The temperature of the shoot tip is the primary variable influencing
the rate at which leaves and flowers develop. The time required for leaf
and flower development can be more accurately predicted when shoot-tip
temperatures are known since shoot-tip temperature frequently deviates
from air temperature. However, shoot-tip temperatures are difficult to
measure directly. Therefore, the objectives of this project are to 1) identify
and measure the critical inputs and outputs from a shoot-tip energy-balance
model, 2) develop an accurate model of shoot-tip temperature based on measured
environmental inputs which are routinely measured in commercial greenhouses,
and 3) compare the reliability of crop timing using air-temperature measurements
with modeled shoot-tip temperature. The anticipated benefits to the floral
industry will be to improve the grower’s ability to meet increasingly narrow
market-date specifications by 1) improving the prediction accuracy of leaf-
and flower-development models, 2) using existing climate-control computers
to provide the proper shoot-tip temperatures, and 3) increasing the grower’s
ability to manage the greenhouse environment with climate-control computers.Introduction/Background

Successful commercial production of greenhouse crops requires that
plants be grown to buyer size and date specifications. Temperature is the
primary environmental factor influencing the rate of plant development
and ultimately the ability to meet date specifications. Accurate timing
of a crop can be jeopardized when plant- shoot temperature differs from
air temperature because plant developmental rate is controlled by temperature
of the shoot meristematic regions, i.e., the shoot tip, not air temperature
(Ritchie and NeSmith, 1991). A knowledge of plant-shoot temperature improves
the ability to precisely time crop development to meet date specifications
(Faust, 1992). A plant-temperature model can describe the relationship
between temperature and rate of plant development, and can be linked with
a climate-control computer, resulting in more accurate control of plant
development and production scheduling. However, control of the greenhouse
temperature is currently based on measurements of air temperature, which
is a problem when plant-temperature models are used because, as shown in
Figure 1, plant-shoot temperatures can deviate significantly from air temperatures
(Faust, 1992). If a system of shoot-tip measurement can be developed, we
believe the next big advance in greenhouse climate control will be to base
greenhouse-temperature control on plant-temperature models. However, accurate
shoot- tip temperature measurement is not nearly as straightforward and
easy as it might initially seem. Direct measurements are difficult because
the shoot-tip region of the plant is small and the shoot-tip region moves
as the plant grows. Therefore, we need either small, frequently moved sensors
or some remote method of measurement. Infrared thermometers have limited
value because of their wide field of view, which results in simultaneous
measurement of shoot tips, leaves, soil, and any other background object.
We believe that plant-shoot temperature may be more accurately modeled
than measured by using greenhouse wet-bulb temperature, solar radiation,
and a sensor which mimics a plant shoot. Wet- bulb temperature and solar
radiation measurements currently are made by most environmental computers
used in greenhouses and the temperature of the artificial plant-shoot sensor
can easily be measured. Once shoot temperature is accurately measured or
estimated, models relating shoot temperature to development rate can be
used to assist with crop scheduling. We propose to model plant-shoot temperature
under greenhouse conditions using plants growing in plugs. Plugs offer
an excellent system in which to study plant temperatures because we believe
if we can model temperature of seedlings, then we can model any plant system
in the greenhouse. Temperature of seedlings in plugs can be influenced
dramatically by actual air temperature, cooling due to evaporation from
the media surface, evaporative cooling from misting, solar radiation, and
longwave energy losses and gains from the greenhouse glazing and high-intensity
supplemental lights. Since these factors can cause changes in the plug’s
microclimate more quickly than the human’s ability to make adjustments,
a model also offers the opportunity to reduce stress losses in plugs and
other plants and increase crop reliability and productivity. This project
is more fundamental in nature than one that is directed at solving a specific
problem. We believe, however, that this more fundamental knowledge may
lead to climate and plant control strategies and systems not yet envisioned.
This project is similar in concept to the one initiated by the PI several
years ago to model chrysanthemum growth and development responses to light
and temperature. Out of that project, the DIF concept for height control
emerged, as did the graphical tracking system of height-control decision
support. Both are widely used in the greenhouse industry and probably neither
would exist today without the foundation of information acquired from that
research project, because neither were envisioned at the start of that
project.

