Agricultural and Food Science in Finland

A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D

Vol. 9 (2000): 71–77.

A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D

Vol. 9 (2000): 71–77.

Research Note

Estimation of soil nitrate in the spring as a basis
for adjustment of nitrogen fertiliser rates

Jouko Sippola
Agricultural Research Centre of Finland, Resource Management Research, Environmental Resources,

FIN-31600 Jokioinen, e-mail:jouko.sippola@mtt.fi

The performance of SOILN model, which simulates soil nitrogen dynamics, was evaluated in respect
to its ability to predict nitrate content in soil in spring when growing barley. The data obtained in
three year nitrogen fertiliser experiments on different soil types was used. Model was parametrised
using the data of the first experimental year and validation results obtained in following years are
presented. The results of the simulations of the springtime nitrate in the root zone showed a reasona-
bly small deviation from the measured values. The coefficient of determination, R2 = 0.56 was signif-
icant. The regression equation was y = 4 + 0.66x where the constant term was not significantly differ-
ent from zero and the slope deviated from zero. The mean value of measured nitrate in the root zone
in spring was 16 kg/ha and that from the simulation was 15 kg/ha showing that the mean values were
close to each other. The mean of deviations between measured and simulated values was 4 kg/ha and
the maximum deviation 9 kg/ha. It is concluded that simulation estimated springtime soil nitrate
concentration with reasonable confidence that further testing of estimating soil nitrate concentration
in spring for adjustment of nitrogen fertilisation using SOILN model should be continued.

Key words: simulation models, nitrate nitrogen, nitrogen fertilisers, barley

Introduction

Nitrogen is the plant nutrient which has the great-
est influence on crop yield. Because of the im-
portance of nitrogen there is an urgent need for
soil test methods to optimise fertiliser applica-
tions. This is not only for economy in nitrogen
use, but also to minimise the risk of leaching of
nitrate to surface- and groundwater. In Central
Europe the so called Nmin-method is used to

analyse soil mineral nitrogen and to adjust ni-
trogen fertilisation accordingly (Wehrman and
Scharpf 1979). In a previous study this method
did not prove to be useful in Finnish conditions
when growing cereals following cereals, but in
other types of crop rotation mineral nitrogen may
continue to accumulate (Sippola and Yläranta
1985). From a practical point of view, the time
span during which it is possible to sample soil
before sowing is very limited in Finland because
of soil freezing and this means that a method

mailto:jouko.sippola@mtt.fi

A G R I C U L T U R A L A N D F O O D S C I E N C E I N F I N L A N D

Sippola, J. Estimation of soil nitrate as a basis for adjustment of nitrogen fertiliser rates

based on soil sampling in spring is not feasible.
The SOILN simulation model has been de-

veloped to describe nitrogen dynamics in the soil
(Johnsson et al. 1987). The use of simulation to
estimate the springtime mineral nitrogen content,
taking account of the depleted situation at har-
vest and the climatic conditions which follows
could, if applicable, provide a fast way to obtain
information for adjusting basic nitrogen fertilis-
er recommendations according to the actual re-
quirement.

The objective of this investigation was to find
out if it is possible by using the SOILN model
and weather data from outside the cropping sea-
son to predict the soil nitrate content which could
be used as a substitute for measured nitrate lev-
els for optimising nitrogen fertiliser application
rates.

Material and methods

The experimental data used to calibrate and val-
idate the SOIL and SOILN models were obtained
from 11 nitrogen fertilisation experiments with
barley carried out during 1981–1990 in research
stations of the Agricultural Research Centre of
Finland. Details of soil properties and yield data
have been published previously (Sippola and
Yläranta 1985, Lindén et al. 1992, Kätterer et
al. 1999). Heavy clay, sandy clay, silty clay and
fine sand soils were represented. Soils were sam-
pled in three layers to 60 or 70 cm depth early in
spring before sowing and in autumn after har-
vest for nitrate and moisture contents. In some
of the experiments a monthly sampling interval
was adopted. An additional soil sampling in
spring following the third experimental year was
made to gather more data for validation. Barley
was the experimental crop and data obtained
from 80 or 90 kg/ha nitrogen treatments were
used in this study. Temperature and rainfall data
were obtained from weather stations located at
each Experimental Station. Only for these two
driving variables daily data was needed when the

CNUMD = –1 option of the SOIL model was
used (Jansson 1991). For wind speed, humidity
and cloudiness an average value on each site was
taken.

