Predicting the effect of irradiance and temperature on the flower diameter of greenhouse grown Chrysanthemum

Scientia Horticulturae 99 (2004) 319–329

Predicting the effect of irradiance and temperature on the flower diameter of greenhouse grown Chrysanthemum Margit Nothnagl∗ , Andrea Kosiba, Rolf U. Larsen Department of Crop Science, Swedish University of Agricultural Sciences, P.O. Box 44, S-230 53 Alnarp, Sweden Accepted 26 March 2003

Abstract A model was developed describing the influence of irradiance and temperature in the greenhouse on the size of Chrysanthemum flowers. In the model, flower diameter increment was related to a development index ranging from zero (start of SD) to unity (anthesis). The growth of the visible flower was divided into two different phases. In the first phase a linear function described the growth and development of the early visible flower bud, while the second phase, representing the opening process of the flower was best described with a monomolecular growth function. The effect of the climate on the two growth phases was modelled using empirical climate functions. Data, collected from a light and a temperature experiment, showed that low light integrals and temperatures above 20 ◦ C had a retarding effect on flower growth. When the model was fitted to the observed data from the light experiment the R2 -values varied from 0.999 to 0.965. Even the simulated diameter values matched the observed values from the temperature experiment well (R2 -values from 0.998 to 0.966). When validated on independently collected data from two trials, the model could simulate the variations in the data with R2 -values of 0.993 and 0.997. © 2003 Elsevier B.V. All rights reserved. Keywords: Chrysanthemum indicum; Chrysanthemum morifolium; Flower size; Growth model; Greenhouse climate; Dendranthema

1. Introduction The demand from society for sustainable production methods in protected cultivation has resulted in an increased interest for biological control in the greenhouse. Since greenhouse cultivation often deals with the production of monocultures, the interaction between a crop, ∗

Corresponding author. Tel.: +46-40-415370/372773; fax: +46-40-460441. E-mail address: [email protected] (M. Nothnagl). 0304-4238/$ – see front matter © 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0304-4238(03)00096-7

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a pest and a predator, should be reasonably simple to describe and predict using system analysis and crop modelling. Such a model, linked to a greenhouse climate computer should in theory make it possible to optimise both climate conditions and predator application in order to minimise crop damages and delay of production. In 2000, a project was initiated at the Swedish University of Agricultural Sciences with the aim of describing the interaction between pot chrysanthemum (Chrysanthemum×indicum), Western flower thrips (Frankliniella occidentalis) and the predatory mite Amblyseius cucumeris. Damages caused by the Western flower thrips appear as scars and deformations on leaves and mostly on the flower petals. As a result the size of the flower head is affected by a thrips infestation. Greenhouse climate also affects flower size. Any model attempting to describe the damaging effect of thrips on the flower must therefore also account for climate effects. Flower bud development in Chrysanthemum is affected by different light and temperature conditions. Low light levels delay bud development and high light levels accelerate it (Cockshull and Hughes, 1971; Hidén and Larsen, 1994). The fastest flower bud development after initiation is achieved when temperatures are held around 18–20 ◦ C (de Jong, 1978a; Adams et al., 1998). The relationship between daily light integral and mean day temperature, and its general effect on the rate of flower development of different chrysanthemum cultivars has previously been described in a prediction model developed by Larsen and Persson (1999). However, only a few studies have been done concerning the actual growth of the flower head. Cockshull and Hughes found in 1971 that the number of florets formed on the receptacle was modified by light level during the initial period of floret formation. Another study showed that standard cultivars produced heavier flower heads when grown during summer than during winter (Ben-Jaacov and Langhans, 1971). Karlsson et al. (1989) pointed out that flower area increases linearly as PPF increases and that it reaches its maximum under the optimal temperature conditions of about 18–20 ◦ C. Higher or lower temperatures had a negative effect on flower growth. It was also shown that in some cut flower cultivars, low night temperatures resulted in a larger flower diameter, a higher flower area per plant, and heavier flowers (Carvalho and Heuvelink, 2001). The aim of the following study was to develop a model that describes the effect of temperature and irradiation on the growth of the flower head in chrysanthemum. At a later stage, this model is to be linked to a larger model describing the crop, pest and predator system.

