Calibration procedure for a potato crop growth model using information from across Europe

e c o l o g i c a l m o d e l l i n g 2 1 1 ( 2 0 0 8 ) 209–223

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Calibration procedure for a potato crop growth model using information from across Europe T. Heidmann a,∗ , C. Tofteng b , P. Abrahamsen b , F. Plauborg a , S. Hansen b , A. Battilani c , ˇ e , W. Mazurczyk f , J.D.R. Ruiz b , J. Takaˇ ´ c g , J. Vacek h J. Coutinho d , F. Dolezal a

Department of Agroecology, Faculty of Agricultural Sciences, University of Aarhus, Research Centre Foulum, P.O. Box 50, DK-8830 Tjele, Denmark b Department of Agricultural Sciences, Faculty of Life Sciences, University of Copenhagen, Agrovej 10, DK-2630 Taastrup, Denmark c Consorzio di Bonifica de secondo grado per il Canale Emiliano Romagnolo, Via E. Masi, 8, I-40137 Bologna, Italy d Department of Soil Science, Universidade de Tras-os-Montes ´ e Alto Douro, Ap. 1012, 5001-911 Vila Real, Portugal e Research Institute for Soil and Water Conservation Prague, Zabovreska 250, CZ-156 27 Praha 5, Zbraslav, Czech Republic f Plant Breeding and Acclimatization Institute, Jadwisin, 05-140 Serock, Poland g Hydromelioration State Enterprise, Vrakunska ´ 29, SK-825 63 Bratislava 211, Slovakia h Potato Research Institute Havl´ıcˇ kuv ˚ Brod Ltd., Dobrovsk´eho 2366, 580 01 Havl´ıcˇ kuv ˚ Brod, Czech Republic

a r t i c l e

i n f o

a b s t r a c t

Article history:

In the FertOrgaNic EU project, 3 years of field experiments with drip irrigation and fertigation

Received 29 November 2006

were carried out at six different sites across Europe, involving seven different varieties of

Received in revised form

potato. The Daisy model, which simulates plant growth together with water and nitrogen

10 September 2007

dynamics, was used to simulate the field experiments. An initial potato parameterisation

Accepted 11 September 2007

was generated from an independent dataset and was used for site-specific calibrations. At

Published on line 22 October 2007

those sites where the same variety was used for all 3 years, the calibration of the initial potato model was based on the first 2 years using the last year for validation. From the site-

Keywords:

specific parameterisations, shared traits were extracted into a common parameterisation.

Potato growing

This resulted in a list of the parameters that six independent people found had the most

Field experiments

impact on the simulations. This common parameterisation we argue is a valuable platform

Modelling

for adaptation of the Daisy model to new potato varieties or for the improvement of the

Daisy

existing parameter set. The procedure is then, as a starting point, to focus the calibration

Parameterisation

process on the recommended list of parameters to change. We demonstrate this approach by

Calibration

showing the procedure for recalibrating three varieties using all relevant data from the sites.

FertOrgaNic

We believe these new parameterisations to be more robust, because they indirectly were

Drip irrigation

based on information from the six different sites. We claim that this procedure combines

Fertigation

both local and specific modeller expertise in a way that results in more robust and general

Nitrogen fertilisation

parameterisations than if the common parameterisation step had been skipped. © 2007 Elsevier B.V. All rights reserved.

1.

Introduction

The presented work was part of the EU-funded project FertOrgaNic, whose main objective was to develop a new growing

∗

Corresponding author. Tel.: +45 89991730; fax: +45 89991200. E-mail address: [email protected] (T. Heidmann). 0304-3800/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolmodel.2007.09.008

system with enhanced use of various organic fertilisers supplemented with mineral nitrogen (N). The application of the additional N was based on improved management strategies for drip irrigation and N fertigation to increase the water

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and N-use efficiency and hence reduce the risk of losses to the environment (http://www.fertorganic.org/). The new system was tested by conducting 3 years of field experiments with seven different potato varieties (Solanum tuberosum L.) cv. Agata, cv. Agria, cv. Filea, cv. Folva, cv. Kennebec, cv. Solana, and cv. Triada under different management strategies at six different sites across Europe. The data from the field experiments were analysed using the Danish simulation model Daisy (Hansen et al., 1990, 1991; Abrahamsen and Hansen, 2000). Simulation models are valuable tools for evaluating the effect of different management practices on crop yield and environment. Some models are used as research tools for studying crop growth and water and N dynamics but also as basis for legislation and regulation by policy-makers when environmental effects of crop production are estimated. Daisy is developed from field experiments and has been tested in several situations (De Willingen, 1991; Verecken et al., 1991; ¨ Diekkruger et al., 1995; Svendsen et al., 1995; Smith et al., 1997). The tests have shown a good agreement between measured and simulated results of water and nitrogen dynamics and biomass production. Crop-specific information on most of the main crops in Danish agriculture is available from field experiments and included in the crop modules of the model. A potato module did exist in the model, but due to large differences between varieties in the FertOrgaNic experiments, there was a need for parameterisation of more than one variety. Therefore, to use the Daisy model for analysing the FertOrgaNic field data, new potato modules were developed for each site and parameterised with the help from modellers from each experimental site. The accuracy of process-oriented crop-growth models depends on the soundness of the representation of physiological processes and the parameter values in their mathematical representations (Zhai et al., 2004). Using a model in different climate zones such as the Mediterranean and Scandinavian often requires the calibration of the model parameters (Botterweg, 1995). The calibration involves estimation of model parameters from field data to improve the fit between model and data, such as in Kabat et al. (1995) where several potato growth models were calibrated using the same data set. The adjustment of the parameters in crop models to field data is essential, but it is often not possible to adjust all model parameters (Wallach et al., 2001). The first step in a calibration procedure is to decide which and how many model parameters

