, the ‘missing N’ (16e27%) might, actually, not have disappeared

Table 2 Mean, standard deviation (s.d.) and minimum and maximum values of variables registered at ‘experiment’-level (combination of year  location) Mean (s.d.)

Minimum

Maximum

Grass NminH,0 (kg N ha1, 0e100 cm) U0 (kg N ha1) Acrit (kg N ha1) rini (kg kg1) P (mm)

26.0 128.1 243.2 0.83 476

(9.2) (36.7) (114.1) (0.11) (117)

8.0 69.7 83.0 0.65 329

46.0 194.0 450.0 0.99 685

Maize NminH,0 (kg N ha1, 0e60 cm) U0 (kg N ha1) Acrit (kg N ha1) rini (kg kg1) P (mm)

36.0 120.3 38.6 0.71 307

(27.5) (44.5) (50.5) (0.13) (87)

9.0 42.0 0.0 0.37 147

144 225.0 169.2 1.00 636

25

in grass (Table 4). Their combined introduction raised R2adj by almost 60% points (Model II.3 versus I.2). Adopting NminH,0 alone increased R2adj by 20e47% points (Series II versus Series I), whereas parameter U0 alone increased R2adj by 36% (Model I.3 versus I.2) and 11% (Model II.3 versus II.2) points. The gradual trend of R2adj increasing with model complexity observed in grass was less pronounced in maize. Further, Tables 3 and 4 show that models based on Acrit (Models I.5, II.5) performed about equal to those based on rini and U0 (Models I.4, II.4), for both crops. This was to be expected, given the relations between these parameters (Appendix). (The agreement between the models based on Eqs. (4) and (5), respectively, suggests that the chosen U(A) function is a good approximation of actual uptake.) 4. Discussion Variation in NminH,0 was much larger in maize than in grass, and the values themselves were higher, too (Table 2), despite the shallower depth of soil sampled (0e60 cm in maize versus 0e100 cm in grass). The importance of NminH,0 in maize, relative to grass, is explained by the respective N-uptake patterns of the two crops. Cut grass swards maintain a vigorous vegetative growth and associated N-absorption throughout the

Table 3 Percentage of variance accounted for (R2adj , %), and estimated regression parameters for Models I.1eI.5 (NminH,0 not included as regressor) and Models II.1eII.5 (NminH,0 included as regressor) for grass on sandy soils Grass Model I.1. I.2. I.3. I.4. I.5.

A AU A  (U  U0) riniA  (U  U0) m (A  Acrit)2

Model II.1. A II.2. A  U II.3. A  (U  U0) II.4. riniA  (U  U0) II.5. m (A  Acrit)2

c (kg ha1) estimate

s.e.

b estimate

s.e.

R2adj

0.8 110.7 24.1 36.0 33.0

6.2 3.9 4.0 2.9 3.0

0.240 0.592 0.661 0.809 0.728

0.015 0.036 0.032 0.030 0.027

50.8 53.7 63.9 75.9 75.9

a estimate 0.20 3.77 0.93 1.18 1.07

s.e. 0.19 0.12 0.12 0.10 0.10

b estimate 0.226 0.638 0.645 0.806 0.726

s.e. 0.015 0.033 0.030 0.030 0.027

R2adj 51.0 61.5 66.7 76.0 75.6

H.F.M. Ten Berge et al. / Environmental Pollution 145 (2007) 22e30

26

Table 4 Percentage of variance accounted for (R2adj , %), and estimated regression parameters for Models I.1eI.5 (NminH,0 not included as regressor) and Models II.1eII.5 (NminH,0 included as regressor) for maize on sandy soils Maize Model I.1. I.2. I.3. I.4. I.5.

A AU A  (U  U0) riniA  (U  U0) m (A  Acrit)2

Model II.1. A II.2. A  U II.3. A  (U  U0) II.4. riniA  (U  U0) II.5. m (A  Acrit)2

c (kg ha1) estimate

s.e.

b estimate

s.e.

