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Author(s)

First Name Middle Name Surname Role Email

David H Fleisher ASABE member

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USDA-ARS 10300 Baltimore Avenue / Beltsville, MD 20705

USA

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Dennis J Timlin Coauthor [email protected]

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USA



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Yang Yang ASABE member

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University of Maryland Wye Research and Education Center, University of Maryland, Queenstown, MD

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V.R. Reddy Coauthor [email protected]

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Publication Information

Pub ID Pub Date

073013 2007 ASABE Annual Meeting Paper


An ASABE Meeting Presentation

Paper Number: 073013

Simulation of Potato Gas Exchange Using SPUDSIM

David H. FleisherCrop Systems and Global Change, USDA-ARS, Beltsville, MD 20705 USA

Dennis J. TimlinCrop Systems and Global Change, USDA-ARS, Beltsville, MD 20705 USA

Yang YangWye Research and Education Center, University of Maryland, Queenstown, MD

V.R. ReddyCrop Systems and Global Change, USDA-ARS, Beltsville, MD 20705 USA

Written for presentation at the2007 ASABE Annual International Meeting

Sponsored by ASABEMinneapolis Convention Center

Minneapolis, Minnesota17 - 20 June 2007

Abstract. SPUDSIM is a new potato model derived from an older USDA-ARS model, SIMPOTATO, developed to incorporate new advances in the knowledge of plant growth and development. Modifications incorporated in SPUDSIM focus at simulating canopy growth and development at the individual leaf level and include routines for individual leaf appearance rates and leaf expansion as a function of leaf physiological age and plant assimilate status. Coupled sub-models for leaf level photosynthesis, transpiration, and stomatal conductance were used to replace the older radiation use efficiency approach. A radiative transfer routine that estimates intercepted photosynthetically active radiation for sunlit and shaded leaves was also added. During each time increment, net photosynthetic rate is estimated for sunlit and shaded leaf area. Photosynthate is partitioned among leaves in the canopy according to leaf age, potential expansion, and plant assimilate status. Assimilate allocation to branches, roots, and tubers proceeds according to fixed partitioning coefficients defined in SIMPOTATO. Remaining photosynthate is used to support the appearance of new leaves or branches in the canopy according to predicted demand. Whole plant gas exchange and harvest data from SPAR (soil-plant-atmosphere research) chamber experiments conducted at USDA-ARS Beltsville, MD were used to evaluate SPUDSIM predictions. Results indicate that SPUDSIM accurately captures potato growth and developmental responses over a wide range of temperatures and will be suitable for a variety of applications involving complex soil-plant-atmospheric system relationships.

Keywords. Potato, Models, Simulation, Decision Support, Photosynthesis, Gas Exchange


IntroductionThe United States is the 5th largest potato growing country in the world, producing 19.7 million metric tons on 453,000 ha in 2006 (USDA, 2007). As with other agricultural crops, there are significant risks and challenges involved in potato production due to uncertainties with climate, pests, and other pressures. As operations increase in size and complexity, farmers are required to manage, interpret, and make decisions upon large amounts of information. Fluctuating market prices, costs of fertilizers, pesticides and irrigation, environmental impact concerns from agricultural practices, land-use pressures, and projected climate change factors create additional demands on farmers, crop consultants, policy planners and scientists. Over the past 40 years, mechanistic, process level computer models have been developed that attempt to mimic crop responses to climatic and management factors. Complex, mechanistic crop models are needed to encapsulate knowledge on the soil-plant-atmosphere system, test hypotheses, evaluate the behavior of complex agricultural systems, and study alternative production scenarios under different climactic, management, and geographic locations (Reddy and Reddy, 1998). These models are typically integrated with computerized decision support systems to help manage and interpret large amounts of complex information in order to help farmers reduce risk (Uehara and Tsuji, 1998; Timlin et al., 2002; Wang et al., 2002). However, many crop models are still at an early stage of development and do not necessarily include state-of-the-art science due to (a) lack of perceived need to incorporate this new information, (b) lack of resources, or (c) other knowledge gaps that prevent adoption of new research in the models. By including this new information into the models, more reliable predictions of growth and development in response to climactic and nutritional stresses can be obtained.

