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

Ž .Geoderma 105 2002 367–383www.elsevier.comrlocatergeoderma

Modeling biomass growth, N-uptake andphenological 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, Australiac 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 fieldswere simulated in this study. The crop growth model Soil–Plant–Atmosphere System SimulationŽ .SPASS was integrated in Expert-N and adapted for the simulation of potato growth. The aim ofthe study was to investigate the extent to which the SPASS model, tested thus far only for winterwheat, is suitable for the simulation of potato crops. In addition to re-parameterization of themodel, minor modifications, such as description of phenological development, assimilate partition-ing, nitrogen uptake and leaf senescence were carried out without changing the overall structure ofthe model. The SPASS model was calibrated using data from a potato field experiment carried outin 1996 at the Research Station Scheyern, which examined the effects of various fertilizationapplications on the growth and yield of two potato varieties, AChristaB and AAgriaB, representingearly and late maturity classes, respectively. Distinctions between AChristaB and AAgriaB wererealized by variable parameter values concerning phenological development, assimilate partition-ing and nitrogen concentration in tubers. The model’s ability to predict potato yields and nitrogenuptake was compared with actual values obtained in different years at other fields of the Research

Ž .Station only AAgriaB . Simulation results show that the SPASS model was able to describe theeffect of different N fertilizer applications on potato growth and nitrogen uptake. Differencesbetween the two potato varieties could be adequately predicted, and tuber yields and nitrogenuptake well predicted. However, estimated modeling efficiencies suggest that further improve-ments are due. Crucial components of the model are the control of root nitrogen uptake and theregulation of the distribution of assimilates to different plant organs. To obtain a broader basis forthe verification of the corresponding simulation modules, further experiments addressing optimal

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

0016-7061r02r$ - see front matter q2002 Elsevier Science B.V. All rights reserved.Ž .PII: S0016-7061 01 00113-6

( )S. Gayler et al.rGeoderma 105 2002 367–383368

nitrogen concentrations in plant organs are necessary. Efforts to refine the SPASS model shouldbe concentrated on a dynamical description of the partitioning pattern of assimilates, including adirect response of the partitioning pattern to changing environmental conditions. q 2002 ElsevierScience 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 managementstrategies that maximize crop production and minimize environmental degrada-tion. An appropriate complement to experimental data is the utilization ofsimulation models, which can provide an efficient interpretation of data, andalso analyze the behavior of agricultural systems under diverse environmentalconditions. Investigations using models are faster and more economical thanexperimental studies alone, and models represent helpful tools through whichdecision-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 inŽagroecosystems is the modeling system Expert-N Engel and Priesack, 1993;

.Baldioli et al., 1995; Stenger et al., 1999 . It consists of several modules forsimulating different processes in the soil–plant–atmosphere system, which canbe coupled together in various combinations. Defined interfaces exist betweenthe single-process modules, which must be kept if new modules are integrated inthe modeling system.

During the last years, Expert-N was successfully used to simulate watertransport and nitrogen turnover processes within the scope of the FAM ResearchNetwork on Agroecosystems. Water transport modules were adapted to thespecial conditions at the sampling locations within the Research Station Schey-

Ž .ern Priesack et al., 1999 , and many experiments were carried out to provideŽ .the data required by Expert-N Scheinost et al., 1997 . To consider crop growth

in an explicit way and to thus extent the performance of Expert-N, now theprocess-oriented model for the description of growth and uptake processes of

Ž .field crops, Soil Plant Atmosphere System Simulation SPASS , was integratedin the modeling system. Implementation of SPASS was carried out subject to themodular structure of Expert-N system, to allow the combination of SPASS withthe soil modules already available in Expert-N. SPASS is intended to be ageneric model for simulating crop growth. A simulation model may termedAgenericB, if it simulates several functionally and structurally equivalent systemssolely through the use of different parameter values. This approach encourages

