simulation of growth and development processes of spring wheat in response to co2 and ozone for...

European Journal of Agronomy 10 (1999) 231–247

Simulation of growth and development processes of springwheat in response to CO2 and ozone for different sites andyears in Europe using mechanistic crop simulation models

F. Ewert a,*, M. van Oijen b, J.R. Porter aa Royal Veterinary and Agricultural University, Department of Agricultural Sciences, Agrovej 10, 2630 Taastrup, Denmarkb Wageningen Agricultural University, Department of Theoretical Production Ecology, PO Box 430, 6700 AK Wageningen,

The Netherlands

Accepted 18 November 1998

Abstract

The response of crop growth and yield to CO2 and ozone is known to depend on climatic conditions and is difficultto quantify due to the complexity of the processes involved. Two modified mechanistic crop simulation models(AFRCWHEAT2-O3 and LINTULCC), which differ in the levels of mechanistic detail, were used to simulate theeffects of CO2 (ambient, ambient ×2) and ozone (ambient, ambient ×1.5) on growth and developmental processesof spring wheat in response to climatic conditions. Simulations were analysed using data from the ESPACE-wheatproject in which spring wheat cv. Minaret was grown in open-top chambers at nine sites throughout Europe and forup to 3 years at each site.

Both models closely predicted phenological development and the average measured biomass at maturity. However,intermediate growth variables such as biomass and leaf area index (LAI) at anthesis, seasonal accumulatedphotosynthetically active radiation intercepted by the crop (SIPAR), the average seasonal light use efficiency (LUE)and the light saturated rate of flag leaf photosynthesis (Asat) were predicted differently and less accurately by the twomodels. The effect of CO2 on the final biomass was underestimated by AFRCWHEAT2-O3 due to its poor simulationof the effect of CO2 on tillering, and LAI.LINTULCC overestimated the response of biomass production to changesin CO2 level due to an overprediction of the effect of CO2 on LUE. The measured effect of ozone exposure on finalbiomass was predicted closely by the two models. The models also simulated the observed interactive effect of CO2and ozone on biomass production. However, the effects of ozone on LAI, SIPAR and Asat were simulated differentlyby the models and less accurately with LINTULCC for the ozone effects on LAI and SIPAR. Predictions of thevariation between sites and years of growth and development parameters and of their responses to CO2 and ozonewere poor for both AFRCWHEAT2-O3 and LINTULCC. It was concluded that other factors than those consideredin the models such as chamber design and soil properties may have affected the growth and development of cv.Minaret. An analysis of the relationships between growth parameters calculated from the simulations supported thisconclusion. In order to apply models for global change impact assessment studies, the difficulties in simulating biomassproduction in response to CO2 need to be considered. We suggest that the simulation of leaf area dynamics deservesparticular attention in this regard. © 1999 Elsevier Science B.V. All rights reserved.

Keywords: Climatic conditions; CO2; Growth and development; Mechanistic simulation models; Ozone; Spring wheat

* Corresponding author. Tel. : +45 3528-3377; fax: +45 3528-2175; e-mail: [email protected]

1161-0301/99/$ – see front matter © 1999 Elsevier Science B.V. All rights reserved.PII: S1161-0301 ( 99 ) 00013-1

232 F. Ewert et al. / European Journal of Agronomy 10 (1999) 231–247

1. Introduction Europe and in up to 3 years at each site (Hertsteinet al., 1999). We used two crop simulation modelsto simulate the interactive effects of CO2, ozoneAtmospheric CO2 and ozone concentrations

have been predicted to increase further over the and climatic conditions on biomass production. Inthe present paper, we analyse the simulated effectsnext decades (IPCC, 1992, 1994). Significant

effects of these gases on the growth and yield of of CO2 and ozone on the underlying growth anddevelopmental processes for different site andwheat crops have been described several times

(Cure and Acock, 1986; Darrall, 1989; Lawlor and years. We attempt to improve the understandingof the variable responses of crop growth andMitchell, 1991; Saxe, 1991). However, the magni-

tude of these effects was found to vary among development to increased CO2 and ozone concen-trations and thus contribute to research on globalseasons and locations, even for single cultivars.

Climatic conditions were reported to affect the change impact assessment studies.response of crop growth and development to bothCO2 (Lawlor and Mitchell, 1991; Bowes, 1993)and ozone (Heagle et al., 1988). 2. Experimental

Mechanistic crop simulation models are oftenused to understand, describe and quantify the 2.1. Plant material and experimental treatmentscomplexity of climatic effects on growth and devel-opmental processes. In the present study, we The present study is based on a series of open-

top chamber (OTC) experiments, which aimed toused two mechanistic crop simulation models,AFRCWHEAT2 and LINTUL, which were investigate the effects of CO2 and physiological

stresses on wheat growth and development forextended to enable simulations to be made of theeffects of CO2 and ozone on wheat growth and different climatic conditions. The experiments were

performed at nine sites throughout Europe and indevelopment. The two models have been validated(Spitters and Schapendonk, 1990; Porter, 1993) up to 3 years at each site (Table 1.). An extended

description of all ESPACE-wheat experiments isbut differ from each other in the degree of mecha-nistic detail to simulate growth and developmental given in Hertstein et al. (1999). All OTC experi-

ments had a randomized design with two or threeprocesses. Crop simulation models have been usedto predict the effects of elevated CO2 and climate replicates and a number of treatments differing

among sites. For the present analysis, we selectedchange on yield (Adams et al., 1990; Semenovet al., 1993). However, the experimental basis on four treatments, two CO2 concentrations (ambient

and 2× ambient) and two ozone concentrationswhich to validate global change scenarios for fieldconditions is limited (Lawlor and Mitchell, 1991). (ambient and 1.5× ambient) (Table 1). Plants of

spring wheat cv. Minaret were grown according toFew attempts have been made to model the effectsof ozone on crop growth and yield using process- a standard protocol agreed between the partners

of the project. However, sowing density was notorientated crop simulation models ( Kobayashi,1997; Krupa and Kickert, 1987; Kickert and constant among sites and years and varied from