Potato models generally simulate crop growth and development by using a ‘big-leaf’ approach. Increases in total canopy leaf area are based on inputs for environment and plant nutritional status (e.g. International Benchmark Sites Network for Agrotechnology Transfer, 1993; Kooman and Haverkort, 1995; Hodges et al., 1992; Shaykewich et al., 1998). Daily gains in plant dry weight are obtained by multiplying an estimate for canopy light interception (based on leaf area) by a conversion factor known as radiation use efficiency (RUE, g carbohydrate (CHO) MJ-1 daily intercepted radiation). This value can be reduced by additional empirical factors that approximate limiting effects of plant nutritional status, water content, and temperature on growth rate. Conceptual carbon (C) pools for total leaf and stem dry mass are then computed through the use of empirical partitioning coefficients as opposed to predicting individual leaf appearance, expansion, and duration. RUE based models are popular and have been successfully applied to a variety of studies for many crops. However, factors such as leaf nitrogen content, water stress, senescence, elevated [CO2] and rising air temperatures play significant roles in influencing plant photosynthetic rate at daily and shorter time-scales, and cannot be mechanistically accounted for with an RUE approach (Demetriadeshah et al., 1992, 1994). In addition, such an approach can over-estimate daily growth rate due to the nonlinearity of leaf response to light (Thornley, 2002).

Over the past few years, a new potato model, SPUDSIM, has been developed. SPUDSIM is based on a series of modifications to an older ARS potato model, SIMPOTATO (also known as SIMGUI), that follows the general RUE based modeling approach outlined above (Hodges et al., 1992). These modifications primarily focus on replacing RUE and big-leaf method to simulate canopy growth and development with an individual leaf level approach. Modifications include simulation of individual leaf appearance on different stems in the canopy (Fleisher et al, 2006a), individual leaf expansion as a function of leaf physiological age and plant assimilate status (Fleisher and Timlin, 2006), and incorporation of a leaf-level coupled model for photosynthesis, transpiration, and stomatal conductance (Soo and Lieth, 2003). This paper focuses on the details of these modifications, provides preliminary comparison between model predictions with experimental gas exchange data, and discusses the future modifications planned for the model.

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Materials and Methods

i. DataThe majority of new modifications incorporated in SPUDSIM come from experiments conducted in daylit soil-plant-atmosphere research (SPAR) chambers at USDA-ARS facilities in Beltsville, MD in 2003 through 2006. Data from field studies and literature have been used to validate modeling sub-components where appropriate. Daylit SPAR chambers were constructed from clear acrylic, transparent to natural sunlit, had a 1 m2 cross-sectional area, and a total chamber volume of 3360 L. Air temperature and relative humidity were monitored and controlled with TC2 controllers (Environmental Growth Chambers, Ohio USA). A dedicated Sun SPARC5 work station (Sun Microsystems, Mountainview, CA) logged environmental data (air and soil temperatures, atmospheric CO2 concentration ([CO2]), and photosynthetically active radiation (PAR, in µmol m-2 s-1)) every 300 s. Mass flow controllers in each chamber were used to maintain [CO2] at desired levels during the day. Each chamber has a dedicated infrared gas analyzer, permitting continuous monitoring of whole plant carbon dioxide fluxes at 5 minute intervals during the course of the season. This permits calculation of gross and net canopy photosynthetic rates. Additional details on SPAR chamber operation and related calculations can be found in Reddy et al., (2001).

ii. SPUDSIM

SPUDSIM contains most of the same phenological components and carbon allocation routines as the original SIMPOTATO model. SPUDSIM was coded in C++ and runs on an hourly time-step. The model has been integrated with 2DSOIL, a modular, comprehensive two-dimensional soil simulator that is specifically designed to be integrated with existing crop models (Timlin et al., 1996). 2DSOIL modules can simulate water, solute, heat and gas movement as well as plant root activity in a two-dimensional profile. Coupling SPUDSIM with 2DSOIL allows simulation of the soil-plant-atmosphere continuum.