Ž .modelers to determine general properties of the class of systems similarities

( )S. Gayler et al.rGeoderma 105 2002 367–383 369

and to view individual systems as variations, rather than as separate entitiesŽ .Reynolds et al., 1989 . In case of crop models, a generic model structure can beestablished by recognizing the general process common to all crops and thisoverall model structure may be then applied to all crop types. Functionallyequivalent crop species can be simulated solely by model re-parameterizationusing species-specific parameters. Although divergences in physiological and

Ž .ecological principles between crop classes e.g. cereals and root crops mayrequire modifications of single process formulations, the overall model structureshould remain unchanged. Thus, the integration of one generic crop model intothe Expert-N modeling system provides a more efficient way to simulate severalcrop species rather than the integration of numerous single species models.

Ž .SPASS has been thoroughly tested for winter wheat by Wang 1997 beforeits integration into Expert-N. The goal of the present study was to modify theSPASS model to simulate potato crops by investigating the changes in processformulations required for this modifications and, further, to test the reliability ofmodel predictions. Therefore, SPASS was calibrated using data from a potatocrop fertilization experiment carried out within the FAM program. In order totest the reliability of model predictions, simulated tuber yields and nitrogenuptake were compared with experimental results obtained in different years atother fields at the Research Station Scheyern.

All simulations reported in this paper were carried out by combining the cropŽmodel SPASS with soil modules according to the LEACHN model Hutson and

.Wagenet, 1991 , also implemented in the Expert-N modeling system. LEACHNis based on the Richards-Equation and describes the one-dimensional, verticalwater and nitrogen transport in the unsaturated soil zone, as well as nitrogentransformation processes.

2. Material and methods

2.1. Experimental data

In order to parameterize the SPASS model and test its suitability to simulatepotato growth, we used experimental data from a detailed study carried out in

Ž .the 1996 vegetation period April to October at seven investigation plots inŽScheyern description of the Research Station Scheyern, see Schroder et al.,¨

. 22001, same issue . The investigation plots were 30 m in size. Two varieties ofpotato, AChristaB and AAgriaB, representing early and late maturity classes,

Ž . Ž .respectively ZADI, 1999 , were planted at the same time 21st April, 1996 andŽ .treated with various amounts of calcareous ammonium nitrate CAN . All

conditions received 150 kg Nrha of fertilizer, applied in the following temporalŽ .schedule: i N150, where the entire amount of fertilizer was applied 4 days


Ž .after sowing; ii N50–100 for 50 kg Nrha fertilization at planting and 100 kgŽ .Nrha at emergence time AChristaB: 17th May 1996; AAgriaB: 24th May 1996

Ž . Ž .and iii N50–50–50 indicating fertilization of equal quantities 50 kg Nrha atŽ .planting, emergence and at 20-cm growth height 10th June 1996, only AAgriaB .

No fertilizer was applied in the N0-condition. Measurements of plant biomassŽ . Ždry matter and plant nitrogen concentrations separated into tubers and vegeta-

.tive parts above ground were realized six times during the growing period fromŽ .April to October for a detailed description, see Maidl et al., 2001, same issue .

Reliability of model predictions was tested with data other than that used formodel calibration. Potato yields and nitrogen uptakes measured in fields A15Ž . Ž . Ž .4.7 ha , A17 6.0 ha and A18 6.5 ha in between 1993 and 1995, and in 1997and 1998 were compared with simulated results. Potato production in thesefields was carried out according to the guidelines of integrated crop manage-ment. In each case, mineral nitrogen fertilization was done once per vegetationperiod, some weeks after planting. The amount of fertilizer varied between 37and 127 kg nitrogenrha and depended on soil nitrogen conditions at fertilizationtime.

2.2. Crop growth model

Plant processes simulated by SPASS are phenological development, photo-synthesis, respiration rate, assimilate partitioning and biomass growth, canopyand root system development, senescence, water uptake and nitrogen uptake. Adetailed description of the SPASS model including all equations and its applica-

Ž .tion to simulate wheat crop growth is provided by Wang 1997 . For themodeling of potato crop in this study, some of the modules were modifiedslightly, as compared with the SPASS Wheat model as indicated below.