120 to 380 plants m−2. Low sowing densities wereKrupa, 1991). We know of no simulation studiesthat tried to account for the complexity of the in OTC experiments where plants were grown in

pots, i.e. Giessen (1994–1996) and Braunschweigeffects of CO2 ozone and climate variability ongrowth and developmental processes in wheat. (1994). Since it was not possible to use a standard

soil in all experiments, each site had to use a localThe results presented in this paper are an integ-ral part of the ESPACE-wheat project in which soil. However, in all selected treatments (Table 1),

water and nutrients were supplied to avoid anyexperimental and modelling activities were com-bined to investigate and simulate the effects of additional stresses. Pests and weeds were con-

trolled as required. All chambers were suppliedCO2 and physiological stress, due to elevated ozoneexposure, on spring wheat cv. Minaret grown in with non-filtered ambient air. CO2 and ozone

exposure started after plants had emerged andopen-top chambers (OTC) at nine sites throughout

233F. Ewert et al. / European Journal of Agronomy 10 (1999) 231–247

Table 1Sites, geographical coordinates and treatment fractors of the ESPACE-wheat experiment considered in the present analysis. Detailedinformation about treatment factors are given in Hertstein et al. (1999)

Site Latitude Longitude Treatment factors

1994 1995 1996

Braunschweig, D 52.3°N 10.4°E CO2, ozone CO2, ozone CO2, ozoneCarlow, IRE 52.8°N 6.9°W CO2, ozone CO2, ozoneGiessen, D 50.6°N 8.7°E CO2, ozone CO2, ozone CO2, ozoneGothenburg, Sa 57.9°N 12.4°E CO2, ozone CO2, ozone CO2, ozonePau, F 43.0°N 0.5°W CO2, ozone CO2 ozoneRoskilde, DK 55.7°N 12.1°E CO2 CO2 CO2Sutton Bonington, UK 52.8°N 1.2°W CO2, ozone CO2, ozoneTervuren, B 50.8°N 4.5°E CO2, ozoneb CO2, ozone CO2, ozoneWageningen, NL 52.0°N 5.7°E CO2, temperaturec CO2, temperature

a Spring wheat cv. Dragon was grown in OTCs instead of spring wheat cv. Minaret.b Ozone treatments refer to charcoal-filtered and non-filtered air.c Two levels of temperature were investigated; the normal OTC temperature and an OTC temperature cooled by about 2°C to

simulate open field temperature conditions

continued until maturity. CO2 and ozone concen- were measured destructively at growth stagesDC31, anthesis and maturity for all OTCs.trations were recorded continuously in each treat-

ment. Climate data such as temperature, radiation However, most sites performed two and moreintermediate harvests to obtain additional meas-and humidity were measured continuously inside

and outside the chambers throughout all seasons. urements of plant growth.Based on measurements of LAI and photosyn-

thetically active radiation (PAR), we calculated2.2. Biological measurementsthe intercepted photosynthetically active radiation(IPAR) (Thornley, 1976; Gallagher and Biscoe,The present study refers to selected measure-

ments of crop growth and development, which 1978), aswere performed at each site with a standard pro-cedure. However, only a few sites were able to IPAR=PAR[1−exp(−kLAI)].perform all measurements in all years. Thus, thenumber of data for the different growth and devel- The light extinction coefficient, k, was taken as

0.65 (Monteith, 1969). An extinction coefficient ofopmental variables analysed in this paper is notconstant and is indicated in the corresponding k=0.6 was measured for spring wheat cv. Minaret

grown at Braunschweig in 1996 (Burkart, pers.tables.The developmental stages of emergence, the commun.). IPAR was integrated over the season

to give the cumulative amount of PAR interceptedbeginning of stem elongation (DC31), anthesisand maturity were recorded according to a decimal by the crop (SIPAR). The light use efficiency

(LUE) was calculated for each treatment by divid-code (Tottman and Broad, 1987). An area of 50plants was marked in each chamber, and a develop- ing the total above-ground biomass measured at

maturity by SIPAR calculated from emergence tomental stage was considered to be reached whenit was passed by 50% of the plants of this area. maturity.

Leaf photosynthesis was measured atFive plants in the marked area were selected torecord the cumulative leaf number on the main Braunschweig, Giessen, Tervuren, Sutton

Bonington and Wageningen using commerciallystems and the number of tillers per plant twice aweek. Tiller number per plant, green leaf area available portable systems. The present paper

refers to selected results of leaf photosynthesisindex (LAI) and above-ground biomass per plant


measurements described in detail by Mitchell stages. Phenological development is calculatedsimilarly in the two models based on thermal timeet al. (1999).totals between stages. Detailed descriptions of themodels are given in Porter (1984, 1993) and Weir2.3. Structure of the modelset al. (1984) for AFRCWHEAT2 and in van Oijenand Goudriaan (1997) for LINTUL.We used two process-orientated complex

crop simulation models, AFRCWHEAT2 and Both models were extended to enable simula-tions to be made of the crop response to CO2 andLINTUL, to model and simulate the effects of

CO2 and ozone on biomass production of spring ozone. These developments are described in detailin Ewert and Porter (1997) and van Oijen andwheat in response to varying climatic conditions.

Both models simulate several growth and develop- Goudriaan (1997) and are indicated in the namesof the models, AFRCWHEAT2-O3 andmental processes with different levels of mecha-

nistic detail. One major difference between the two LINTULCC (CC—climate change). The cropgrowth response to CO2 is realized by using themodels is the simulation of LAI. AFRCWHEAT2

simulates LAI by considering the processes of leaf biochemical model of Farquhar et al. (1980) whichin both models is combined with a stomatal modeland tiller emergence, growth and senescence