2. Material and methods To be able to quantify flower growth non-destructively, the diameter of the macroscopically visible terminal flower bud of Chrysanthemum indicum ‘Lompoc’ was chosen as a state variable. We originally assumed that the increment in flower diameter would follow a traditional sigmoid growth function. A preliminary model was therefore developed and experimental data were collected from greenhouse experiments for the estimation of model parameters. However, during the course of the analysis the model theory had to be modified in order to fit the collected data. This resulted in the present two-phase model. The final model was verified by comparing model simulations with the collected data used for the parameter estimations. The model was finally validated by comparing model simulations with independent data from new trials.

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maximum flower diameter (mm)

100 90

D maxf

Linear Phase

Monomolecular Phase

80 70 60 50 40 30 20 10 0 0.0

Dcrit Indcrit IndS

0.2

0.4

0.6 0.8 1.0 1.2 development index

1.4

1.6

Fig. 1. The different growth phases of a Chrysanthemum × indicum ‘Lompoc’ flower under optimal conditions.

2.1. Model theory In the model we chose to relate the increment in terminal flower diameter D, not to time, but to a development index Ind, ranging from zero to unity. In the index, zero corresponds to the start of short day treatment while unity is anthesis. In the present case anthesis was defined as the first row of disc florets being fully opened. The index approach was chosen to simplify the model so that the effect of climate on the rate of flower development could be excluded. It also made it possible to link this model to a model of flower development previously developed (Larsen and Persson, 1999). The rate of diameter increment, RD , can thus be defined as dD RD = (1) d Ind In the model the growth of the visible terminal flower bud is divided into two different phases. The first phase starts when the flower bud becomes visible, IndS , and continues until a critical index value, Indcrit , is reached. This phase follows an almost linear growth pattern (Fig. 1). In the initial part of the second phase, starting at Indcrit , the flower bud increases very rapidly in diameter but the process will eventually slow down as the flower reaches its final size. During both phases the growth in diameter is influenced by the daily light integral I, and the mean day temperature T. In the model we chose to describe diameter growth during the first, linear growth phase with the following expression: dD (2) = klin fIlin fTlin for IndS ≤ Ind ≤ Indcrit d Ind where klin is a maximum growth rate while fIlin and fTlin are the climate functions. In the second growth phase different light and temperature regimes will not only affect the shape of the growth curve but also the final flower diameter. This can be simulated by

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relating the growth rate to a maximum potential growth rate, dDmax /d Ind in the following way: dDmax dD for Ind > Indcrit (3) = f I fT d Ind d Ind mon mon where fImon and fTmon are the climate functions representing the effect of the light and temperature integral, respectively. Since the growth pattern of the second phase resembles that of monomolecular growth (Thornley and Johnson, 1990), the following expression was used to describe the maximum growth rate: dDmax = kmon (Dmaxf − Dmax ) (4) d Ind The Dmax is the diameter of a flower with maximum growth and kmon determines how fast the maximum diameter will reach its final value, Dmaxf (Fig. 1). In its integrated form the equation has the following appearance: Dmax = Dmaxf − (Dmaxf − Dcrit ) e−kmon Ind

(5)

where Dcrit is the Dmax -value at Indcrit . The light and temperature functions for the linear phase are described using the following functions: fIlin = 1 − e−αI

for I > 0

(6)

fTlin = A + BT for 14 ◦ C < T > 24 ◦ C

(7)

Eq. (6) is a saturation curve where α determines how fast an upper asymptote, in this case unity, is reached and I is the daily light integral (Fig. 2a). The mean day temperature, T, has a linear effect on flower growth during the first growth phase (Eq. (7)). The parameter A is a constant while B is the slope of the function (Fig. 2b). For the monomolecular growth phase the two climate functions are denoted: fImon = 1 − (1 − fI0 ) e−CI

for I > 0

(8)

1.0

1.0

0.8

0.8

fI lin

fT lin

0.6

0.6

0.4

0.4 2

R =0.999

0.2

0.0 0

(a)

1000

2000

2

R =0.986

0.2

3000

4000

5000

6000

daily light integral (mmol m-2d-1)

0.0

7000

14

(b)

16

18

20

22

24

mean day temperature (˚C)

Fig. 2. The linear flower growth phase in Chrysanthemum × indicum ‘Lompoc’ is modelled by multiplying a light (a) and a temperature function (b) with a maximum growth rate.