2.3. Phenological deÕelopment

SPASS describes three internal developmental phases: germination to emer-gence, emergence to flowering and flowering to maturity. A given genotyperequires varying number of days to complete a developmental process. Thenumber of days having optimal climatic conditions needed to complete adevelopmental phase is defined here as physiological developmental daysŽ .PDDs . PDDs determine the maximal development rates in the different phases.The actual rate of development is affected by temperature and, for several crops

Žduring the vegetative stage, also by photoperiod and vernalization e.g. winter.wheat . For potato, the effect of photoperiod length on flowering date is almostŽ .none Penning de Vries et al., 1989 , but date of tuber growth start is affected by

photoperiodism. Day lengths exceeding 12-h delay tuberization, whereby earlyŽvarieties are less sensitive to increasing photoperiod than late varieties Griffin


.et al., 1993 . In simulating potato growth, therefore, an additional tuberizationrate, R , is introduced in the model to estimate the time point where assimilatetub

transfer to tubers begins. Developmental rates during the three developmentalphases are calculated as follows:

° fT; for germination to emergence

PDD1

fT~ ; for emergence to floweringR s ,dev PDD2

fT; for flowering to maturity¢PDD3

Žwhere f represents the temperature response of the developmental rate fT Tw x.equals 1 at T and is zero beyond the interval T ,T . PDD is theopt min max 1

minimum number of physiological developmental days from germination toemergence and can be estimated by multiplying the maximal growth rate of the

w x w xsprout, Spr cmrday , and the planting depth of the seed potato d cm :ext,max seed

PDD sd Spr .1 seed ext ,max

PDD and PDD are the minimal numbers of physiological developmental days2 3

from emergence to flowering and from flowering to maturity, respectively.Emergence occurs if ÝR s1, flowering starts if ÝR s2, and maturity isdev dev

reached if ÝR s3. Spr , PDD and PDD are species-specific parame-dev ext,max 2 3

ters.Calculation of tuberization rate begins, if emergence is reached:

f fT PR s ,tub PDDtub

where

° 1 ; if h F12hphp

~ y4 h yhŽ .f s ,max phpP 1yexp ; else¢ ž /h y12Ž .max

is the photoperiod response function, h the photoperiod in hours and h thephp max

maximal photoperiod at which no tuberization occurs. The number of physio-logical developmental days needed for tuberization are estimated from PDD2


and a variety-dependent parameter f : PDD s f PDD . Tubers begin totub tub tub 2

grow, if ÝR Gs1.tub

2.4. Photosynthesis

As in other crop models, daily canopy photosynthesis is determined in threeŽ . Ž .steps: i calculation of photosynthesis rate of a unit leaf area, ii calculation of

Ž .momentary photosynthesis rate of the whole canopy and iii calculation of thedaily total photosynthesis of the canopy.

Leaf photosynthesis rate is related to the intercepted photosynthetic activeradiation, temperature and leaf nitrogen level. Values for the maximum leafgross photosynthesis rate at optimal light and temperature conditions andoptimal nitrogen supply of the leaf are summarized by Penning de Vries et al.Ž .1989 . The dependence of photosynthesis on light interception is described by a

Ž .negative exponential curve Goudriaan and Van Laar, 1994 . The effect ofnon-optimal temperatures or nitrogen shortage is described by reduction factors.Daily canopy photosynthesis is calculated by integration of the leaf photosynthe-sis through the canopy and over time.