are to be adjusted. Wallach et al. (2001) mentioned different approaches to this: (1) to decide a priori on a small number of ¨ parameters to be adjusted (Sievanen and Burk, 1993), (2) to do a sensitivity analysis of the model and adjust the most sensitive parameters (Van der Perk, 1998), (3) to start with a small number of parameters and then add additional parameters, one at a time, if they reduce residual variance (Sumner et al., 1997) and (4) to adjust as many parameters as necessary to fit the model within a fixed margin (Hanson et al., 1999). The personal interest and competence of the model developer may result in modules of varying detail (Botterweg, 1995). Often water transport is relatively well known and well described in the model, whereas the processes related to carbon/N turnover and crop growth are more complex and difficult to parameterise. The model user is seldom mentioned as an independent factor that might affect the results of a calibration/validation process, but the calibration also depends on the specialisation and the experiences of the model user (Botterweg, 1995). Different model users applying the same model will often calibrate the model in different ways. They make a series of decisions, which are most often subjective. A reasonable fit of the measured data can be achieved with different parameter values. Botterweg (1995) stated that complex multi-process models cannot be validated in general, but must be calibrated separately for each site. It is an advantage if several independent users are involved in the calibration of a model. Vanclooster et al. (2000) stated that the application of the same model by different users could allow finding the most essential parameters of the components of the model. Boesten (2000) found, when model users applied different pesticide leaching models to the same data set, that the variability caused by the model user’s choice of parameter values was in many cases so large that it overruled the conceptual differences between the models. Therefore, it can be helpful for the model user to follow a defined calibration procedure and know which parameters are the most important when he starts the calibration process. This paper presents the results of a calibration of the Daisy model on potato experiments from six locations in Europe, but only results from Denmark, Poland, Portugal, and the Czech Republic are shown. Six local model users made the calibrations. The objective of the present study was (1) to calibrate the Daisy model, so that it can be applied across Europe using potato as a model crop, (2) to develop a calibration procedure and show results achieved with this method, and (3) to identify

Table 1 – Average yearly sums of precipitation, reference evapotranspiration, global radiation, mean temperature and soil type (USDA textural classes) at the sites Czech Republic Precipitation, annual (mm) Ref. evapotranspiration, annual (mm) Global radiation (MJ m−2 ) Mean temperature, annual (◦ C) Soil type (USDA)

Denmark

Poland

Portugal

Italy

Slovakia

754

1045

576

1159

857

557

598

563

537

–

1038

887

4052

3479

3535

–

5505

4447

10.1

7.9

7.9

12.8

14.0

9.8

Loam

Loamy sand

Sandy loam, loamy sand

Sandy loam, loamy sand

Loam

Silt loam

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the most important model parameters that require attention in the initial stage of model parameterisation.

2.

Materials and methods

2.1.

Experimental design and measurements

Data used in the present study were extracted from drip and fertigation experiments with potatoes carried out during 2003–2005 at six experimental sites in Denmark, Poland, Slovakia, the Czech Republic, Italy, and during 2003–2004 in Portugal (Table 1). Table 2 describes the design of the experiments. Treatments T1 and T6 represented the zero fertilisation level needed to calculate the efficiency and the recovery ratio of both fertilisation and irrigation inputs in treatments T2–T5. T2 and T3, which were without and with irrigation, respectively, were control plots to assess the separate effect of the organic N supply. T4 and T5 received a basic dressing of organic manure (same amounts as in T2 and T3), full irrigation, but also an additional mineral N supply by means of different fertigation strategies. Fertigation is mineral N dissolved in the irrigation water and applied through the drip lines. The new potato growing system was in its design and development governed by some common protocols, which were followed at all the experimental sites (details are explained after the list). • The new system was focused on different strategies of N application whereas other nutrients (P, K, etc.) were applied as in conventional systems. • The organic N source (slurry, FYM, compost, etc.) was incorporated at a depth of 0–0.1 m, preferably in spring before potato planting. • Medium early varieties of potatoes were studied, i.e. table potatoes, potatoes for crisps or chips. The mother tube was planted at maximum 8 cm depth (before ridging) with around 0.75 m ridge distance and around 0.33 m between potatoes in the ridge, corresponding to around 40,000 plants per ha. • The amount of organic N needed was estimated based on the N balance for the field.

211

• Expected N-mineralisation from the soil organic matter during the growing season was estimated based on local experience. • The organic N was ridged up around the potato mother tubers in the first out of two ridging operations. • The drip line was placed centrally on the partly built ridge and a second ridging operation was carried out to cover the drip line with up to around 0.1 m soil. • Mineral N concentration in the final fertigation solution should be around 0.1–0.2% N by the time it reaches the potato roots. • To avoid toxicity towards the potato roots, the electric conductivity of the solution used for fertigation could not be higher than 3 dS m−1 . • Application of irrigation and N-fertigation was guided by the FertOrgaNic Decision Support System (DSS) developed during the project (cf. http://www.fertigation.org/). Two N fertigation strategies were explored. • After harvesting the potato field, a catch crop such as winter rye, winter wheat or rye-grass was sown. Fig. 1 shows the difference between the two fertigation strategies, T4 and T5, in the Danish experiment. Half of the expected N-fertiliser demand was applied as pig slurry to the fertigated treatments at planting. After emergence, two different fertigation strategies were applied: a static approach, T4, which suggests a distribution of the additional nitrogen based on standard recommendations, and a dynamic approach, T5, with N applied later in the growing season, and based on a simple daily N-balance-estimation method (Battilani et al., 2006). In the present study, the following measurements were carried out in all the treatments. Mineral N (ammonium and nitrate) was determined in soil samples representing soil depth from 0 m to (at most sites) 0.75 m. The sampling was carried out at least in early spring and again after harvest of the potato. Soil mineral N was also sampled in the soil solution with suction cells (0.75 m below soil surface) five to six

Table 2 – Factorial design of the drip and fertigation experiments, where organic nitrogen was supplied from an organic source (FYM, slurry or another source as compost) Treatment Organic N Mineral N Irrigation

T1

T2

T3

T4

T5

T6

0 0 0

0.5 0 0

0.5 0 1

0.5 0.5* 1

0.5 0.5** 1

0 0 1

Mineral nitrogen was supplied in the irrigation water through drip lines (fertigation). The levels are given as 0 (no input), 1 (full/optimal input based on estimates from a water or nitrogen balance calculation) and 0.5 (half the optimal input based on estimates from a nitrogen balance). ‘*’ and ‘**’ are N-fertigated treatments with the use of two different application strategies.

Fig. 1 – An example of the distribution of the mineral nitrogen in the experiments, when using the static approach (T4) and the dynamic approach (T5) in Denmark during 2004.

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times during the growing season. Dry matter (DM) and total N content of leaves, stems and tubers were measured five to six times during the growing season. Tuber DM and total N content were measured at harvest. Soil water content and tension were measured at two depths (at most sites approximately 0.15 and 0.38 m below top of ridge). Methods of measuring soil water content varied from site to site. Among the methods used there were gravimetric sampling, Theta probes (DeltaT devices, Cambridge, UK), TDR (time domain reflectometry) probes with both manual and automatic readings with the use of the Tektronix 1502C cable tester (Tektronix Inc., Beaverton, OR, USA), neutron scattering, and Virrib sensors (AMET consortium, Velke´ B´ılovice, Czech Republic). Soil water tension was mostly measured with standard tensiometers, but at some sites also with Watermark sensors (Irrometer Co., Riverside, CA, USA). Weather data – precipitation, air temperature, global radiation, wind speed and relative humidity – were mostly measured locally at the sites or taken from the nearest weather station.

