Hort. Environ. Biotechnol. 53(3):193-203. 2012. DOI 10.1007/s13580-012-0054-y

Research Report

Simulation of Year-round Plant Growth and Nutrient Uptake in Rosa hybrida over Flowering Cycles 1*

Wan Soon Kim

2

and J. Heinrich Lieth

1

Department of Environmental Horticulture, University of Seoul, Seoul 130-743, Korea 2 Department of Plant Sciences, University of California, Davis, CA 95616, USA *Corresponding author: [email protected]

Received April 17, 2012 / Revised April 26, 2012 / Accepted April 27, 2012 GKorean Society for Horticultural Science and Springer 2012

Abstract. Cut flower roses grown hydroponically in greenhouses produce flowers year-round in flushes, indicating changes in the plant biomass during each flowering cycle. Due to the cyclical nature of productivity, it is difficult to optimize the supply of nutrient to the plants in hydroponic system. To address this challenge, this study was conducted to develop an integration model using three sub-models (shoot growth, root growth, and nutrient uptake), evaluate the developed models with experimental data, and predict the year-round changes in plant growth and nutrient uptake in rose plants. Parameters for the models were estimated using non-linear regression by fitting data collected from experiments. The nutrient uptake rate of six macro-nutrients (NO3-N, NH4-N, P, K, Ca, and Mg), and the root growth rate relying on the shoot growth from self-rooted one-year old ‘Kardinal’ roses were collected. As results of simulation, the maximum nutrient uptake potential (mMm-2d-1) was predicted for macro-nutrients of 17.07 in NO3-N, 12.67 in K, 12.22 in NH4-N, 4.39 in Ca, 3.12 in P, and 1.57 in Mg. Simulations using the developed models were well matched with real plant responses in plant growth and nutrient uptake in short-term (one flowering cycle) and longterm (year-round). Additional key words: cut rose, Michaelis-Menten function, nutrient uptake potential, root growth

Introduction Increased efficiency of fertilization in agriculture is required to mitigate environmental pollution and to conserve resources. Current management practices for fields, greenhouses, and nursery crops generally involve the use of considerable amount of fertilizers. In intensely managed systems, such as greenhouse roses (Rosa hybrida L.) production, excessive fertilization can be led to over 2,000 kg of nitrogen leached per hectare per year (Raviv and Lieth, 2008). In order to reduce fertilizer inputs, growers need to optimize irrigation/fertilization timing and application rates, as well as using the closed irrigation system. Hydroponically-grown cut flower roses are often managed to produce a flush of harvestable stems in time for particular seasons and dates. This production system indicates changes in biomass production of the rose plants during each flowering cycle. Flowering could be initiated through breaking apical dominance by harvesting, cutting back, or bending existing shoots. Kim and Lee (2002) found that it generally took 4-8

weeks to reach harvestable maturity of new flower shoots depending on varieties and environmental conditions. Therefore, the whole-plant biomass of cut roses can be possibly changed by different stages of flower shoot growth during each flowering cycle, and the pattern of biomass change is cyclically repeated in harvesting period (Zeislin and Moe, 1990). In previous studies, modeling study for nutrient uptake of cut roses during flowering cycles was reported (Lorenzo et al., 2000). Bougoul et al. (2000) developed the model of NO3- uptake of ‘Sweet Promise’ roses using a modified Pump-Leak-Buffer model, which accounted for active and passive absorption. The model accurately predicted NO3uptake of young rose plants over four days, but was not applied across a flowering cycle. In the mathematical model developed by Silberbush and Lieth (2004), NO3 - and K+ uptake of hydroponically grown ‘Kardinal’ cut roses was predicted, and the flower shoot growth was described by a logistic equation, while the root parts were assumed to have constant dry weight (equilibrium between new growth and senescence).

Wan Soon Kim and J. Heinrich Lieth

194

Most reported studies have been only focused on nutrient (nitrate) uptake of cut roses (Bougoul et al., 2000; Mattson et al., 2008; Silberbush and Lieth, 2004). Currently the study for the uptake of the other essential nutrients is not much conducted. The year-round dynamics of nutrient uptake over flowering cycles of roses grown in hydroponics system will provide the basis for improving the efficiency of nutrient recycling system, minimizing environmental impacts, and sustaining cut roses productivity and quality. As an excellent means to predict plant performance under variable conditions, especially a limited number of plant characteristics, crop models are useful (Gao et al., 2012). This holds in particular for models that deal with functional-structural relationship in a plant, especially cut roses (Buck-Sorlin et al., 2011; Mashonjowa et al., 2010). The objective of this study was to develop and calibrate a simulation model of nutrient uptake to take into account the year-round dynamic changes in nutrient demand for flowering cycles in cut rose production system. The simulation model was developed by coupling a whole-plant growth model with a nutrient uptake model on six macronutrients, NO3-N, NH4-N, P, K, Ca, and Mg.