2.5. Respiration, assimilate partitioning and biomass growth

Simulation of respiration, assimilate partitioning and biomass growth wasŽ .done according to the method of Penning de Vries et al. 1989 and Groot

Ž .1987 . The amount of carbohydrates available for growth and respiration isdefined as the sum of the carbon gain from daily photosynthesis and there-translocation of mobilizable starch in the vegetative organs. To simulatepotato, additional retranslocation from starch reserves in the seed potato wasintroduced into the model. One part of the available assimilates is used formaintenance respiration. Carbohydrates required to maintain plant organs areassumed to be proportional to the respective plant organ biomass. The other partis partitioned to the different plant organs, and will be used for biomass growthand for growth respiration. Growth respiration is estimated to be proportional tothe newly formed biomass. Potato-specific parameters for maintenance respira-tion, growth respiration and growth efficiencies were taken from Penning de

Ž .Vries et al. 1989 . Allocation of assimilates to the different plant organs isdependent on the actual development stage. The pattern of assimilate distribu-tion is simulated by using fixed values at defined phenological stages andperforming a linear interpolation between these points.

2.6. Canopy and root system formation

Growth of leaf area is simulated by transforming the newly formed leafbiomass with the specific leaf weight; specific leaf weight varies with develop-


ment stage. A nitrogen stress factor is considered to account for reduced leafarea growth in case of nitrogen shortage.

Increase in the root depth is dependent on the species-specific root extensionrate, temperature and soil water content in the rooting layer. We assumed thatroot extension occurs only if assimilate transport to roots takes place.

Total root length produced within a day is calculated using the root biomassgrowth rate and a specific root lengthrweight ratio. The newly formed root

Ž .length is then distributed in soil profile according to Jones et al. 1986 .

2.7. Senescence

ŽSenescence simulation is based on the SUCROS-model 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 islower than a critical leaf area index, and is maximal when the leaf area indexexceeds twice the critical leaf area index. The relative death rate due to aging is

n Žzero, as long as Ý R FD is true D is the developmentalemergence dev senesc senesc.stage at which leaf senescence begins . Once senescence due to aging begins,

the relative death rate increases with advancing developmental stage and ismaximal at maturity. In simulating potato processes, an additional parameter isintroduced into the model, which is dependent on the maturity class of thevariety and modifies the form of this death rate curve. The actual leaf death rateis set to the larger one of senescence due to aging and senescence due toshading, multiplied with two factors accounting for the effects of temperatureand nitrogen stress on leaf senescence. High temperatures and nitrogen shortageaccelerate senescence.

Root death begins after flowering and is estimated by a constant root turnoverrate multiplied by a factor accounting for nitrogen stress effects.

2.8. Water uptake

Ž .Root water uptake was simulated based on the method of Ritchie et al. 1987Ž .and Jones et al. 1986 . If sufficient water is present in soil, transpiration is

determined by plant water demand. The potential transpiration rate underoptimal water supply is taken as plant water demand and depends on leaf areaand weather conditions. Different modules for determining potential transpira-tion are available in the Expert-N modeling system. Water uptake can be limitedeither by water shortage in soil or by the maximal uptake rate for water by unitlength of root. For each soil layer, potential root water uptake is calculated fromactual available soil water and the total root length in this layer. The totalpotential root water uptake by plants is the cumulative sum of water uptake inall layers. If the potential root water uptake by plants is greater than potential


transpiration, then actual transpiration is equal to potential transpiration. Other-wise, 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 plantorgans, nitrogen availability in the rooted soil layers and the maximal nitrogen

Ž .uptake rate per unit root length. According to Groot 1987 , potential nitrogendemand of vegetative plant parts depends on biomass amount and on optimumand minimum nitrogen concentrations in different organs. Under conditions ofoptimal soil nitrogen supply, nitrogen concentration in the plant tissue reachesan optimal value and the plant attains its maximum growth rate. Thus, nitrogenconcentration limits are regarded as species-specific parameters.

