CORN
Nitrogen Concentration at Maturity—An Indicator of Nitrogen Status in Forage Maize
Reproduced from Agronomy Journal. Published by American Society of Agronomy. All copyrights reserved.
Antje Herrmann* and Friedhelm Taube
ABSTRACT
a concentration of dairy farming with intensive silage
maize production. Previous and current efforts on the national and state level to reduce N emissions to the environment include various measures, such as the obligation under the Fertilizer Act to record nutrient balances
at the farm gate or field level and enacted policies such
as the large-scale monitoring of environmental conditions (Bundesregierung, 1996). In the future, the allocation of subsidies to producers will be linked to the respect of environmental, food safety, animal and plant
health, and animal welfare standards as well as the requirement to keep all farmland in good agricultural and
environmental condition (“cross-compliance”) (Karnitschnig, 2002; European Commission, 2004). The successful implementation of such subsidy allocation policies
requires the availability of efficient and reliable indicators of sound nutrient management. With respect to the
N status of crops, a favorable indicator should be (i) sensitive to N deficiency as well as overfertilization, (ii) easy
to determine and to update for in-season adjustments of
N management at the farm level, and (iii) suitable for
broad-scale monitoring and assessment programs.
Many studies have been conducted to explore the potential of plant tissue sampling methods as an indicator
of N status. The chlorophyll meter technology (Piekielek et al., 1995; Fox et al., 2001) provides point measurements only, which limits its suitability for quantifying the
N status of entire fields or for a broad-scale monitoring
(Bausch and Duke, 1996). Moreover, this method is not
capable of detecting luxury N uptake since maize plants
achieve a maximum chlorophyll content irrespective of the
level of overfertilization (Dwyer et al., 1995). This serious
disadvantage applies also to remote-sensing techniques,
which allow a large-area monitoring with the possibility of
assessing spatial variability within a field (Bausch and
Duke, 1996; Blackmer and Schepers, 1996; Osborne et al.,
2002). The end-of-season stalk nitrate test has proven
effective for the assessment of the N status of maize from
one-fourth milkline growth stage to 3 wk after maturity
(Binford et al., 1992; Hooker and Morris, 1999). The
concept of a critical N concentration provides another
indicator, which can be applied over a wider time frame.
Plénet and Lemaire (1999) suggested restricting its validity to the period from emergence to silking plus 25 d.
Herrmann and Taube (2004) recently showed that this
indicator is valid until silage maturity. The underlying
concept assumes the existence of a minimal N concen-
The increased awareness of potential impacts of agricultural activities on nonpoint source pollution has increased the demand for agroenvironmental policy measures and for scientifically sound indicators
to control their implementation. Our objective was to investigate whether
N concentration of maize (Zea mays L.) at silage maturity, a routinely
recorded quality parameter, can serve as an end-of-season indicator
of N status. Based on 29 field trials on sandy soils in northern Germany,
we derived a critical N concentration at silage maturity (CNC), i.e.,
the minimum N concentration necessary for maximum yield production. A quadratic-plateau function describing dry matter (DM) yield
as a function of N concentration allowed for the exclusion of all data
sets not responsive to N fertilization or with luxury N uptake. For
the remaining pooled data points, a mixed-model analysis provided
parameter estimates describing N concentration (Nc) as an exponential function of relative DM yield (Wrel), namely Nc ⫽ 4.4141·
exp (0.0086·Wrel). Setting Wrel ⫽ 100 provided a CNC of 10.5 g N
kg⫺1 DM. This value is in good agreement with results in the literature,
which indicates the relative robustness of CNC with respect to a wide
range of environmental conditions and genotypes. The CNC constant
can be used to evaluate and monitor the end-of-season N status on
a large-area scale. Applied to an extended set of silage quality data
of northern Germany, it revealed that forage maize production is
characterized by significant excess of N supply in this region and
leaves ample opportunity for reduction in N use without risk of any
yield loss.
T
he intensification of agricultural production has
been accompanied by severe N contamination of
ground and surface water, marine waters, and the atmosphere (Matson et al., 1997). In western Europe, agricultural land is now by far the leading source of nitrate in
rivers and aquifers (OECD, 2001). In Germany, the
nonpoint pollution of surface waters caused by agriculture corresponds to 30% of the N fertilizer applied to
arable land (Umweltbundesamt, 2003). In the last 15 yr,
N surpluses decreased by 40% between 1987 and 2000.
Nevertheless, intensive fertilization and a high concentration of animal husbandry keeps the N surpluses high
with an average of 117 kg N ha⫺1 in 2000.
Nonpoint N emissions show maximum values in regions where high livestock population density coincides
with sites vulnerable to leaching. This situation applies
especially to northwestern Germany, which is characterized by sandy soils, shallow groundwater tables, and by
Inst. of Crop Sci. and Plant Breeding, Grass and Forage Sci./Organic
Agric., Christian-Albrechts-Univ. Kiel, Olshausenstr. 40, D-24098 Kiel,
Germany. Received 19 Feb. 2004. *Corresponding author (aherrmann@
email.uni-kiel.de).
Abbreviations: CNC, critical nitrogen concentration at silage maturity;
DM, dry matter; LUFA, Agricultural Analytical and Research Institute; Nc, nitrogen concentration; Ncth, threshold nitrogen concentration; Nmin, soil mineral nitrogen; NNI, nitrogen nutrition index; W,
dry matter yield; Wrel, relative dry matter yield.