Materials and Methods 6LPXODWLRQ 0RGHO 'HYHORSPHQW The model dynamically simulates the whole-plant growth and essential nutrient uptake. To develop this model, various aspects of the conceptual dynamic system for nutrient uptake were simplified using assumptions so as to focus on the trends of nutrient uptake over flowering cycles year-round. Cut flower roses during flowering cycles consist of three main components: a ‘static base’ (main stem, residual stem and leaf materials), an above-ground ‘growing component’ (flowering shoots), and the root system. The model assumed the fully grown rose plant with flowering cycles under constant static base. Driven by light and air temperature, therefore, the simulation model was developed by synchronization of three sub-models: shoot growth, root growth, and nutrient uptake over flowering cycles. 6KRRW *URZWK 0RGHO Our shoot growth model is based on ‘short-cut’ light use efficiency concept. Light use efficiency (LUE) is the ratio of net dry mass (DM) produced per day to the amount of intercepted light I (photosynthetically active radiation: PAR, -1 -1 in MJ). Shoot LUE (g DMMJ d ) can be written as: LUE =

dW dt

1 I (1 - e-kLAI)

Eq. 1

where dW/dt is shoot growth rate (dry mass increase, g DM

-1

-kLAI

d ), and I (1 - e ) is light intercepted by plant canopy. Q -2 -1 (MJm d ) is calculated from daily sum of PAR I and leaf area index (LAI) (Monteith, 1994): -kLAI

Q = I (1 - e

)

Eq. 2

where k is extinction coefficient for I, given as 0.65 (Goudriaan and Van Laar, 1994). Therefore, daily shoot growth rate, dW/dt can be expressed as: dW = LUE Q dt

Eq. 3

This equation may also be expanded to include temperature factor, fTemp: dW = LUE Q fTemp dt

Eq. 4

where fTemp is a function of temperature to calculate relative plant growth ratio (RGR) in the range of plant survival temperature, 5Gto 40G(Shin et al., 2000). Also, it can be assumed that LUE is a function of light, I. This function may be exponentially decreased: LUE = LUEmax,o e-aQ

Eq. 5

where a is a coefficient and LUEmax,o is the theoretically maximal value of LUE on I = 0. Therefore, final shoot growth rate, dW/dt is finally described as follows: dW -aQ = LUEmax,o e Q ftemp dt

Eq. 6

5RRW *URZWK 0RGHO Root growth was hypothesized to be dependent on shoot growth in terms of a sink for photosynthate. After flowering shoot harvest, dying some parts of the root system could be one example. Previous study (Kim and Lee, 2008; Mattson et al., 2008) showed that the growth of new roots of cut roses temporarily stopped after flowering shoot was harvested, and then the root growth remarkably decreased. The former phenomenon is called ‘root-delay’ and the latter phenomenon is called ‘root-decay’. Later, new roots regrew depending on flowering shoot growth, and it is called ‘root-grow’. The pattern of root growth is repeated over flowering cycles in cut rose production system. Our modeling work on root growth is empirically approached with ‘Delay and Decay’ concept. In the root growth model,

Hort. Environ. Biotechnol. 53(3):193-203. 2012. -2

-1

we separated root growth (g DMm d ) into three stages based on harvest, budbreak, and shoot growth during a flowering cycle: ‘root - delay’ time zone from shoot harvest (Rdelay, 0) to end of delay (Rdelay, end), ‘root - decay’ time zone from end of delay (Rdelay, end) or start of decay (Rdecay, 0) to end of decay (Rdecay, end), and ‘root - grow’ time zone is from end of decay (Rdecay, end) or start of root growth (Rgrow, 0) to end of root growth (Rgrow, end) or shoot harvest (Rdelay, 0). ‘R’ is a function of daily integrated temperature (degree hour, DH). DH was assumed to repeat cyclically from previous harvest to next harvest. Conceptually, root growth rate -1 (g DMd ) during root-delay time zone, dWRdelay/dt, can be described as follows: dWRdelay dt

