Modeling biomass growth, N-uptake and phenological development of potato crop

Geoderma 105 Ž2002. 367–383 www.elsevier.comrlocatergeoderma

Modeling biomass growth, N-uptake and phenological development of potato crop S. Gayler a,) , E. Wang b, E. Priesack a , T. Schaaf c , F.-X. Maidl d a

Institute of Soil Ecology, GSF-National Research Center for EnÕironment and Health, D-85764 Neuherberg, Germany b Agricultural Production Systems Research Unit, Toowoomba, QLD 4350, Australia c Zentralstelle f ur ¨ Agrardokumentation und-information, D-53144 Bonn, Germany d Agronomy and Plant Breeding, Technical UniÕersity of Munich, D-85350 Freising-Weihenstephan, Germany

Abstract Using the modeling tool Expert-N, daily fluxes of water, carbon and nitrogen in potato fields were simulated in this study. The crop growth model Soil–Plant–Atmosphere System Simulation ŽSPASS. was integrated in Expert-N and adapted for the simulation of potato growth. The aim of the study was to investigate the extent to which the SPASS model, tested thus far only for winter wheat, is suitable for the simulation of potato crops. In addition to re-parameterization of the model, minor modifications, such as description of phenological development, assimilate partitioning, nitrogen uptake and leaf senescence were carried out without changing the overall structure of the model. The SPASS model was calibrated using data from a potato field experiment carried out in 1996 at the Research Station Scheyern, which examined the effects of various fertilization applications on the growth and yield of two potato varieties, AChristaB and AAgriaB, representing early and late maturity classes, respectively. Distinctions between AChristaB and AAgriaB were realized by variable parameter values concerning phenological development, assimilate partitioning and nitrogen concentration in tubers. The model’s ability to predict potato yields and nitrogen uptake was compared with actual values obtained in different years at other fields of the Research Station Žonly AAgriaB .. Simulation results show that the SPASS model was able to describe the effect of different N fertilizer applications on potato growth and nitrogen uptake. Differences between the two potato varieties could be adequately predicted, and tuber yields and nitrogen uptake well predicted. However, estimated modeling efficiencies suggest that further improvements are due. Crucial components of the model are the control of root nitrogen uptake and the regulation of the distribution of assimilates to different plant organs. To obtain a broader basis for the verification of the corresponding simulation modules, further experiments addressing optimal

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Corresponding author. Fax: q49-89-31873376. E-mail address: [email protected] ŽS. Gayler..

0016-7061r02r$ - see front matter q 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 1 6 - 7 0 6 1 Ž 0 1 . 0 0 1 1 3 - 6

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nitrogen concentrations in plant organs are necessary. Efforts to refine the SPASS model should be concentrated on a dynamical description of the partitioning pattern of assimilates, including a direct response of the partitioning pattern to changing environmental conditions. q 2002 Elsevier Science B.V. All rights reserved. Keywords: Generic plant modeling; Potato growth; Phenological development; Nitrogen uptake

1. Introduction One of the main topics in agronomic research is to find management strategies that maximize crop production and minimize environmental degradation. An appropriate complement to experimental data is the utilization of simulation models, which can provide an efficient interpretation of data, and also analyze the behavior of agricultural systems under diverse environmental conditions. Investigations using models are faster and more economical than experimental studies alone, and models represent helpful tools through which decision-making processes in sustainable agricultural systems occur. However, in order for simulation models to be useful instruments in agricultural practice, comparison to field experimental data is essential. A powerful tool for simulating daily fluxes of water, carbon and nitrogen in agroecosystems is the modeling system Expert-N Ž Engel and Priesack, 1993; Baldioli et al., 1995; Stenger et al., 1999. . It consists of several modules for simulating different processes in the soil–plant–atmosphere system, which can be coupled together in various combinations. Defined interfaces exist between the single-process modules, which must be kept if new modules are integrated in the modeling system. During the last years, Expert-N was successfully used to simulate water transport and nitrogen turnover processes within the scope of the FAM Research Network on Agroecosystems. Water transport modules were adapted to the special conditions at the sampling locations within the Research Station Scheyern ŽPriesack et al., 1999., and many experiments were carried out to provide the data required by Expert-N ŽScheinost et al., 1997. . To consider crop growth in an explicit way and to thus extent the performance of Expert-N, now the process-oriented model for the description of growth and uptake processes of field crops, Soil Plant Atmosphere System Simulation Ž SPASS. , was integrated in the modeling system. Implementation of SPASS was carried out subject to the modular structure of Expert-N system, to allow the combination of SPASS with the soil modules already available in Expert-N. SPASS is intended to be a generic model for simulating crop growth. A simulation model may termed AgenericB, if it simulates several functionally and structurally equivalent systems solely through the use of different parameter values. This approach encourages modelers to determine general properties of the class of systems Ž similarities.