Published in Agron. J. 97:201–210 (2005).
© American Society of Agronomy
677 S. Segoe Rd., Madison, WI 53711 USA
201
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AGRONOMY JOURNAL, VOL. 97, JANUARY–FEBRUARY 2005
tration necessary to achieve a maximum crop growth
rate. This required minimal concentration is defined as
the critical N concentration. The current N status at any
time can now be expressed by the N nutrition index (NNI),
defined as the ratio of actual N over the corresponding
critical N concentration value (Lemaire and Gastal,
1997). The end-of-season stalk nitrate test as well as the
critical N concentration are well suited to record the N
status and to distinguish N deficiency from N excess situations. Both methods, however, require a considerable
effort of sampling and processing, which for all practical
purposes prohibits their routine use as a regional monitoring tool.
In Germany, most dairy farmers routinely make use
of the services of an existing network of private and official consultancy agencies to conduct a forage quality
analysis of their silages. The data on N concentrations
of maize silages are therefore widely available to them.
Provided that the changes of N concentration during ensiling are negligible, this information could be used by
farmers to calculate their individual NNI, and based on
this value, they could adjust their N management in the
following growing season (“learning-by-doing” strategy). Furthermore, policy makers could include this extensive data set into their effort of large-scale monitoring of the overall N status of silage maize production.
There is, however, one obstacle to this otherwise promising approach, namely the necessity of recording DM
yield parallel to N concentration.
The main objectives of the present study therefore
were (i) to circumvent the necessity of yield recording
by quantifying the relationship between relative DM
yield and N concentration of the whole crop at silage
maturity, based on data sets collected in nine experiments on sandy soils in northern Germany; (ii) to derive
a CNC value, defined as the critical N concentration at silage maturity, i.e., the minimal N concentration at maximum relative yield; (iii) to monitor and assess the status
of N management in silage maize production in northern
Germany by applying the derived CNC value to silage
quality data provided by several Agricultural Analytical
and Research Institutes (LUFA); and (iv) to discuss the
suitability of this new approach.
MATERIALS AND METHODS
Field Experiments
The study was based on data obtained in nine multiyear
field experiments conducted by five different institutions at
nine different sites in northern Germany, displayed on the
map in Fig. 1. The research objectives of the experiments differed substantially. Table 1 lists for each field trial some details
with respect to N fertilization treatments, hybrids, soil conditions, and cropping history. Experiment 1 was conducted by
our department as part of the interdisciplinary research project
“Karkendamm” (Taube and Wachendorf, 2000) where N flows
in the soil–plant–animal system were analyzed for specialized
dairy farms with varying production intensities and management strategies. Experiments 2 to 9 were performed by the
Agricultural Chambers of Schleswig-Holstein, of Hannover,
and of Weser-Ems, and the University of Applied Sciences of
Kiel. Their purpose was to study the effects of amount and
Fig. 1. Locations of the field experiments in Schleswig-Holstein and
Lower Saxonia, Germany: Schuby (1), Ostenfeld (2), Karkendamm
(3), Bremervoerde (4), Bramstedt (5), Berkhof (6), Dasselsbruch
(7), Celle (8), and Markhausen (9).
timing of fertilizer application on yield, forage quality, and
soil mineral N (Nmin) content after harvest.
Sites 1 to 5 and 9 are characterized by a moderate maritime
climate with wet, cool summers, mild winters, and only slight
temperature fluctuation. Average daily temperatures range
between 7.8 and 8.7⬚C and annual precipitation between 716
and 865 mm. The climate prevailing on Sites 6 to 8 is more
continental (average daily temperature: 8.8 to 8.9⬚C; rainfall:
698 to 725 mm). All experiments were conducted on sandy
soils where maize was grown either as monoculture (Exp. 1,
2, 3, 9) or in a crop rotation followed by other arable crops
(Exp. 4–8). While Exp. 1 to 3 and 9 were performed on the
same plots each year, experimental sites of Trials 4 to 8 have
changed over the years.
Fertilizer treatments included applications of mineral N
fertilizer or combinations of mineral N and slurry (cattle or
pig). With respect to mineral N application, fixed treatments
were used in Exp. 1 to 3, regardless of the Nmin content. In
contrast, fertilization of Exp. 4 to 9 was aligned with a fixedtarget N fertilization level, taking into account the soil N release, i.e., Nmin in spring. With Nmin values ranging from 4
to 81 kg N ha⫺1, actual application rates varied substantially.
Slurry was generally applied before sowing and mineral N
fertilizer given in split doses at key growth stages, i.e., at the
one- and six-leaf stage if not specified differently in Table 1.
Phosphorus and K fertilization levels and further crop management measures were applied according to the common agricultural practice to allow for potential production, i.e., no
other factor was limiting except N. In field experiments that
included slurry treatments, P and K application were adjusted
for all treatments to align with the highest slurry application
rate to eliminate potential effects of P and K supply on maize
yield and quality. Cultivars used in the experiments covered
the range of early to midearly silage maturity. With only a few
exceptions, the field trials did not receive any irrigation. Experiments were generally conducted in a randomized complete
block design or a split-plot design with three to four replications.
