A simple model for simulation of growth and development in faba beans (Vicia faba L.) 2. Model evaluation and application for the assessment of sowing date effects

Eur. J. Agron., 1995, 4(2), 187-195

A simple model for simulation of growth and development in faba beans (Vicia faba L.) 2. Model evaluation and application for the assessment of sowing date effects H. Sti.itzel* Institut fiir Pfianzenbau und Griinland (340), Universitiit Hohenheim, Fruwirthstr. 23, D-70593 Stuttgart, Germany

Accepted 23 November 1994

*

Present address: lnstitut fiir Gemlisebau, Universitat Hannover, Herrenhauser Strasse 2, D-30419 Hannover, Germany

Abstract

An evaluation of a faba bean model was undertaken to obtain indications whether the description of the physiological relationships in the model and the assumptions made realistically described the effects of variation in sowing date. Comparisons of simulated with experimental data for indeterminate and determinate cultivars of Vicia faba revealed that the model made good predictions of leaf area index, total and fruit dry matter until early June. Thereafter, the model consistently overestimated dry matter production. This was presumed to be due to aphid and virus infestation for which no provisions were made in the model. Linear reductions of light use efficiency from the third decade of June on resulted in satisfactory model results for the indeterminate cultivar. In the determinate cultivar, the pest apparently accelerated leaf senescence which had also to be taken account of in the model. Simulations of systematic variations of sowing date revealed that the observed yield reductions due to delayed sowing were probably due to pests when a constant thermal duration of the reproductive phase was assumed. When the thermal duration of the reproductive phase was decreased with later sowing dates, yields decreased even without pests. Key-words : Simulation model, Vicia faba, sowing date.

INTRODUCTION To evaluate whether the description of the physiological relationships and the assumptions on which a model is based are realistic, model predictions have to be compared with independent experimental data. Agreement between model and experimental results, however, can not be interpreted as proof of the correctness of the model (Thomley and Johnson, 1990). In contrast, differences between model and experimental results indicate that causal relationships and assumptions are either incorrect or incomplete. In the model described previously (Stiitzel, 1995) sowing date may affect yield in various ways, e.g. flowering may occur earlier with delayed sowing as a consequence of longer days and higher temperatures. Root : shoot partitioning may be modified due to different growth rates and vapour pressure deficits at comparable development stages. Also, the duration of ISSN II6I-030I/95/02/$ 4.001© Gauthier-Villars - ESAg

the reproductive phase may be shorter when temperatures increase in summer. Though delayed sowing generally tends to reduce yields in Vicia faba (Thompson and Taylor, 1977 ; Barry and Storey, 1979; McVetty et al., 1986), a sowing date slightly later than the optimum may not cause yield depressions (Barry and Storey, 1979), whereas increasing delays result in overproportional yield reductions (McVetty et al., 1986). One reason for the positive effect of early sowing is the longevity of the leaves (Pilbeam et al., 1989) because green area duration after flowering and grain yield are related (Husain et al., 1988). Also, the risk of drought stress, diseases and frost in autumn increases with delayed sowing (Kondra, 1975 ; Marcellos and Constable, 1986). The model described earlier (Stiitzel, 1995) was tested under weather conditions different from those prevailing when calibration data were obtained, so that incorrect or incomplete physiological relationships and

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assumptions could be identified. Further, the model was used to evaluate in a systematic manner effects of delayed sowing on yield of faba beans.

MATERIALS AND METHODS Experiment The field experiment was conducted in 1989 at the Institut fiir Pftanzenbau und Grtinland at Hohenheim, Southern Germany, situated about 400 m above sea level with a slight (ca. 2 per cent) slope to the south. The topsoil was a sandy loam, the subsoil a loamy clay. Long-term averages for annual mean temperature and annual precipitation are 8.5 oc and 687 mm, respectively. Weather data for the growing season 1989 are shown in Figure 1.