1.0

1.0

0.8

0.8

0.6

f T mon

f I mon

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fI0

0.4

0.6 0.4

2

2

R =0.994

0.2

0.2

0.0 0

(a)

323

1000

2000

3000

4000

0.0

5000

daily light integral (mmol m-2 d-1)

R =0.944

14

(b)

16

18

20

22

24

mean day temperature (˚C)

Fig. 3. Light (a) and temperature functions (b) for the monomolecular flower growth phase in Chrysanthemum × indicum ‘Lompoc’. By multiplying these functions with a maximum growth rate, the influence of climate on the growing process can be simulated.

fTmon = 1 − e−β(Tmax −T)

for T < Tmax

(9)

Eq. (8) is again a saturation curve, where the parameter C is a constant and fI0 is the function value when the light integral is zero (Fig. 3a). The constant Tmax (Eq. (9)) illustrates a maximum temperature above which no growth occurs (Fig. 3b). The parameter β is a constant. 2.2. Experimental procedure Two trials were conducted in the experimental greenhouses at the Swedish University of Agricultural Sciences at Alnarp in southern Sweden, to collect data for parameter estimations. Cuttings of C. indicum ‘Lompoc’ (by courtesy of Edo Plant) were rooted in long day conditions (16 h photoperiod, natural day length extended with 8 h SON-T lamps, 44 ␮mol s−1 m−2 ) at 20 ± 2 ◦ C and transplanted in a commercial peat compost (Hasselfors K) in 11 cm pots, with one cutting per pot. After 2 weeks, when the roots were well developed, the plants were transferred to the different climate treatments. At the start of the experiments the plants were pinched above the fifth leaf. During the remainder of the trials the plants were exposed to short photoperiods (10 h light per day) to induce flowering. Short photoperiods were achieved by covering the plants with darkening fabric (LS XLS Obscura, Ludvig Svensson AB, Kinna, Sweden). The plants were irrigated once every day and given a liquid fertiliser once every week (Superba Plant 1:100). In the irradiation experiment the plants were exposed to four different shading conditions, 100, 40, 15 and 10% respectively of the light transmitted inside the greenhouse. This was done at a set point temperature of 20 ◦ C. The shading treatments were obtained by positioning the plants under frames covered with different layers of white shading cloth. Each treatment consisted of 10 plants (Table 1). During the temperature experiment 10 plants per treatment were exposed to natural short day conditions (autumn and winter) in four different greenhouse chambers with the set point temperatures 15, 18, 21 and 24 ◦ C.

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Table 1 Days to anthesis of a Chrysanthemum × indicum ‘Lompoc’ flower at different climate treatments Shading (%)

Mean daily light integral (mmol m−2 per day)

Mean day temperature (◦ C)