Data Sampling
In Exp. 1, maize yield and quality data were recorded every
2 wk throughout the growing season. The present study, however, used only the data collected at silage maturity, which
203
HERRMANN & TAUBE: AN INDICATOR OF N STATUS AT SILAGE MATURITY
Table 1. Locations, years, N treatments, hybrids, preceding crops, and soil types in the N fertilization experiments. For combined N
fertilization treatments, i.e., mineral N and slurry application, the N amount (kg N ha⫺1) is specified with respect to the mineral (m) and
organic (o) fraction.
Reproduced from Agronomy Journal. Published by American Society of Agronomy. All copyrights reserved.
Experiment no.
Field trial site/institution
Year
1
Site: Karkendamm (53ⴗ55ⴕ N,
9ⴗ55ⴕ E, 14 m above sea level)
Grass and Forage Sci./
Organic Agric., Univ. of Kiel
1997–2001
2
Site: Schuby (54ⴗ31ⴕ N, 9ⴗ28ⴕ E,
20 m above sea level)
Chamber of Agric. SchleswigHolstein
Site: Ostenfeld (54ⴗ18ⴕ N,
9ⴗ46ⴕ E, 14 m above sea level)
Faculty of Agric., Univ. of Appl.
Sci., Kiel
1997–1998
3
4
Site: Berkhof (52ⴗ36ⴕ N, 9ⴗ43ⴕ E,
50 m above sea level)
Chamber of Agric. Hannover
5
Site: Bramstedt (52ⴗ52ⴕ N,
8ⴗ46ⴕ E, 20 m above sea level)
Chamber of Agric. Hannover
6
Site: Bremervoerde (53ⴗ29ⴕ N,
9ⴗ9ⴕ E, 10 m above sea level)
Chamber of Agric. Hannover
7
Site: Celle (52ⴗ37ⴕ N, 10ⴗ5ⴕ E,
55 m above sea level)
Chamber of Agric. Hannover
8
9
N fertilization treatments
1993–1997
• 0, 30, 60, 90, 110, 150, 190 kg N
ha⫺1 (m)
• 30 (m) ⫹ 80 (o, applied as cattle
slurry) kg N ha⫺1
• 30 (m) ⫹ 160 (o) kg N ha⫺1
• 110 (m) ⫹ 80 (o) kg N ha⫺1
1995–1998
• 0 kg N ha⫺1
• 100 kg N ha⫺1 (m) minus Nmin§
(early spring)
• 140 kg N ha⫺1 (m) minus Nmin
(early spring)
• 180 kg N ha⫺1 (m) minus Nmin
(early spring)
• 140 kg N ha⫺1 (m) minus Nmin
(May)
• 180 kg N ha⫺1 (m) minus Nmin
(May)
• 120 kg N ha⫺1 (m) minus Nmin
(sowing) ⫹ 60 kg N ha⫺1 (m)
(presidedress)
• 180 kg N ha⫺1 (o) minus Nmin
(sowing)
• 120 kg N ha⫺1 (o) minus Nmin
(sowing) ⫹ 60 kg N ha⫺1 (o)
(presidedress)
1995, 1996, 1998 see Berkhof (Exp. 4) additional
in 1996 and 1998: 180 kg N
ha⫺1 (o ⫹ Didin¶) minus
Nmin (sowing)
1997–1998
Hybrid†
0, 50, 100, 150 kg N ha⫺1 (m)
Naxos (S 220)
combined with 0, 20, and 40 m3
cattle slurry (2.4, 1.8, 3.4, 3.7,
3.3 kg N m⫺3 in 1997, 1998,
1999, 2000, and 2001)
Antares (S 190,
0, 70, 110, 150 kg N ha⫺1 (m)
K 220)
Preceding crop‡
Soil type
maize
humous sand
maize
sand
Alarik (S 210,
K 220)
maize
loamy sand
1995: Pirat (S 230,
K 220)
1996: Magister
(S 250, K 260)
1997, 1998: Banguy
(S 240, K 240)
1995: winter
barley
1996: carrot
1997: potato
1998: carrot
1995: loamy sand
1996–1998: sand
1995: Legat (S 230,
K 240)
1996: Lenz (S 240,
K 240)
1998: Banguy
(S 240, K 240)
see Berkhof (Exp. 4) additional
Banguy (S 240,
in 1998: N fertilization according
K 240)
to Nitrachek#
1995
see Berkhof (Exp. 4)
Oural (S 220,
K 220)
Site: Dasselsbruch (52ⴗ33ⴕ N,
10ⴗ0ⴕ E, 39 m above sea level)
Chamber of Agric. Hannover
1996–1998
see Berkhof (Exp. 4)
Site: Markhausen (52ⴗ56ⴕ N,
7ⴗ50ⴕ E, 12 m above sea level)
Chamber of Agric. Weser-Ems
1994–1997
mineral N treatments:
• 0, 80, 160, 240 kg N ha⫺1 (m)
• 180 kg N ha⫺1 minus Nmin
combined mineral and organic
(cattle slurry):
• 30 (m) ⫹ 50 (o) kg N ha⫺1
• 30 (m) ⫹ 130 (o) kg N ha⫺1
• 30 (m) ⫹ 210 (o) kg N ha⫺1
combined mineral and organic
(pig slurry):
• 30 (m) ⫹ 50 (o) kg N ha⫺1
• 30 (m) ⫹ 130 (o) kg N ha⫺1
• 30 (m) ⫹ 210 (o) kg N ha⫺1
1996: Lenz (S 240,
K 240)
1997, 1998: Banguy
(S 240, K 240)
Magda (S 220,
K 240)
1995: winter
1995: sand
wheat
1996 and 1998:
1996: winter rye
sandy loam
1998: maize
1997: winter
rapeseed
1998: winter
barley
winter barley
humous sand
1996: maize
1997: ley
1998: maize
1996: sand
1997, 1998:
humous sand
maize
humous sand
sand
† German maturity rating system developed from the FAO system in 1998: silage maize cultivars to be released in Germany receive two rating numbers,
based on the dry matter content of the whole crop (S) and the grain (K).