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and to view individual systems as variations, rather than as separate entities ŽReynolds et al., 1989.. In case of crop models, a generic model structure can be established by recognizing the general process common to all crops and this overall model structure may be then applied to all crop types. Functionally equivalent crop species can be simulated solely by model re-parameterization using species-specific parameters. Although divergences in physiological and ecological principles between crop classes Ž e.g. cereals and root crops. may require modifications of single process formulations, the overall model structure should remain unchanged. Thus, the integration of one generic crop model into the Expert-N modeling system provides a more efficient way to simulate several crop species rather than the integration of numerous single species models. SPASS has been thoroughly tested for winter wheat by Wang Ž 1997. before its integration into Expert-N. The goal of the present study was to modify the SPASS model to simulate potato crops by investigating the changes in process formulations required for this modifications and, further, to test the reliability of model predictions. Therefore, SPASS was calibrated using data from a potato crop fertilization experiment carried out within the FAM program. In order to test the reliability of model predictions, simulated tuber yields and nitrogen uptake were compared with experimental results obtained in different years at other fields at the Research Station Scheyern. All simulations reported in this paper were carried out by combining the crop model SPASS with soil modules according to the LEACHN model Ž Hutson and Wagenet, 1991., also implemented in the Expert-N modeling system. LEACHN is based on the Richards-Equation and describes the one-dimensional, vertical water and nitrogen transport in the unsaturated soil zone, as well as nitrogen transformation processes.

2. Material and methods 2.1. Experimental data In order to parameterize the SPASS model and test its suitability to simulate potato growth, we used experimental data from a detailed study carried out in the 1996 vegetation period ŽApril to October. at seven investigation plots in Scheyern Ž description of the Research Station Scheyern, see Schroder et al., ¨ 2001, same issue.. The investigation plots were 30 m2 in size. Two varieties of potato, AChristaB and AAgriaB, representing early and late maturity classes, respectively ŽZADI, 1999., were planted at the same time Ž 21st April, 1996. and treated with various amounts of calcareous ammonium nitrate Ž CAN. . All conditions received 150 kg Nrha of fertilizer, applied in the following temporal schedule: Ž i. N 150, where the entire amount of fertilizer was applied 4 days

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after sowing; Žii. N50–100 for 50 kg Nrha fertilization at planting and 100 kg Nrha at emergence time ŽAChristaB: 17th May 1996; AAgriaB: 24th May 1996. and Žiii. N50–50–50 indicating fertilization of equal quantities Ž 50 kg Nrha. at planting, emergence and at 20-cm growth height Ž 10th June 1996, only AAgriaB .. No fertilizer was applied in the N0-condition. Measurements of plant biomass Ždry matter. and plant nitrogen concentrations Ž separated into tubers and vegetative parts above ground. were realized six times during the growing period from April to October Žfor a detailed description, see Maidl et al., 2001, same issue. . Reliability of model predictions was tested with data other than that used for model calibration. Potato yields and nitrogen uptakes measured in fields A15 Ž4.7 ha., A17 Ž6.0 ha. and A18 Ž 6.5 ha. in between 1993 and 1995, and in 1997 and 1998 were compared with simulated results. Potato production in these fields was carried out according to the guidelines of integrated crop management. In each case, mineral nitrogen fertilization was done once per vegetation period, some weeks after planting. The amount of fertilizer varied between 37 and 127 kg nitrogenrha and depended on soil nitrogen conditions at fertilization time. 2.2. Crop growth model Plant processes simulated by SPASS are phenological development, photosynthesis, respiration rate, assimilate partitioning and biomass growth, canopy and root system development, senescence, water uptake and nitrogen uptake. A detailed description of the SPASS model including all equations and its application to simulate wheat crop growth is provided by Wang Ž 1997. . For the modeling of potato crop in this study, some of the modules were modified slightly, as compared with the SPASS Wheat model as indicated below. 2.3. Phenological deÕelopment SPASS describes three internal developmental phases: germination to emergence, emergence to flowering and flowering to maturity. A given genotype requires varying number of days to complete a developmental process. The number of days having optimal climatic conditions needed to complete a developmental phase is defined here as physiological developmental days ŽPDDs. . PDDs determine the maximal development rates in the different phases. The actual rate of development is affected by temperature and, for several crops during the vegetative stage, also by photoperiod and vernalization Ž e.g. winter wheat.. For potato, the effect of photoperiod length on flowering date is almost none ŽPenning de Vries et al., 1989., but date of tuber growth start is affected by photoperiodism. Day lengths exceeding 12-h delay tuberization, whereby early varieties are less sensitive to increasing photoperiod than late varieties Ž Griffin