(Porter, 1984; Weir et al., 1984). The time frame (Wong et al., 1979). AFRCWHEAT2-O3 uses anextended version of this stomatal model whichfor these processes is set by developmental stages

of the shoot apex, such as floral initiation, double considers the effect of vapour pressure deficit onstomatal conductance (Leuning, 1995). Thus,ridge and terminal spikelet. Leaf area dynamics

are modelled with less mechanistic detail in the increasing the CO2 concentration increases leafphotosynthesis in both models. A direct effect ofpresent version of the LINTUL model. Both LAI

and biomass allocation are modelled as in increasing assimilate production, due to elevatedCO2 on LAI is considered in LINTULCC but notSUCROS1 (Goudriaan and van Laar, 1994) where

the rate of increase of leaf area is calculated as the in AFRCWHEAT2-O3.Long-term ozone exposure affects leaf photo-biomass allocation to leaves multiplied by a con-

stant specific leaf area, with the exception of early synthesis (Reich, 1987; Darrall, 1989; Saxe, 1991;Heath, 1994; Pell et al., 1994) and enhances leafleaf growth (LAI<1), which is driven by air

temperature. In both models, leaf photosynthesis senescence (Lehnherr et al., 1987; Grandjean andFuhrer, 1989; Ojanpera et al., 1992; Fangmeieris modelled using the biochemical model of

Farquhar et al. (1980). However, the crop growth et al., 1993; Nie et al., 1993; Sandelius et al., 1995).However, the underlying mechanisms remainrate is modelled differently in the two models.

LINTUL simply calculates the crop growth rate unclear. In both models, we assume that ozonedamage is caused by ozone uptake. Ozone uptakein daily steps by multiplying the amount of light

intercepted by the canopy by the crop light use reduces the light saturated rate of leaf photosynthe-sis in AFRCWHEAT2-O3 and induces a short-efficiency (LUE) (Monteith, 1977). The LUE was

treated as a constant in the original version of term response which is reversible-dependent on theleaf age. AFRCWHEAT2-O3 also considers aLINTUL (Spitters and Schapendonk, 1990). For

the analysis of the ESPACE-wheat experiments, long-term response to ozone exposure by introduc-ing a relationship between integrated ozone uptakeLUE was modelled to depend on environmental

conditions such as CO2, light and temperature. and leaf senescence. The ozone effect is modelledin LINTULCC assuming a decrease in rubiscoAFRCWHEAT2 divides the crop canopy into

layers calculated in integer steps of LAI. The concentration. Ozone uptake also increases thecosts of detoxification and repair processes. Inintercepted radiation is modelled as proposed by

Charles-Edwards (1978). Photosynthesis is calcu- both models, the plant response to ozone dependson plant development.lated for every hour and for each layer and accu-

mulated over the day. In both models, biomass The time step of both models is 1 day. Themodels run with daily input data of minimum andallocation patterns change with developmental


maximum temperature, radiation, humidity (the rity on average 1.1 and 0.5 days, respectively,earlier than observed, whereas both stages werelast only for AFRCWHEAT2-O3), mean CO2

concentration, mean ozone (only LINTULCC ) predicted later (1.3 and 1.6 days) by LINTULCC(Table 2). The goodness of the site- and year-and maximum ozone concentration (only

AFRCWHEAT2-O3), which were recorded in specific prediction of developmental stages isexpressed by the standard deviations of differenceseach experiment. Both models were parameterized

for spring wheat cv. Minaret (Ewert and Porter, between simulated and observed data. Althoughboth models used the thermal time concept to1997; van Oijen and Goudriaan, 1997).

In the present analysis, we compare measured simulate developmental stages, predictions ofanthesis and maturity differed between the models.and simulated absolute values of different growth

and developmental variables of the ambient air Standard deviation of differences between simu-lated and observed data were lower (1 day forchambers. The measured and simulated effects of

CO2 and ozone on these variables are calculated anthesis and 2.8 days for maturity) for predictionsmade with AFRCWHEAT2-O3 as compared toin relative terms by dividing the value in the CO2

or ozone treatment by its corresponding value in predications made with LINTULCC (Table 2).Both models consider the effect of high temper-the ambient air chamber.atures on phenological development differently.Since mean daily temperatures of 30°C andhigher were measured on several days of the experi-3. Resultsment, the slightly better simulations ofAFRCWHEAT2-O3 may be due to the assump-3.1. Phenological developmenttion considered in this model that temperaturesabove 27°C have a decreasing contribution to theSpring wheat cv. Minaret was sown between

March and May, depending on the site and year. thermal time. This effect is not considered inLINTULCC. Generally, the variation of develop-Plants emerged on average 12 days after sowing.

The development stages DC31, anthesis and matu- mental stages among site and years was simulatedrather poorly by the two models, particularly forrity were reached on average 34, 61 and 97 days,

respectively, after plant emergence. A more DC31 and maturity. We think this may be due toa subjective interpretation of the occurrence ofdetailed description of phenological development

of spring wheat cv. Minaret grown in this experi- these stages, which might have varied amongexperimental groups.ment is given in Ewert and Pleijel (1999).

Simulations started at emergence. Both The effects of CO2 and ozone on phenologicaldevelopment were not observed in the experimentsmodels predicted DC31 about 3 days earlier than

observed (Table 2). Simulations of anthesis and and are not considered in the models.maturity differed between the two models.AFRCWHEAT2-O3 predicted anthesis and matu- 3.2. Leaf emergence and tillering

Leaf emergence and tillering were investigatedTable 2

at most sites of the ESPACE-wheat projectMean deviation (md) and standard deviations (sd) of differ-and are described in detail in Ewert and Pleijelences between simulated and observed dates of developmental

stages (days) of spring wheat cv. Minaret grown in ambient air (1999). Both processes are considered inchambers at different sites and years for two models (n=24) AFRCWHEAT2-O3 to simulate LAI. Leaf emer-

gence is simulated by relating the thermal rate ofTreatment DC31 Anthesis Maturity

leaf emergence to the rate of change of day lengthmd sd md sd md sd at emergence (Baker et al., 1980). By adjusting

the model to the present cultivar, the final numberAFRCWHEAT2-O3 −3.2 8.2 −1.1 5.3 −0.5 7.4 of main stem leaves was simulated closely to theLINTULCC −3.0 8.5 1.3 6.3 1.9 10.2

observations (Table 3). Effects of CO2 and ozone


Table 3Means and standard deviations of observed and simulated final leaf number on main stems and of the numbers of tillers (at DC31and anthesis) and ears (at maturity) per plant of spring wheat cv. Minaret grown in ambient air chambers at different sites and years.Simulations were made with ARFCWHEAT2-O3

Development stage Mean Standard deviation n

Obs. Sim. Obs. Sim. Sim.−Obs.