Eq. 7

=a

where a is constant, same as root growth rate at shoot harvest. In root-decay time zone, root growth rate, dWRdecay/ dt, can be expressed as: dWRdecay a = (DHRdecay, end - DHRdecay, t) dt DHRdecay, end Eq. 8 where DHRdecay, end is daily integrated temperature at Rdecay, and DHRdecay, t is daily integrated temperature at Rdecay, t. Root growth rate during root-grow time zone, dWRgrow/dt, can be expressed as: end

dWRgrow = F(dW/dt) dt

Eq. 9

where dWRgrow/dt is a function of shoot growth, dW/dt [Equation 6 (Eq. 6)], and is calculated by using empirical relationship between shoot growth and root growth. 1XWULHQW 8SWDNH 0RGHO Nutrient absorption by plant roots is often expressed mathematically using enzyme kinetics as in the MichaelisMenten function (Barber, 1995; Claasen and Barber, 1974) as in Eq. 10: I (C) =

Imax (C - Cm) Km + (C - Cm) -2

-1

Eq. 10

Where I (mMm d ) is net influx of the nutrient by the -2 -1 root, Imax (mMm d ) is the maximum influx rate of the nutrient, C (mM) is the nutrient concentration at root surface, Cm (mM) is the minimum nutrient concentration at which no net influx occurs, and Km is Michaelis-Menten

195

constant, which represents the nutrient concentration at 2 one-half of Imax. In Eq. 10, the unit area (per m ) of I and Imax means the root surface area (RSA). Therefore, a simple simulation model can be expressed by coupling Eq. 10 with 2 RSA (m per plant) and the simulation time step (ǻt, day) as: I (C) =

Imax (C - Cm) RSA ǻt Km + (C - Cm)

Eq. 11

The plant’s relative demand for macro-nutrients such as NO3-N, NH4-N, P, K, Ca, and Mg can be changed by varying Imax according to the current nutrient concentration C in the biomass of flower shoots and the current stage during a flowering cycle. The unit Imax in Eq. 11 can be scaled in terms of plant dry mass (DM). Therefore, Eq. 11 can be expressed as: I (C) =

Imax (C - Cm) DMplant ǻt Km + (C - Cm) -2

Eq. 12

-1

where DMplant (g DMm d ) is plant growth, calculated as the sum of shoot growth (Eq. 6) and root growth (Eq. 7, 8, or 9). The parameters need to be estimated and fitted for model calibration. ([SHULPHQWDO 6HW8S IRU 'DWD &ROOHFWLRQ DQG $QDO\VLV ([SHULPHQW  6KRRW DQG 5RRW *URZWK The first experiment was set up in order to prove that the inference ‘root growth (dWRgrow/dt) in root-grow time zone is a function of shoot growth (dW/dt)’ as in Eq. 6. Ten self-rooted one-year old ‘Kardinal’ rose plants were established in each 8-L container with air bubbling system for solution culture. Twenty containers were provided and filled with the -1 same nutrient solution (EC 1.0 dSm ) containing nutrients (NO3 7.0, NH4 0.5, H2PO4 0.5, K 3.0, Ca 2.0, Mg 1.0, and SO4 1.0 mM), and micronutrients (Hoagland and Arnon, 1950). The solution was kept aerated by continuously bubbling air into the solution. The experimental set-up was placed in two controlled environment chambers (18 h -2 -1 photoperiod at 700 ȝmoLm s PAR with daily mean temperature 23) in the Department of Plant Sciences, University of California, Davis, USA. Nutrient solutions were replaced every week, and one group of ten plants was selected for destructive harvest every week for two flowering cycles (90 days). Root length and mean radius were measured using the line intersect method (Tennant, 1975). Root surface area was calculated by assuming the roots to be cylindrical. In addition, shoot length, dry mass,