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et al., 1993. . In simulating potato growth, therefore, an additional tuberization rate, R tub , is introduced in the model to estimate the time point where assimilate transfer to tubers begins. Developmental rates during the three developmental phases are calculated as follows:

°

fT

PDD1 R dev s~

fT PDD 2 fT

¢PDD

; for germination to emergence ; for emergence to flowering , ; for flowering to maturity

3

where f T represents the temperature response of the developmental rate Ž f T equals 1 at Topt and is zero beyond the interval w Tmin ,Tmax x.. PDD1 is the minimum number of physiological developmental days from germination to emergence and can be estimated by multiplying the maximal growth rate of the sprout, Sprext,max wcmrdayx, and the planting depth of the seed potato d seed wcmx: PDD1 s d seed Sprext ,max . PDD 2 and PDD 3 are the minimal numbers of physiological developmental days from emergence to flowering and from flowering to maturity, respectively. Emergence occurs if Ý R dev s 1, flowering starts if Ý R dev s 2, and maturity is reached if Ý R dev s 3. Sprext,max , PDD 2 and PDD 3 are species-specific parameters. Calculation of tuberization rate begins, if emergence is reached: R tub s

fT f P

,

PDDtub

where

°

1

y4 Ž h max y h php . f P s~ 1 y exp Ž h max y 12.

¢

ž

/

;

if h php F 12 h

;

else

,

is the photoperiod response function, h php the photoperiod in hours and h max the maximal photoperiod at which no tuberization occurs. The number of physiological developmental days needed for tuberization are estimated from PDD 2

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and a variety-dependent parameter f tub : PDDtub s f tub PDD 2 . Tubers begin to grow, if Ý R tub Gs 1. 2.4. Photosynthesis As in other crop models, daily canopy photosynthesis is determined in three steps: Ži. calculation of photosynthesis rate of a unit leaf area, Ž ii. calculation of momentary photosynthesis rate of the whole canopy and Ž iii. calculation of the daily total photosynthesis of the canopy. Leaf photosynthesis rate is related to the intercepted photosynthetic active radiation, temperature and leaf nitrogen level. Values for the maximum leaf gross photosynthesis rate at optimal light and temperature conditions and optimal nitrogen supply of the leaf are summarized by Penning de Vries et al. Ž1989.. The dependence of photosynthesis on light interception is described by a negative exponential curve ŽGoudriaan and Van Laar, 1994. . The effect of non-optimal temperatures or nitrogen shortage is described by reduction factors. Daily canopy photosynthesis is calculated by integration of the leaf photosynthesis through the canopy and over time. 2.5. Respiration, assimilate partitioning and biomass growth Simulation of respiration, assimilate partitioning and biomass growth was done according to the method of Penning de Vries et al. Ž 1989. and Groot Ž1987.. The amount of carbohydrates available for growth and respiration is defined as the sum of the carbon gain from daily photosynthesis and the re-translocation of mobilizable starch in the vegetative organs. To simulate potato, additional retranslocation from starch reserves in the seed potato was introduced into the model. One part of the available assimilates is used for maintenance respiration. Carbohydrates required to maintain plant organs are assumed to be proportional to the respective plant organ biomass. The other part is partitioned to the different plant organs, and will be used for biomass growth and for growth respiration. Growth respiration is estimated to be proportional to the newly formed biomass. Potato-specific parameters for maintenance respiration, growth respiration and growth efficiencies were taken from Penning de Vries et al. Ž 1989. . Allocation of assimilates to the different plant organs is dependent on the actual development stage. The pattern of assimilate distribution is simulated by using fixed values at defined phenological stages and performing a linear interpolation between these points. 2.6. Canopy and root system formation Growth of leaf area is simulated by transforming the newly formed leaf biomass with the specific leaf weight; specific leaf weight varies with develop-