The basic weather data needed to run SPUDSIM include daily solar radiation, maximum and minimum temperature, relative humidity and rainfall. Management inputs include planting and emergence date, planting density and depth, seed reserve at planting, row spacing, cultivar, amount, type and incorporation depth of crop residue, and in-season fertilization and irrigation information. Soil inputs include initial, saturated, wilting and upper limit of field capacity volumetric water contents, mineral ammonium and nitrate concentrations, and soil pH of each user defined soil horizon.

At each time-step, SPUDSIM reads in the appropriate input data and simulates plant development, gas exchange, carbon allocation, and organ initiation as indicated in Figure 1. Routines that are different between SPUDSIM and SIMPOTATO are shaded. The model keeps iterating until either harvest date, maturity date, or other user-specified end point is reached. The frequency and type of model outputs can be specified by the user and include, but are not limited to, dry weights of all organs, transpiration, photosynthetic rate, assimilate status, leaf and lateral branch numbers, and leaf area index.

iii. Modifications

a. Leaf appearance rate

Data from the literature (Kirk and Marshall, 1992) and daylit SPAR and field experiments were used to model leaf appearance rates on potato mainstem and lateral branches as detailed in Fleisher et al. (2006a). Rates followed a nonlinear response with temperature and were modeling using a modified β distribution

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function (Yan and Hunt, 1999), and take the form shown in equation (1). Rates accumulate at an hourly basis in SPUDSIM using the previous 24 h average air temperature (°C).

(1)

where: r – leaf appearance rate (leaves plant-1 day-1) Rmax – maximum leaf appearance rate (leaves plant-1 day-1); 0.96 Tmax – ceiling temperature where r = 0; 39.5°C Topt – optimum temperature where r = Rmax; 27.2°C T – average daily temperature from previous 24 h (°C)

A comparison with experimental data is shown in Figure 2. As implemented in the model, leaves can appear on any lateral or mainstem branch – i.e., each branch accumulates leaf appearance rate separately, as long as there is sufficient plant assimilate supply to support the new organ. The appearance of lateral branches is assumed to have the same temperature response.

b. Leaf expansion

Individual leaf expansion rate was modeled using a modification of an organ expansion routine introduced by Ng and Loomis (1984). The routine simulates individual leaf expansion primarily as a function of genetic potential and temperature, with external factors for nutrient, water, and plant assimilate supplies limiting the expansion (Fleisher and Timlin, 2006):

(2)where:RA RAmax A f(age) f(T) f(C)

– rate of leaf expansion, [cm2 d-1]– maximum relative rate of area expansion, [cm2 cm-2 d-1]; 10– leaf area, [cm2]– physiological age dependent expansion rate, [cm2 cm-2]; 0 to 1– air temperature affect on cell division and expansion, [unit less, 0 to 1]– affect of assimilate supply on potential leaf expansion, [unit less, 0 to 1]

It is assumed that all leaves have the same potential to expand to the same maximum size and at the same potential rate, but are limited by nutrient, temperature, water, and plant assimilate supply. A comparison of experimental and model predictions of individual leaf area versus time in potato are shown in Figure 3 (in this case, equation (2) was tested directly against experimental data). The expansion of each leaf is tied to carbon demand through the use of specific leaf area (SLA, cm2 leaf g-1 dry weight). In SPUDSIM, SLA decreases with physiological age of the leaf; thus, more carbon is required to support an equivalent increase in area of mature versus newly initiated leaves.

c. Gas exchange

The RUE approach in SIMPOTATO was replaced with a leaf-level gas exchange routine that requires inputs for [CO2], PAR, temperature, relative humidity, and wind speed. For estimating the fraction of PAR incident at the leaf surface, the canopy is divided into sunlit and shaded leaf fractions, following the descriptions in Sinclair et al. (1976) and Campbell and Norman (1998).

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Photosynthetic rate and transpiration are estimated per unit leaf surface based on the work of Kim and Lieth (2003) who coupled a biochemical model of photosynthesis for C3 leaves (de Pury and Farquhar, 1997; Harley et al., 1992; Farquhar et al., 1980) with a model of stomatal conductance (Ball et al., 1987) and an energy budget equation at the leaf surface (Campbell and Norman, 1998). Separate carbohydrate (CHO) assimilation rates are computed for sunlit and shaded leaves, and then multiplied by leaf area index to provide the total carbon assimilate pool for the plant at the current time-step.