196

Wan Soon Kim and J. Heinrich Lieth

and leaf area were measured as indicators of shoot growth. ([SHULPHQW  1XWULHQW 8SWDNH 3RWHQWLDO The second experiment was conducted to evaluate nutrient uptake potential of cut rose plants by calibrating the nutrient uptake model as in Eq. (10). The values of Imax, Km, and Cm was estimated and fitted on six macronutrients (NO3-N, NH4-N, P, K, Ca, and Mg) according to flowering cycles. Eight self-rooted one-year old ‘Kardinal’ roses were established in each 25-L container with air bubbling system for solution culture. Nutrient solution (EC values of 0.7, 1.4, and 2.1 dSG -1 m ) was applied to experiment treatments. The nutrient solution was the same as in the first experiment (shoot and root growth). The experimental design was a randomized complete block with three replications of eight plants each. The plant materials were used with only four five-leaflet 2 leaves (0.035 m as leaf area), primary flowering shoots, and finely trimmed roots. The experiment was conducted in a glasshouse at the Department of Environmental Horticulture in University of Seoul, Korea. Nutrient solutions were replaced every week, and deionized water was added to maintain the volume. After each nutrient solution replacement, one group of eight plants was selected for destructive harvest. The harvested plants were analyzed for macronutrient uptake rates. The following methods of analysis were employed: the diffusion conductivity method + for NO3 and NH4 , the flame emission method with anion + 2+ 2+ absorption spectrophotometer for K , Ca , and Mg , and the stannous chloride colorimetric method with a Brinkman PL800 colorimeter for H2PO3 . To evaluate the developed nutrient uptake model, the same designed experiment was conducted with the different concentrations of nutrient solution -1 (EC values of 0.8, 1.6, and 2.4 dSm ). 0RGHO 6LPXODWLRQ Using the above equations, a simulation model was developed to predict flowering shoot elongation, plant growth, and nutrient (NO3-N, NH4-N, P, K, Ca, and Mg) absorption of roses over time for flowering cycles under the environmental conditions of solar irradiance and temperature in a greenhouse in Suwon, Korea. Simulation was carried out in an Excel spreadsheet. The initial conditions for simulation were designed to be similar or equal to the crop experimental conditions. The number of flowering shoots was set as one per plant. The 2 base leaf area and dry mass were assumed to be 0.03 m (the approximate area of four mature five-leaflet leaves) and 0.088 g, respectively. The initial concentration of nutrient -1 solution was EC 1.0 dSm , and NO3 8.7, NH4 1.0, H2PO3 2+ 1.0, K 4.6, Ca 2.2, and Mg 0.8 mM. The flowering cycle resumed when daily integrated temperature reached 1,035

degree hour (DH), indicating flowering shoot harvest time. Daily integrated temperature (DIT) was counted only in the range of plant survival temperature from 5Gto 40G(Shin et al., 2001). After flowering shoot harvest, the ‘root-delay’ time zone lasted until DIT reached 69 DH. The ‘root-decay’ time zone continued until DIT became 161 DH. The DITs of 69 DH and 161 DH indicated the time for the new flowering shoot budbreak and the unfolded leaf emergence in the shoot, respectively. 6WDWLVWLFDO $QDO\VLV Statistical analyses were conducted with Statistical Analysis System (version 9.1, SAS Institute Inc., Cary, NC, USA). Parameter values in the model equations were estimated using SAS procedure, non-linear regression routine (NLIN). Graph module analyses were performed using Sigma Plot software (Systat Software, Inc., Chicago, IL, USA).

Results and Discussion 6KRRW *URZWK 0RGHO &DOLEUDWLRQ Light use efficiency (LUE) in Eq. 5 exponentially decreased due to a reduction of LUE at high light intensities, based on light saturation of photosynthesis at the leaf level although to a much lesser extent, at crop level (Heuvelink et al., 2002). Increased light intensities resulted in a larger fraction of direct radiation, which was less efficient than diffuse radiation (Gijzen, 1992). The estimates for the parameters LUEmax,o and a in Eq. 6 were 2.0203 and 0.1151 g (DM -1 MJ ), respectively, obtained by fitting the data using the SAS procedure NLIN (Kim and Lee, 2002). The fit obtained 2 (R = 0.85) reflects homogeneity of 32 plants in a group used in the calibration study (Fig. 1). Theoretically, the maximum -1 value of LUE was predicted to be 2.0203 g DMMJ at

Fig. 1. Predicted light use efficiency in relation to daily intercepted light (PAR) of cut rose plants. Theoretically, maximum value of -1 LUE at I = 0 is 2.0203 g DMᨿMJ (n = 32). PAR is photosynthetically active radiation.

Hort. Environ. Biotechnol. 53(3):193-203. 2012.

intercepted light I = 0. Leaf area index (LAI) in Eq. 1 to Eq. 6 was easily calculated by the data of leaf area per plant and plant density. Leaf area was predominantly simulated as a function of the plant development stage or of simulated leaf dry mass (Marcelis et al., 1998). A Michaelis-Menten kinetics relationship between leaf area per shoot and flowering shoot DM was shown in Fig. 2. The estimated equation was described as: Leaf area 2 (m per shoot) = 0.1211 (shoot DM - 0.1232) / [3.63 + (shoot 2 DM - 0.1232)]. The value 0.1211 m was predicted as the maximum potential leaf area per shoot, and theological shoot DM was 0.1232 g when the first leaf began to be unfolded. Finally, LAI also could be simulated as a function of shoot 2 DM per unit area (m ). In rose plants, temperature is well known to be more effective on plant development (flowering speed) than plant growth (biomass accumulation and elongation) (Kim and Lee, 2008). The temperature factor in Eq. 4, fTemp, is a

197

function of temperature to relative plant growth ratio (RGR in range of 0 to 1). The temperature factor was estimated using the published data (Shin et al., 2001). They determined that the temperature zone for survival of cut rose plants ranged from 5Gto 40. The fTemp was developed as 2 a hyperbolic function: RGR (0 to 1) = -0.0031AT + 0.1435AT - 0.6429, hyperbolic curve showed the vertex at 23Gin the plant survival temperature zone from 5Gto 40.