2.2.

Data quality

The primary purpose of this work was not statistical comparisons between the treatments. The funding did not allow repetitions in the experiment, although it was managed at some sites through other sources. We hope, that running the same experiment on multiple sites will compensate for that. Furthermore, local experts at each site gave an estimate of the uncertainty based on experience from earlier experiments. Instead, we focus on the calibration process using data from several sites in Europe. The data was of varying quality because of unexpected incidents in at least one of the experimental years, e.g. occurrence of extreme heat in Italy, an anaerobic layer in Czech Republic, a hard pan in Denmark, and initial problems with the measuring techniques. The method described in this paper is partly an attempt to compensate for the shortcoming of the site-specific data set by combining data from all six sites.

2.3.

The Daisy model

Daisy is a one-dimensional soil–plant–atmosphere system model designed to simulate water balance, heat balance, solute balance and crop production in agro-ecosystems subjected to various management strategies (Hansen et al., 1990, 1991, 2001; Abrahamsen and Hansen, 2000). The waterbalance model comprises a surface-water balance and a soil-water balance. The surface-water model includes submodels for snow accumulation and melting, interception, throughfall, evaporation of water in the crop canopy, infiltration, and surface runoff. The soil-water balance includes water flow in the soil matrix as well as in macropores. It finally includes water uptake by plants and a model for drainage to pipe drains. The heat-balance model simulates soil temperature and freezing and melting in the soil. The solute-balance model simulates transport, sorption and nitrogen transformation processes including mineralisation–immobilisation, nitrification and denitrification, sorption of ammonium, uptake of nitrate and ammonium, and leaching of nitrate

and ammonium. The crop-production model simulates plant growth and development, including the accumulation of DM and N in different plant parts, the development of leaf-area index and the distribution of root density. The agricultural management model allows complex management scenarios to be built. An overview of recent developments, parameterisations and applications of the model can be found in Hansen et al. (2001) and Jensen et al. (2001). In the present context, the mineralisation–immobilisation and the crop-growth models are of special interest. The turnover of soil organic matter and the subsequent mineralisation or immobilisation of mineral N is simulated by a conventional multi-pool model that is based on the carbon turnover. The model considers three distinguishable types of organic matter, viz., newly added organic matter (AOM), soil microbial biomass (SMB) and soil organic matter (SOM). Especially, the latter constitutes a vast number of organic compounds. In order to be able to apply first-order kinetics, AOM, SMB and SOM are divided into two sub-pools each (SOM may be split into three sub-pools, but the third pool is considered inert). The model considers a new set of AOM pools for each addition of organic matter. This type of model is known to require quite a number of parameters. The present model parameterisation is based on Mueller et al. (1998) and Bruun et al. (2003) and is supplemented by results obtained in the present project. The potato model presented in this paper is a new development. It is based on the generic crop model included in Daisy (Hansen et al., 2001). The structure of the crop model is shown in Fig. 2. In the figure, solid lines represent flows of matter and the dashed lines represent information flows. It is noted that the model considers leaf, stem, root and storage organs, where the latter in the present case are tubers. Furthermore, it is noted that the main plant-growth processes considered are photosynthesis, respiration (both maintenance respiration and growth respiration, the latter arising from conversion of assimilates into structural DM), partitioning of assimilates, and leaf and root death. Partitioning, leaf and root death, senescence, N stress (stress factors) and canopy structure are all influenced by the crop development stage (not shown in the diagram). The photosynthesis model is based on a simple light response curve (Goudriaan, 1982) for a single leaf characterised by an initial light-use efficiency and a maximum photosynthesis rate (Fm). The canopy photosynthesis is estimated by assuming the light distribution within the canopy predicted by applying Beers law and assuming a light distribution over the day that is proportional to the local extraterrestrial radiation. The canopy is divided into a number of layers. Assuming the properties of each crop canopy layer to be equal to the properties of a single leaf, the gross photosynthesis is calculated by accumulating the contribution from the individual layers. The gross photosynthesis is calculated for each hour of the day and then accumulated to daily gross photosynthesis. The basic parameterisation of the potato model is based partly on literature values and partly on calibration, applying the model to a dataset kindly provided by Dr. D.K.L. MacKerron of the Scottish Crop Research Institute (SCRI). This dataset comprised 2 years, 1984 and 1985, with six and

e c o l o g i c a l m o d e l l i n g 2 1 1 ( 2 0 0 8 ) 209–223

213

Fig. 2 – Overview of the carbon flow in the generic crop model included in Daisy.

five N-fertilisation levels, respectively, of fully irrigated potatoes of the cultivar Maris Piper. These data have previously been used in connection with a workshop “Modelling and parameterisation of the soil–plant–atmosphere system – A comparison of potato growth models” which was reported by Kabat et al. (1995). Especially, the model CROPWATN presented at this workshop by Karvonen and Kleemola (1995) resembles the structure of the generic crop model in Daisy (Fig. 2), and hence results from this model application have served as input to the parameterisation of the new Daisy potato model.

2.4.

Calibration procedure

The main purpose of the FertOrgaNic project was the development of a computer-based DSS as well as practical guidelines for using drip irrigation and a combination of organic fertiliser and fertigation. However, as an additional product of the experiments, we also wanted to use the data for potato modelling in a European context. We saw the challenge as a social one, how to combine the “central” model-specific knowledge (mainly the Daisy model developers at the University of Copenhagen, Faculty of Life Sciences (KU-LIFE)), with the distributed site-specific knowledge. This led to a four-stage calibration procedure: (1) The first stage was to create a common basis for the project. A comprehensive experimental data set was available from SCRI, from which the experts from KU-LIFE made a basic potato parameterisation (referred to below as “Scottish parameterisation”). KU-LIFE also hosted a Daisy workshop for all the local site experts, to give them a basic understanding of how to work with and calibrate the model. (2) The local experts made site-specific calibrations using the basic potato parameterisation. At the sites where the same

variety was used for all 3 years, the first 2 years were used for calibration and the third for validation. (3) Under the assumption that all the potato varieties share some traits, the model experts at KU-LIFE created a common parameterisation by merging the various site parameterisations. The first result was a list of parameters that were changed during the site-specific calibrations compared to the initial parameterisation. Furthermore, the merge was performed by looking at the site-specific values for each parameter to extract the one we found most representative for the set as a whole. (4) Using this common parameterisation as a new base, the model experts at KU-LIFE derived a new parameterisation for the three varieties that had the best data sets. Only one of the three varieties (Agria) was used at multiple sites. It may be difficult to separate the site and variety for the two other varieties. However, all three recalibrated varietyspecific models are now less site specific, because they are based on the common potato parameterisation developed in step 3, and thus indirectly incorporating data from all sites.