R2adj

24.6 86.5 27.0 35.6 40.9

4.2 4.0 3.2 2.9 3.0

0.433 0.329 0.662 0.979 1.099

0.032 0.045 0.036 0.053 0.067

34.0 13.4 49.2 49.8 43.8

a estimate 1.17 2.14 1.09 1.18 1.27

s.e. 0.06 0.06 0.05 0.05 0.05

b estimate 0.305 0.327 0.491 0.732 0.840

s.e. 0.019 0.028 0.027 0.043 0.051

R2adj 68.5 61.0 72.6 70.4 69.7

but was accounted for in the term aNminH,0, or at least partly so. To test this, we ran Models II.4 and II.5 with fixed coefficient a ¼ 1. In grass, b increased to 0.838 and 0.738 for the two models, respectively. In maize, the new b-values were 0.827 and 1.007, respectively. So, the conclusion that (rini  r)A overestimates (NminH  NminH,0) was not based on a numerical artefact arising from compensation between the regression parameters, except perhaps in Model II.5 for maize where it appears that b becomes 1 if a is fixed at 1. Given the observed R2adj and the values of the regression coefficients, Models II.4 and II.5 perform best for grass and maize. For grass alone, Models I.4 and I.5 are equally good. Central to Models II.4 and II.5 is the absence of an NminH response to subcritical N-rates. To illustrate that this is no gross simplification, Figs. 2 and 3 (grass and maize) depict the difference (NminH  NminH,0) versus the exceedance of the critical N-rate. 4.1. Relation between nitrogen surplus and NminH The difference between N-input A and N-offtake U is referred to as the N-surplus. Until 2006, threshold values for N-surplus were used in Dutch legislation to reduce nitrate

leaching (e.g., Schro¨der et al., 2003; Van der Meer, 2001). Models I.2 and II.2 are surplus-based models, and these allow to inspect how well the two supposed indicators for nitrate leaching hazard (NminH and N-surplus) are related. This study shows a large discrepancy between the two. There is a large gap between their respective magnitudes (both crops), as well as a weak correlation (notably in maize; Fig. 4). N-surplus is, for most data points, much smaller than NminH. This is because U0 is included in the total offtake U, while the corresponding source e that is, N-mineralization e is not accounted for. This makes that (A  U ) grossly underestimates NminH, resulting in relatively large values of regression coefficients a (Model II.2) and c (Model I.2). Scatter in the relation between N-surplus and NminH arises largely as a result of pooling data from sets (experiments) with different U0. As demonstrated by Fig. 4, segregation of data into U0-classes brings significant improvement (see also Table 4, Model I.3 versus I.2). Variation in U0 has such dispersing effect because enhanced N-mineralization reduces N-surplus (larger U0 and hence larger total offtake U ) while it increases NminH (via increased NminH,0). This dispersal occurs most pronounced in crops where mineralization proceeds well beyond the crop

maize

grass

250.0

400 300 200 100 -600

-400

-200

0

0

200

400

600

800

-100

exceedance of critical N-rate (kg/ha) Fig. 2. Increment of residual inorganic soil N above residual inorganic soil N found in unfertilised plots of the same experiment (NminH  NminH,0), in response to the exceedance (A  Acrit) of the critical N-rate in cut grass experiments. See also Eq. (5). NminH refers to depth interval 0e100 cm and time of last harvest. Data points are treatment means. All data are from sandy soils.

NminH - NminH,0 (kg/ha)

NminH - NminH,0 (kg/ha)

500

200.0 150.0 100.0 50.0 -200.0

-100.0

0.0 0.0

100.0

200.0

300.0

400.0

-50.0 -100.0

exceedance of critical N-rate (kg/ha) Fig. 3. Increment of residual inorganic soil N above residual inorganic soil N found in unfertilised plots of the same experiment (NminH  NminH,0), in response to the exceedance (A  Acrit) of the critical N-rate in silage maize experiments. See also Eq. (5). NminH refers to depth interval 0e60 cm and time of harvest. Data points are treatment means. All data are from sandy soils.