Fig. 2. Predicted leaf area by shoot dry mass of cut rose plants. Theoretically, maximum leaf area per shoot leaf area (LA) at shoot DM = 0 is 0.1211 g DMᨿMJ-1 (n = 98). R2 = 0.95.

5RRW *URZWK 0RGHO &DOLEUDWLRQ During vegetative growth, the shoots and roots of plants have well-defined roles: the shoots assimilate carbon, while the roots acquire mineral nutrients and water. If a reduction in the supply of nitrogen occurs, a greater proportion of plant growth is directed or partitioned to the roots. The shoot and root specific activities directly depend on the environmental conditions, light and temperature in particular. The absolute rate of photosynthesis can be proportional to the absolute rate of nitrogen uptake. As environmental conditions are changed, the plant adjusts its shoot and root biomass in order to maintain this balance (Thornley and Johnson, 1990). The hypothesis that the root growth is dependent on the shoot growth in terms of a sink for photosynthate in rose plants (Mattson et al., 2008) was empirically confirmed through the first experiment as in Fig. 3. After the flowering th shoots were harvested on the 45 day after previous harvest, nd the root growth was maintained without decrease until 52 day after previous harvest (‘root-delay’). The root growth th remarkably decreased in a week until 59 day after previous harvest (‘root-decay’). Then, new roots grew again depending on flowering shoot growth (‘root-grow’). Finally this pattern of root growth was repeated over flowering cycles in a week ‘root-delay’ as in Eq. 7 and another week ‘root-decay’ as in Eq. 8. In Fig. 4, root DM (%) showed an exponential decrease

Fig. 3. Comparison of root growth (dry mass gᨿm-2) and shoot -2 growth (dry mass gᨿm ) of cut rose plants over flowering cycles (n = 6).

Fig. 4. Ratio of root dry mass (DM) to shoot dry mass (DM) of cut rose plants as expressed by exponential decrease. Theoretically, maximum root DM ratio at shoot DM = 0 is 28.757 % (n = 84).

Wan Soon Kim and J. Heinrich Lieth

198

-1

with increased shoot DM (R = 0.61). It could be caused by relatively high allocation of total plant biomass to roots in the early stage of shoot growth (Fitter and Hay, 1987). The equation of the relationship between root DM and shoot DM -0.063shoot DM was expressed as: 28.757e , where 28.757 was theoretically the maximum ratio of root DM to shoot DM. dWRgrow/dt in Eq. 9 could be estimated by this exponential function. In the nutrient uptake model as in Michaelis - Menten kinetics, RSA serves as a key coupler between the plant growth model and the nutrient uptake model. Fig. 5 showed that measured RSA was well correlated to root DM with an 2 empirical linear regression (R = 0.95). Therefore, RSA could be predicted by means of empirical approach, described as a linear function of root DM with the slope of 0.0747: RSA 2 (m ) = 0.0747 × new root DM.

m , directly decreased the plant biomass including 25.3% in shoot DM, 21.6% in root DM, 23.6% in shoot height, and 28% in RSA compared to high nutrient concentration of 2.1 -1 dSm . During a flowering cycle, nutrient uptake rate by the rose plants showed an exponential increase followed by a constant linear growth phase (Fig. 6E), whereas the nutrient -1 -1 uptake efficiency (mMm RSAd ) was the highest at the previous harvest followed by rapid decrease around the middle of a flowering cycle (around the 4th week after previous harvest) (Fig. 6F). It has been hypothesized that decreased nutrient absorption during the middle of a flowering cycle may be due to the competition within the plant for photosynthesis; new flower shoots may limit carbohydrates available for root growth or ion uptake (Cabrera et al., 1995). As expected, maximum nutrient uptake occurred in NO3 , + 2+ 2+ + followed by K , NH4 , Ca , H2PO3 , and Mg in order. The nutrient uptake potentials of cut rose plants for the macro-nutrients were identified by calibration of the MichaelisMenten function as in Eq. 10 through experiment 2. The parameter values for maximum nutrient uptake rate (Imax) of NO3-N, NH4-N, P, K, Ca, and Mg were determined by maximum influx concentrations observed in Experiment 2 (Table 1). In addition, Km and Cm were estimated using SAS procedure NLIN by fitting the observed nutrient uptake, DM, and RSA data to I as in Eq. 12 (where Eq. 10 and 11 are substituted into the Eq. 12 for Imax scaled in terms of plant DM). As all parameters were estimated, the nutrient uptake model (Eq. 12) showed that maximum daily nutrient -2 -1 uptake potential (mMm RSAd ) in cut ‘Kardinal’ rose plants was up to 17.07 in NO3-N, 12.67 in K, 12.22 in NH4-N, 4.39 in Ca, 3.12 in P, and 1.57 in Mg (Fig. 7). The predicted uptake patterns for NO3-N, NH4-N, P, K, Ca, and Mg matched well with the measured data in Experiment 2.