Final leaf number 7.7 8.1 0.37 0.27 0.40 14Tiller and ear number plant–1

DC31 3.6 5.1 1.58 0.92 1.13 16Anthesis 3.8 3.5 1.91 1.00 1.42 21Maturity 2.2 3.0 0.87 0.76 0.61 23

on leaf emergence were neither observed nor observed and simulated LAI and phenologicaldevelopment. Similarly to the other sites and yearssimulated.

Tiller and ear number per plant was recorded in ESPACE-wheat, DC31 was recorded a few daysafter LAI started to increase with a maximum rateat DC31, anthesis and maturity (Table 3).

AFRCWHEAT2-O3 overestimated tillering at (Fig. 1a). The two model simulations clearly reflectthis relationship(Fig. 1a). The observed maximumDC31 by 1.5 tiller per plant compared to the

observations and simulated tiller number moreclosely to the observations at anthesis (Table 3).Finally, the model predicted an average of 0.8more ears per plant at maturity than was observed(Table 3). The variation in tillering among sitesand years was predicted to be less when comparedto observations (Table 3). There was a significantpositive effect of CO2 on the number of tillers andears per plant at anthesis and maturity at differ-ent sites (Ewert and Pleijel, 1999). SinceAFRCWHEAT2-O3 does not include an effect ofCO2 on tillering, the observed response of tilleringto CO2 was not simulated. A significant effect ofozone exposure on tiller number was not observedand was also not simulated.

3.3. Leaf area index

Canopy development is the result of the pro-cesses of leaf and tiller emergence, growth andsenescence. Since canopy growth and senescenceare related to phenological development, we firstinvestigated whether the models could reproduce

Fig. 1. Observed and simulated dynamics of (a) LAI and (b)the observed relationships between the dynamicsabove-ground biomass of spring wheat cv. Minaret grown atof LAI and the occurrence of developmentalTervuren in 1995. Points are observations with error bars ofstages. We selected data from ambient air cham-±SE and are not shown when smaller than symbol size. Lines

bers of one experiment (Tervuren, 1995) where are simulations of AFRCWHEAT2-O3 (—) and LINTULCCLAI was measured frequently throughout the (- - -). Vertical arrows indicate the observed and simulated devel-

opment stages DC31, anthesis and maturity.season to indicate the relationships between


of LAI was reached several days before anthesis, LAI–time curve. If we compare the simulatedmaximum LAI with the observed maximum, thewhich was simulated accurately by

AFRCWHEAT2-O3. LINTULCC simulated both LAI simulations will be closer to the observations.Both models, particularly LINTULCC, predictedanthesis and maximum LAI later than observed

and also simulated both events to have occurred a much smaller variation in LAI than was observedin the experiments (Table 4). High values of theat about same time. This was different to the

observations and the simulations with standard deviation of differences between observedand simulated data indicated a poor site- and year-AFRCWHEAT2-O3. Thus, the two models differ

with respect to the coordination of LAI and pheno- specific prediction of LAI for both models(Table 4).logical development. Also, LINTULCC simulated

a longer leaf area duration than did There were very few significant effects of CO2and ozone on LAI observed at DC31, whichAFRCWHEAT2-O3 (Fig. 1a).

Analysing the simulations of LAI in response was closely reproduced by AFRCWHEAT2-O3.However, LINTULCC simulated an increase into climatic variation and increasing CO2 and ozone

concentrations, we compared observed and simu- LAI for the high CO2 treatment (data not shown).A significant response of LAI to CO2 and ozonelated LAI at DC31 and anthesis for the different

sites and years. The observed experimental average exposure was measured at different sites at anthe-sis. Doubling the CO2 concentration increased LAIof LAI at DC31 was 2.3 m2 m−2, which was closely

predicted by the two models (Table 4). The simula- by on average 15% (Table 4), and LINTULCCsimulated an increase of about 11% (Table 4). Ations were less accurate for the average LAI at

anthesis (Table 4). AFRCWHEAT2-O3 and small increase of LAI (2%) was simulated byAFRCWHEAT2-O3 (Table 4), a result basicallyLINTULCC overpredicted the LAI at anthesis by

on average 1.2 m2 m−2 and 0.9 m2 m−2, respec- due to the process of tillering, where no directresponse to CO2 is included in the model. Ozonetively (Table 4). However, the simulations with

AFRCWHEAT2-O3 refer to the total green area exposure reduced LAI at anthesis by an averageof 7%. The same reduction was also simulated byindex, which was not measured in the experiments.

For LINTULCC, the co-ordination between the AFRCWHEAT2-O3 (Table 4). Very little responseof LAI to ozone was simulated by LINTULCCdynamics of LAI and phenological development

differed from observations. Thus, in analysing the (Table 4). The observed variation in CO2 andozone effects on LAI among sites and years wassimulations of LAI at anthesis, we did not compare

the same points on the observed and simulated always higher than the simulated variation.

Table 4Means and standard deviations of observed and simulated LAI (m2 m−2) at different development stages from ambient air chambersand of observed and simulated effects of CO2 and ozone on LAI at anthesis of spring wheat cv. Minaret grown at different sitesand years

Treatment Development Observed Simulated nstage

Mean sd AFRCWHEAT2-O3 LINTULCC

Mean sd sd (Sim. Mean sd sd (Sim.−Obs.) −Obs.)