‡ Winter barley, Hordeum vulgare L.; carrot, Daucus carota L. subsp. sativus (Hoffm.) Arcang.; potato, Solanum tuberosum L.; winter wheat, Triticum
aestivum L.; winter rye, Secale cereale L.; winter rapeseed, Brassica spp.
§ Nmin, soil mineral nitrogen.
¶ Didin, nitrification inhibitor, added to slurry.
# Nitrachek, reflectometer for calculating nitrate concentration in plant sap.
was assessed on the basis of DM content of the whole crop,
optimally ranging between 300 and 350 g kg⫺1, depending on
genotype. In Exp. 2 to 9, data on maize yield and forage
quality were recorded at silage maturity stage only. In all
experiments, aboveground biomass sampling was performed
using a plot chopper, and representative subsamples were
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AGRONOMY JOURNAL, VOL. 97, JANUARY–FEBRUARY 2005
subsequently dried to constant weight at 65⬚C. All samples
were fine-ground using a Cyclotec mill, which was fitted with a
1-mm screen. Nitrogen content of the samples was determined
using the near infrared spectroscopy technology (NIRSystems
5000 monochromator, Foss-NIRSystems, Silver Spring, MD,
USA), using software from Infrasoft International (ISI, Port
Matilda, PA, USA). Calibration and validation subsets were
analyzed for N with the Kjeldahl method according to the
procedure of Naumann and Bassler (1993).
Selection of Data Sets
Our study intended to first derive a relationship between
the standardized, relative DM yield and the N concentration
of forage maize at silage maturity. This should then allow
determination of the CNC value, i.e., the minimum N concentration required for achieving maximum relative DM yield.
Only site-year combinations covering the whole range of N
supply from N deficiency conditions up to luxury N consumption are suitable for determining the relationship between
relative DM yield and N concentration. We therefore, in a
first step, separated the informative from the noninformative
data sets. This was achieved by fitting to each site-year combination a quadratic-plateau function, which described the response of DM yield to N concentration.
W(Nc) ⫽
Nc ⫹ a · Nc
冦 a ⫹ a · wmax
0
1
2
2
Nc ⱕ Ncth (quadratic)
Nc ⬎ Ncth (plateau)
[1]
⫺1
where W denotes the DM yield (t DM ha ), Nc is the N concentration (g N kg⫺1 DM), Ncth is the threshold N concentration (g N kg⫺1 DM) at which the quadratic part passes into
the plateau part, and a0, a1, a2, and wmax are curve parameters.
While the quadratic part represents those situations where
further increase in N supply is paralleled by an increase in
DM yield, the plateau part of the function describes the nonN-limiting growth conditions, i.e., additional N supply results
in an increased N concentration but no increase of biomass.
Estimates of the function parameters were calculated for each
site-year combination using PROC NLIN of SAS (version 8.2).
With respect to our parameter estimation results, three cases
may be distinguished: (i) parameters could not be estimated
for the quadratic and plateau parts; (ii) the quadratic part
could be fitted, but no DM yield maximum (i.e., the plateau
part) was attained; and (iii) the complete quadratic-plateau
function with all parameters, namely Ncth, a0, a1, and a2, and
wmax, could be estimated. For our purposes, all data sets (siteyear combinations) belonging to the first or second group are
noninformative since for our subsequent analysis, we need to
know the DM yield-to-N-concentration relation in the responsive part of that relationship and at the same time we need
to know the threshold value Ncth, where this responsive part
ends. All noninformative data sets were therefore discarded
from further analysis.
The remaining informative site-year combinations constituted the data pool for the determination of the relative DM
yield-to-N-concentration relationship and the derivation of a
CNC value. Before pooling these data sets, however, the DM
yields had to be standardized since the yield potential may
differ substantially between sites or years. For each site-year
combination, the estimated DM yield wmax ⫽ W(Ncth) of the
corresponding plateau part was assumed to be the maximum
yield. Each DM yield value W(Nc) of a data set therefore was
standardized with respect to the corresponding wmax value, i.e.,
it was converted into a relative yield Wrel(Nc) ⫽ 100·W(Nc)/
wmax.
Derivation of the Critical Nitrogen
Concentration Value
After standardization of the DM yields in all informative
data sets, these data were pooled to quantify the relationship
between relative DM yield and N concentration. For the purpose of finding that relationship, however, only those data
points belonging to the quadratic (responsive) parts are essential. Hence, all data points with N concentrations above their
corresponding threshold value Ncth were discarded before the
pooling of the data.
For fixed conditions of soil and climate, one may express the
relationship between relative DM yield and N concentration as
an exponential function of the form
Nc ⫽ A · e B·Wrel
[2]
where Nc is the N concentration of the maize crop at silage
maturity (g N kg⫺1 DM), Wrel denotes the relative DM yield
scaled from 0 to 100, and A and B are two parameters.