Time to anthesis days

0 60 85 90 0 0 0 0

4825.1 1641.9 513.5 392.5 987.4 987.4 987.4 987.4

21.7 21.7 21.7 21.7 14.4 18.0 20.8 23.3

56 60 70 77 61 61 59 75

In both experiments the PPFD (Li-Cor Quantum Sensor) and temperature (Pt-100 sensor) were monitored continuously using a data logger. The diameter of the terminal flower buds and flowers was recorded twice a week using an electronic calliper. 2.3. Statistical procedure To be able to relate flower growth to a plant development index, instead of time, the index value Indi for each recording occasion i was calculated using the following formula: di Indi = (10) da where da is the number of days from start of short day treatment until anthesis, while di is the number of days from start of short day treatment to the time of the recording. The curve fitting and parameter estimation of the linear growth phase of the model followed a procedure consisting of the following steps. First, linear regressions were done where mean values of the terminal flower diameter of the plants from the different experimental treatments were plotted against the corresponding index values. Only data belonging to the growth phase were used. The estimated regression coefficients, kN , from the regressions corresponding to each treatment, N, were then divided by the highest kN -value estimated from each experiment separately. This resulted in two sets of data consisting of relative kN -values belonging to the two experiments. These data were now plotted against the corresponding temperature and irradiation treatments from each experiment separately. The parameter α, of the fIlin (Eq. (6)) was estimated using a non-linear regression analysis on the relative kN -values from the irradiation experiment. Accordingly, the parameters A and B of the fTlin (Eq. (7)) were estimated using linear regression analysis on the relative kN -values corresponding to the temperature experiment. Finally, the klin (Eq. (2)) was estimated using a non-linear regression analysis on all original data from the experiments, where α, A and B were fixed to the values presented in Table 2. In the curve fitting and parameter estimation of the monomolecular growth phase, data corresponding to the growth phase and to each treatment were first plotted against the corresponding index values. Eq. (5) was then fitted to each plot using a non-linear regression analysis. The final Indcrit value was calculated as the mean value of the estimations from

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Table 2 Results of parameter estimations for a flower growth model of Chrysanthemum × indicum ‘Lompoc’ Parameter klin kmon Dmaxf (mm) Indcrit IndS α (mmol−1 m2 per day) A B (◦ C−1 ) fI0 C (mmol−1 m2 per day) β (◦ C−1 ) Tmax (◦ C)

Estimate 18.24 22.25 74.2 0.89 0.24 0.004 1.125 −0.008 0.65 0.0024 0.385 27.3

ASS ±0.213 ±7.587 ±5.295 ±0.018 ±0.031 ±0.00006 ±0.0138 ±7.069e−4 ±0.091 ±0.0008 ±0.120 ±1.482

all treatments. A new series of non-linear regressions were done where Indcrit was fixed to the value in Table 2. This resulted in estimated parameter values of kmon (kmonN ) and Dmaxf (Df N ) corresponding to each individual treatment. The final value of kmon was calculated as the mean value of the kmonN estimates while Dmaxf was estimated from model simulations. All linear regression analysis and graphical estimation were done using the graphical software package Origin 5.0 (Microcal Inc.) while the PROC NLIN of PC/SAS for Windows, version 8.1 (Statistical Analysis Systems Institute) was used for the non-linear regression analysis. Model simulations were done using a fourth-order Runge–Kutta integration method with a fixed step size of 1 day in the simulation programme POWERSIM 2.02. To verify the simulations the predicted values were compared with observed data in a linear regression analysis. 2.4. Validation Two new experiments were conducted to collect data for a validation of the model. In the first experiment plants were grown at the set point temperature of 21 ◦ C and under natural short day conditions. In the second experiment the set point temperature was 18 ◦ C and the plants were kept at a photoperiod of 10 h. The flower diameter values, simulated by the model were compared with the observed values in a linear regression analysis.

3. Results The collected data indicate that there are two evident growth phases in the terminal flower bud (Figs. 4 and 5). After the first flower bud was just visible the bud diameter increased steadily in a linear manner. During the second growth phase that corresponds to the opening of the flower, the flower diameter changed rapidly. Both climate factors, irradiation and temperature, had an effect on the final flower diameter value whereas they did not influence flower growth significantly during the earlier

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80

70

70

60 50

60 -2

392 mmol m d

50

-1

40 2

flower diameter (mm)