Ambient air chamber DC31 2.34 0.97 2.63 0.99 1.39 2.34 0.36 0.96 16Anthesis 3.7 1.53 4.9 1.07 1.78 4.6 0.39 1.47 22

CO2 effect, low ozone (relative) Anthesis 1.15 0.39 1.02 0.01 0.39 1.11 0.04 0.39 22Ozone effect, low CO2 (relative) Anthesis 0.93 0.23 0.93 0.06 0.22 0.99 0.01 0.23 15


3.4. Intercepted photosynthetically active radiation accumulated IPAR by 2% due to CO2 exposure(Table 5) which is basically due to the small effect(IPAR) and light use efficiency (LUE)of CO2 on LAI simulated by this model (Table 5).Ozone fumigation reduced the accumulated IPARWe used the same radiation data measured at

each site and the same extinction coefficient (k= in the experiment on average by about 4%.AFRCWHEAT2-O3 simulated a reduction of 3%.0.65) to calculate the observed and simulated

photosynthetic active radiation intercepted by the No effect of ozone on the accumulated IPAR wassimulated by LINTULCC, which we explain bycrops. Thus, the differences in the accumulated

IPAR between measured and simulated data are the small effect of ozone on LAI simulated by thismodel (Table 5). Simulated variations in thedue to differences in the dynamics of the observed

and simulated LAI. The spring wheat crops response of the accumulated IPAR to CO2 andozone were, for both models, smaller than theintercepted on average 392 MJ PAR m−2 in the

ambient air chambers (Table 5). The models simu- observed variation.Final biomass is the product of the radiationlated an accumulated IPAR of 359 MJ PAR m−2

(AFRCWHEAT2-O3) and 442 MJ PAR m−2 intercepted by the crop and the efficiency of con-version of radiation to dry matter. An average(LINTULCC ). We have shown that for

AFRCWHEAT2-O3, the co-ordination between seasonal LUE of 3.2 g MJ−1 was calculated fromthe measurements in the ambient air chambersphenological development and LAI was similar to

the observations (Fig. 1a). Since the model did not (Table 5). This value appears to be rather highand will be discussed later. Dead leaves that fellunderestimate LAI at anthesis (Table 4), we con-

clude that the model must have underestimated off before maturity harvest were not considered inthe calculation of LUE due to limited data avail-leaf area duration. The canopy stayed longer green

in the experiment than was simulated by this able. Since the two models do not account for theloss of material, a comparison of observed andmodel. LINTULCC predicted a long leaf area

duration (Fig. 1a) which explains the differences simulated LUE should take into account that thesimulated LUE (Table 5) would be smaller if bio-between the simulated and observed data and

between the two model simulations. mass was simulated considering the loss of deadleaves. However, since the values of simulatedDoubling the CO2 concentration increased

the accumulated amount of IPAR by 5%, LUE were calculated in the same way for the twomodels, a model intercomparison is still validwhich was closely predicted by LINTULCC.

AFRCWHEAT2-O3 simulated an increase in the (Table 5). The average LUE simulated by

Table 5Means and standard deviations of observed and simulated accumulated intercepted photosynthetically active radiation (SIPAR,MJ m−2) and LUE (g MJ−1) from ambient air chambers and of observed and simulated effects of CO2 and ozone on IPAR andLUE of spring wheat cv. Minaret grown at different sites and years

Variable Treatment Observed Simulated n



SIPAR Ambient air chamber 392 97 359 60.7 80 442 68 76.3 20CO2 effect, low ozone (relative) 1.05 0.17 1.02 0.01 0.18 1.04 0.01 0.18 20Ozone effect, low CO2 (relative 0.96 0.07 0.97 0.03 0.069 1.00 0.00 0.073 13

LUE Ambient air chamber 3.2 0.66 3.1 0.37 0.63 2.4 0.17 0.63 20CO2 effect, low ozone (relative) 1.26 0.15 1.21 0.03 0.14 1.31 0.06 0.14 20Ozone effect, low CO2 (relative) 0.96 0.07 0.96 0.04 0.08 0.94 0.03 0.08 13


AFRCWHEAT2-O3 was close to the observed photosynthesis are different in the two models,value (Table 5) and higher than the LUE simulated and this might explain some of the differences inby LINTULCC. There was less variation in LUE the simulations. AFRCWHEAT2-O3 uses the orig-of the ambient air chambers simulated by the two inal parameter set for the photosynthesis modelmodels than was observed (Table 5). In particular, given by Farquhar et al. (1980). The temperatureLINTULCC simulated very little variation in LUE sensitivity of this model was re-assessed by Longamong sites and years. (1991) to give a temperature optimum of leaf

LUE was observed to have increased on average photosynthesis, which is lower than the optimumby 26% due to increasing CO2 concentration. This calculated from the original set of parameters.response was 5% lower for AFRCWHEAT2-O3 These changed parameters were used inand 5% higher for LINTULCC (Table 5). There LINTULCC and explain the higher photosyntheticwere small differences between the models in the rate simulated by this model compared to thesimulated effect of ozone on LUE. AFRC- simulations made with AFRCWHEAT2-O3 sinceWHEAT2-O3 predicted an average reduction in the temperatures at Sutton Bonington were closerthe LUE of 4% due to ozone exposure, which was to the optimum assumed in LINTULCC than toalso observed in the experiments (Table 5). A the assumed optimum in the other model.slightly higher response was simulated by Differences in the simulations of the light saturatedLINTULCC (6%) (Table 5). Again, both models rate of leaf photosynthesis, particularly in thesimulated a variation in LUE among sites and variation of Asat with time, might also be due toyears, which was smaller than the variation the response of leaf photosynthesis to the vapourobserved in the experiment (Table 5). pressure deficit, which is only considered in

LUE is primarily determined by processes AFRCWHEAT2-O3. The process of senescencerelated to leaf photosynthesis. Since both CO2 and was also simulated differently by the two models.ozone are known to affect the light saturated rate The light saturated rate of leaf photosynthesisleaf photosynthesis (Asat), we analysed the simu-

decreased with increasing leaf age inlated responses of Asat to CO2 and ozone exposure.AFRCWHEAT2-O3. No such decrease was simu-lated in LINTULCC, where crop senescence only3.5. Leaf photosynthesisreduces the amount of green leaf area, and not thephotosynthetic capacity of leaves.The extended analysis of leaf photosynthesis,