The soil hydraulic properties required by the
SOIL model were obtained using the PLOTPF
program and soil texture and organic matter data
at each site (Jansson 1989). Soil moisture deter-
mined in laboratory was used to check the suc-
cess of simulations when SOIL model parame-
ters were adjusted. In addition to the soil hydrau-
lic properties, soil moisture values were rather
sensitive to parameters describing maximum root
depth and critical tension for reduction of water
uptake. Their optimised values depending on soil
type ranged from –0.6 to –0.8 and from 200 to
2800, respectively.

Parameters, such as organic carbon and total
nitrogen, used in the SOILN model were ob-
tained by soil analysis (Jansson et al. 1991). In
addition, site specific values for C/N ratio, ini-
tial values for ammonium and nitrate, for litter
carbon and nitrogen, for humus carbon and ni-
trogen, based on samples collected before the
start of the experiment were used. Results of the
first of the three experimental years were used
when parameter values were adjusted to give
proper results for each site. Simulation results
for yield and nitrogen uptake of grain and straw
in addition to soil nitrate concentrations were
compared with measured data when parameters
were adjusted to improve agreement. The param-
eter for radiation use efficiency (PHOEFF) was
used to adjust yield level and it ranged from 1.5
to 2.2 in different experimental sites. The distri-
bution of accumulated biomass between straw
and grain was adjusted with parameter AGRAIN
which ranged from 0.016 to 0.025. Several pa-
rameters describing soil organic matter flows
were adjusted to be site specific to improve the
agreement between simulated and measured ni-
trate values. Their ranged as follows: CNORG
(carbon-nitrogen ratio of microorganisms and hu-
mified products) 10–14, HUMK (humus miner-
alisation rate) 0.00002–0.00015, LITEFF (effi-
ciency of the internal synthesis in litter) 0.2–0.7,
LITHF (litter carbon humification fraction) 0.2–

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Vol. 9 (2000): 71–77.

0.8, LITK (litter decomposition rate) 0.02–0.035,
DENPOT (potential rate of denitrification) 0.02–
0.12, DFRAC1 (fraction of potential denitrifi-
cation in plough layer) 0.2–0.6, UPMA (fraction
of mineral nitrogen available for immobilisation
and plant uptake) 0.04–0.08. However, many of
the model parameters were those standard val-
ues provided with the program and found valid
in Swedish conditions (Jansson et al. 1991). A
more thorough site specific parametrisation was
not pursued because the aim of the study was to
test if the SOILN model could be useful in the
situations where site and soil properties are
known only rather generally. Finally, the results
of the second and third years were used to eval-
uate the validity of the simulation results.

Results and discussion

Moisture contents predicted by
the SOIL model

The results of the simulated soil moisture con-
tents for the clay soil in Jokioinen show that the

simulated values in the surface layers follow the
drying out of the soil in summer and increase in
moisture content following autumn rains, but
periodical sampling does not adequately describe
the total picture (Fig. 1). Figure 2 indicate that,
according to regression model, 58 % of meas-
ured variation in soil moisture in the plough layer
of a silty clay is explained by simulated values.
The constant term in the regression equation
deviates to a quite large degree from zero, and
the slope is much less than one. Because soil

Fig. 1. Measured (squares) and simulated moisture contents in plogh layer of a clay soil in Jokioinen.

Fig. 2. Comparison between measured and simulated mois-
ture contents in plogh layer of a clay soil in Jokioinen.

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Sippola, J. Estimation of soil nitrate as a basis for adjustment of nitrogen fertiliser rates

water flows are used as driving variables in the
SOILN model, a good agreement between sim-
ulated and measured values is required. Devia-
tions of the results of the SOIL model from meas-
ured values decreases the reliability of simulat-
ed nitrate estimates.

Nitrate concentrations predicted
by SOILN model

As shown in Figure 3, the nitrate concentration
of the plough layer in the clay soil at Jokioinen
follows the increase caused by fertiliser appli-
cation, but in 1985 the measured value is low
and 1986 high compared to the simulated value.
Crop uptake decreases the soil nitrate concen-
tration effectively, and at harvest concentrations
are low similar to those at spring. Simulations
follow the measured values closely at this stage.
Later in autumn, there is some accumulation of
nitrate in soil which simulations do not follow,
predicting lower concentrations in both years.
In winter when soil is frozen, there is no change
in the simulated nitrate concentration. The ac-

cumulated nitrate shown by measured values
have decreased by spring to a similar low level
what simulation predicted.