Respiration losses due to growth and maintenance for each organ are then substracted from the assimilate pool at each time-step. Growth respiration is based on 30 to 40% of organ growth demand largely following the work of Ng and Loomis (1984). Maintenance respiration is computed as a function of temperature, organ type and mass, and metabolic activity. Carbon allocation and demand of various organ classes is primarily a function of leaf expansion and tuber demand as defined in SIMPOTATO (Hodges, 1992).

iv. Testing of SPUDSIMGas exchange and harvest data comes from a daylit SPAR chamber study conducted in 2004. Certified potato (solanum tuberosum L. cv. Kennebec) seed tubers were planted in a 50:50 peat/vermiculite potting medium in 15L pots at a depth of 5 cm. Plants were selected for uniformity based on main-stem leaf count, thinned to a single main-stem at a density of 12 pots (12 stems) per chamber. SPAR chambers were set to one of six different day/night temperature regimes: 14/10, 17/12, 20/15, 23/18, 28/23, and 34/29°C with a 16- and 8-h day / night thermoperiod. Plants were harvested at approximately 60 DAE and dry weights were obtained for each organ class. Additional details can be found in Fleisher et al. (2006b).

Results and Discussion

SPUDSIM predictions for daily assimilation during the course of the season were reasonably accurate. Correlation coefficients for 1:1 comparisons of predicted versus measured data were 0.8 for 14/10°C, 0.7 for 17/12, 20/15, 23/18, and 28/23°C and 0.3 for the 34/29°C treatment. An illustration is provided for the 17/12 and 28/23°C treatments (Figure 4). The dips in the gas exchange data at 151,152, 156, 157, 164, and 175 day of year were due to reduced daily PAR as a result of cloud cover. The poor correlation in the 34/29°C treatment is discussed in more detail below.

The model also reproduces diurnal patterns of gas exchange. Figure 5 shows daily net photosynthetic rate for the 23/18°C treatment at 20, 40, and 60 DAE. In the 20 and 40 DAE, the model shows an increased sensitivity to high levels of PAR (greater than 1100 µmol m-2 s-1) which may indicate too high a response of the gas exchange routines to high light levels.

Nonetheless, SPUDSIM does a reasonable job in simulating the biomass totals at harvest for most temperature treatments (Table 1). The model reproduced the experimentally measured responses to temperature, with the largest amounts of biomass (mostly attributed to tuber yield) occurring at the coolest temperatures. Model predictions were within 15% of total and tuber dry mass values for all temperatures except the 14/10 and 34/29°C treatments. At the 14/10°C treatment, the model over-predicted tuber yield response, most likely a function of too large an increase in leaf area at that temperature (data not shown). Over-prediction of dry mass at the 34/29°C treatment was most likely a result of the model lacking an insufficient response to high heat stress on leaf expansion, as the leaves from the plants in this treatment were visually curled and stunted.

Table 1: Comparison of experimental (actual) dry mass (g plant-1) versus model predictions for each organ type including senesced leaf mass. Standard deviations are provided.

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Treatment (°C)

Total Tuber Stem Leaf SenescedActual Model Actual Model Actual Model Actual Model Actual Model

14/10 93±18 112 75±16

93 2±1 4 12±3 11 0 0

17/12 103±20

92 80±19

78 3±1 4 15±1 8 1±2 0

20/15 103±21

101 79±18

82 5±2 5 13±3 11 3±2 4

23/18 87±30 84 51±22

63 8±4 5 11±4 12 5±5 4

28/23 64±17 59 24±10

23 10±5 6 17±3 20 5±3 4

34/29 13±3 20 0 0 9±4 9 3±3 6 5±4 2

The results presented here indicate SPUDSIM can provide accurate predictions of potato growth and development over a wide range of temperature conditions. The responses to warmer environments suggest SPUDSIM can also be used to simulate production in the tropics (Manrique et al., 1989). However, the current simulations assume non-limiting nutrient and water. The incorporation of leaf-level canopy growth and development routines, and replacement of the RUE approach with a state-of-the-art leaf-level gas exchange / energy balance approach, provides a basis to include more recent knowledge on the response of various plant processes to nutrient and water stress. For example, our lab is currently testing alternate theories on the response of stomatal conductance to drought stress as mediated by either abscisic acid signaling from the root system or reduced bulk leaf water potential (Yang et al., 2007). In either case, the effect of drought stress on gas exchange can directly be modeled. These changes, along with methods to simulate nitrogen and water uptake, and their associated limiting effects on organ expansion and initiation, are currently being incorporated into SPUDSIM.