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ment stage. A nitrogen stress factor is considered to account for reduced leaf area growth in case of nitrogen shortage. Increase in the root depth is dependent on the species-specific root extension rate, temperature and soil water content in the rooting layer. We assumed that root extension occurs only if assimilate transport to roots takes place. Total root length produced within a day is calculated using the root biomass growth rate and a specific root lengthrweight ratio. The newly formed root length is then distributed in soil profile according to Jones et al. Ž 1986. . 2.7. Senescence Senescence simulation is based on the SUCROS-model Ž Van Keulen et al., 1992., and occurs either due to aging or when the leaves shade each other. The relative death rate due to shading is neglected as long as the leaf area index is lower than a critical leaf area index, and is maximal when the leaf area index exceeds twice the critical leaf area index. The relative death rate due to aging is n zero, as long as Ýemergence R dev F Dsenesc is true Ž Dsenesc is the developmental stage at which leaf senescence begins.. Once senescence due to aging begins, the relative death rate increases with advancing developmental stage and is maximal at maturity. In simulating potato processes, an additional parameter is introduced into the model, which is dependent on the maturity class of the variety and modifies the form of this death rate curve. The actual leaf death rate is set to the larger one of senescence due to aging and senescence due to shading, multiplied with two factors accounting for the effects of temperature and nitrogen stress on leaf senescence. High temperatures and nitrogen shortage accelerate senescence. Root death begins after flowering and is estimated by a constant root turnover rate multiplied by a factor accounting for nitrogen stress effects. 2.8. Water uptake Root water uptake was simulated based on the method of Ritchie et al. Ž 1987. and Jones et al. Ž1986.. If sufficient water is present in soil, transpiration is determined by plant water demand. The potential transpiration rate under optimal water supply is taken as plant water demand and depends on leaf area and weather conditions. Different modules for determining potential transpiration are available in the Expert-N modeling system. Water uptake can be limited either by water shortage in soil or by the maximal uptake rate for water by unit length of root. For each soil layer, potential root water uptake is calculated from actual available soil water and the total root length in this layer. The total potential root water uptake by plants is the cumulative sum of water uptake in all layers. If the potential root water uptake by plants is greater than potential

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transpiration, then actual transpiration is equal to potential transpiration. Otherwise, actual transpiration is equal to the potential root water uptake. 2.9. Nitrogen uptake In the model, plant nitrogen uptake depends on the nitrogen demand by plant organs, nitrogen availability in the rooted soil layers and the maximal nitrogen uptake rate per unit root length. According to Groot Ž 1987. , potential nitrogen demand of vegetative plant parts depends on biomass amount and on optimum and minimum nitrogen concentrations in different organs. Under conditions of optimal soil nitrogen supply, nitrogen concentration in the plant tissue reaches an optimal value and the plant attains its maximum growth rate. Thus, nitrogen concentration limits are regarded as species-specific parameters. For the simulation of nitrogen translocation to tubers, the equation for the calculation of nitrogen demand of storage organs Ž here tubers. is replaced with the method of the SIMPOTATO-model by Hodges et al. Ž 1992. , which uses optimum nitrogen concentrations, also for tubers. The method previously utilized by SPASS is based on the number of storage organs Ž grains. and a potential nitrogen accumulation rate per individual grain, which was developed for the simulation of grain crop growth and is not suitable for potato. The total amount of translocatable nitrogen in the vegetative plant parts, estimated from the difference between actual and minimum nitrogen concentrations in vegetative plant organs, is used to fulfill the nitrogen demand of tubers.