H.F.M. Ten Berge et al. / Environmental Pollution 145 (2007) 22e30

27

maize 300

NminH, 0-60 cm (kg/ha)

250 200 150 100 50

-250

-200

-150

-100

-50

0

0

50

100

150

N-surplus (A-U) kg/ha Fig. 4. Residual inorganic soil N (NminH) in maize plotted versus N-surplus defined as applied N-rate minus crop N-offtake (A  U ). Each data point is the treatment mean within one experiment. Data are grouped by classes of soil N-supply (observed crop N-offtake in unfertilised plots, U0).  50  U0 < 100 kg ha1; 6 100  U0 < 150 kg ha1; : 150  U0 < 200 kg ha1. The three lines represent the linear regression relations within each of the three classes. Broken line is 1:1 line. NminH refers to depth interval 0e60 cm and to time of harvest. All data are from sandy soils.

NminH,0 0-60 cm (kg/ha)

N-uptake phase e such as maize. The positive correlation between U0 and NminH,0 is illustrated by Fig. 5, for all unfertilised maize plots in the combined data set. Obviously, short term trials with varying N-rates (as in this study) are characterised by the absence of equilibrium between N-input and U0. The above dispersal mechanism may, therefore, be less pronounced in real farming conditions where such equilibrium is more likely to exist and where, as a consequence, N-surplus could be better correlated with losses to the environment. Nevertheless, poor correlation between N-surplus and nitrate leaching has been reported, too, from monitoring studies outside the context of N-rate trials, notably in arable systems (e.g., Lord et al., 2002). Oenema et al. (2003, 2005) also reported such lack of correlation (for nitrate leaching rather than NminH), mostly based on simulated data. The above mechanism may help to understand why the correlation is often poor and sometimes even reverse. maize

160.0 140.0 120.0 100.0 80.0 60.0 40.0 20.0 0.0 0.0

50.0

100.0

150.0

200.0

250.0

N-offtake from unfertilised soil (kg/ha) Fig. 5. Residual inorganic soil N in maize observed in unfertilised plots (NminH,0), versus crop N-offtake in the same plots (U0). Each data point is the mean of zero-N (unfertilised) plots in one experiment (one year at one location). NminH,0 refers to depth interval 0e60 cm and to time of harvest. The line represents the linear regression relation y ¼ 0.41x 12.1 (R2 ¼ 0.45). All data are from sandy soils.

4.2. Relation between rini and NminH So far, we have not discussed the direct relation between rini and NminH. Parameter rini may vary among locations and years due to variations in N-losses (in-season leaching, denitrification, volatilisation and immobilisation). In the grass data we found a weak but significant ( p < 0.001) positive correlation between rini and NminH. This implies that more efficient crop N-uptake is associated with larger amounts of residual soil N. That might seem contradictory, but is explained as follows by two mechanisms. First, larger rini reduces Acrit because less N-input is required to satisfy crop N-demand, and so to reach the point where apparent N recovery drops below rini. Lower Acrit increases the exceedance (A  Acrit) at any given N-rate (A), and so enhances the accumulation of inorganic N (Eq. (5)). Second, larger rini implies that the ‘lost fraction’ (1  rini) is smaller, which leaves more N as NminH (see also Fig. 1). This second mechanism implies that larger rini leads to larger NminH, not only at given N-rate but also at given exceedance of Acrit. This is confirmed by the data ( p ¼ 0.012). Both mechanisms are implied in Eqs. (4) and (5). For the two explanations to be valid, they should share the same prerequisite: that the amount (1  rini)A remains indeed unavailable to contribute to NminH, as was already verified previously. Though N corresponding to (1  rini)A does not seem to contribute to NminH, this does not preclude the possibility that part of this N contributes to nitrate leaching. Leaching by rain during the growing season may render this N inaccessible for crop uptake, thus lowering rini. N-rates smaller than Acrit are, therefore, not necessarily ‘safe’ in terms of nitrate leaching. The available data do not allow extensive investigation of this aspect. Ten Berge et al. (2002) have shown for some of these grassland experiments, however, that effects on nitrate leaching were small indeed in the subcritical range of N-rates (though not entirely absent).