30

30 20

10

10 0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

-2

1642 mmol m d

60

-1

2

40

R =0.999

30

30

20

20

10

10

0 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

-2

4825 mmol m d

0.8

1.0

1.2

1.4

1.6

-1

50

50 40

0 0.0 80

R =0.996

70

70 60

-1

2

R =0.987

20

0 0.0 80

-2

513 mmol m d

40

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 0.0

2

R =0.965

0.2

0.4

0.6

development index Fig. 4. Results of a flower growth experiment at different irradiation levels and fitting of a model to the data. The mean day temperature for all four treatments lay close to 21.7 ◦ C. Solid lines indicate model simulations while symbols are observations (n = 8). Vertical bars show standard deviations.

stages of flower development. At the highest light level the flowers reached a final diameter of approximately 70 mm. The plants exposed to the lowest light level had a diameter of approximately 55 mm (Fig. 4). A similar pattern was observed in the temperature experiment (Fig. 5). At temperatures close to 15 ◦ C flowers became larger (approximately 70 mm) and at higher temperatures (23.3 ◦ C) the final flower size was about 55 mm. The differences between the treatments were proportional throughout both growth phases. The results of the model parameter estimation are given in Table 2. All the flower buds of all treatments became visible at an index value of 0.24 (IndS ). The switch from the first, linear phase to the second growth phase occurred at an index value of 0.89 in all treatments. The model fitted the data from the irradiation experiment reasonably well with R2 -values ranging from 0.999 to 0.965 (Fig. 4). The simulations show that the model had a tendency of underestimating the final flower diameter of the plants exposed to the highest light levels. In the temperature trial the model was able to predict 96–99% of the variation in the data within the different treatments (Fig. 5). 3.1. Validation results The model fitted the data from two separate experiments well with R2 -values from 0.993 to 0.997 (Fig. 6).

M. Nothnagl et al. / Scientia Horticulturae 99 (2004) 319–329 80

80

70

70

60

60

50

flower diameter (mm)

50

14.4˚C

40 30

30

20

20

10

10

0 0.0 80

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

70

60

60 50

20.8˚C

40 30

30

20

20

10

10 0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.2

0.6

0.8

1.0

1.2

1.4

1.6

0.6

0.8

1.0

1.2

1.4

1.6

2

R =0.998

0 0.0

1.6

0.4

23.3˚C

40

2

R =0.992

0 0.0

2

R =0.966

0 0.0 80

70

50

18˚C

40

2

R =0.995

327

0.2

0.4

development index Fig. 5. Results of a flower growth experiment at different temperatures and fitting of a model to the data. The daily light integral during this experiment lay close to 987 mmol m−2 per day. Solid lines indicate model simulations while symbols are observations (n = 8). Vertical bars show standard deviations.

80

70

70 flower diameter (mm)

flower diameter (mm)

80

60 50

Validation 1

40

458 mmol m d 20.8˚C

-2

30 20

-1

2

R =0.993

10 0 0.0

60

Validation 2

50

1035 mmol m d 19.7˚C

40 30

-2

-1

2

R =0.997

20 10

0.2

0.4

0.6

0.8

1.0

1.2

development index

1.4

1.6

0 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

development index

Fig. 6. The results of two validation trials for a flower growth model in Chrysanthemum × indicum ‘Lompoc’. Solid lines indicate model simulations while symbols are observations. Vertical bars show standard deviations.

4. Discussion The increment of the flower bud diameter is determined by the growth of different parts of the inflorescence. The first phase mainly involves the growth of the receptacle and the bracts surrounding it. This is most likely a sigmoid growth process that starts at flower initiation,

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development index value zero, and reaches its highest value when all the disc florets have reached anthesis. The diameter recordings are only measuring the intermediate part of this sigmoid curve, which seems to be almost linear. Once the ray florets start opening they will dominate the diameter increment. The second growth phase starts at Indcrit which was determined to index value 0.89, corresponding to flower development stage G, “Ray florets starting to open” as described by Larsen and Persson (1999). The strong growth of the ray florets results in the diameter increment following the new monomolecular growth pattern. As a result we do not get a traditional sigmoid growth pattern for the total process and that is why we have to use a two-phase model. The effect of climate on the diameter increment should be seen in relation to the two phases. Since the first phase involves the growth and cell division of the receptacle, it will be dependent of the availability of assimilates. The sub-function fIlin probably reflects this relationship. Although we chose to also have a temperature function, fTlin , this function actually shows that the temperature effect is only marginal on the linear phase of growth. However, there is a temperature response on the timing of the growth that is not shown by the model. The second phase mainly involves the expansion of the cells of the ray florets. This process will be dependent of the osmotic properties of the cells and the plant. Both the availability of assimilates in the plant in relation to the water potential and the temperature will have an effect. This is reflected in the two climate functions described in Fig. 3. A series