2.5.

Evaluation of simulation results

Evaluation of the agreement between observed and modelled variables included qualitative as well as quantitative techniques. The agreement between model results and observations was evaluated visually from plots of observed and simulated data. Furthermore, the root mean square error (R.M.S.E.) was calculated as an objective measure. R.M.S.E. was calculated as ((xs − xo )2 )/n)0.5 , where xs is the simulated value, xo the observed value, and n is the number of observations. A normalised R.M.S.E., named N.R.M.S.E., was obtained by dividing R.M.S.E. by the mean xo of observations. N.R.M.S.E. was calculated for shoot and tuber DM and total N content in all treatments from simulations and observations made

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continuously during the growing season. Hence, a separate value of NMRSE was obtained for each of the four quantities indicated, each treatment and each year. An N.R.M.S.E. value of 25% and below was defined as acceptable agreement. The comparison between the initial and the recalibrated parameterisations was made by grouping the calculated N.R.M.S.E.’s for the main data in all treatments (i.e. in T1–T5, crop DM and N content divided into shoots and tubers) into categories below 10, 20 and 30% and above 30%, respectively. Correct simulation of nitrogen uptake was given higher priority than dry matter production because of the nitrogen aspects of the project (FertOrgaNic).

3.

Results

In this section, the calibration procedure is described in detail. The calibration was divided into two stages: a sitespecific and a variety-specific parameterisation, but only the variety-specific parameterisation will be described in detail. Simulation results for treatment T4 were chosen as a reference for parameterisation. T4 was the static fertigation approach applied to the fully irrigated and fully fertilised treatments (Fig. 1).

3.1.

Site-specific parameterisation

At each site a local modeller calibrated the potato model. The site-specific measurements from years 2003 and 2004 were used for the calibrations, and the calibrations were initially based on the Scottish parameterisation. This resulted in parameterisations that were both site and variety specific. The calibration procedures for Denmark, the Czech Republic, and Poland are briefly described below. A detailed description can be found in the FertOrgaNic report (Abrahamsen et al., 2006). Table 3 shows the varieties used at the different sites and key parameter values of the initial Scottish parameterisation compared to the resulting site-specific parameterisations for the Danish, Czech, and Polish data. PenPar1 is the root penetration parameter, DSLAI05 a parameter that forces the leaf area index (LAI) to be 0.5 at a specified development stage (DS) during initial growth, SpLAI the specific leaf area

(two values are given, one at the start of the season and the other one at its end), Fm the maximum assimilation rate, DSRate1 the rate of development from emergence until tuberisation, and DSRate2 is the rate of development from tuberisation until harvest. The meaning of the key parameters is described in detail in Abrahamsen (2006) and Abrahamsen et al. (2006).

3.2.

Simulation results for Denmark

The parameters in the Danish site-specific parameterisation that were adjusted compared with the Scottish one were the temperature sum from planting to emergence (EmrTsum), DSRate2, Fm, SpLAI, and the partitioning of assimilates. The photosynthetic rate may vary significant depending on potato variety (Shapendonk et al., 1989; Ierna and Mauromicale, 2006). A new parameter nitrogen stress limit was introduced. The purpose of this parameter is to allocate more assimilate to the tubers when the crop is stressed. Belanger et al. (2001) found, that water and N deficiencies increased biomass partitioning to tubers. Table 3 shows the key parameter values of the resulting Danish site-specific parameterisation. It is noted that the SpLAI of the Danish Folva parameterisation is smaller than the corresponding value found in the Scottish Maris Piper parameterisation indicating the leaves of the Folva are thicker than the leaves of the Maris Piper. SpLAI is based on experimental data, viz., leaf weight and leaf area index. Furthermore, it is noted that the estimated Fm of the Folva is larger than the estimated Fm of the Maris Piper, which is in agreement with the thicker leaves of the Folva and hence presumably a higher content of RuBPCase per unit surface area. Penning de Vries et al. (1989) suggest, that the Fm-leaf thickness dependency can be taken into account by assuming proportionality between Fm and leaf thickness. This suggestion is in good agreement with our finding. The progress of the tuber DM development in T4 was well simulated (N.R.M.S.E. 0.06–0.08, see also Fig. 3a and c), although the simulated final tuber yield in 2003 was too high. Also the simulation of the shoot development resulted in good agreement between observed and simulated values (N.R.M.S.E. 0.13–0.17). The N content of the tubers was well simulated for T4 during 2003 and 2004 (N.R.M.S.E. 0.11–0.13, see also Fig. 3b

Table 3 – Comparison of some key parameters in the Scottish and the site-specific parameterisations Parameter PenPar1a (cm/(dg C days)) DSLAI05b SpLAIc (m2 /g DM) Fmd (g CO2 /(m2 h)) DSRate1e DSRate2f

Scottish

Danish Folva

Czech Agria

Polish Triada

0.30 0.15 0.033, 0.033 3.0 0.07 0.009

0.30 0.15 0.020, 0.020 5.0 0.07 0.012

0.07 0.16 0.033, 0.023 3.0 0.07 0.009

0.06 0.25 0.020, 0.016 4.0 0.05 0.009

For SpLAI, start and end values for the function are given. a b c d e f

Root penetration parameter. Parameter that forces the leaf area index to be 0.5 at a certain development stage. Specific leaf area index. Maximum assimilation rate. Rate of development from emergence to tuberisation. Rate of development from tuberisation to harvest.

e c o l o g i c a l m o d e l l i n g 2 1 1 ( 2 0 0 8 ) 209–223

215

Fig. 3 – Site-specific simulation results of crop dry matter and nitrogen together with soil mineral nitrogen for the treatment T4 in Denmark during the calibration years 2003 (a and b), and 2004 (c and d), and during the validation year 2005 (e and f).

and d). The simulations of shoot-N content during 2003 were too high (N.R.M.S.E. 0.48) but were well simulated during 2004 (N.R.M.S.E. 0.19). A validation of the final site parameterisation was made using the observations from 2005, but the results for the validation year were not as good as for the two calibration years. The DM of the tubers and shoots was acceptably simulated only in T4 (both N.R.M.S.E.’s being 0.21), but for the other treatments the simulated tuber yields were generally low (not shown). The simulation of shoot and tuber-N content in 2005 was not acceptable (N.R.M.S.E. 0.31–0.42), as the observed tuber-N content was very low compared with the two other years (Fig. 3b, d and f). Observations in the field this year showed a root depth of only around 0.25 m. The maximum root depth of the potato was set in the model to 0.50 m in all

years. If the maximum root depth in the model was reduced to take account of this, the results improved for T4 but not for the stressed treatments (not shown). It seems that the model and its parameterisation based on the calibration from the two other years still need some improvement to describe the crop development during 2005.