1XWULHQW 8SWDNH 0RGHO &DOLEUDWLRQ In the Experiment 2, the ‘Kardinal’ rose plants had average flowering cycles (45-day) regardless of nutrient concentration -1 (EC 0.7 to 2.1 dSm ) and sigmoid increase patterns in the shoot and root growth (Figs. 6A, 6B, 6C, and 6D). On the other hand, relatively low nutrient concentration, 0.7 dS

3ODQW *URZWK DQG 1XWULHQW 8SWDNH 6LPXODWLRQ 6KRUW7HUP During a flowering cycle, the developed shoot growth model as in Eq. 6 simulated shoot growth (dry mass accumulation) and shoot length (elongation) over time to compare measured data from additional experiments for model evalu-

Fig. 5. Correlation between new root dry mass (DM) and root surface area (RSA) of cut rose plants (n = 41). 2

Table 1. Parameter values for Michaelis-Menten function (Claasen and Barber, 1974) used in this study. Parameters were estimated using SAS procedure NLIN (non-linear regression routine) (version 9.1, SAS Institute Inc., Cary, NC, USA). 3DUDPHWHU

(VWLPDWH 5VTXDUH 121

1+1

3

.

&D

0J

,PD[

 

 

 

 

 

 

.P

  

  

 

  

 

 

 

 

 

 

 

 

P0ᨿPᨿG P0

&P P0

Hort. Environ. Biotechnol. 53(3):193-203. 2012.

A

B

C

D

E

199

F

Fig. 6. Plant growth and macro-nutrient uptake of self-rooted one-year old ‘Kardinal’ rose plants during a flowering cycle (n = 24). A: shoot growth, B: root growth, C: shoot length, D: root surface area, and E and F: nutrient uptake per plant (E) and per m-2 RSA (F).

ation (Fig. 8). The simulated patterns of shoot growth during a flowering cycle matched well with the measured data (R2 = 0.91). However, the model overestimated the shoot growth during the first half of the cycle, and then slightly under-

estimated, suggesting that mobilization of stored reserves occurs to a greater degree than a simulated model (Marcelis et al., 2005). Simulated shoot elongation was fairly representative of measured values (R2 = 0.97).

200



Wan Soon Kim and J. Heinrich Lieth



Fig. 7. Measured (symbols) and predicted (solid lines) nutrient uptake rate of NO3-N, NH4-N, P, K, Ca, and Mg by ‘Kardinal’ rose plants grown in the nutrient solution with various concentrations (n = 30). Predicted values were estimated by Michaelis-Menten function -2 -1 in order to calculate nutrient uptake potential, Imax (mMᨿm ᨿd ), which is the maximum influx rate of the current nutrient concentration.

Fig. 8. Simulation for shoot growth (left) and shoot length (right) of ‘Kardinal’ rose plants using the developed model as in Eq. 6. Measured values (n = 5) were data from dry mass and length of five plants harvested every week during a flowering.

Hort. Environ. Biotechnol. 53(3):193-203. 2012.

The nutrient uptake model as in Eq. 12 was used to predict nutrient absorption of cut rose plants for six macronutrients in a flowering cycle (Fig. 9). The simulation patterns in nutrient uptake concentration were representative of measured values for six macro-nutrients, especially K. However, the model overestimated NO3-N and Mg uptake concentration during the first three weeks of the cycle, and then well estimated the measured values. The simulation overestimated K uptake absorbed by plant during the entire cycle, while

201

simulated NH4-N, P, and Ca uptake concentrations were less than measured values during the second half of the cycle. The measured values used for evaluation of our developed models were collected from the additional experiment of which treatment was designed with nutrient solution con-1 centration of EC 0.8, 1.6, and 2.4 dSm . It seems reasonable that the variation of the measured data was observed. The underestimated simulation at nutrient uptake could be explained by ‘luxury nutrient consumption’ concept, defined as ‘absorption

Fig. 9. Simulation for nutrient uptake of NO3-N, NH4-N, P, K, Ca, and Mg of ‘Kardinal’ rose plants using the developed model as in Eq. 12. Measured values (n = 5) were data from nutrient uptake of five plants every week during a flowering.