3. Simulation results 3.1. Model calibration Whereas most of the parameter values required for model parameterization were taken from Penning de Vries et al. Ž1989. , Ritchie et al. Ž 1987. and Jones et al. Ž 1986., those values required for simulating phenological development, assimilate partitioning and nitrogen uptake had to be estimated. Model calibration was done on the basis of data measurements from a fertilization study as previously described. Parameter values required for simulating phenological development were deduced from observed phenological stages and from the course of daily mean temperature during the experiment Ž Table 1. . The coefficients of assimilate partitioning were adjusted to the observed experimental course of vegetative biomass and tuber growth. Different partitioning coefficients were used in simulating calculations to represent the early variety AChristaB and the rather late variety AAgriaB ŽTable 2. .

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Table 1 Parameter values for phenological development Parameter Sprext,max PDD 2 PDD 3 f tub h max

Description

Value

maximal growth rate of the sprout phenological developmental days from emergence to flowering phenological developmental days from flowering to maturity time parameter of tuberization maximal daylength for tuberization

Unit

Agria

Christa

0.4 26

0.45 23

cmrday 8C day

37

38

8C day

0.7 18.8

0.5 24

– h

Parameters for the nitrogen uptake submodel were estimated from experimental data. A wide range of nitrogen concentrations in the different plant organs were found, consequent to the various fertilizer applications. Values ranging from 4.7% to 6.2% were observed in leaves at the start of the growing period, and from 1.3% to 3.0% at the start of leaf senescence. In stems, nitrogen concentration levels were between 3.1% and 4.9% at the start, and between 0.9% and 1.8% at the end of the growing period. Both potato varieties AAgriaB and AChristaB demonstrated different nitrogen concentrations in tubers Ž 1.2% to 3.1% and 0.6% to 1.9%, respectively.. Simulation results were optimized by varying the concentration limit parameters within the range of the measured values ŽTable 3.. Simulated biomass weight and experimental data from the plot experiments are shown in Figs. 1 and 2 for AChristaB and AAgriaB, respectively. Biomasses were separated in vegetative biomass aboveground and biomass of tubers, whereby the biomass of aboveground fruits of potato was not modeled separately and hence included in the vegetative biomass. As in actual experiments, model predictions provided very similar tuber yields for fertilizer applications N 150 and N50–100 in case AChristaB, and N 150, N50–100 and N50–50–50 in case AAgriaB. The N0-variant yield was Table 2 Proportion of assimilates partitioned to various organs of AChristaB and AAgriaB potato crop in different phenological stages Ž%. Values for AAgriaB are shown in brackets. Developmental stage

Emergence

Tuberization start

Flowering start

Senescence start

Maturity

Root Leaves Stem Tubers

70 15 15 0

50 22.5 Ž20. 27.5 Ž30. 0

30 14 17.5 Ž21. 38.5 Ž35.

15 0 0 85

0 0 0 100

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Table 3 Parameter values of nitrogen concentration limits for AChristaB and AAgriaB Ždependent on developmental stage. in kgŽN.rkg Parameter

Description

Value Agria

Nlv,opt Nst,opt Nrt,opt Ntb,opt Nlv,min Nst,min Nrt,min

optimal nitrogen concentration in leaves optimal nitrogen concentration in stems optimal nitrogen concentration in roots optimal nitrogen concentration in tubers minimal nitrogen concentration in leaves minimal nitrogen concentration in stems minimal nitrogen concentration in roots

Christa

0.015–0.067 0.01–0.067 0.01–0.02 0.025–0.03 0.015–0.02 0.005–0.025 0.005–0.025 0.001

markedly reduced in both cases, and was also well simulated by the model. In addition, the higher AAgriaB tuber yields as compared to AChristaB were also well reproduced by the model. However, model predictions tended to overestimate tuber yield for AAgriaB in conditions of limited soil N. Furthermore, the model did not predict a decreased tuber yield at the end of the growing period, which was particularly noticeable for the variety AChristaB. In the SPASS-model, no process for tuber senescence is considered, since under usual agricultural management potatoes are harvested at maximal tuber biomass.

Fig. 1. Simulated and experimental biomass weight Ždry matter. in plot experiments for potato variety AChristaB, under three different N-fertilizer treatments during the 1996 vegetation period.