3.3.

Simulation results for the Czech Republic

As for the Danish calibration, the changes made in the Czech calibration were mostly based on direct observations. The emergence was adjusted using EmrTsum, but DSRate1 and DSRate2 were kept unchanged (Table 3). PenPar1, DSLAI0.5, partitioning of assimilates, and values for potential and critical N content were changed. The same value of initial SpLAI

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Fig. 4 – Site-specific simulation results of crop dry matter and nitrogen together with soil mineral nitrogen for the treatment T4 in Czech Republic (a and b) and Poland (c and d) during the validation year 2005.

as in the Scottish parameterisation (0.033 m2 /g DM) was used for the Czech potato until a development stage of 0.75. After this stage SpLAI was linearly decreased until a development stage of 1.3, ending the season with a value of 0.023 m2 /g DM. The nitrogen stress limit parameter was not used in the Czech parameterisation. Measurements and simulations of DM production and N content in the crop are shown for T4 for the validation year 2005 only (Fig. 4a and b) together with mineral N in the 0–0.5 m soil layer. The simulation of T4 did show good agreement with the measured data, especially at the beginning of the growing season, although the simulated tuber-DM yield was too low (Fig. 4a). In general, the N dynamics were well simulated (Fig. 4b).

3.4.

Simulation results for Poland

As with the two previous calibrations, the Polish calibration started with correcting the time of emergence by EmrTsum. Other changed parameters were DSRate1, PenPar1, SpLAI, and Fm (Table 3). Assimilate partitioning was also changed compared to the Scottish potato. The crop showed a poor development in leaves and stems, but not in tubers in some of the treatments with N stress (not shown), and as in the Danish calibration, it was handled with the parameter nitrogen stress limit. In Fig. 4c and d measurements and simulations of crop DM production and N content are shown for T4 for the validation year 2005. Simulated values for crop growth in the initial phase were too low, even though the final yields were acceptably simulated.

3.5.

Common parameterisation

To benefit from the expertise and knowledge of the individual modellers, the information from the site-specific calibrations was combined into a common parameterisation of potato growth. This resulted in a collection of in total 23 changed parameters compared to the Scottish parameterisation, and the complete list can be found in Abrahamsen et al. (2006). The changed parameters were from sub-models governing phenology, root system, photosynthesis, assimilate partitioning, leaf development, N content in the crop, and N uptake. In general, the crop model is physical based and empirical, although some sub-models are founded in the plant biology. The method used for developing the common parameterisation was to find the most representative value for each parameter, taking into account known problems in the different datasets. To find a common value of SpLAI, for example, all measured values of SpLAI were plotted on the same figure (Fig. 5). Almost all of the local modellers used a pattern of SpLAI development that started at a constant level at emergence and then started to decrease after some time. Hence, this form of SpLAI was adopted for the common parameterisation. A visual inspection indicated that a decrease started around a development stage of 1.5. Linear regression was performed for the interval DS from 1.0 to 1.5 and from 1.5 to 2.0 and was used as the basis for assessing the final form of the common function. To find common values of other parameters where we did not have direct measurements, the site-specific parameter values were plotted and the most representative value was used. This was for example done for the potential N concentration in the crop, where the Czech

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so that initial growth was determined by the development stage only, which explicitly responds to air temperature. The mechanistic concept of initial growth in the original model seems inadequate and was replaced by an empirical sub model for initial growth, which also was used in an earlier version of the model. This makes the crop model more robust. The resulting common parameterisation is thought to be a more robust basis for any new calibration, as it is built on knowledge and experience extracted from sites across Europe. The intention is that it provides a basis for variety-specific parameterisations.

3.6.

Fig. 5 – The distribution of the specific LAI for the common potato parameterisation is based on data from all sites.

Fig. 6 – The potential nitrogen concentrations chosen for the different site-calibrations. The site-specific calibrations (thin lines) refer to the three different varieties used at the Slovakian site.

A new calibration based on the common parameterisation was performed for three potato varieties: Agria, Folva and Triada. Agria was grown in the Czech Republic all 3 years and in Portugal and Slovakia in 2003. Folva was only grown in Denmark and Triada only in Poland. Our idea was that by using the common parameterisation as a base, these new parameterisations would to a higher degree reflect the variety, and be less sitespecific and thus more representative for use across Europe. The data from 2005 were included in the variety-specific calibration, because the data sets had already been used for validation of the site-specific calibrations, and the trends in the data sets were then already known. Another reason was that the validation of the 2005 data for Poland revealed that there were conditions governing the potato growth that were not covered by the previous site-specific calibration. The variety-specific recalibration procedures are briefly described in the following three sections. Table 4 shows key parameter values for the common parameterisation and the values resulting from the variety-specific calibration for the varieties Folva, Agria, and Triada.

3.7. distribution was chosen to represent the common function (Fig. 6). One feature was changed as a result of the validation of the Polish site. The validation process showed that the procedure for starting growth was too sensitive to the weather conditions. Therefore, the common parameterisation was adjusted

Variety-specific parameterisation

Folva parameterisation, Denmark

The variety Folva was only grown in Denmark. The recalibration was based on the parameters from the common parameterisation. An adjustment of EmrTsum from 425 to 330 resulted in correct plant emergence. The DSRate1, which determines tuber initiation, was kept at 0.06, corresponding to

Table 4 – Comparison of some key parameters in the common and the recalibrated parameterisations Parameter a

PenPar1 (cm/(dg C days)) DSLAI05b SpLAIc (m2 /g DM) Fmd (g CO2 /(m2 h)) DSRate1e DSRate2f

Common

Folva (DK)

Agria (CZ, PT, SK)

Triada (PL)

0.06 0.2 0.02, 0.006 4.0 0.06 0.009

0.06 0.5 0.022, 0.006 4.8 0.055 0.011

0.06 0.5 0.02, 0.006 4.0 0.06 0.09

0.06 0.4 0.015, 0.009 5.0 0.06 0.009

For SpLAI, start and end values for the function are given. a b c d e f

Root penetration parameter. Parameter that forces the leaf area index to be 0.5 at a certain development stage. Specific leaf area index. Maximum assimilation rate. Rate of development from emergence to tuberisation. Rate of development from tuberisation to harvest.