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factor

levels

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Factor

Level

Sowing date

7 March 28 March 19 April

Main plot

Plant density

18.5 plants m- 2 74 plants m- 2

Sub plot

Cultivar

Herz Freya (indeterminate) Ticol ('topless', deterntinate

Sub-sub plots

Replicates

4

Blocks

Model The model of growth and development used here was described by Sttitzel (1995). Light extinction coefficients were calculated as functions of leaf area index (Figure 5 in Sttitzel, 1995). During model evaluation, alterations to the model were shown to be necessary. Adaptation of specific leaf area (SLA) was performed usi ng measured values (Figure 2) for each cultivar, sowing date and density, averaged over the growing season. When calculating average SLA, values from harvests 1-3 were obtained by double weighing since leaf area has a much greater impact on light interception in young stands with low LA I.

MODEL EVALUATION

30 4.

Date

Figure 1. Mean day temperatures and photosynthetically-active radiation (moving averages over 10 days), and precipitation/10 days at Hohenheim, 1989.

The experiment was planted by hand in isometric stands (equal distances between all plants) in a splitplot design with three sowing dates, two plant densities and two cultivars (Table 1). All sub-sub-plots were 2 m wide and 2.8 m (74 plants m- 2 ) or 5.6 m (18.5 plants m- 2 ) long. Plots were sampled five times during the growing season and at physiological maturity. Sampled areas were 0.432 m2 (at 18.5 plants m-2 ) and 0.325 m2 (at 74 plants m- 2 ). All plants were partitioned into roots and shoots and a random subsample of three shoots was further separated into leaves, stems (including petioles), inflorescences, pod husks and seeds. At physiological maturity, all plants harvested were separated into their organs. Dry weights of all plant parts were determined. Leaf areas were measured with aLi-Cor 3100 leaf area meter.

Initial simulations with the input constants used for 1987 and 1988 (Table 1 in Sttitzel, 1995) resulted in considerable deviations from measured leaf area indices, LAI (Figure 3). This was due to differences between assumed (Table 1 in Sttitzel, 1995) and actual specific leaf areas, SLA. Usi ng values from Table 2 significantly improved the model predictions (Figures 4 and 5). In cv. Herz Freya, simulated net total dry matter WTN (standing dry matter including roots, but without shed leaves) using input constants from Table 2 agreed well with measured data until harvest 3 which took place from 21 to 23 June (Figure 4, dashed lines; harvest 3 is represented by the second and third data points from left for the April and March sowings, respectively). Subsequently, simulated dry matter exceeded the measured. Consequently, fruit dry matter WPO was overestimated by the model from harvest 4 onwards (second data points from left in Figure 4). The later the crops were sown, the greater the discrepancies between measured and simulated total and fruit dry matter at physiological maturity (first data points Eur. J. Agron.

189

Faba bean model evaluation

Herz Freya 320

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March; L A sowing Figure 2. Variation of measured specific leaf area SLA during the growing season; D • sowing 7 March; 0 sowing 28 2 2 19 April 1989; open symbols: 18.5 plants m- , closed symbols 74 plants m- ; bars: standard error.

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Measured values are Figure 3. Simulated (curves) and measured (points) leaf area index, LA! in cvs. Herz Freya and Tical sown at three dates. error. standard bars: 1; Table (1995), Stiitzel from areas leaf specific replicates; 2 means of

from right in Figure 4). Interestingly, simulated leaf areas never differed significantly from the measured. Hence, the model estimated the light intercepting surface correctly throughout the growing season. In contrast, the conversion of intercepted radiation into dry matter was calculated correctly only until harvest 3. The lower light use efficiency from the third decade of June on may have been due to effects of aphid Vol. 4,

ll

0

2- 1995

infestation, which began during late May. Aphid con1 trol with Pirimicarb (300g ha- ) sprayed on 23 May and 16 June was incomplete. The aphids (mainly Aphis fabae) caused leaf deformation and leaf chlorosis consequent on infection by bean yellow mosaic virus. It can thus be assumed that the photosynthetic efficiency of the canopy was reduced (Boote et al., 1983).