For the simulation of nitrogen translocation to tubers, the equation for theŽ .calculation of nitrogen demand of storage organs here tubers is replaced with

Ž .the method of the SIMPOTATO-model by Hodges et al. 1992 , which usesoptimum nitrogen concentrations, also for tubers. The method previously uti-

Ž .lized by SPASS is based on the number of storage organs grains and apotential nitrogen accumulation rate per individual grain, which was developedfor the simulation of grain crop growth and is not suitable for potato. The totalamount of translocatable nitrogen in the vegetative plant parts, estimated fromthe difference between actual and minimum nitrogen concentrations in vegeta-tive 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 parameterizationŽ . Ž .were taken from Penning de Vries et al. 1989 , Ritchie et al. 1987 and Jones

Ž .et al. 1986 , those values required for simulating phenological development,assimilate partitioning and nitrogen uptake had to be estimated. Model calibra-tion was done on the basis of data measurements from a fertilization study aspreviously described. Parameter values required for simulating phenologicaldevelopment were deduced from observed phenological stages and from the

Ž .course of daily mean temperature during the experiment Table 1 .The coefficients of assimilate partitioning were adjusted to the observed

experimental course of vegetative biomass and tuber growth. Different partition-ing coefficients were used in simulating calculations to represent the early

Ž .variety AChristaB and the rather late variety AAgriaB Table 2 .


Table 1Parameter values for phenological development

Parameter Description Value Unit

Agria Christa

Spr maximal growth rate of the sprout 0.4 0.45 cmrdayext,max

PDD phenological developmental days 26 23 8C day2

from emergence to floweringPDD phenological developmental days 37 38 8C day3

from flowering to maturityf time parameter of tuberization 0.7 0.5 –tub

h maximal daylength for tuberization 18.8 24 hmax

Parameters for the nitrogen uptake submodel were estimated from experimen-tal data. A wide range of nitrogen concentrations in the different plant organswere found, consequent to the various fertilizer applications. Values rangingfrom 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, nitrogenconcentration levels were between 3.1% and 4.9% at the start, and between0.9% and 1.8% at the end of the growing period. Both potato varieties AAgriaB

Žand AChristaB demonstrated different nitrogen concentrations in tubers 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 measuredŽ .values Table 3 .

Simulated biomass weight and experimental data from the plot experimentsare shown in Figs. 1 and 2 for AChristaB and AAgriaB, respectively. Biomasseswere separated in vegetative biomass aboveground and biomass of tubers,whereby the biomass of aboveground fruits of potato was not modeled sepa-rately and hence included in the vegetative biomass.

As in actual experiments, model predictions provided very similar tuberyields for fertilizer applications N150 and N50–100 in case AChristaB, andN150, N50–100 and N50–50–50 in case AAgriaB. The N0-variant yield was

Table 2Proportion of assimilates partitioned to various organs of AChristaB and AAgriaB potato crop in

Ž .different phenological stages %Values for AAgriaB are shown in brackets.

Developmental Emergence Tuberization Flowering Senescence Maturitystage start start start

Root 70 50 30 15 0Ž .Leaves 15 22.5 20 14 0 0Ž . Ž .Stem 15 27.5 30 17.5 21 0 0

Ž .Tubers 0 0 38.5 35 85 100


Table 3ŽParameter values of nitrogen concentration limits for AChristaB and AAgriaB dependent on

. Ž .developmental stage in kg N rkg

Parameter Description Value

Agria Christa

N optimal nitrogen concentration in leaves 0.015–0.067lv,opt

N optimal nitrogen concentration in stems 0.01–0.067st,opt

N optimal nitrogen concentration in roots 0.01–0.02rt,opt

N optimal nitrogen concentration in tubers 0.025–0.03 0.015–0.02tb,opt

N minimal nitrogen concentration in leaves 0.005–0.025lv,min

N minimal nitrogen concentration in stems 0.005–0.025st,min

N minimal nitrogen concentration in roots 0.001rt,min

markedly reduced in both cases, and was also well simulated by the model. Inaddition, the higher AAgriaB tuber yields as compared to AChristaB were alsowell reproduced by the model. However, model predictions tended to overesti-mate tuber yield for AAgriaB in conditions of limited soil N.

Furthermore, the model did not predict a decreased tuber yield at the end ofthe growing period, which was particularly noticeable for the variety AChristaB.In the SPASS-model, no process for tuber senescence is considered, since underusual agricultural management potatoes are harvested at maximal tuber biomass.

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


Ž .Fig. 2. Simulated and experimental biomass weight dry matter in plot experiments for potatovariety 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 weeksafter physiological maturity.