According to the model of Farquhar et al.measured for different treatments of CO2, ozone(1980), and using the original parameters, dou-and nitrogen supply at different sites in ESPACE-bling the CO2 concentration increases the light-wheat, which we refer to in this paper, is given bysaturated rate of photosynthesis by about 70% atMitchell et al. (1999). The general characteristics25°C. This response is strongly dependent on tem-of simulating leaf photosynthesis were similar forperature and light intensity. For the period fromall sites; the experiment at Sutton Bonington inflat leaf emergence until the beginning of flag leaf1996 is chosen here as an example to analyse thesenescence, AFRCWHEAT2-O3 simulated anlight saturated rate of photosynthesis of the flagaverage increase in Asat due to CO2 elevation ofleaves in response to CO2 and ozone simulated byabout 48% (Fig. 2). LINTULCC simulated forthe two models.the same period an average increase in Asat ofThe light saturated rate of photosynthesis inabout 35% (Fig. 2). These relatively low values ofthe ambient air chambers was simulated differentlythe simulated increase in Asat are mainly causedby the two models (Fig. 2). Asat was higher andby temperatures being sub-optimal for the experi-varied more among days in the simulations withment at Sutton Bonington and, in the case ofLINTULCC compared to the simulations withLINTULCC, by its choice of photosyntheticAFRCWHEAT2-O3. Both models used the sameparameters [Long (1991) instead of Farquhar et al.approach as that of Farquhar et al. (1980) to(1980)]. For different sites in ESPACE-wheat, ansimulate leaf photosynthesis. However, parameters

that describe the temperature sensitivity of leaf average increase of about 48% due to CO2 eleva-


Fig. 2. Responses of light saturated rate of photosynthesis (Asat) to CO2 and ozone for flag leaves of spring wheat cv. Minaret grownat Sutton Bonington in 1996 simulated with AFRCWHEAT2-O3 and LINTULCC.

tion was measured for leaf photosynthesis under dependent on CO2 concentration. AFRC-WHEAT2-O3 simulated an enhanced decline inlight saturated conditions at DC31 (Mitchell

et al., 1999). the light saturated rate of photosynthesis due toozone exposure, which was less pronounced in theBoth models simulated a reduction in the light

saturated rate of photosynthesis due to ozone elevated CO2 treatment compared to the ambientCO2 treatment (Fig. 2). No decline in Asat withexposure (Fig. 2). However, this effect was simu-

lated as less pronounced in the high CO2 as increasing leaf age was simulated by LINTULCC.However, the model simulated a reduction in Asatcompared to the ambient air treatment (Fig. 2).

AFRCWHEAT2-O3 simulated an average ozone- due to ozone exposure, which increased withincreasing leaf age. This response was less pro-induced reduction of about 10% in the low CO2

treatment and of about 3% in the elevated CO2 nounced in the high CO2 treatments compared tothe ambient CO2 treatment (Fig. 2).treatment. The corresponding simulations of

LINTULCC were 12 and 6% for the low and highCO2 treatments. Results from the measurements 3.6. Biomassat Sutton Bonington, Giessen and Tervuren con-firm the simulated reduction in Asat due to ozone Similarly to our analysis of the simulations of

the effects of CO2 and ozone on LAI, we firstexposure (Mitchell et al., 1999).Ozone exposure was measured to have affected compared the simulated and measured dynamics

of dry matter production of the ambient air cham-the decline in photosynthesis with time withexposed leaves senescing faster than untreated bers. This is again shown for the experiment at

Tervuren in 1995 (Fig. 1b). The early growth fromplants (Mitchell et al., 1999). The results furtherindicate that this response to ozone exposure was emergence until DC31 was predicted closely by


the two models (Fig. 1b). For the period from produced before and after anthesis. AFRC-WHEAT2-O3 simulated about 15% of the finalDC31 until anthesis, AFRCWHEAT2-O3 simu-

lated biomass production closely to the observed biomass to be produced between anthesis andmaturity, which was lower, compared to the simu-data (Fig. 1b). LINTULCC simulated less bio-

mass than observed (Fig. 1b) and simulated by lations with LINTULCC (36%) and the measure-ments (33%). AFRCWHEAT2-O3 simulated LAIAFRCWHEAT2-O3. This was mainly due to the

lower LAI predicted by the model as compared to at anthesis, which was higher than the LAI simu-lated with LINTULCC and the observed LAIthe observed and simulated (AFRCWHEAT2-O3)

data. There were also differences in simulating (Table 4). This might explain the high amount ofbiomass produced until anthesis as it was simulatedbiomass production between the two models for

the period from anthesis to maturity. Both models by this model. LINTULCC simulated a smallerLAI at anthesis and a smaller LUE compared tofinally predicted a biomass at maturity, which was

close to the observed value [Fig. 1(b)]. However, AFRCWHEAT2-O3. Thus, the high amount ofbiomass produced after anthesis simulated by theLINTULCC simulated a higher contribution of

biomass produced between anthesis and maturity model can only be explained by the leaf areaduration, which was simulated to be longer withto the final biomass at maturity harvest than

AFRCWHEAT2-O3, which was mainly due to a LINTULCC, compared to AFRCWHEAT2-O3.Both models and particularly LINTULCC simu-longer leaf area duration simulated by

LINTULCC in comparison to the other model. lated less variation in biomass at anthesis andmaturity than observed (Table 6). The high valuesThe measured experimental averages of the total

above ground biomass at anthesis and maturity of the standard deviation of the differences betweensimulated and observed data indicate for bothwere 824 and 1230 g m−2, respectively (Table 6).

Both models slightly underpredicted final biomass models the rather poor site-specific prediction ofbiomass at anthesis and maturity (Table 6).at maturity (1150 g m−2 with AFRCWHEAT2-O3

and 1100 g m−2 with LINTULCC) (Table 6). The Doubling the CO2 concentration increased thebiomass production in the experiment on averagebiomass at anthesis was simulated higher

(973 g m−2) with AFRCWHEAT2-O3 and lower by nearly 30% at anthesis and maturity (Table 6).AFRCWHEAT2-O3 simulated CO2 induced sti-(706 g m−2) with LINTULCC as compared to the

measurements. Thus, both models differed from mulations in biomass production at anthesis (18%)and maturity (23%) which were lower than theeach other with respect to the amount of biomass

Table 6Means and standard deviations of observed and simulated above ground biomass (g m−2) measured at anthesis and maturity fromambient air chambers and of observed and simulated effects of CO2 (doubling) and ozone on above ground biomass of spring wheatcv. Minaret grown at different sites and years