The modifications incorporated into SPUDSIM update the previous potato model, SIMPOTATO, to include more state of the art knowledge on plant response to the production environment. Once fully validated, SPUDSIM will provide more accurate responses to the off-nominal growth conditions typically encountered by farmers, improving the model’s importance as part of a computer based decision support system. The integration of SPUDSIM with the 2DSOIL two-dimensional soil simulator will permit evaluation of complex soil-plant-atmosphere system issues in a more in-depth basis. These include the impact of climate change, cropping rotations, nutrient dynamics and movement in soil, and assessment of conservation practices on soil health.

ConclusionSPUDSIM is a new USDA-ARS potato model that incorporates recent advances in individual leaf appearance, growth, and gas exchange. These modifications essentially replace the big leaf, radiation use efficiency (RUE) approach used in older potato models with a more mechanistic depiction of canopy growth and development. Specifically, routines for individual leaf appearance rates and individual leaf expansion were developed using experimental data. A biochemical model of photosynthesis, stomatal conductance, and transpiration were coupled and incorporated in SPUDSIM to predict photosynthetic rate and transpiration for both shaded and sunlit leaves in the potato canopy. Preliminary analysis of SPUDSIM results indicate the model predicts plant growth and development responses over a broad range of temperatures. Planned additions to the model include components for water and nitrogen stress on gas exchange, organ expansion, and initiation, and are expected to make the model suitable for simulating off-nominal conditions typically experienced by farmers. SPUDSIM has been integrated with a two-dimensional soil simulator in order to more effectively study impacts of climate change, crop

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rotations nutrient dynamics, and evaluation of conservation and other management practices on soil quality.

References

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Campbell, G.S. and J.M. Norman. 1998. The light environment of plant canopies. in An introduction to environmental biophysics. Springer, New York. pp: 247-281.

de Pury, D.G.G. and G.D. Farquhar. 1997. Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant, Cell and Env. 20: 537-557.

Demetriadeshah, T.H., M. Fuchs, E.T. Kanemasu, and I.D. Flitcroft. 1994. Further discussions on the relationship between cumulated intercepted solar-radiation and crop growth. Agric. For. Meteorol. 68:231-242.

Demetriadesshah, T.H., M. Fuchs, E.T. Kanemasu, and I. Flitcroft. 1992. A note of caution concerning the relationship between cumulated intercepted solar-radiation and crop growth. Agric. For. Meteorol. 58:193-207.

Farquhar, G.D., S. von Caemmerer, and J.A. Berry. 1980. A biochemical model of photosynthetic CO2

assimilation in leaves of C3 species. Planta 149: 78-90.

Fleisher, D.H. and D.J. Timlin. 2006. Modeling expansion of individual leaves in the potato canopy. Agri. and Forest Meteorology 139: 84-93.

Fleisher, D.H., R.M. Shillito, D.J. Timlin, S-H Kim, and V.R. Reddy. 2006a. Approaches to modeling potato leaf appearance rate. Agron. J. 98:522-528

Fleisher, D.H., D.J. Timlin, and V.R. Reddy. 2006b. Temperature influence on potato leaf and branch distribution and on canopy photosynthetic rate. Agron. J. 98: 1442-1452.

Harley, P.C., R.B. Thomas, J.F. Reynolds, and B.R. Strain. 1992. Modeling photosynthesis of cotton grown in elevated CO2. Plant, Cell and Env. 15: 271-282.

Hodges, T. 1992. A modular structure for crop simulation models: implemented in the SIMPOTATO model. Agronomy Journal, 84:911-915.