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the common parameterisation. Fm was at the start increased from 4.0 to 5.0, because the yield level was generally too low. The parameter DSLAI0.5 was equal to 0.5 in the common parameterisation, which resulted in correct initial growth and the final yields were acceptably simulated. However, the partitioning of assimilate had to be adjusted, so that less assimilate was allocated to the leaves and stems after the tuber initiation and hence more to the tubers. Afterwards, the simulated LAI was too high and the value of the factor that makes the SpLAI dependent on DS (SpLAIMod) was changed slightly at development stage 1.5 from 1.0 to 1.1. The result was that the simulated yields were generally too high and to improve them, Fm had to be reduced from 5.0 to 4.8. Finally, the parameterisation was refined based on the calculated N.R.M.S.E. This resulted in an earlier beginning of leaf senescence, from DS 1.50 to 1.48. Furthermore, a small decrease in DSRate1 from 0.060 to 0.055 and an increase of DSRate2 from 0.09 to 0.11 were introduced (Table 4). This resulted in a parameterisation that was better than the first site-specific parameterisation (Tables 5 and 6). The improvements applied particularly to 2005 and to the plant-N content. However, the simulations of the stressed treatments (T1 and T2) were not as good as the simulations of T4 (not shown). Fig. 7 shows the final simulation results for treatment T4 in 2005. The final Folva parameterisation can be found on the Daisy homepage.

3.8.

Table 6 – Number of model and data comparisons distributed into ranges of N.R.M.S.E. when the recalibrated parameterisation (per variety) and data from all treatments (T1–T6, DK: only T1–T5) during 2003–2005 were used N.R.M.S.E. Denmark, Folva Shoot—dry matter Tuber—dry matter Shoot—N content Tuber—N content Total of 60 Agria Shoot—dry matter Tuber—dry matter Shoot—N content Tuber—N content Total of 112 Poland, Triada Shoot—dry matter Tuber—dry matter Shoot—N content Tuber—N content Total of 68

0–10%

0–20%

0–30%

Above 30%

1 3 0 2

6 8 6 5

9 12 9 8

6 3 6 7

6

25

38

22

1 2 0 0

6 10 2 7

11 21 8 13

17 7 20 15

3

25

53

59

0 0 0 0

0 6 0 6

2 14 2 11

15 3 15 6

0

12

29

39

Agria parameterisation

The potato variety Agria was grown in the Czech Republic, Portugal, and Slovakia. The inclusion of data from all these sites was an opportunity to create a potato parameterisation that was less site-specific. The data sets contained some

Table 5 – Number of model and data comparisons distributed into ranges of N.R.M.S.E. when the site-specific parameterisation (irrespective of variety) and data from all treatments (T1–T6, DK: only T1–T5) from 2003 to 2005 were used N.R.M.S.E. Denmark, Folva Shoot—dry matter Tuber—dry matter Shoot—N content Tuber—N content Total of 60 Agria Shoot—dry matter Tuber—dry matter Shoot—N content Tuber—N content Total of 112 Poland, Triada Shoot—dry matter Tuber—dry matter Shoot—N content Tuber—N content Total of 68

0–10%

0–20%

0–30%

Above 30%

0 3 0 0

4 5 3 6

10 12 6 9

5 3 9 6

3

18

37

23

0 4 0 0

2 11 3 10

9 20 7 17

19 8 21 11

4

26

53

59

0 0 0 0

0 6 0 3

3 12 2 5

14 5 15 12

0

9

22

46

problems, described in the following, that were necessary to take into account during the calibration. The measurements from T4 and T5 from the Czech Republic in 2004 were not considered relevant, because this year an anaerobic soil layer was observed in these treatments, caused probably by wrong placement and unfavourable composition of the slurry. It seems, that this has negatively affected the potato production. During the original site-specific calibration of the Portuguese site, we had to estimate both the amount of irrigation applied as well as the initial content of mineral nitrogen in the soil, because the measured values for both were too uncertain. When revisiting the Portuguese site for the varietyspecific Agria parameterisation, we choose to use the same estimations of irrigation and mineral nitrogen. No measurements were made early in the growing season 2003 in Slovakia and, therefore, the initial growth and start of tuberisation were uncertainly determined. The steps in the calibration procedure were to calibrate: (1) emergence, (2) initial growth, (3) SpLAI, (4) the influence of air temperature on DSRate1 and DSRate2, and (5) assimilate partitioning. Emergence was adjusted by EmrTsum. Using the common parameterisation, the growth just after emergence was too fast for almost all treatments in the Czech Republic. A value of 0.5 for DSLAI05 gave a better initial growth. Common values of SpLAI were used, as these were in good agreement with the Czech measurements. For the Slovak data set, the common values were too low and for the Portuguese data set they seemed to be too high, which indicates that the common values may be a reasonably good across-Europe average. The next calibration step was to ensure that tuberisation time is correctly simulated. The potatoes grown in the Czech Republic generated tubers much faster than the pota-

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Fig. 7 – Simulation results of crop dry matter and nitrogen together with soil mineral nitrogen for the variety Folva in treatment T4 in Denmark during 2005 (recalibration). The late measured value is total tuber dry matter measured at the final harvest.

toes grown in Portugal and Slovakia. At the Czech site, it took 12–18 days from emergence, while it took 34 days in Portugal and 25 days in Slovakia. Tuber initiation is very dependent on temperature and day length (Ritchie et al., 1995; Haverkort and Kooman, 1997). The air temperatures after emergence were higher at the sites in Portugal and Slovakia than at the site in the Czech Republic. Thus, the data may indicate that air temperatures above a certain threshold delay tuberisation. To be able to simulate this effect, the parameter TempEff1 governing the temperature effect on the development rate before tuberisation was adjusted accordingly. This was done by feeding an optimisation program with all five sets of air temperatures and the corresponding number of days before tuberisation. The value of TempEff1 that gave the best match was chosen. One must add, however, that the mother tubers in the Czech experiment were chitted, i.e., their physiological age was higher. This circumstance may have affected not only the onset of emergence but also the onset of tuberisation (Villafranca et al., 1998). It is also possible to include an effect of day length on tuberisation in the model, but it was omitted because we assume that the effect of the variable mother tubers was important in our experiments across Europe and resulted in insufficient data quality for parameterisation of this effect. Using these parameter values, the simulated DM production was too high at the end of the growing season in Portugal and Slovakia, but not in the Czech Republic. The excessively high DM production simulated in Portugal and Slovakia may have been a consequence of too late leaf senescence in the simulations, when compared to observations. To lower the production rate, leaf senescence had to be increased for Portugal and Slovakia, but this would cause a worse simulation for the Czech conditions, because the senescence was well simulated for the Czech potatoes. To solve these conflicting trends, the parameter governing the temperature effect after tuberisation was adjusted so that high temperatures speed up phenological aging. This resulted in earlier simulated leaf senescence for potatoes from Portugal and Slovakia, without affecting the potatoes from the Czech Republic, and all the simulations were improved. The Portuguese data indicated an even faster aging rate but this could not be achieved in simulations without causing problems for the simulations of