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Table 2. Input constants for simulations 1989"

Herz Freya 74 plants - 2

18.5 plants m-2

Date of emmergence b Initial plant density (m-2 ) Specific leaf area (cm 2 g- 1 Duration of reproductive phase (°C d) ' Duration of reproductive phase (°C d) d Daily reduction of light use efficiency after day 170 (%) e

Ticol 18.5 plants - 2

74 plants m- 2

Sowing 7.3

Sowing 28.3

Sowing 19.4

Sowing 7.3

Sowing 19.4

Sowing 7.3

Sowing 28.3

Sowing 19.4

Sowing 7.3

Sowing 19.4

122 16.0 250 I 000 I 000

128 17.0 250

140 16.0 250

140 60.0 250

000 I 000

I 000

000 I 000

125 17.0 270 000 I 100

128 18.0 250 I 000 800

140 15.0 250 I 000 600

124 53.0 280

000 I 000

121 60.0 280 000

000 I 100

140 51.0 270 I 000 700

1.5

1.5

1.5

1.5

1.5

1.5

3

3

1.5

1.5

all input constants not mentioned were calculated as described by Stiitzel (1995) b Julian date (days after 1. January) ' without consideration of aphid and virus damages (dashed lines in Figs. 4 and 5) d with consideration of aphid and virus damages (solid lines in Figs. 4 and 5), values adapted to leaf area curves in Figs. 4 and 5 ' with consideration of aphid and virus damages (solid lines in Figs. 4 and 5), values adapted to total dry matter curves in Figs. 4 and 5. a

Aphids may be presumed to act in accordance with the following chain : Cause of } damage { (e.g. number of aphids)

~

Direct } effects on system { components

No data are available to simulate the aphid population dynamics. However, an attempt was made to quantify the direct aphid and indirect (virus) effects on the functioning of the leaf apparatus. If this were the only effect of the pest, it should be possible to estimate the consequences for the whole system and the partitioning of dry matter into yield organs. The aphid and virus effects mentioned above were macroscopically visible from the middle of June. It was assumed that they resulted in a decrease in light use efficiency of 1.5 per cent per day from Julian day 170 (19 June). With this assumption the model estimated total and fruit dry matter satisfactorily (Figure 4, solid lines) except at 74 plants m-2 in the first sowing. Leaf areas were almost fully developed by day 170, so that the assumption had no significant impact on LAI. For cv. Ticol, model predictions of total and fruit dry matter were satisfactory until harvest 3 (Figure 5, dashed lines). As in Herz Freya, fruit dry matter was overestimated during the reproductive phase. This was pronounced in the later harvests, even more than with Herz Freya. The build-up of green leaf area was estimated realistically by the model, but there were

~

{Effects } on whole system

~

{ Indirect effects } on relevant systern components (yield)

increasing discrepancies between simulated and measured leaf areas in the second part of the growing season. For the crops of the first sowing that were least damaged by the pest, the model underestimated leaf area during the reproductive phase. The opposite was found in crops sown later. It may be speculated that for early sown crops the thermal time from flowering to complete leaf senescence is longer than 1000 °Cd, as assumed here on the basis of the 1987/88 experiments that were sown in mid April. Figure 4 indicates this for the early sowing of Herz Freya. For early sown Ticol, the assumption of 1100 °Cd for the duration of the reproductive phase, TPF, resulted in better agreement between simulated and measured leaf areas (Figure 5, solid lines). For Ticol crops sown at 18.5 plants m-2 in late March and mid April, however, satisfactory estimates of leaf area were obtained when TPF was set to 800 °Cd (second planting) and 600 °Cd (third planting). In the high density of the third sowing, TPF set to 700 °Cd gave the most realistic leaf area estimates. On this basis and assuming a daily reduction in light use efficiency of 1.5 per cent as in Herz Freya, total and fruit dry matter production of the first sowEur. J. Ag ron.