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4.3. Effect of precipitation on NminH All above results and comments refer to models without the precipitation variable, P. Introduction of P as additional linear regressor in the respective models of Series I showed that P had a significant effect on NminH. Depending on the model, it reduced NminH in grass by 2e22 kg ha1 per 100 mm, and in maize by 10e13 kg ha1 per 100 mm. P vanished as a significant regressor after including NminH,0 (Models II.1eII.5). We conclude that part of the effect of rainfall is implicit in NminH,0. With this variable included, the effect of P reduces to 3 to þ7 kg (grass) and 7 to þ5 (maize) kg ha1 per 100 mm of precipitation. (It was investigated whether the precipitation surplus [P  ET], with ET for evapotranspiration, was a better regressor than P. After all, it is only the surplus of precipitation that might wash inorganic N downward. [P  ET], however, gave no better results.) 5. Conclusions Selected combinations of A, U, U0, rini and NminH,0 explained up to 75% of the total variance in NminH, in grass as well as in maize. Without loss of model performance, parameters rini and U0 can be replaced by Acrit, in combination with a fixed crop-specific value for m. N-rate alone explained 51% (grass) and 34% (maize) of the variance in NminH. In maize, NminH,0 alone can make up large part of total NminH. This is attributed to continued mineralization after tasseling, when crop N-absorption ceases. The effect of N-rate on NminH can be ignored at N-rates below Acrit, the range where the apparent N recovery r remains constant and equal to rini. Mean values of Acrit were 243 kg ha1 for grass and 39 kg ha1 for maize. At N-rates exceeding Acrit, NminH builds up in proportion to the difference rini  r. On average, about 80% of this difference is found as inorganic soil N at harvest. This holds for both grass and maize. N-surplus and NminH are both regarded as indicators for nitrate leaching hazard. Correlation between the two indicators was only weak in this study, especially in maize. This was largely caused by variation in soil N-supply, expressed as N-offtake from unfertilised plots, U0. Variation in U0 results from variation in soil fertility and weather conditions, if data from different locations and years are pooled, as in this study. Scatter due to varying U0 is more pronounced in maize because a positive correlation exists for this crop between U0 and NminH,0. Because larger U0 reduces N-surplus, this correlation disrupts e in composed data sets e the positive association that would otherwise exist, at given site-year, between N-surplus and NminH with increasing N-rates. Farmers’ practice is to apply 150 to over 200 kg N per ha to maize. This is well above the average critical N-rate found here (39 kg ha1). In grassland systems, too, many farmers apply more N than the mean critical N-rate of 243 kg ha1. This study shows (Models 4 and 5 in Tables 3 and 4) that a reduction of N-rate to Acrit will have a considerable impact on NminH in

both crops. Acrit, however, varies (Table 2) and this would complicate its use as threshold in agri-environmental legislation. A simpler alternative would be to allow N-rates based on expected soil N-supply, thus accounting for part of the variation in Acrit. High N-rates on fertile soils would then be avoided, for example on soils with U0 larger than 100 kg ha1 in maize (Fig. 4). This would require, obviously, reliable methods to estimate U0. The use of animal manures in maize should also be restricted on such soils, to mitigate late season mineralization. Such measures, aiming to reduce NminH,0, would take effect only in the course of years. Catch crops after maize have a more immediate effect. Their use is now compulsory in dairy farms in the Netherlands. The results in this study are based on regression analysis which limits the scope for extrapolation to new situations; further work should aim to generalise balance approaches further. Yet the study exposes, we believe, principal mechanisms and factors which must be addressed if we wish to reduce residual inorganic soil N. The models for NminH may hold for other field crops, too, albeit with different parameter values. In cereals and other crops already harvested in summer, for example, NminH,0 would be lower than in maize because an important fraction of annual mineralization occurs after harvest and is thus not expressed in NminH,0. There, agronomic measures to reduce the buildup of inorganic soil N towards the onset of drainage are of similar nature, but their implementation is easier in cereals than in maize due to the earlier time of harvest.

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