Literature Review

Energy Balance

Energy-balance modeling has been a useful tool for analyzing the environmental
influence of physiological processes on plants. Energy-balance modeling
involves quantifying all the physical processes that result in heat transfer
into and out of a plant tissue. These models of energy flow are extremely
useful since actual measurements of energy transfer can be difficult to
make because of the special equipment required and the difficulty of making
temperature measurements without influencing plant temperatures. Consequently,
models based on physical principles can result in more accurate temperature
estimates, i.e., have a lower percentage of error, than actual measurements
(Parkinson and Day, 1990). The key concepts and components of energy-balance
models are described in sections below.

General Model

Temperatures can be predicted using energy-balance models by estimating
the temperature at which the energy gain by the plant equals the energy
loss. Energy (In) = Energy (Out) (1) The components of the energy-balance
model can then be put in Eqn. 1 SW+LW+L(Dew)+S=LW+L(Trans)+S (2) where
SW represents shortwave radiation, LW represents longwave radiation, L
represents latent heat exchange as a result of transpiration or dew formation,
and S represents sensible heat transfer.

Radiation

The sun emits radiation at wavelengths between 300 and 3,000 nm which
is commonly referred to as shortwave radiation. The maximum shortwave radiation
incident on the Earth’s surface at sea level is about 1,000 W m-2. A single
leaf absorbs approximately 50% of the shortwave radiation and shortwave
radiation is usually the single largest source of energy incident on the
plant. According to the Stephan-Boltzmann law, all objects at temperatures
above absolute zero emit radiation at wavelengths above 3,000 nm, which
is termed longwave radiation. Energy = e * a (T)^4 (3) where e represents
the emissivity of a leaf, a is the Stephan-Boltzmann constant, and T is
temperature on the Kelvin scale. Longwave radiation incident on the plant
is a result of the temperature of the surrounding environment.

Plant temperature must be known to calculate the longwave radiation
emitted by a plant. The importance of longwave radiation can be misleading
if expressed on the basis of gross radiation. For example, the longwave
energy emitted by a plant at 30′C is 459 W m-2, but if the plant is at
the same temperature as the surrounding environment, then 459 W m-2 are
also incident on the plant, resulting in a net radiation of zero. If net
longwave radiation is reported, then the relative importance of longwave
radiation is more evident (Mellor et al., 1964). Longwave radiation becomes
relatively more important to the energy balance of a plant whenever the
plant temperature differs greatly from its surrounding environment. A large
net radiation loss can occur when the glazing material of the glass becomes
cold because of the exposure to a clear sky. Alternatively, a large net
radiation gain can occur when the glazing becomes hot because of the absorption
of solar radiation. Radiation incident on a surface can be transmitted,
reflected or absorbed. The exact degree to which each of the three occurs
is dependent on the location of the sun (solar altitude and azimuth), the
angle of the surface with respect to the radiation source, and the material
of the surface. Takakura (1989) developed a model which can be used for
evaluating the transmission, reflectance, and absorption of greenhouse
structures; however, it can only be used to compare isolated situations.
It cannot be used in dynamic environments for the purpose of environmental
control, or for estimating plant-shoot temperature.

Calculation of the energy balance of different plant parts requires
the knowledge of the radiation incident on each part. Models have been
developed which predict radiative transfer within plant canopies (Norman
and Welles, 1983; Woolley, 1971). Typically, Beer’s law (Eqn. 4) is used
to determine the extinction of radiation as it passes through a plant canopy.
R(t) = Ri * ln (-k*L) (4) where R, represents the shortwave radiation transmitted
through a leaf, Ri is the radiation incident on the leaf, k is an extinction
coefficient, and L is the leaf-area index. Leaf angle influences the percentage
of radiation which is absorbed. The extinction coefficient is used to quantify
the general canopy structure, i.e., the degree to which the leaves are
arranged horizontally of vertically. Latent heat transfer Energy can be
exchanged between the plant and the surrounding air during the phase change
of water from a liquid to a gas or vice versa. Transpiration results in
a loss of energy, while dew formation results in an energy gain. Transpiration
can be described in the general form of Flux = Driving Gradient / Resistance.
(5) where flux equals rate of transpiration, the driving gradient is the
difference in water vapor pressure between the plant tissue and the surrounding
air expressed in a mole fraction, and the resistance is equal to the reciprocal
of the conductance. Conductance of water occurs primarily through the stomata.
Several models have been developed to predict stomatal conductance under
different environmental conditions (Takakura et al., 1975; Warritt et al.,
1980), since direct measurements are difficult to make (Pearcy et al.,
1991).