International Benchmark Sites for Agrotechnology Transfer, 1993. A simulation model for potato growth and development: SUBSTOR-Potato, Version 2.0. Res. Rep. Ser. 02. Dep. of Agron. and Soil Sci. Univ. of Hawaii, Honolulu.

Kim, S-H. and H. Lieth. 2003. A coupled model of photosynthesis, stomatal conductance and transpiration for a rose leaf (Rosa hybrida L.). Annals of Botany 91: 771-781.

Kirk, W.W. and B. Marshall. 1992. The influence of temperature on leaf development and growth in potatoes in controlled environments. Ann. Appl. Biol. 120:511-525.

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Kooman, P.K. and A.J. Haverkort. 1995. Modeling development and growth of the potato crop influenced by temperature and daylength: LINTUL-POTATO. p. 41-60. In A.J. Haverkort and D.K.L. MacKerron (eds.). Potato Ecology and Modeling of Crops Under Conditions Limiting Growth. Kluwer Acad. Publ. Boston.

Manrique, L.A. D.P. Bartholomew, and E.E. Ewing. 1989. Growth and yield performance of several potato clones grown at three elevations in Hawaii: I. plant morphology. Crop Sci. 29: 363-370.

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Reddy, K.R., J.T. Baker, V.R. Reddy, J. McKinion, L. Tarpley, and J.J. Read. 2001. Soil-plant-atmosphere-research (SPAR) facility: A tool for plant research and modeling. Biotronics 30: 27-50.

Reddy, K.R. and V.R. Reddy. 1998. Cotton phenology and growth processes: model development. Proceedings of the World Cotton Research Conference -2. Athens, Greece, September 6-12, 1998. pp 526-529.

Shaykewich, C.F., G.H.B. Ash, R.L. Raddatz, and D.J. Tomasiewicz. 1998. Field evaluation of a water use model for potatoes. Can. J. Soil Sci. 78: 441-448.

Sinclair, T.R., Murphy, C.E., and K.R. Knoerr. 1976. Development and evaluation of simplified models for simulating canopy photosynthesis and transpiration. Journal of Applied Ecology 13: 813-829.

Thornley, J.H.M. 2002. Instantaneous canopy photosynthesis: Analytical expressions for sun and shade leaves based on exponential light decay down the canopy and an acclimated non-rectangular hyperbola for leaf photosynthesis. Annals of Bot. 89: 451-458.

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Yang, Y., D. Timlin, D.H. Fleisher, and V.R. Reddy. 2007. Simulating Canopy Evapotranspiration and Photosynthesis of Corn Plants Under Different Water Status Using a Coupled MaizeSim+2DSOIL Model. In preparation.

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Figure 1: General implementation of SPUDSIM. Shaded boxes represent major modifications to SIMPOTATO.

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Read in data:-weather-management-soil status

Compute developmental rates:-Whole plant phenology-Branch appearance rate-Leaf (nodal unit) appearance rate-Tuber initiation / bulking rate

Compute canopy gas exchange:-Estimate sunlit / shaded leaf area fraction-Gross photosynthetic rate-Transpiration rate

Subtract maintenance respiration costs

Increment time step

Is there sufficient CHO to satisfy all organ

growth rates?

Reduce potential growth for all organs based on developmental stage and assimilate status

Allocate CHO to each organ:-Give young leaves first priority-Subtract growth respiration costs

Initiate new organs based on developmental rate and availability of plant assimilate

Output data to file

No

Yes

Figure 2: Comparison of leaf appearance rates (with standard errors) versus observed average daily temperature for three SPAR chamber experiments (D0, D1, D2) and data from Kirk and

Marshall (1992) (KM.) The nonlinear temperature response model based on the modified beta distribution (equation (1)) is the solid line. Figure comes from Fleisher et al. (2006).

Figure 3: Measured (symbols) and simulated (lines) individual leaf area versus days after appearance for a SPAR experiment with 6 different day/night temperatures.

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Figure 4: Comparison of daily net CO2 accumulation for the season between measured and predicted (SPUDSIM) data for the 17/12 (top) and 28/23°C (bottom) treatments.

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Figure 5: Comparison of diurnal patterns in net photosynthetic rate between measured and predicted data at 20, 40, and 60 DAE for the 23/18°C treatment.

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