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Fig. 2. Simulated and experimental biomass weight Ždry matter. in plot experiments for potato variety AAgriaB, under four different N-fertilizer treatments during the 1996 vegetation period. Symbols in the diagrams are the same as in Fig. 1.

In this study, harvesting occurs only at the beginning of October, some weeks after physiological maturity. The course of biomass growth during the first half of the growing period was predicted somewhat better for AAgriaB than for AChristaB, with the exception of tuber biomass measurements for AAgriaB on August 5th. However, these measurements appear to be artifactual, since such a marked increase in tuber biomass Žappropriately 7000 kg Ždry matter.rha in 3 weeks. as indicated by subsequent measurements is unlikely to occur. For AChristaB, the model underestimated tuber biomass and tended to overestimate aboveground vegetative biomass in the first weeks of vegetation, whereas a better fit for the ratio of these components appeared for AAgriaB. The high experimental tuber yields observed for the AAgriaB condition N50–50–50 in September were not predicted by the model. Here, the observed value seems very high compared to preceding and subsequent measured results. The course of nitrogen concentration in potato tubers and vegetative aboveground organs was well simulated by the model, over a wide range of the growing period for both varieties AChristaB and AAgriaB ŽFigs. 3 and 4, respectively.. Differences in soil nitrogen availability were reflected by the

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Fig. 3. Simulated and experimental nitrogen concentrations of vegetative biomass aboveground and tubers for potato variety AChristaB, under three different N-fertilizer treatments during the 1996 vegetation period.

various simulated nitrogen concentrations. The simulated increases in nitrogen levels in vegetative plant parts at the start of the growing period could not be verified, due to the lack of experimental data. This effect may not be real but caused instead by a model underestimation of maximal root nitrogen uptake rate. 3.2. Test of reliability of model predictions After calibrating the model, simulation runs were carried out to predict potato yields for different years and different fields at the Research Station Scheyern : A15 Ž 1995., A17 Ž 1993, 1997. and A18 Ž 1994, 1998. . Simulated yields Ž tubers. and nitrogen uptake were compared with actual experimental data. Input data required by the model Ž soil properties, weather data, start values and information about fertilization and tillage. were provided by FAM Database Ž 2000. . Data from one measurement point per field were used for simulations, assuming these to be representative for whole field. As no data were available for AChristaB production, only model predictions for AAgriaB could be tested. Simulated tuber yields and nitrogen uptake together with obtained experimental data are shown in Table 4. Model predictions meet experimental values

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Fig. 4. Simulated and experimental nitrogen concentrations of vegetative biomass aboveground and tubers for potato variety AAgriaB, under four different N-fertilizer treatments during the 1996 vegetation period. Symbols in the diagrams are the same as in Fig. 3.

within "10% accuracy in most cases. Only simulation results for nitrogen uptake in 1993 and tuber yield in 1997 exceeded this range. A suitable statistical measure for assessing the model’s ability to predict an observable quantity is the modeling efficiency ME Ž Mayer and Butler, 1993. : n

Ý ž Ok y O / ME s ks1

n

2

n

y Ý Ž O k y Pk . ks1

Ý ž Ok y O /

2

2

,

ks1

were the O k are measured values, Pk are predicted values of the same quantity and O is the mean value of all n measurements. ME estimates the extent to which a model is able to provide better predictions than the simple assumption Pk s O. If a model accurately meets all measurements Ž Pk s O k for all k ., ME equals 1. ME equals 0, if a model simply predicts the mean value of the measurements. The values of the modeling efficiency for tuber yield and nitrogen uptake reached in this simulation study are shown in Table 4. Both values are noticeably greater than zero. Thus, the SPASS model is able to

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Table 4 Measured and simulated potato yields Žtubers. for different years and different fields at the Research Station Scheyern Year

1993 1994 1995 1997 1998 Modeling efficiency

Field

A17 A18 A15 A17 A18

Measured

Simulated

Yield wtrhax

N-uptake wkgrhax

Yield wtrhax

N-uptake wkgrhax

7.63 10.11 6.95 8.65 8.05

113.0 153.0 105.8 104.1 118.7

7.96 9.60 7.77 7.45 8.75 0.48

91.5 144.3 108.3 98.4 127.6 0.61

predict satisfactorily tuber yields and nitrogen uptake in several potato fields at the Research Station Scheyern.