the two other sites. Stem loss of dry matter over time was indicated by some measurements but this process was not included in Daisy. Instead, allocating less assimilates to the stem simulated this trend. Tables 5 and 6 show that the N.R.M.S.E. values of the Agria simulation were almost the same for both the sitespecific and the variety-specific recalibrated simulations, but the actual simulation results were very different. Fig. 8 shows the simulation results of the Agria variety for the Czech treatment T4 for 2005. In comparison with the results of the site-specific calibration (Fig. 4a and b), the simulated N contents of the crop now better coincide with the measured ones, except for the total N content in tubers at harvest, and the agreement was better for the other treatments (not shown). The simulated DM contents were not improved. For the Agria site-specific parameterisations, the N.R.M.S.E. values were calculated from the simulations using three different parameterisations—one for each site, but for the recalibrated simulations only one parameterisation was used. For the Portuguese simulations, the start phase was improved, but the final production was often too high. The final Agria parameterisation can be found on the Daisy homepage.

3.9.

Triada parameterisation, Poland

The potato parameterisation for Triada was based only on the Polish field experiments. The validation of the Polish site-specific parameterisation showed problems in predicting the initial growth, especially for the non-irrigated treatments. The initial growth was low, and this influenced the rest of the simulation period. It seems that initiation of growth in Daisy was too sensitive to weather conditions just after emergence. The validation year 2005 received about 60% less radiation during the period just after emergence than the two preceding years. In the recalibration procedure, the initial growth function was made independent of the light intensity. This was justified by knowledge found by Villafranca et al. (1998), that the initial growth of the potato can be very dependent on the variable state of the mother tubers. The steps in the recalibration process were: (1) to get emergence and the start of tuberisation correct, (2) to adjust root development relative

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Fig. 8 – Simulation results of crop dry matter and nitrogen together with soil mineral nitrogen for the variety Agria in treatment T4 in Czech Republic during 2005 (recalibration). The late measured value is total tuber dry matter measured at the final harvest.

to measured root depths and soil moisture, (3) to determine SpLAI from measurements, (4) to adjust Fm, (5) to adjust initial growth, (6) to change the partitioning of assimilate, (7) to set limits for N uptake, (8) to make the phenological rate more dependent on temperature, and (9) to make the crop less sensitive to water stress. The first step was to adjust the emergence time by EmrTSum. After that, a correct prediction of tuberisation start was found using the common development rate (Table 4). Root measurements of 2005 indicated a deeper root penetration at emergence than in 2003 and 2004. This was interpreted as being a consequence of a deeper sowing depth of the mother tuber. Setting the sowing depth of the mother tuber to 0.30 m in 2005 instead of 0.20 m used in the common parameterisation gave better agreements between simulated and measured root development. The measurements of LAI and DM of leaves were used to calculate SpLAI, and this resulted in a lower value than the one used in the common parameterisation (Table 4). The new value of SpLAI gave a reduction in production that had to be compensated for by increasing Fm from 4.0 to 5.0 g CO2 /(m2 h). In general, Fm has been the parameter used for fitting the general level of production, after adjustment of directly measured parameters. Ierna and Mauromicale (2006) found that the photosynthetic rate was positively associated with the aboveground biomass. The simulation results for 2005 were still very poor, especially during the start of the

growing period. Increasing the value of DSLAI05 from 0.2 to 0.4 gave a better result during 2005. This, unfortunately, had a slightly negative effect on the success of 2003 and 2004 simulations. The common definitions of assimilate partitioning parameters resulted in too much DM in stem compared to DM in leaves, and, therefore, the partitioning parameters had to be changed. Measurements of soil mineral N in the upper 0.60 m showed a lower boundary of 30–40 kg N/ha. The potato parameterisation was accordingly modified by setting the parameters NH4 root min and NO3 root min, specifying the minimum concentrations needed near the root surface for uptake to happen. By changing the leaf-senescence rate it was possible to get even better predictions of the 2003 and 2005 treatments. Unfortunately, this was not the case for the 2004 treatments, where an increased leaf-senescence rate did not match the measurements, resulting in too low a production. This could indicate that the crop phenological aging in 2004 was slower than in 2003 and 2005. The year 2004 was colder than 2003 and 2005 in the period from April to mid-July. To simulate the apparent difference in the phenological aging, the parameterisation was modified to give maximum developing rates at 20 ◦ C. Due to poor simulations of the water stressed treatments (T1 and T2, not shown), the parameterisation of crop response to water stress was changed. A linear relation between water

Fig. 9 – Simulation results of crop dry matter and nitrogen together with soil mineral nitrogen for the variety Triada in treatment T4 in Poland during 2005 (recalibration).

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stress and assimilate production is used by default in Daisy. Mogensen (1980) showed that the assimilate production might be less sensitive to water stress than this relation indicated. This last refinement improved the simulation results (Tables 5 and 6), especially for 2005. The simulation results for treatment T4 can be seen in Fig. 9. The final Triada parameterisation can be found on the Daisy homepage.

4.

Discussion

This study describes a calibration method that can be used to model potato growth in Europe. Following the steps in the procedure can be helpful for model users when they want to develop and parameterise new potato modules, so they can be applied satisfactorily at different locations. The parameterisations resulting from the calibration can be used as starting points for new calibrations of potatoes for other soils, climates, and management practices.

4.1.