Treatment Development stage Observed Simulated n



Anthesis Ambient air chamber 824 263 973 167 248 706 113 242 21CO2 effect, low ozone (relative) 1.28 0.31 1.18 0.04 0.32 0.96 0.015 0.16 21Ozone effect, low CO2 (relative 0.88 0.16 0.98 0.014 0.16 0.96 0.015 0.16 14Ozone effect, high CO2 (relative) 0.96 0.22 0.99 0.003 0.22 0.98 0.007 0.21 12

Maturity Ambient air chamber 1230 315 1150 233 290 1100 195 288 24CO2 effect, low ozone (relative) 1.29 0.18 1.23 0.05 0.17 1.36 0.08 0.17 24Ozone effect, low CO2 (relative 0.94 0.12 0.94 0.03 0.11 0.95 0.03 0.12 16Ozone effect, high CO2 (relative 0.95 0.15 0.96 0.018 0.15 0.97 0.013 0.15 13


measured responses (Table 6). The model did not the efficiency of the conversion of the interceptedsimulate the observed response of the LAI to radiation into dry matter. The accumulated IPARCO2 exposure (Table 4), which might explain that depends very much on LAI. We have shown thatthe model underpredicted the effect of CO2 eleva- both models closely predicted the average mea-tion on biomass production. LINTULCC pre- sured biomass at maturity. However, the measureddicted the same biomass response to CO2 exposure variation in final biomass was simulated less accu-at anthesis as was observed (Table 6). However, rately. From the measurements of the presentthe CO2 effect on biomass production was overesti- ESPACE-wheat experiments, we found a positivemated at maturity (Table 6). This increase in the relationship between final biomass in ambient airCO2 response after anthesis was simulated because chambers and LAI measured at anthesispost-anthesis temperatures were generally high, (r2=0.39, Fig. 3a, Table 7). A much stronger rela-which increased the effect of CO2 on photosynthe- tionship between these two variables was calcu-sis and LUE. lated from the simulations of the two models,

Ozone exposure in the ambient CO2 treatments r2=0.76 for AFRCWHEAT2-O3 and r2=0.83 forwas measured to have decreased biomass on LINTULCC (Table 7), indicating the importanceaverage by about 12% at anthesis and by about

of LAI at anthesis for the final biomass in the6% at maturity (Table 6). Both models predictedsimulations. A positive relationship was founda similar reduction in biomass at maturity due tobetween measured biomass at maturity in ambientozone exposure for the non-elevated CO2 treat-air chambers and the measured accumulated IPARments (Table 6). However, the effect of ozone on(r2=0.42, Fig. 3b, Table 7). A similar relation-biomass production at anthesis was predicted lessship between these two variables (r2=0.47) wasaccurately (2% with AFRCWHEAT2-O3 and 4%calculated from the simulations withwith LINTULCC ) (Table 6). We found it difficultAFRCWHEAT2-O3 (Table 7). From the simula-to explain the high reduction of biomass due totions made with LINTULCC we calculated a muchozone exposure measured at anthesis and, thus,closer relationship between final biomass and accu-the rather poor predictions. There is no evidencemulated IPAR (r2=0.73, Table 7) indicating thatin the literature indicating a response of biomassthe variation in final biomass was mostly explainedto long-term ozone exposure being higher beforeby the variation in the accumulated IPAR. Thethan after anthesis. In the elevated CO2 treatments,variation in LUE hardly accounted for the varia-ozone exposure decreased biomass by only 4% at

anthesis and by 5% at maturity. This interaction tion in biomass calculated from the simulationsbetween the effects of CO2 and ozone on biomass made with LINTULCC, which was different toproduction was also simulated by the two models the observations and to the simulations from(Table 6). Both models simulated a variation AFRCWHEAT2-O3 (Table 7).among sites and years of the effects of CO2 and The measured variation in the CO2 effect onozone on biomass production at anthesis and biomass was mainly explained by a variation inmaturity, which was smaller than the observed the CO2 effect on LUE (Fig. 4a, Table 7), whichvariation (Table 6). One explanation is that other was also calculated from the simulations of thefactors than those considered in the models might two models (Table 7). We could not derive ahave affected the processes of biomass production relationship between the CO2 effect on biomassand, thus, the responses of biomass production to and the CO2 effect on the accumulated IPAR fromCO2 and ozone exposure. However, to support the measurements (Fig. 4b, Table 7) and from thethis argument, we think it necessary to analyse the simulations of AFRCWHEAT2-O3 (Table 7).relationship between simulated growth and devel- However, a relationship between CO2 responsesopment processes. of these two variables was calculated from the

simulated data of LINTULCC (r2=0.50, Table 7).3.7. Relationships among growth parametersThe measured variation in the effect of ozone

fumigation on biomass was explained by both theBiomass production is the product of theamount of radiation intercepted by the crop and variation in the response of the accumulated IPAR


Fig. 3. Observed responses of above ground biomass at maturity to a) LAI at anthesis and b) IPAR accumulated over the seasonfor spring what cv. Minaret grown in ambient air OTC at different sites and years in Europe. For parameters of regressions see Table 7.

Table 7Results of the linear regression analysis of observed and simulated data of the relationships between LAI at anthesis, SIPAR andLUE and biomass and of the relationships between the CO2 and ozone effects on SIPAR, LUE and biomass for spring wheat cv.Minaret grown at different sites and years. a and b, parameters of the linear model y=a+bx. r2, regression coefficient.ns, not significant

Relationships Data source Linear regression n

a b r2 p-value

Biomass vs. LAI at anthesisa Obs. 155.6 717.0 0.39 0.003 20AFRC 143.6 202.2 0.76 <0.001 25LINTUL −1017 464.6 0.83 <0.001 24

Biomass vs. SIPARa Obs. 181.2 2.75 0.42 0.002 20AFRC 242.1 2.41 0.47 <0.001 25LINTUL −7.2 2.43 0.73 <0.001 24

Biomass vs. LUEa Obs. 121.8 351.4 0.39 0.003 20AFRC 178.9 313.5 0.34 0.0023 25LINTUL ns 24

CO2 effect on biomass vs. CO2 effect on SIPAR Obs. ns 20AFRC ns 25LINTUL −3.47 4.66 0.50 <0.001 24