The course of biomass growth during the first half of the growing period waspredicted somewhat better for AAgriaB than for AChristaB, with the exception oftuber biomass measurements for AAgriaB on August 5th. However, thesemeasurements appear to be artifactual, since such a marked increase in tuber

Ž Ž . .biomass appropriately 7000 kg dry matter rha in 3 weeks as indicated bysubsequent measurements is unlikely to occur. For AChristaB, the model under-estimated tuber biomass and tended to overestimate aboveground vegetativebiomass in the first weeks of vegetation, whereas a better fit for the ratio ofthese components appeared for AAgriaB. The high experimental tuber yieldsobserved for the AAgriaB condition N50–50–50 in September were not pre-dicted by the model. Here, the observed value seems very high compared topreceding and subsequent measured results.

The course of nitrogen concentration in potato tubers and vegetative above-ground organs was well simulated by the model, over a wide range of the

Žgrowing period for both varieties AChristaB and AAgriaB Figs. 3 and 4,.respectively . Differences in soil nitrogen availability were reflected by the


Fig. 3. Simulated and experimental nitrogen concentrations of vegetative biomass abovegroundand tubers for potato variety AChristaB, under three different N-fertilizer treatments during the1996 vegetation period.

various simulated nitrogen concentrations. The simulated increases in nitrogenlevels in vegetative plant parts at the start of the growing period could not beverified, due to the lack of experimental data. This effect may not be real butcaused 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 potatoyields for different years and different fields at the Research Station Scheyern :

Ž . Ž . Ž . Ž .A15 1995 , A17 1993, 1997 and A18 1994, 1998 . Simulated yields tubersand nitrogen uptake were compared with actual experimental data. Input data

Žrequired by the model soil properties, weather data, start values and information. Ž .about fertilization and tillage were provided by FAM Database 2000 . Data

from one measurement point per field were used for simulations, assuming theseto be representative for whole field. As no data were available for AChristaBproduction, only model predictions for AAgriaB could be tested.

Simulated tuber yields and nitrogen uptake together with obtained experimen-tal data are shown in Table 4. Model predictions meet experimental values


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

within "10% accuracy in most cases. Only simulation results for nitrogenuptake in 1993 and tuber yield in 1997 exceeded this range. A suitable statisticalmeasure for assessing the model’s ability to predict an observable quantity is the

Ž .modeling efficiency ME Mayer and Butler, 1993 :

n n2 2

O yO y O yPŽ .ž /Ý Ýk k kks1 ks1MEs ,n

2O yOž /Ý k

ks1

were the O are measured values, P are predicted values of the same quantityk k

and O is the mean value of all n measurements. ME estimates the extent towhich a model is able to provide better predictions than the simple assumption

Ž .P sO. If a model accurately meets all measurements P sO for all k , MEk k k

equals 1. ME equals 0, if a model simply predicts the mean value of themeasurements. The values of the modeling efficiency for tuber yield andnitrogen uptake reached in this simulation study are shown in Table 4. Bothvalues are noticeably greater than zero. Thus, the SPASS model is able to


Table 4Ž .Measured and simulated potato yields tubers for different years and different fields at the

Research Station Scheyern

Year Field Measured Simulated

Yield N-uptake Yield N-uptakew x w x w x w xtrha kgrha trha kgrha

1993 A17 7.63 113.0 7.96 91.51994 A18 10.11 153.0 9.60 144.31995 A15 6.95 105.8 7.77 108.31997 A17 8.65 104.1 7.45 98.41998 A18 8.05 118.7 8.75 127.6Modeling 0.48 0.61efficiency

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

4. Discussion and conclusions

In the present study, the crop model SPASS was adapted to simulatepotato-growth processes. To accomplish this, a novel parameterization withpotato-specific parameters and minor modifications of some process formula-tions were required, as compared to the wheat version of the SPASS-model. The

Ž .following modifications were carried out: i in the submodel for the simulationof phenological development, an additional variable, the tuberization rate, was

Ž .introduced; ii the pool of assimilates available for growth processes wasŽ .extended to include translocatable starch of the seed potato; iii a new parame-

ter, dependent on the maturity class of the considered potato variety, wasŽ .introduced for the estimation of leaf senescence rate; and iv estimation of the

nitrogen demand of storage organs was replaced with the method used in theSIMPOTATO-model.