To include soil and climatic conditions into the model, we
regarded site and year as good representations of soil and climatic conditions. Rather than entering them as two separate
variables, we only distinguished different site-year combinations. This was necessary because the available experimental
data were not cross-classified, i.e., trials on different sites were
not conducted over all years; also, the plots at some sites changed
over years. With the site-year effects random, the resulting
model equation was
Ncij ⫽ (␣ ⫹ ai) · exp[(␤ ⫹ bi) · Wrelij)] · e*ij
[3]
where (ai, bi) is the parameter vector of the random effect of
site-year combination i, Ncij denotes the N concentration of
the jth observation in the ith site-year combination, Wrelij ⫽
Wrel(Ncij) is the corresponding relative DM yield, and e*ij and
eij are the corresponding error terms. Logarithmic transformation of Eq. [3] results in the linear model: ln(Ncij) ⫽ (␣ ⫹
ai) ⫹ (␤ ⫹ bi)·Wrelij ⫹ eij. Using PROC MIXED in SAS, we
obtained estimates for all parameters involved. The desired
CNC value, defined as the N concentration Nc(Wrel) at maximum relative DM yield, is then obtained by setting Wrel ⫽
100 in the exponential function of Eq. [2].
RESULTS AND DISCUSSION
The Informative Data Pool
Figure 2 displays the DM yield and the corresponding
N concentrations for the 29 site-year combinations available for the estimation of the CNC value. A considerable
variation in soil mineralization capacity among these
experiments most likely caused the N treatment effects
to be confounded with those of soil N release. In 8
out of 29 cases, our attempts to fit a quadratic-plateau
function failed because N treatment effects were completely overridden by strong soil N release so that all
data points were located within the “plateau part” of the
function. This occurred for the data sets Berkhof 1995,
Bramstedt 1995–1996, Celle 1995, Ostenfeld 1993, Schuby
1998, and Markhausen 1994 and 1996. In all these cases,
high yield levels of the untreated control plots (no N
fertilization) furnish strong evidence of a high mineralization potential. Seven of these sites even showed a
control DM yield higher than 84% of the corresponding
maximum yield in the years specified above. Five additional data sets had to be discarded because only a qua-
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HERRMANN & TAUBE: AN INDICATOR OF N STATUS AT SILAGE MATURITY
205
Fig. 2. Relationship between whole-crop N content [g N kg⫺1 dry matter (DM)] at silage maturity and DM yield (t DM ha⫺1) at each site-year.
Parameters of the quadratic-plateau function are given if model fit was successful, namely the threshold N concentration (Ncth) where the
quadratic part passes into the plateau and the corresponding plateau yield wmax. Points are treatment means over three or four replicates.
Control treatments (0 kg N ha⫺1) are displayed by open symbols. The critical N concentration at silage maturity of 10.5 g N kg⫺1 DM is
represented by dashed vertical lines. M.F. ⫽ model failed to fit, N.A. ⫽ data excluded due to drought, and N.P. ⫽ plateau estimation failed.
dratic, but no plateau part, could be determined. These
data sets included Bramstedt 1998, Bremervoerde 1998,
Dasselsbruch 1998, and Markhausen 1995 and 1997. In
all five site-year combinations, the highest treatment
level was fixed to 180 kg N ha⫺1. The failure of model
fit was either caused by an insufficient amount of N
applied or by the relatively large random fluctuation
of DM yield compared with the small variation of the
N content.
One data set (Ostenfeld 1994) had to be excluded because the maize in this trial experienced severe drought
conditions during silking and in the early grain-filling
stage. Such severe stress conditions are known to hamper usual uptake, assimilation, and translocation of N
(Justes et al., 1994). This may distort the relationship
between biomass and N concentration, which otherwise
seems to be quite robust for a wide range of experimental conditions (Plénet and Lemaire, 1999).
For the 15 (out of 29) remaining informative data sets,
the parameters a0, a1, and a2, and wmax of the quadratic
as well as the plateau part, could be estimated according
to the response function W(Nc), as introduced in Eq. [1].
Figure 2 displays the calculated threshold values Ncth and
the corresponding estimated DM yield values wmax ⫽
W(Ncth) for all 15 site-year combinations. Estimated
maximum yields (plateau values), which ranged between
10.6 and 18.3 t ha⫺1, indicate that the yield potential
differed considerably among the 15 site-year combinations. They were, however, in accordance with findings
of Schröder (1999) and Ogola et al. (2002) for comparable soils and climatic conditions.
Nitrogen Concentration as a Function of
Dry Matter Yield
The data of all informative sets contained a total of
147 pairs of (Wrel, Nc), 45 of these had Nc values greater
than their corresponding Ncth. Discarding them left us a
pooled set of 102 remaining points for conducting a
meta-analysis using a random coefficient model (Eq. [3]).
A statistically meaningful relationship between relative DM yield and N concentration at silage maturity
could be estimated for the remaining 102 data points
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AGRONOMY JOURNAL, VOL. 97, JANUARY–FEBRUARY 2005
Fig. 3. Relationship between relative dry matter (DM) yield and N concentration (g N kg⫺1 DM), and corresponding regression statistics; the
dashed lines indicate the critical N concentration at silage maturity (CNC), i.e., the N concentration at maximum relative yield.