In opposite to the clay soil, in the fine sand
soil at Peipohja the simulation predicted more
mineralisation during late autumn than what was
shown by measurement (Fig. 4). However, in the
spring the simulated values were in good agree-
ment with the measured values which ranged
from 3.5 to 11.2 kg/ha in the 20 cm plough lay-
er. This because nitrate losses due to leaching
are clearly simulated in the porous find sand soil
in late autumn and early spring.

Generally, the small number of measured data
available for parameter derivation had its restric-
tions in arriving at optimal parameter values.
This was indicated by values for different layers
deviating occasionally quite considerably. Low
nitrate concentration in deeper layers did not
change the total content in root zone to a great
deal, however.

The results of the simulations of the spring-
time nitrate in the root zone at 11 sites show a
reasonable small variation from the measured
values (Fig. 5). The coefficient of determination,
R2 = 0.56 is significant. The slope of the regres-

Fig. 3. Measured (squares) and simulated nitrate concentrations in plogh layer of a clay soil in Jokioinen.

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Vol. 9 (2000): 71–77.

sion equation is less than one indicating also
deviation from a perfect agreement. When com-
paring measured and simulated soil mineral ni-
trogen in spring in winter wheat trials in United
Kingdom Whitmore and Addiscott found a much
higher regression coefficient 0.947 (Whitmore
and Addiscott 1985). However, the correlation
coefficient r = 0.78 was of the order found in the
present study.

The mean value of measured nitrate in spring
was 16 kg/ha and that of the simulated 15 kg/ha
showing that mean values were close together
(Table 1). The mean of deviations between meas-
ured and simulated values was 4 kg/ha, and the
maximum deviation 9 kg/ha. From a practical
point of view, deviations of less than 10 kg/ha
are not of importance, and also errors in deter-
mining soil mineral nitrogen may be even larger
due to soil variability (Otter-Nacke and Kuhl-
man 1991). Compared to results obtained in the
current study Otter-Nacke and Kuhlman found
spring mineral nitrogen values that were clearly
higher than in the present study. The deviation
between measured and simulated mineral nitro-
gen contents ranged from 14 to 19 kg/ha. They
concluded that these values deviate too much to

Fig. 4. Measured (squares) and simulated nitrate concentrations in plogh layer of a fine sand soil in
Peipohja.

Fig. 5. Comparison between measured and simulated root
zone nitrate content in spring. Results from 11 sites with
various soil types, one or two observations each.

Table 1. Soil nitrate in spring before sowing in root zone
kg/ha. 11 sites over 1 or 2 years during 1983–90. N = 20.

Measured Simulated Difference

Mean 16 15 4
Standard error 1.5 1.3 0.6
Standard deviation 6.6 5.9 2.5
Minimum 3 7 0
Maximum 27 26 9

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Sippola, J. Estimation of soil nitrate as a basis for adjustment of nitrogen fertiliser rates

be used with any confidence as an aid when
making nitrogen fertiliser recommendations.
When testing the DAISY model, Jensen et al.
(1996) observed a somewhat closer agreement
between measured and simulated mineral nitro-
gen values, with a mean difference of 11 kg/ha
for a large number of observations. However,
they concluded that results deviated too much
to be used as tool in fertiliser recommendations.

Based on the results of the present study, it
may be concluded that deviations of simulated

from measured values are sufficiently small that
further testing of the SOILN model for estimat-
ing soil nitrate concentration in spring as a basis
for adjustment of nitrogen fertiliser application
should continue. Tests should include recommen-
dations for practical scale farming and evalua-
tion of the results obtained.

Acknowledgements. The financial support for the project
by The Academy of Finland and Nordic Council of Minis-
ters (NMR) is gratefully acknowledged.

References
Jansson, P.-E. 1989. Manual and help library for PLOTPF.

Swedish University of Agricultural Sciences, Dept.
of Soil Sciences, Mimeogr. 7 p.

– 1991. SOIL model. User’s manual. Swedish Univer-
sity of Agricultural Sciences, Dept. of Soil Sciences,
Communications 91: 7.

–, Eckersten, H. & Johnsson, H. 1991. SOILN model.
User’s manual. Swedish University of Agricultural
Sciences, Dept. of Soil Sciences, Communications
91: 6.