The energy loss from transpiration is then calculated by the following
equation: Transpiration rate * Energy per mole of water evaporated = Energy
loss (6) Evaporation of water from the soil surface can also influence
the temperature of plant tissue near the soil surface. Agronomic crop models
have used soil temperature to predict leaf-unfolding rate while the meristem
is at or near the soil surface (Law and Cooper, 1976). Models have been
developed to predict water loss from transpiration and evaporation for
a crop as the leaf-area index changes (Ritchie and Johnson, 1990). A transpiration
model based on greenhouse tomatoes has been developed to manage greenhouse
climates (Stanghellini, 1987).

Sensible Heat Transfer

Conductive heat transfer refers to the transfer of energy from molecule
to molecule, while convective heat transfer refers to transfer of heat
resulting from the movement of bodies of a liquid or gas. Conductive and
convective heat transfer are difficult to separate; therefore, they are
often linked. Conductive/convective heat transfer can be expressed in the
same general formula as that of transpiration. Flux = K * Driving Gradient
/ Resistance (7) where K is the thermal conductivity of air or water, the
driving gradient represents the difference between plant and air temperature,
and the resistance is the thickness of the boundary layer of still air
surrounding the plant. The thickness of the boundary layer is determined
by the wind speed and the morphology of the plant as shown in the following
equation: __________________________________________ g(bl) = C * /wind
speed / length perpendicular to wind (8) where g(bl) is the boundary layer
resistance, and C is a constant which is dependent on the shape of the
plant structure, e.g. , a plane, cylinder, or sphere (Nobel, 199 1). The
amount of energy transferred by conductive/convective heat transfer is
generally considered a small percentage of the total energy budget (Mellor
et al., 1964); however, it is largely dependent on plant morphology. For
example, plant structures which have a low water volume and a high surface
area equilibrate rapidly with the surrounding air, while plant structures
with a high water volume and low surface area transfer relatively little
energy via conduction/convection.

Influence of Temperature on Plant-Development Rate Temperature is the
primary variable used to predict rates of development (Kiniry et al., 1991).
Photoperiod (Major et al., 1975), light intensity (Friend et al., 1962),
water stress (Hodges and French, 1985), and nutrition (Snyder and Bunce,
1983) have also been included in plant-development models. Vegetative development
is often quantified by measuring leaf-appearance rate (Grueber et al.,
1986), which refers to the reciprocal of the number of days required for
one leaf to appear, or unfold, at the apical shoot. Leaf-appearance rate
varies considerably between species (Table 1) and also between cultivars
of the same species (Snyder and Bunce, 1983). Leaf-appearance rate has
a base temperature, or threshold, at which increased temperature results
in a linear increase of leaf-appearance rate until an optimum is reached
(Table 1), at which point further increase in temperature results in a
rapid decrease in leaf-appearance rate. The fastest rate of development
under any average daily temperature occurs at a constant day and night
temperature (Coligado and Brown, 1975; Erwin and Heins, 1990). Fluctuating
temperatures result in a slower rate of development whenever temperatures
fall outside of the linear temperature range (Erwin and Heins, 1990).

Development is a result of the accumulation of responses to small time
intervals; consequently, frequent measurements of the plant environment
would be useful in predicting development over a longer period of time
(Karlsson et al., 1991). Table 1. Comparison of the base temperature, the
optimum temperature, and the interval between the appearance of leaves
at the optimum temperature. Species Base Optimum Interval between leaves
appearing temp. temp. at the optimum temperature (C) (C) (days) Banana
8 27 10.3 (Allen et al., 1988) Hibiscus 8 3 1 4.3 (Karlsson et al., 199
1) Wheat 0 25 4.0 (Baker et al., 1986) Maize 8 32 1.6 (Watts, 1972) Chrysanthemum
0 30 1.8 (Karlsson et al., 1989a) Sunflower - - 1.1 (Rawson and Hindmarsh,
1982) Sugar beet 1 20+ 1.7 (Milford et al., 1985) Easter lily 1 30 0.4
(Karlsson et al., 1988) Flower induction, initiation, and development can
be positively and negatively influenced by several environmental factors
including temperature, light intensity, photoperiod, nutrition, and water
stress.