(Fig. 3). While the model fits satisfactorily in the upper
yield range, a systematic deviation between observed
and predicted values is apparent in the lower range, i.e.,
for relative DM yield values below 70%. This is mainly
due to the imbalanced data structure. In Berkhof, Dasselsbruch, and Bremervoerde, the DM yields of the control treatments (no N application) were consistently high
compared with the corresponding maximum yield (plateau value), exceeding 73% of the latter. This effect is
most likely caused by specific environmental conditions
of these sites, e.g., soil type, mineralization capacity,
manuring history, crop rotation, and climate. In contrast, for the Schuby, Ostenfeld, and Karkendamm experiments, DM yield of the control treatments in most
site-year combinations fell substantially below 70% of
the corresponding plateau values.
Besides environmental conditions, the main research
objectives of the experiments and their design may have
contributed to the systematic deviation in the lower
range. In Berkhof, Dasselsbruch, and Bremervoerde, the
applied N treatments did not equidistantly cover the
whole range from severe underfertilization to excess of
N. The underrepresentation of low yield levels affected
parameter estimation in the mixed-model analysis since
slope and intercept of the regression between relative
DM yield and N concentration were determined separately for each site-year combination. Estimations of
the fixed effects resulted in an intercept that deviated
significantly for the site-year combinations having relative yield values in the 30 to 70% range (Schuby, Karkendamm, and Ostenfeld). Therefore, for lower yield
levels, the derived function has to be interpreted cautiously.
With decreasing N input, i.e., in the lower yield level
range, the N content of the crop is assumed to approximate a minimum value. The study by Plénet and Cruz
(1997) specified a minimum N concentration of 7 g N
kg⫺1 DM for maize, which is in agreement with the value
of 8 g N kg⫺1 DM suggested by Lemaire and Gastal
(1997) for structural plant N concentration. In the present study, the minimum N contents estimated (5.7 g N
kg⫺1 DM) and observed (6.4 g N kg⫺1 DM) for a relative
DM yield of 30% were lower (Fig. 3). CERES-Maize
(Jones and Kiniry, 1986) specifies a minimum N concentration of 4.5 g N kg⫺1 DM from silking until physiological maturity, which however, refers to the stover. This
value is in agreement with the stover data of the Karkendamm experiment, ranging between 2.1 and 4.7 g N kg⫺1
DM for the zero-fertilization treatment.
Critical Nitrogen Concentration at
Silage Maturity
For our primary objective of quantifying the CNC
constant, i.e., the CNC, only the quality of the model fit
in the upper range of relative DM yield is of importance.
While lower relative DM yield levels showed considerable deviations between observed and predicted N concentrations, our model performed sufficiently well in
the crucial range of relative DM yield values above 70%
(Fig. 3). It was therefore possible to calculate a CNC
value for silage maize. Setting Wrel ⫽ 100, we obtain
a CNC constant of 10.5 g N kg⫺1 DM.
We compared this result with Plénet and Lemaire
(1999). Their study provides two regression functions
describing the relationship between DM yield and critical N concentration. The first function covers the period
from the 10-leaf stage up to silking plus 25 d (Table 2).
Extrapolating this regression line to silage maturity by
assuming a DM yield of 24 to 25 t DM ha⫺1, one obtains
a CNC value of 10.4 to 10.5 g N kg⫺1 DM, which is
practically identical to our estimations. The second regression line in Plénet and Lemaire (1999) spans from
10-leaf stage up to silage maturity (Table 2), but the
207
HERRMANN & TAUBE: AN INDICATOR OF N STATUS AT SILAGE MATURITY
Table 2. Two regression equations from Plénet and Lemaire (1999) on the relationship between critical N concentration (Ncrit) [g N
kg⫺1 dry matter (DM)] and biomass (W ) (t DM ha⫺1) during the growth period of silage maize using the model Ncrit ⫽ a·W⫺b.
No.
Reproduced from Agronomy Journal. Published by American Society of Agronomy. All copyrights reserved.
1
2
Growth period
a
b
r2
n
10 leaves to silking ⫹ 25 d
10 leaves to silage maturity
33.90 ⫾ 0.80
34.05 ⫾ 0.77
⫺0.368 ⫾ 0.015
⫺0.373 ⫾ 0.013
0.99
0.99
22
26
authors doubt the validity for later grain-filling stages.
For this regression, the CNC was 10.2 to 10.4 g N kg⫺1
DM, which again coincides quite well with our analysis.
In the crop simulation model CropSyst (Stöckle and
Nelson, 2000), maize, which is well supplied with N, has
a N concentration ranging from 7.0 g N kg⫺1 DM up to
a maximum of 14 g N kg DM⫺1 at grain maturity. Our
CNC estimate fits well into this range. The CERESMaize model (Jones and Kiniry, 1986) sets the critical
N concentration of stover to 9.6 g N kg⫺1 DM at the
end of the effective grain-filling period. This seems
rather high compared with the whole-crop N concentrations in the other studies mentioned above. Nitrogen
concentrations of the stover in optimally fertilized plots
of the Karkendamm data, for instance, ranged from 4.0
to 5.6 g N kg⫺1 DM. In summary, the obtained CNC
value of 10.5 g N kg⫺1 DM seems to be in good agreement with several other studies. To achieve the CNC
level of N, i.e., to attain a maximum DM yield, the
amount of N application needed in our field trials ranged
from 100 to 180 kg N ha⫺1 for the responsive sites.