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Faba bean model evaluation

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Vol. 4,

0°

2- 1995

192

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Eur. J. Agron.

193

Faba bean model evaluation

ing and at the high density of the third sowing was estimated realistically by the model (Figure 5, solid lines). At the low densities of the second and third sowing, better agreement with measured total and fruit dry matter was obtained when light use efficiency was reduced by 3 per cent for each day after day 170. Also, final grain yield estimates of the model showed little deviations from measured values (Figure 6).

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JD1 = 95.5 ( :±:: 4.17)- 0.61 ( :±:: 0.134)* DSO, 2

r = 0.95 Assuming absence of pests and a thermal duration of the reproductive phase of 1000 °Cd, grain yield was only slightly reduced by delayed sowing from early March on (Figure 7a). For May sowings, yields tended to increase again, due to more rapid vegetative growth. Increasing plant densities resulted in about 40 g m-2 seed yield increase per 20 plants, independent of the time of sowing. Probably more realistic than a constant thermal duration of the reproductive phase, is a decrease in TPF with delayed sowing. Assuming that the duration of the reproductive phase is 1 100 °Cd on 1 March and decreases by 2 °Cd with each day delay in sowing, the model calculates a reduction in grain yield of about 20 per cent between early March and late April (Figure 7b), irrespective of plant density. If light use efficiency is reduced at a rate of 1.5 per cent per day to account for pests and TPF is maintained at 1 000 °Cd for all sowing dates, grain yields decrease substantially with delayed sowing (Figure 7c). Also, the effects of higher plant densities on yield decrease. If, additionally, TPF is assumed 1 100 acd on 1 March and is reduced by 2 acd for each day delay in sowing, effects of delayed sowing are stronger (Figure 7d).

WSE measured (g m·21

DISCUSSION Figure 6. Comparison between simulated and measured grain yields, WSE

SYSTEMATIC ASSESSMENT DATE EFFECTS

OF

SOWING

With the above findings a more systematic assessment of sowing date effects is possible. This is attempted using the 1989 data for cv. Herz Freya, for which only the reduction in light use efficiency from Julian day 170 had to be assumed to account for pest damages. To examine to what extent increasing seed rates can compensate for delayed sowing, plant densities were varied from 20 to 60 plants m-2 . Since the model predicts development and growth only from a given date of emergence, DEM, or date of reaching a leaf area of 50 cm 2 per plant, JD1, respectively, empirical relationships between DEM and JD1, and date of sowing, DSO, were calculated for the conditions of 1989 at Hohenheim : DEM = 34.1 ( :±:: 2.24)- 0.18 ( :±:: 0.050)* DSO, 2

r = 0.87 Vol. 4, n° 2- 1995

One of the objectives of this evaluation was to assess whether the model performs satisfactorily in situations different from those underlying its development. Certainly, the number of data sets used to evaluate the model was limited, but gave important indications of effects not or not adequately included in the model. Based on the results of the 1987 and 1988 experiments, and on published approaches (cf. Penning de Vries et al., 1989), only little variation in specific leaf area, SLA, was assumed. This appeared justified, since effects of temperature that increased SLA and radiation that reduced SLA (Kasim and Dennett, 1986 ; Acock et al., 1979) are correlated under field conditions. The relatively low specific leaf area at the first harvests of the early plantings may have been a consequence of the low temperatures and high irradiance levels in early spring. However, this does not account for the low SLA at later harvests. Thus, a large proportion of the variation in SLA appears related to pest incidence, but cannot be explained causally. When the experimentally determined specific leaf area was used as input, the model produced realistic estimates of leaf area index and dry matter production until harvest 3.

194

H. Stiitzel

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Figure 7. Simulated dependence of grain yield produced by cv. Herz Freya on date of sowing. planting density, and assumptions on pest damage and the thermal duration of the reproductive phase, TPF; (a) no pests, TPF = 1000 °Cd for all sowing dates; (b) no pests, TPF = I 100 °Cd at 1 March, decreasing by 2 °Cd for each day thereafter; (c) with pests (reduction of light use efficiency by 1.5 per cent for each day after Julian day 170), TPF = I 000 °Cdfor all sowing dates; (d) with pests (reduction of light use efficiency by 1.5 per cent for each day after Julian day 170), TPF = I 100 °Cd at 1 March, decreasing by 2 °Cd for each day thereafter.