202

Wan Soon Kim and J. Heinrich Lieth

at a higher rate than required to sustain growth’ (Lambers et al., 1998). The exponential equation used to describe shoot growth (Eqs. 1 and 2) allows for luxury consumption to take place. This assumption may be valid as relative growth rate of rose plants were not influenced under wide range of N, P, and K tissue concentration (Mattson et al., 2008). Considering the variable data and luxury consumption, the developed simulation model proved to be a good performance to predict nutrient uptake over time during flowering cycles. 3ODQW *URZWK DQG 1XWULHQW 8SWDNH 6LPXODWLRQ
was developed to predict the whole-plant growth and six macro-nutrients absorption of ‘Kardinal’ rose plants over time during flowering cycles. Modified Michaelis-Menten function as a nutrient uptake model was coupled with wholeplant growth model using plant DM and RSA as couplers. The model successively simulated the dynamic changes in year-round nutrient uptake of six macro-nutrients from real environmental data, daily integrated radiation and greenhouse temperature in Suwon, Korea in 2009 (Fig. 10). In addition, whole-plant growth including shoot and root growth, and flowering shoot elongation were simulated over flowering cycles. The year-round simulation allowed us to predict our rose production system with macroscopic view considering

Fig. 10. Year-round simulation of plant growth and nutrient uptake of ‘Kardinal’ rose plants using the simulation model as in Eq. 12 over time during flowering cycles under real environmental condition of irradiance (daily light integral) and greenhouse temperature (heating at 14ഒ or less) in Suwon, Korea, 2009.

Hort. Environ. Biotechnol. 53(3):193-203. 2012.

the environmental conditions. For instance, the possible flower harvest is six times a year, and flower growth is fast in summer season and delayed in winter season. Flower shoot length is short in summer compared to those in winter. Production cost also is affected by seasonal change. This information can be obtained mainly from long-term simulations on the rose production system. Due to the incorporation of Michaelis-Menten kinetics into the plant growth model, the predicted nutrient uptake by the model is responsive to nutrient solution concentration from rhizosphere. Therefore, the developed model used in this study can be powerful if the model is connected to predictive models for remobilization ability of base compartments in relationship to plant age. It would be expected that as base stem biomass increases so does its potential for storage and remobilization, which would be necessary to understand declines in macro-nutrient absorption rate during the middle of a crop cycle in roses (Lorenzo et al., 2000). In conclusion, to predict nutrients uptake over flowering cycles, a simulation model was developed and derived by irradiance, temperature, and synchronization of three submodels: shoot growth, root growth, and nutrient uptake. Shoot growth model was developed with ‘short-cut’ light use efficiency concept. Root growth model was empirically approached with ‘Delay and Decay’ concept. Nutrient uptake model was modified from Michaelis-Menten function. ‘Kardinal’ cut rose plants decreased 25.3% in shoot DM, 21.6% in root DM, 23.6% in shoot height, and 28% in RSA at low nutrient -1 -1 concentration of EC 0.7 dSm , compared to EC 2.1 dSm . The maximum nutrient uptake potential was predicted up to -2 -1 17.07 mMm d in NO3-N, 12.67 in K, 12.22 in NH4-N, 4.39 in Ca, 3.12 in P, and 1.57 in Mg. The developed models were well calibrated and estimated by fitting measured data from experiment to the models, and also evaluated by matching between simulated values and measured values in real experimental system. In this study, the developed models showed efficient simulation in a short-term (one 2 flowering cycle as in Figs. 8 and 9, R = 0.80-0.97) and in a long-term (year-round over flowering cycles as in Fig. 10). Acknowledgements: This work was supported by the University of Seoul 2009 Research Fund.