Johnsson, H., Bergström, L., Jansson, P.-E. & Paustian,
K. 1987. Simulated nitrogen dynamics and l o s s e s
in a layered agricultural soil. Agriculture, Ecosystems
and Environment 18: 333–356.

Jensen, C., Stougaard, B. & Ostergaard, H. 1996. The
performance of the Danish simulation model DAISY
in prediction of Nmin at spring. Fertilizer Research
44: 79–85.

Kätterer, T., Heidman, T., Sippola, J., Haugen, L.E., Borg,
G.C., Haraldsen, T.K. & Blombäck, K. 1999. Agricul-
tural experimental sites. In: Nitrogen processes in
arable and forest soils in the Nordic countries. Field

scale modelling and experiments. ThemaNord 1999:
560. p. 41–52.

Lindén, B., Lyngstad, I., Sippola, J., Soegaard, K. & Kjel-
lerup, V. 1992. Nitrogen mineralization during the
growing season. Swedish Journal of Agricultural
Research 22: 3–12.

Otter-Nacke, S. & Kuhlmann, H. 1991. A comparison of
the performance of N simulation models in the pre-
diction of Nmin on farmers’ fields in the spring. Ferti-
lizer Research 27: 341–347.

Sippola, J. & Yläranta, T. 1985. Mineral nitrogen reserves
in soil and nitrogen fertilization of barley. Annales
Agriculturae Fenniae 24: 117–124.

Wehrman, J.G. & Scharpf, H.C. 1979. Der Mineralstick-
stoffgehalt des Bodens als Massstab für den Stick-
stoffdüngerbedarf (Nmin-Methode). Plant and Soil 52:
109–126.

Whitmore, A.P. & Addiscott, T.M. 1985. Computer simu-
lation of changes in soil nitrogen during under a crop
of winter wheat. In: Neeteson, J.J. & Diltz, K. (eds.).
Assesment of nitrogen fertilizer requirement. Haren:
Institute for Soil Fertility. p. 133–137.

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Vol. 9 (2000): 71–77.

SELOSTUS
Maassa olevan nitraattitypen arviointi simulointimallin avulla

Jouko Sippola
Maatalouden tutkimuskeskus

Typpi on kasvinravinne, joka ratkaisevimmin vaikut-
taa satoon. Typelle ei kuitenkaan ole käytössä yleis-
tä viljavuusanalyysimenetelmää kuten muille ravin-
teille. Menetelmä olisi hyvin tarpeellinen typpilannoi-
tuksen optimoimiseksi taloudellisuuden ja ympäris-
tövaikutusten suhteen. Keski-Euroopassa maasta
määritetään mineraalityppi keväällä ennen lannoitus-
ta. Meillä menetelmä on epäkäytännöllinen, koska
roudan sulamisen ja kylvöjen välinen aika on lyhyt.
Simulointimallin käyttö kevään mineraalityppimää-
rän arvioimiseen talven ja varhaiskevään sääolojen
sekä maan ominaisuuksien perusteella on yksi mah-
dollisuus. Tutkimuksessa selvitettiin SOILN simu-
lointimallin kykyä ennakoida kevään nitraattityppi
ohran typpilannoituskokeiden aineistoissa, joissa
maan mineraalityppi oli määritetty. Kokeita oli kaik-

kiaan 11 Maatalouden tutkimuskeskuksen eri tutki-
musasemilla vuosina 1981–1990.

Tulosten mukaan simulaatiomalli ennusti juuris-
tovyöhykkeen nitraatin määrän kohtuullisella tark-
kuudella. Kaikkien kokeiden nitraattipitoisuuden mi-
tattu keskiarvo oli 16 kg/ha ja simulointien vastaava
keskiarvo 15 kg/ha. Mitattujen ja simuloitujen arvo-
jen suurin poikkeama oli 9 kg/ha ja keskimääräinen
poikkeama 4 kg/ha. Siten poikkeama on käytännön
lannoitustarkkuutta ajatellen riittävän pieni, jotta
menetelmän hyödyllisyyden tarkastelua on perustel-
tua jatkaa. Tällöin tarkasteluun tulee ottaa mukaan
typpilannoitussuositusten teko käytännön viljelyksille
ja tilanteisiin, jossa on käytetty eloperäisiä lannoit-
teita.

Title
Introduction
Material and methods
Results and discussion
References
SELOSTUS