This suggests that the other relationships and assumptions of the model are adequate. However, further research is needed to systematically elucidate the effects of (a) the physical environment, i.e. temperature, radiation and water supply, and (b) pests and diseases on SLA. The investigations suggested simple ways to quantify pest effects. Through systematic modification of one parameter, light use efficiency, from a given date dry matter production of cv. Herz Freya was described realistically. The fact that the model estimated fruit and grain dry matter satisfactorily with this modification included indicates that the dry matter partitioning pattern was not modified by the pest. The simple assumption of a linear reduction of light use efficiency with time is crude, since it includes the loss of cell sap as well as the reduction in photosynthesis caused

by aphids and the virus, respectively (Cammel and Way, 1983 ; Cockbain, 1983). Quantitative epidemiological knowledge is necessary to model the relationship between the pest population and the reduction in net photosynthesis or leaf area (Ticol) in a more mechanistic way. The approach of Rossing et al. (1989) with Sitobion avenae may be a good starting point. On the basis of a known population density of S. avenae, weight per aphid and sap extraction rate, dry matter loss from the crop can be calculated. However, the model requires input of the number of aphids per culm. Combination with a model of population dynamics would reduce the effort of counting aphids. Interestingly, sufficiently accurate results were obtained when light use efficiency was reduced by the same proportion for all three planting dates for Herz Freya. It is known that the bean aphid (Aphis fabae) Eur. J. Agron.

Faba bean model evaluation

attacks thin stands more severely than dense crops (Cammel and Way, 1983). Hence, it may be expected that later sown crops, particularly those at 18.5 plants m-2 would be damaged more severely than earlier sown plots, as observed in Ticol. In fact, a slight tendency towards a similar behaviour of Herz Freya may be deduced from the fact that total and fruit dry matter in the first sowing at 18.5 plants m- 2 and in the third sowing at 74 plants m- 2 were somewhat underestimated, and overestimated in the third planting at 18.5 plants m- 2 . However, density by sowing date interactions with respect to pest population were probably masked by the compensating effects of new leaf production in the indeterminately growing Herz Freya, but not in Ticol. One important objective of this model evaluation was to identify the range of ecological conditions under which assumptions about processes that are little understood are valid. The assumptions that the duration of the reproductive phase is either independent of sowing date or constantly decreasing with time certainly need further experimental confirmation. This will be difficult, since the physiological causes of senescence are not known. If high saturation deficits of the air or limited soil water availability accelerate senescence (Farah, 1981 ; Poulain et al., 1989), later sown crops would be expected to have shorter thermal durations of the reproductive phase, even in the absence of diseases and pests. This situation would correspond to the assumption made in Figures 7b and 7d. If photosynthesis is reduced through soil water shortage, as is not very likely under our experimental conditions, modifications of light use efficiency will have to be included in the model also. Under the conditions described here, incidence of pests and diseases is the main cause of yield depressions due to delayed sowing. This means that the efficiency of pest control has to be much higher in later than in early sowings. Good correspondence between measured and simulated crop parameters during the disease-free periods of crop growth indicate that the model can adequately describe yield formation in faba beans, provided specific leaf area can either be given as an input constant or simulated on the basis of physiological relationships not yet sufficiently clear. The main challenge for further model development, however, lies in the incorporation of the physiological effects of abiotic and biotic stresses which commonly occur in faba bean crops. ACKNOWLEDGEMENTS I thank Mrs. E. Taubert-Ott for assistance in the field experiments and Mrs. P. Haider for help in preparing the manuscript. Financial support through the German Research Community (DFG) is gratefully acknowledged. Vol. 4, n" 2 · 1995

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