Literature Cited Barber, S.A. 1995. Soil nutrient bioavailability: A mechanistic approach. 2nd ed. Wiley, New York. Bougoul, S., R. Brunand, and A. Jaffrin. 2000. Nitrate absorptionconcentration of Rosa hybrida cv. Sweet Promise grown in soilless culture. Agronomie 20:165-174. Buck-Sorlin, G., P.H.B. De Visser, M. Henke, V. Sarlikioti, G.W.A.M. van der Heijden, L.F.M. Marcelis, and J. Vos. 2011. Towards a functional-structural plant model of cut-rose: Simulation of light environment, light absorption, photosynthesis and interference with

203

the plant structure. Ann. Bot. 108:1121-1134. Cabrera, R.I., R.Y. Evans, and J.L. Paul. 1995. Cyclic nitrogen uptake by greenhouse roses. Scientia Hort. 63:57-66. Claasen, N. and S.A. Barber. 1974. A method for characterizing the relation between nutrient concentration and flux into roots of intact plant. Plant Physiol. 54:564-568. Fitter, A.H. and R.K.M. Hay. 1987. Environmental physiology of plant. 2nd ed. Academic Press Inc., London. Gao, M., G.W.M. van der Hekjden, J. Vos, B.A. Eveleens, and L.F.M. Marcelis. 2012. Estimation of leaf area for large scale phenotyping and modeling of rose genotype. Scientia Hort. 138:227-234. Gijzen, H. 1992. Simulation of photosynthesis and dry matter production of greenhouse crops. Simulation report CABO-TT no. 28, Wageningen, The Netherlands. Goudriaan, J. and H.H. van Laar. 1994. Modeling potential crop growth processes: Textbook with exercises. Current issues in production ecology 2. Kluwer Academic Publishers, Dordrecht, The Netherlands. Heuvelink, E., J.H. Lee, R.P.M. Buiskool, and L. Ortega. 2002. Light on cut chrysanthemum: Measurement and simulation of crop growth and yield. Acta Hort. 580:197-202. Hoagland, D.R. and D.L. Arnon. 1950. The water-culture method for growing plants without soil. University of California at Berkley, Circ. 347 (revised), p. 32. Kim, W.S. and J.S. Lee. 2002. Evaluation of photosynthate accumulation and distribution and radiation use efficiency in roses in relation to irradiance and night temperature. Acta Hort. 593:129-136. Kim, W.S. and J.S. Lee. 2008. Growth and light use efficiency under different light intensities of cut rose ‘Rote Rose’ as affected by night temperature. Hort. Environ. Biotechnol. 49:226-231. Lambers, H.F., S. Chapin, III, and T.L. Pons. 1998. Plant physiological ecology. Springer-Verlag, New York. Lorenzo, H., M.C. Cid, J.M. Siverio, and M. Caballero. 2000. Influence of additional ammonium supply on some nutritional aspects in hydroponic rose plants. J. Agricultural Sci. 134:421-425. Marcelis, L.F.M., E. Heuvelink, and J. Goudriaan. 1998. Modelling biomass production and yield of horticultural crops: A review. Scientia Hort. 74:83-111. Marcelis, L.F.M., E. Brajeul, A. Elings, A, Garate, and E. Heuvelink. 2005. Modelling nutrient uptake of sweet pepper. Acta. Hort. 691:285-292. Mattson, N.S., J.H. Lieth, and W.S. Kim. 2008. Temporal dynamics of nutrient and carbohydrate distribution during crop cycles of Rosa spp. ‘Kardinal’ in response to light availability. Scientia Hort. 118:246-254. Mashonjowa, E., F. Ronsse, T. Mhzha, J.R. Milford, R. Lemeur, and J.G. Piesters. 2010. The effects of whitening and dust accumulation on the microclimate and canopy behavior of rose plant (Rosa hybrida) in a greenhouse in Zimbabwe. Solar Energy 84:1-23. Monteith, J.L. 1994. Validity of the correlation between intercepted radiation and biomass. Agricultural Forest Meteorolgy 68:213-220. Raviv, M. and J.H. Lieth. 2008. Soilless culture: Theory and practice. Elsevier, London. Silberbush, M. and J.H. Lieth. 2004. Nitrate and potassium uptake by greenhouse roses (Rosa hybrida) along successive flower-cut cycles: A model and its calibration. Scientia Hort. 101:127-141. Shin, H.K., J.H. Lieth, S.H. Kim, and N. Zieslin. 2001. Effects of temperature on leaf area and flower size in rose. Acta Hort. 547: 185-191. Tennant, D. 1975. A test of a modified line intersect method of estimating root length. J. Ecol. 63:995-1001. Thornley, J.H.M and I.R. Johnson. 1990. Plant and crop modelling: a mathematical approach to plant and crop physiology. Oxford University Press, New York, USA. Zeislin, N. and Y. Moe. 1990. Light on roses: A review. Scientia Hort. 43:1-4.