CO2 effect on biomass vs. CO2 effect on LUE Obs. 0.126 0.933 0.75 <0.001 20AFRC −0.008 1.023 0.99 <0.001 25LINTUL −0.190 1.180 0.97 <0.001 24

Ozone effect on biomass vs. ozone effect on SIPAR Obs. −0.19 1.16 0.60 0.0018 13AFRC −0.70 1.70 0.74 <0.001 17LINTUL −10.60 11.60 0.59 <0.001 16

Ozone effect on biomass vs. ozone effect on LUE Obs. −0.153 1.123 0.61 0.0016 13AFRC −0.420 1.390 0.83 <0.001 17LINTUL −0.051 1.050 1.00 <0.001 16

a For ambient air-chamber conditions.

to ozone (r2=0.60) and the variation in the with LINTULCC, a strong relationship(r2=1.00) was calculated between the effect ofresponse of the LUE (r2=0.61) to this pollutant

(Fig. 4c and d, Table 7). Similar results were ozone on biomass and the effect of ozone on LUE.A comparison of the parameters of the regres-obtained from the simulations of AFRC-

WHEAT2-O3 (Table 7). From the simulations sions calculated from the observed and simulated


Fig. 4. Observed responses of the effect of CO2 on above ground biomass at maturity to (a) the effect of CO2 on LUE and (b) theeffect of CO2 on IPAR accumulated over the season and of the effect of ozone on above-ground biomass at maturity to (c) the effectof ozone on LUE and (d) the effect of ozone on IPAR accumulated over the season for spring wheat cv. Minaret grown at differentsites and years in Europe. For parameters of regressions, see Table 7.

data indicates differences between the models the ESPACE-wheat project has been achieved. Wehave performed a complex analysis of the simu-(Table 7). Overall, AFRCWHEAT2-O3 repro-

duced more closely the directions of the relation- lated effects of CO2 and ozone on wheat growthand development in response to different climaticships established from the observations than

LINTULCC (Table 7). In particular, the sensitiv- conditions. Generally, both models closely simu-lated the experimental averages of final biomass.ity of changes in the ozone effect on biomass to

changes in the ozone effect on accumulated IPAR However, the simulated variation in final biomasswas smaller than the measured variation. Thecalculated from the simulations with LINTULCC

differed considerably from the observations and simulations also did not reproduce the pattern inthe variation of biomass production observed insimulations with AFRCWHEAT2-O3 (Table 7).the experiment. Basically, poor simulations areeither due to a poor understanding of physiologicalprocesses or due to insufficiencies in performing4. Discussionthe experiments. Both models used different mech-anisms to simulate growth and development. TheAn extended database of the physical and chem-

ical climate in the chambers and of several biologi- simulations differed from each other with respectto the co-ordination between phenological devel-cal measurements of different sites and years of


opment and crop growth and with respect to prediction of final biomass in response to CO2 andozone, as given above for the simulations of bio-growth parameters such as LAI, LUE, SIPAR,

and the amount of biomass produced before and mass in the ambient air chambers.There is much discussion about the transfer ofafter anthesis. However, in almost all cases, even

when the measured average of a parameter was results achieved in OTC experiments to field condi-tions (Lawlor and Mitchell, 1991). The use ofsimulated closely, the simulated variation among

sites and years was smaller than the observed OTCs mainly alters temperature, radiation andwind profiles. In the present experiment, temper-variation. We also analysed the sensitivity of bio-

mass production to certain growth parameters. ature and radiation were measured continuouslyin the chambers to provide a chamber-specificAgain, the calculated relationships were different

for the two model simulations with input data set for the model simulations. Further,plants in the chambers were supplied optimallyAFRCWHEAT2-O3 closely reproducing the

observed correlations between final biomass and with water to avoid any alteration in growthpatterns due to an effect of a chamber inducedseasonal accumulated IPAR and final biomass and

LUE. However, the observed variation in final change of the wind profile on evapo-transpiration.However, our results seem to indicate that the usebiomass was predicted as poorly as with

LINTULCC. From these results, we derive the of OTCs that differed in design and materialamong the sites and the use of local soil typesidea that other factors than those considered in

the models might have affected growth and devel- might explain part of the variation that we couldnot simulate with the models.opment in the chambers and have caused a varia-

tion in biomass production, which could not be The present study can be assessed as uniquewith respect to the complex investigation of thesimulated by the models. An analysis of the sensi-

tivity of grain yield to the climatic variables which interactive effects of climatic change and increasedCO2 and ozone concentrations on a single cultivarvaried among sites and years provides further

arguments for the hypothesis (van Oijen and of spring wheat grown under near field conditions.The results have indicated that variations in bio-Ewert, 1999).

Both models used different mechanisms to simu- mass production and in the responses of biomassproduction to CO2 and ozone exposure werelate the effects of CO2 and ozone on crop growth

and development. The average crop response to mainly explained by differences in the leaf areadynamics among experiments. Both models usedozone exposure was simulated closely by the two

models. The models also closely simulated the different approached to simulate leaf area indexand duration. However, none of the two gaveobserved interactive effects of CO2 and ozone on

final biomass. However, the average crop response satisfactory results. Thus, we suggest that thesimulation of leaf area dynamics deserves particu-to CO2 was simulated less accurately by both

AFRCWHEAT2-O3 and LINTULCC, which was lar attention.due to different reasons depending on the modeland which should be considered when using themodels for climate change impact assessmentstudies. AFRCWHEAT2-O3 hardly simulated an Acknowledgementseffect of CO2 on canopy development as observedin the experiment and underestimated the CO2 We acknowledge the funding for this project

from the European Commission Environment pro-effect on LUE. LINTULCC overestimated theCO2 effect on LUE and thus the CO2 effect on gramme (contract no.: EV5V-CT93-0301). We also

thank the partners of the ESPACE-Wheat projectfinal biomass. In addition, neither of the modelssimulated the variation in the crop response to of the constructive co-operation. F. Ewert thanks

Hanne Olsen for administrative assistance. J.R.CO2 and ozone exposure as it was measured inthe experiment. We would apply the same argu- Porter acknowledges the support of the Danish

Agricultural and Veterinary Research Council.ment to explain the poor site- and year-specific


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simulation of growth and development processes of spring wheat in response to co2 and ozone for...

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