Some nonresponsive sites, e.g., Markhausen, Bramsted, and Celle, showed high DM yields and N contents
even without N fertilization and were excluded from
further analysis. Most such sites have a long history of
high N input and manuring, which is likely to cause a
high mineralization potential, especially in combination
with humous soils. These characteristics are often encountered in maize-growing regions of northwestern
Germany. Applying the CNC constant to nonresponsive
data sets indicated an overfertilization of the crop (Fig. 2),
which seems to be a quite realistic assessment of the
actual N status.
The development of the CNC indicator was based on
a Kjeldahl method for N determination (Naumann and
Bassler, 1993). We chose this method despite its limitations (N in N–O linkages is only partially determined)
because it is the standard method for N determination
of forage crops used by German LUFA institutes. Applying this method throughout the whole study should
provide consistent results. A CNC determination based
on total N content may give a slightly different CNC
constant.
Assessment of Critical Nitrogen Concentration
as Indicator of Nitrogen Status
The CNC allows, on the one hand, for detection of luxury N consumption if the N concentration exceeds the
calculated critical threshold. On the other hand, the derived functional relationship enables, in case of insufficient N supply, the quantification of relative yield losses.
The CNC value thus provides a diagnostic tool to assess
the N status of the maize crop that can guide maize growers
in adjusting their N fertilization for the following grow-
ing seasons in terms of a “learning-by-doing” strategy.
The results of this study are in good agreement with
those of Plénet and Lemaire (1999), which were based on
different genotypes and environmental conditions. The
derived relationship may therefore be generalized to
other regions with climatic and soil conditions similar
to northwestern Europe, as long as crop management
with respect to tillage and row spacing is compatible. It
remains, however, to be clarified to what extent the
relationship between N concentration and DM yield is
affected by other management practices, such as tillage
or understory crops, since findings in literature are ambiguous (Cusicanqui and Lauer, 1999; Mehdi et al., 1999;
Cox and Cherney, 2001 and 2002; Widdicombe and
Thelen, 2002; Nevens and Reheul, 2003).
Apart from assessing the N status on the farm scale,
one could exploit the CNC value on a large scale to
monitor the “environmental performance” of farmers
in a whole region. At least in Germany, the necessary
data on N concentration of maize silage are routinely
gathered and therefore available. This approach assumes
that no substantial N losses occur during the ensiling
process. Since maize is characterized by a relatively high
DM content, a low buffering capacity, and adequate
levels of water-soluble carbohydrates, we can exclude
any extensive proteolysis during ensiling (McDonald
et al., 1991). In theory, one might even argue that the
N concentration increases due to DM losses caused by
respiration and fermentation. Studies on changes of N
concentration during ensiling of maize are scarce. Bergen et al. (1974) reported a slight increase of N concentration during ensiling of the whole maize plant. The
study of Amos et al. (1996), however, found a negligible
decrease of N content in maize silage compared with
the unfermented forage.
Evaluation of the Nitrogen Status of Silage
Maize Production in Northern Germany
Farmers in Germany routinely send silage samples
for forage quality analysis to a nationwide network of institutes (LUFA institutes), which have a unified and coordinated approach toward analysis and advisory. Based
on forage quality data provided by three of the LUFA
institutes (Kiel, Hameln, and Kassel), the derived CNC
value of 10.5 g N kg⫺1 DM was used to investigate the
current state of N supply in Northern Germany’s forage
maize production. The LUFA institutes are located in
different federal states of northern Germany, each having a large catchment area. The data thus can be assumed
to be a representative sample on the N status of forage
maize production in the northern part of Germany.
The data provided by the LUFA institutes show a clear
tendency for all locations and years for the N concentrations to exceed the CNC value of 10.5 g N kg⫺1 DM in
208
AGRONOMY JOURNAL, VOL. 97, JANUARY–FEBRUARY 2005
Table 3. Frequency distribution of N content [g N kg⫺1 dry matter (DM)] and mean DM content (g kg⫺1), and coefficient of correlation
between DM content and N content (Nc) of maize silage samples analyzed by three Agricultural Analytical and Research (LUFA)
institutes, located in northern Germany; the variable n denotes the samples size.
LUFA Kassel
N content
LUFA Hameln
1999
2000
2001
1999
2000
2000
0.0
1.8
0.0
33.3
49.5
15.3
0.8
1.2
15.5
40.6
36.9
5.0
0.4
1.5
17.8
59.2
20.0
1.0
samples, %
0.3
5.6
32.9
51.4
8.6
1.4
1.0
3.8
18.9
40.1
27.1
9.1
0.8
0.6
3.1
26.1
49.2
20.1
0.0
2.5
26.3
51.4
18.4
1.4
111
98.2
483
82.4
719
80.3
736
61.3
111
76.3
1534
95.4
845
71.1
337.4
⫺0.06
368.0
⫺0.30
333.4
⫺0.10
330.1
⫺0.04
360.6
⫺0.28
Reproduced from Agronomy Journal. Published by American Society of Agronomy. All copyrights reserved.
kg⫺1
gN
DM
⬍8.8
8.8 ⱕ x ⬍ 10.2
10.2 ⱕ x ⬍ 11.6
11.6 ⱕ x ⬍ 13.0
13.0 ⱕ x ⬍ 14.4
⬎14.4
n
ratio of samples with Nc ⬎
critical N concentration ⫹ SE
mean DM content, g kg⫺1
coeff. of correlation of DM
content vs. Nc
LUFA Kiel
1998
most samples by more than one standard error (1.03 g
N kg⫺1 DM), indicating for that region a predominance
for luxury crop N uptake (Table 3). In the region of the
LUFA Kassel, where 4 yr of data were available, a
steady decrease from 98 to 61% of samples with luxury
N uptake could be observed. The pronounced decrease
in the Kassel distribution is most likely due to a reduction of N input, resulting from strict measures of groundwater protection. In the region of LUFA Hameln, where
only 2 yr were analyzed, we find the opposite development with an increasing percentage from 76 to 95%.