Systematic calibration process

Systematic or automatic calibration/validation procedures can often be found in the literature, especially for hydrological modelling and pesticide-leaching models (Refsgaard, 1997; Vanclooster et al., 2000; Abrahamson et al., 2005), whereas automated procedures for crop models are more rare (Hunt and Boote, 1998; Wallach et al., 2001). The first stage in our calibration was to create an initial potato parameterisation that could serve as a starting point for all the simulations. Vanclooster et al. (2000) recommend the good modelling practice (GMP) that records the steps in the modelling process. The main objective of a GMP is to describe what has been done, to justify why it has been done, and to document the modelling process such that the model user and any independent person may repeat the modelling exercise and achieve the same result (Travis, 1995). The first advice in GMP is “The model user is responsible for understanding the model and its appropriate usage”. In the present study, a step toward this target was made at a workshop held in 2003, where the site-specific modellers learned to use the Daisy model. The next advice in GMP is “The model user is responsible for estimating the model parameters and the input for a selected scenario”. Because our model users were local, they had close contact to the persons that were responsible for the field measurements or they undertook the field experiments themselves. This is important for the quality of the input and for the explanation of unexpected situations. The local modellers performed the calibrations with only electronic communication to the model experts. An evaluation of the process with the site-specific calibration by the local modellers showed that electronic communication was insufficient and that direct contact between the site and model experts is necessary. In the present study, the steps in the calibration procedure were described in details and as such agreed well with the last advice in the GMP: “The user is finally responsible for developing modelling reports that contain sufficient information for an independent person to reproduce the results”. The reporting (Abrahamsen et al., 2006) of the detailed analysis of the

221

calibration procedure and parameters use will most likely aid subsequent users of the model. In the present study, the initial procedure was an ordinary calibration/validation process that is often composed of calibration of the model on experimental data from some years and validation of the model on data from another year. The calibration process was an iterative ‘trial and error’ process. It is important that the parameters are evaluated in a logical sequence. At all sites in the present study, the parameters related to soil water were calibrated first and then parameters related to crop growth and N dynamics—a sequence that is often used (Hanson et al., 1999). The next step in our procedure was to combine the resulting parameter sets from the individual calibrations into a new common parameter set. The model developers from KU-LIFE did this work. A strategy like this was also used in a systematic parameterisation of legume crop models where the starting values determining crop development and phenology were from ‘general’ cultivars (Hunt and Boote, 1998). Afterwards, the calibration was iterated using the common parameter set as a starting point. The data from the experiments during 2005 were also included in this analysis. As a first step, six key parameters were inspected and, if necessary, their values, imported from the common parameterisation, were changed. It is recommended to adjust the time of emergence parameter individually because the variable history and actual state of the mother tubers make it difficult to find an automatic universal way how to estimate it. Villafranca et al. (1998) found that the physiological age of the mother tuber can affect the development of the potato as long as until tuberisation. Afterwards, other parameters were modified if this contributed to reduction of N.R.M.S.E. A method like this, starting with a few important parameters and then including more parameters, one at a time as long as the fit between measurements and simulations improves, was described by Wallach et al. (2001) and the same strategy was used by Zhai et al. (2004).

4.2.

Use of parameterisation

If a model user wants to apply the model to a potato crop grown on the same site, then the site-specific specific parameters can be a good starting point. Hanson et al. (1999) described regional or site-specific parameters as parameters that may change according to site. The parameters can differ from site to site depending on soil, climate, and management practice (Abrahamson et al., 2005). Therefore, the parameters must be ¨ estimated separately for each plot (Sievanen and Burk, 1993). In Poland and Denmark, the variety-specific parameterisation corresponds to the site-specific parameters because the variety was only used in experiments at one site. The application of the Daisy model on different sites in Europe in this study resulted in parameters that respond to the local conditions. For an inexperienced user it may be difficult to select the key parameters to calibrate because different combinations of parameters can result in equally good simulations (Botterweg, 1995). The selection of the important parameters was facilitated in this study, where six independent model users from different locations in Europe made their own choices depending on the soil, climate, and management practices at their

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locations. There were six parameters that all individual users needed to change in order to get a good agreement between measurements and simulations. These were defined as the key parameters. Besides these six key parameters, the time of emergence should be adjusted individually as mentioned above. If a model user will calibrate the model on experiments with the same variety he can use the variety-specific parameter set as a starting point. It is expected that this variety-specific parameterisations reflect the variety properties better and are less site-specific, more representative and more suitable for use across Europe. Unlike the site-specific parameterisations, the new variety-specific parameterisations have not yet been validated, as no new data were available. The parameterisation resulting from this procedure can, e.g., be used in scenario studies including simulations of many combinations of soils, climate, crop rotations and management for several years. Before applying a model across Europe it is necessary to test the model on the different climate and soils in several areas of Europe. Adopting the calibration procedure in this study we hope to remove some local bias due to, e.g., inaccuracy in data at a site, and then to be able to create potato variety parameterisations that are more suitable for use in different parts of Europe.

5.

Conclusions

The FertOrgaNic project has resulted in transport and exchange within Europe of know-how of the Daisy simulation system, methods of measurements, and development of a shared scientific language. The project has shown the importance for local modellers of having close contact to persons, who make the field experiments and of detailed knowledge on the conditions under which the experiments were performed. The calibration project started with a Daisy workshop generating a calibration base for the group of modellers. The problem with local modelling is that the parameterisation for a given variety may become too site-specific regarding climate, soil and management. We believe that the two steps of creating the common parameterisation first and then the variety-specific parameterisations have worked well and generated robust and portable parameterisations. These parameterisations are now a part of the Daisy distribution. However, they need to be validated on other experimental data sets with new site partners to check their portability. The calibration procedure we recommend to follow is to start calibrating the water dynamics and then continue with the crop growth and N dynamics. The calibration of the crop growth should be started with the six key parameters identified in this study. In addition, it is always necessary to adjust the emergence time individually, because the initiation of potato growth is very dependent on the history and actual state of the mother tubers. After the key parameters have been parameterised, other more or less subjectively selected parameters can be included in the calibration process until an acceptable agreement is reached between modelled and measured data.

It is recommended to use site-specific parameter sets obtained in the present study as a starting point if the model user wants to calibrate a potato crop from the same country (Denmark, Czech Republic or Poland) for which the parameter set has been developed. The variety-specific parameter sets should be used if the model user wants to calibrate a potato crop of the same variety (Agria, Folva or Triada). The common parameter set is expected to be valid as a starting point for model users from other sites and for the modelling of other potato varieties than mentioned above.

Acknowledgements This work was financed jointly by the EU 5th Framework RTD project (QLK5-2002-01799) FertOrgaNic (Improved organic fertiliser management for high nitrogen and water use efficiency and reduced pollution in crop systems), and national funding from the co-authoring institutes. We are very grateful to Dr. D.K.L. MacKerron of the Scottish Crop Research Institute for his highly valuable comments.

references

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