4. Discussion and conclusions In the present study, the crop model SPASS was adapted to simulate potato-growth processes. To accomplish this, a novel parameterization with potato-specific parameters and minor modifications of some process formulations were required, as compared to the wheat version of the SPASS-model. The following modifications were carried out: Ž i. in the submodel for the simulation of phenological development, an additional variable, the tuberization rate, was introduced; Žii. the pool of assimilates available for growth processes was extended to include translocatable starch of the seed potato; Ž iii. a new parameter, dependent on the maturity class of the considered potato variety, was introduced for the estimation of leaf senescence rate; and Ž iv. estimation of the nitrogen demand of storage organs was replaced with the method used in the SIMPOTATO-model. Until its application in the present study, the SPASS model had been tested only on winter wheat. As simulation of potato growth could be carried out without changing the model structure, results obtained in this study confirm that the SPASS model structure is appropriate as a generic model structure for the simulation of crop growth. However, further model applications to other crop species and at other sites should be carried out in order to achieve a thorough characterization of the effects of crop species, soil types and climatic conditions on model performance. This is necessary to provide a broader applicability and credibility of the SPASS model as a prediction tool for crop growth and nitrogen uptake under different environmental conditions and variable fertilization scenarios.

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Model calibration was carried out for two potato varieties, AChristaB and AAgriaB, representing early and late maturity classes, respectively. Parameterization of the module simulating phenological development was based on time of flowering, an event that can easily be observed in practical applications for most crop species, including most potato varieties. Assuming that time of tuberization depends on time of flowering, the genetic parameters required for simulating phenological development in the SPASS potato version can be easily stated; this is important regarding the intended use of the SPASS model in agricultural practice. Flowering had not been previously considered by comparable potato growth models ŽHodges et al., 1992; Griffin et al., 1993; Kooman and Haverkort, 1995., which use time of tuberization for parameterizing phenological development. A satisfactory agreement between simulated and experimental data for different nitrogen conditions could be reached by adjusting the partitioning coefficients and the parameter values of minimal and optimal nitrogen concentrations in plant organs. In order for the model to achieve applicability for other potato varieties, additional experiments referring to these critical concentrations should be carried out. The reliability of model predictions for the variety AAgriaB was tested using data other than that used for model calibration; data for testing AChristaB were not available. Results provide a modeling efficiency of 0.48 for tuber yield and 0.61 for nitrogen uptake. This indicates that SPASS has reached the ability to predict potato yields, at least for the environmental conditions present at the Research Station Scheyern. However, the rather small values observed for the modeling efficiency suggest that further model improvements are due. Better results and greater model flexibility may be expected if refinements concerning the description of the assimilate partitioning regulation to the different plant organs are realized. In the present version of SPASS and in other comparable crop models, allocation of assimilates is regulated only by the plant developmental stage and is based on experimental observations. Direct responses of the partitioning pattern to changing environmental conditions are scarcely considered. In contrast, Manrique and Bartolomew Ž 1991. demonstrated that changes in biomass partitioning of potato cultivates AKennebecB and ADesireeB were strongly related to a single environmental factor, the minimum daily temperature. Shifts in the pattern of assimilate partitioning were similarly expected, with regard to environmental stress situations Ž elevated atmospheric CO 2- or ozone concentrations. or pathogen disease Ž Herms and Mattson, 1992. . A known strategy for pathogen defence is the synthesis of pathogen-related proteins ŽPR-proteins, Linthorst, 1991.. Woloshuk et al. Ž 1991. found pathogen-induced proteins with inhibitory activity against Phytophthora infestans, a pathogen which has become a widespread problem in potato production. Consequently, new modeling approaches are required to predict the regulation of assimilate partitioning which depend on environmental conditions and include secondary metabolism.

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Acknowledgements Thanks are due to the German Federal Ministry for Education and Research ŽBMBF 0339370. and the Bavarian State Ministry for Science, Research and the Arts for the financial support of the FAM Research Network on Agroecosystems, and the German Research Foundation Ž DFG. , who supports the program SFB 607 AWachstum oder ParasitenabwehrB.

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