Flower development can be quantified by measuring the number of flowers
which develop per inflorescence, stem, or plant, or the time required for
a plant to develop to anthesis. The reciprocal of the time to anthesis
provides a measure of development rate. The response of flower development
to temperature is similar to that of leaf development. Below a threshold
temperature, flowers fail to develop and above it, development increases
linearly with respect to temperature until an optimal temperature is reached.
Flower development decreases rapidly at supraoptimal temperatures. 0.6
0.032 The optimal temperature for flowering can be -z 0 considerably different
than that for leaf development of IS o .5 -0.030 .2 the same species; for
example, the optimum temperature -0.028 0.4 for flower development of chrysanthemum
was 21′C, 2sp; -0.026 a while for leaf development, it was 30′C (Figure
2). 0.3 - 0.024 S Time to anthesis is usually described as a function CL
0,2 & .2 -0.022 2, of average daily temperature; however, high night
Flower temperature can affect flowering independently. O. I - -0.020 Poinsettia
flower initiation was delayed by 21′C night 0 a 0.018 temperature in one
study (Kofranek and Hackett, 1966) 0 5 1 0 I’5 2b 2′5 30 35 and a 23′C
night temperature in another study Temperature (C) (Berghage et al., 1987);
however, a 29′C day temperature Figure 2. Comparison of temperature did
not delay flower initiation or development (Berghage responses for leaf
and flower development et al., 1987). of chrysanthemum (Karlsson et al.,
1989a; Harris and Scott (1969) found that development Karlsson et al.,
1989b). of carnation flowers was a function of bud temperature when bud
and leaf temperature were independently varied. Flower buds which were
9C warmer than leaves flowered 26 days sooner than plants grown with leaves
9′C warmer than flower buds. A low-irradiance, high-temperature environment
is often associated with flower-bud abortion in Easter lily (Mastalerz,
1965). When natural irradiance is low- during winter months, low temperatures
are more effective at producing greenhouse crops. Low temperatures increase
the duration of crop production time, thus allowing , for more efficient
use of the low photosynthate supply (Harris and Scott, 1969).

Objectives

The objectives of this project are:

1) To identify and measure the critical inputs and outputs from a shoot-tip
energy-budget

model.

2) To develop an accurate system of modeling shoot-tip temperature
based on measured environmental inputs from the greenhouse environment.

3) To compare reliability of crop timing using standard air temperature
measurements with modeled shoot temperature.

Outline of Materials and Methods

1. Development of an energy-balance model for a plant shoot in a greenhouse
environment.

Environmental variables measured (sensor):

1) Air temperature - thermocouples

2) Shortwave radiation - pyranometer

3) Longwave radiation - total hemispherical radiometer

4) Relative humidity - dew point hygrometer

5) Wind velocity - anemometer

6) Media water content - lysimeter

General Procedure:

Plant and media temperatures will be measured with 40-gauge chromel-constantan
thermocouples. Each environmental treatment will be independently varied,
and the subsequent changes in plant and media temperature will be recorded.

The critical inputs and outputs of the shoot energy balance will be
identified, and a model will be developed which will predict the effect
of the greenhouse environment on plant-shoot temperature. The model will
be developed such that the sensors commonly used in commercial greenhouse
environmental computers, such as wet-bulb temperature, dry-bulb temperature,
and solar radiation, will be measured and the other components of the energy
balance will be modeled. The measured and modeled components of the plant-shoot
energy balance will be integrated. The plant shoot is in equilibrium with
the greenhouse environment when the energy input equals the energy output.
The plant-shoot temperature at which equilibrium occurs can then be determined
by numerical iteration.

2) Design a sensor (artificial plant) which mimics the temperature
of a plant shoot.