Until its application in the present study, the SPASS model had been testedonly on winter wheat. As simulation of potato growth could be carried outwithout changing the model structure, results obtained in this study confirm thatthe SPASS model structure is appropriate as a generic model structure for thesimulation of crop growth. However, further model applications to other cropspecies and at other sites should be carried out in order to achieve a thoroughcharacterization of the effects of crop species, soil types and climatic conditionson model performance. This is necessary to provide a broader applicability andcredibility of the SPASS model as a prediction tool for crop growth and nitrogenuptake under different environmental conditions and variable fertilization scenar-ios.


Model calibration was carried out for two potato varieties, AChristaB andAAgriaB, representing early and late maturity classes, respectively. Parameteriza-tion of the module simulating phenological development was based on time offlowering, an event that can easily be observed in practical applications for mostcrop species, including most potato varieties. Assuming that time of tuberizationdepends on time of flowering, the genetic parameters required for simulatingphenological development in the SPASS potato version can be easily stated; thisis important regarding the intended use of the SPASS model in agriculturalpractice. Flowering had not been previously considered by comparable potato

Žgrowth models Hodges et al., 1992; Griffin et al., 1993; Kooman and Haverkort,.1995 , which use time of tuberization for parameterizing phenological develop-

ment. A satisfactory agreement between simulated and experimental data fordifferent nitrogen conditions could be reached by adjusting the partitioningcoefficients and the parameter values of minimal and optimal nitrogen concen-trations in plant organs. In order for the model to achieve applicability for otherpotato varieties, additional experiments referring to these critical concentrationsshould be carried out.

The reliability of model predictions for the variety AAgriaB was tested usingdata other than that used for model calibration; data for testing AChristaB werenot available. Results provide a modeling efficiency of 0.48 for tuber yield and0.61 for nitrogen uptake. This indicates that SPASS has reached the ability topredict potato yields, at least for the environmental conditions present at theResearch Station Scheyern. However, the rather small values observed for themodeling efficiency suggest that further model improvements are due. Betterresults and greater model flexibility may be expected if refinements concerningthe description of the assimilate partitioning regulation to the different plantorgans are realized. In the present version of SPASS and in other comparablecrop models, allocation of assimilates is regulated only by the plant developmen-tal stage and is based on experimental observations. Direct responses of thepartitioning pattern to changing environmental conditions are scarcely consid-

Ž .ered. In contrast, Manrique and Bartolomew 1991 demonstrated that changesin biomass partitioning of potato cultivates AKennebecB and ADesireeB werestrongly related to a single environmental factor, the minimum daily tempera-ture. Shifts in the pattern of assimilate partitioning were similarly expected, with

Žregard to environmental stress situations elevated atmospheric CO - or ozone2. Ž .concentrations or pathogen disease Herms and Mattson, 1992 . A known

strategy for pathogen defence is the synthesis of pathogen-related proteinsŽ . Ž .PR-proteins, Linthorst, 1991 . Woloshuk et al. 1991 found pathogen-inducedproteins with inhibitory activity against Phytophthora infestans, a pathogenwhich has become a widespread problem in potato production. Consequently,new modeling approaches are required to predict the regulation of assimilatepartitioning which depend on environmental conditions and include secondarymetabolism.


Acknowledgements

Thanks are due to the German Federal Ministry for Education and ResearchŽ .BMBF 0339370 and the Bavarian State Ministry for Science, Research and theArts for the financial support of the FAM Research Network on Agroecosys-

Ž .tems, and the German Research Foundation DFG , who supports the programSFB 607 AWachstum oder ParasitenabwehrB.

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Documents

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