70

(m m ) flower diam eter

60 50 40 30 20 10 0 14 16

te m

18

pe

rat ure

20

(˚ C

22

)

24

500

4500 4000 3500 -1 ) 3000 -2 d 2500 m l 2000 mo 1500 (m l 1000 gra

h lig

t in

te

Fig. 7. The effect of different light and temperature conditions on flower growth in Chrysanthemum × indicum ‘Lompoc’.

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of simulations have been done to summarise the effect of the climate on the final flower diameter according to the model (Fig. 7). Results from other studies indicate that photoperiod may also affect the flower size (Ben-Jaacov and Langhans, 1971; Langton, 1987). In our experiments we could not see any significant differences in flower diameter between plants subjected to constant or to varying photoperiods under natural daylight conditions. However, the specific experiment was only done under natural short days, probably explaining the lack of response. On the other hand, the model should be used under short day conditions, and we do not see any need to complicate it further by involving the photoperiod. Our study shows that the present model works well on a single cultivar. The question arises if the same model can be used for other cultivars. This will be examined in future experiments. As mentioned in the introduction the flower growth model is also meant to be part of a plant–pest–predator interaction model. From preliminary studies we know that an infestation of Chrysanthemum with F. occidentalis can reduce flower diameter with 13–25% depending on climate conditions. In the continuing experiments different temperature effects will be tested and general quality criteria of the flower will be determined.

Acknowledgements This project was supported by a grant from the Swedish Research Council for Environment, Agriculture Sciences and Spatial Planning (FORMAS).

References Adams, S.R., Pearson, S., Hadley, P., 1998. The effect of temperature on inflorescence initiation and subsequent development in chrysanthemum cv. Snowdon (Chrysanthemum × morifolium Ramat.). Sci. Hortic. 77, 59–72. Ben-Jaacov, J., Langhans, W., 1971. Effect of long photoperiods on flower development of chrysanthemum. Florist’s Rev. 149 (3861), 72–75. Carvalho, S.M.P., Heuvelink, E., 2001. Influence of greenhouse climate and plant density on external quality of chrysanthemum (Dendranthema grandiflorum (Ramat.) Kitamura): first steps towards a quality model. J. Hortic. Sci. Biotechnol. 76 (3f), 249–258. Cockshull, K.E., Hughes, A.P., 1971. The effects of light intensity at different stages in flower initiation and development of Chrysanthemum morifolium. Ann. Bot. 35 (142), 915–926. de Jong, J., 1978a. Invloed temperatuur op bloei jaarrondchrysanten. Vakbl. Bloemisterij 5, 64–65. Hidén, C., Larsen, R., 1994. Predicting flower development in greenhouse grown chrysanthemum. Sci. Hortic. 58, 123–138. Karlsson, M.G., Heins, R.D., Erwin, J.E., Berghage, R.D., Carlson, W.H., Biernbaum, J.A., 1989. Irradiance and temperature effects on time of development and flower size in Chrysanthemum. Sci. Hortic. 39, 257–267. Langton, F.A., 1987. Apical dissection and light-integral monitoring as methods to determine when long-day interruptions should be given in Chrysanthemum growing. Acta Hortic. 197, 31–41. Larsen, R.U., Persson, L., 1999. Modelling flower development in greenhouse chrysanthemum cultivars in relation to temperature and response group. Sci. Hortic. 80, 73–89. Thornley, J.H.M., Johnson, I.R., 1990. Plant and Crop Modelling. Clarendon Press, Oxford.