The increase of N concentrations in the Hameln data
may be attributed to several reasons. The lower mean
DM content, probably a consequence of delayed crop
development, may have led to higher N contents in 2000.
Also, water shortage or possibly higher N input may have
contributed. The Kiel data for the year 2000 showed a
314.1
⫺0.23
316.2
⫺0.19
similar excess of N fertilization with 71% of the samples above the CNC value plus a standard error. Unfortunately, for the 1999 Kiel data, only frequencies were
available with classes differing from those used in our
analysis. But the data exhibit the same luxury N consumption.
Changes in the N concentration pattern can be caused
in principal by two factors, namely by the N management and by weather conditions. Weather can act on
the plant N content directly or indirectly through its effect on crop development. Studies by Struik et al. (1985),
Crasta et al. (1997), and Wilhelm et al. (1999) point out
that the variability of several parameters of grain and forage quality are caused by temperature and water availability. By means of a reduced uptake of N, water deficiency can directly influence the N concentration (De
Willigen and Van Noordwijk, 1995). Whether this effect
Fig. 4. Relationship between dry matter (DM) content (g kg⫺1) and N content (g N kg⫺1 DM) of maize silage samples analyzed by three
Agricultural Analytical and Research Institutes (LUFA), located in northern Germany. The coefficient of correlation is denoted by r, and
the critical N concentration (CNC) (⫹SE) is represented by dashed lines.
Reproduced from Agronomy Journal. Published by American Society of Agronomy. All copyrights reserved.
HERRMANN & TAUBE: AN INDICATOR OF N STATUS AT SILAGE MATURITY
is negative or positive depends on the degree of decreased N uptake relative to a simultaneous reduction
of biomass production (Deinum, 1981).
Maize is quite dependent on temperature and sufficient irradiation. Since northern Germany is a marginal
region, where the conditions for successful forage maize
production are not always met, the stage of maturation
at harvest varies strongly depending on the prevailing
weather conditions. Unfavorable conditions in summer
and early autumn may delay plant development and
therefore lead to suboptimal results at harvest time with
lower DM content and higher N concentrations. To
determine to what extent weather conditions influenced
the N concentration, we compared DM contents with
corresponding N concentrations for each site-year combination and observed only a weak negative correlation,
if any, in some site-year data sets (Fig. 4 and Table 3).
Calculated over all years for a given site, the corresponding correlations were slightly negative (Hameln: r ⫽
⫺0.38) or practically absent (Kassel r ⫽ ⫺0.23). The
low r values can be regarded as negligible, indicating
that N management, not weather, was the important
factor for the crop’s N content.
The CNC value can also be used to detect N shortages.
If we regard a N concentration below 9 g N kg⫺1 DM
as N deficiency, then less than 3% of all our samples
fall below this threshold. Hence, northern Germany
seems to be characterized by excessive N supply rather
than by N deficiency, and optimized nutrient balances
without yield loss may be achieved in forage maize production under conditions of significantly reduced N fertilizer application.
CONCLUSIONS
There is an increasing demand by policy makers for
easy-to-use, scientifically founded, and cost-efficient N
indicators, which allow monitoring and evaluation of
policy measures and which provide information on the
current state of farm N management. Nitrogen balances
are a widespread instrument, implemented in many national, state, or regional action programs in Europe, recommended on the European Union level in terms of soil
surface balances as one of a set of different indicators
as useful tools in the effort to integrate environmental
concerns into the common agricultural policy. The assessment of potential N losses, however, should not be
based solely on N balances, but should be complemented
by additional indicators at various scales to obtain a
more complete insight into the N dynamics (Öborn et al.,
2003; Oenema et al., 2003). With respect to the development of new, suitable instruments, more emphasis
should be put on crop-based indicators, which simultaneously reflect the interactions between the crop and
the soil. In the current study, we demonstrated and discussed that the CNC value might serve as such an easyto-use, powerful indicator, which can perfectly serve a
dual purpose: (i) the improvement of on-farm N management by farmers to adjust N fertilization for the
following maize growing season (“learning-by-doing”
strategy) and (ii) accurate and cost-efficient large-scale
209
monitoring of the N status, based on maize silage N
concentrations, which are widely available in Germany.
Before implementing the CNC value as an agro-environmental indicator of N status into agricultural practice, however, the technique of CNC estimation should
be refined, and the geographical scale for which a homogeneous CNC value can be assumed has to be determined.
The present study was based on data collected on sandy
soils under the climatic conditions of northern Germany.
Additional trials covering a broader range of environmental conditions and more differentiated N treatments
are necessary to validate the CNC approach as a generally applicable method.
ACKNOWLEDGMENTS
We gratefully acknowledge the contribution of the University of Applied Sciences in Kiel and the Agricultural Chambers
of Schleswig-Holstein, Hannover, and Weser-Ems in collecting many of the data used in this analysis. We are indebted
to the LUFA institutes for providing us data on silage quality.
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