General Procedures:

We envision the artificial plant as being a relatively simple sensor
which is placed in a crop canopy. Initially, we plan to use a sensor which
would consist of a column of water sealed in a glass or plastic “straw”.
A thermocouple will be mounted inside the column. The thermal conductivity
and thermal capacity of the sensor will be very similar to that of a plant
shoot because water is the largest component of the plant shoot. The column
will be painted so that it will have an emissivity similar to green plants,
approximately 0.96, and a matrix will be added to avoid water currents
within the column. The sensible heat transfer, shortwave radiation, and
longwave radiation components of the sensor’s energy balance will be made
similar to that of a plant shoot by designing the sensor with dimensions
similar to a plant stem and placing it strategically in the sensor in the
plant canopy. Since the sensor will not mimic transpiration, the influence
of latent heat transfer will be predicted from the energy-balance model.
3) Validation of the energy-balance model and artificial plant in commercial
greenhouses.

General Procedures:

The shoot energy-balance model and the artificial plant sensor will
be taken to commercial greenhouses to be tested for two possible applications.
First, the predicted shoot temperature can be used as a decision-support
tool in which the temperature data is used in plant-development models
to estimate the current developmental progress of a crop. Also, the temperature
setpoint for the next day will be based upon the developmental rate that
is necessary for the crop to be ready on the projected market date. Second,
the predicted shoot-temperature data can be used as the setpoint for climate
control. When used in this fashion, the model will serve as a means for
maintaining the desired plant-shoot temperature rather than constant air
temperature.

Facilities and equipment available

The Floriculture research group at MSU has invested in the instrumentation
necessary to conduct this project. Instrumentation includes Campbell Scientific
CR10 dataloggers for sensor measurement, a REBS total hemispherical radiometer
for net radiation measurement, a General Eastern dew point hygrometer for
dew point measurement, a hot-wire anemometer for air speed measurement,
36- and 40- gauge chromel-constantan thermocouples for shoot-tip temperature
measurement, and a LI-COR pyranometer for shortwave radiation measurement.
The dataloggers are linked to a data-acquisition computer running Campbell
Scientific software that has real-time graphics displaying the immediate
measurements of the sensors wired to the datalogger. Computer hardware
and software are available for data analysis and modeling. The data collected
for the energy-balance model will be at the Plant Science Research Greenhouses
on the Michigan State University campus. Commercial greenhouses in Michigan
will provide a variety of greenhouse structures to collect additional data
and to validate the energy-balance model.

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Berghage, R., R. Heins, W. Carlson, and J. Biernbaum. August, 1987.
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Erwin, J.E., and R.D. Heins. 1990. Temperature effects on lily development
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Faust, J.E. 1992. Modeling leaf and inflorescence development of the
African violet (Saintpaulia ionantha Wendl.). MS Thesis. Michigan State
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Friend, D.J., V.A. Helson, and J.E. Fischer. 1962. Leaf growth in Marquis
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Grueber, K.L., W.E. Healy, H.B. Pemberton, and H.F. Wilkins. 1986.
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Harris, G.P., and M.A. Scott. 1969. Studies on the glasshouse carnation:
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Hodges, T., and V. French. 1985. Soyphen: Soybean growth stages modeled
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Karisson, M.G., R.D. Heins, and J.E. Erwin. 1988. Quantifying temperature-controlled
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Karlsson, M.G., R.D. Heins, J.E. Erwin, R.D. Berghage, W.H. Carlson,
and J.A. Biernbaum. 1989a. Temperature and photosynthethic photon flux
influence chrysanthemum shoot development and flower initiation under short-day

conditions. J. Amer. Soc. Hort. Sci. 114(1):158-163. Karlsson, M.G.,
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1989b. Irradiance and

temperature effects on time of development and flower size in chrysanthemum.
Sci. Hort. 39:257-267. Karlsson, M.G., R.D. Heins, J.0. Gerberick, and
M.E. Hackmann. 1991. Temperature driven leaf unfolding rate in Hibiscus
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development. Crop Sci. 15:174-179.

Mastalerz, J.W. 1965. Bud blasting in Lilium longiflorum. Proc. Amer.
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Mellor, R.S., F.B. Salisbury, and K.L. Raschke. 1964. Leaf temperature
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Parkinson, K.J., and W. Day. 1990. Design and testing of leaf cuvettes
for use in measuring photosynthesis and

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10:25-30.

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Budget

Graduate Assistantship (Half-time Graduate Assistantship) $15,254

Undergraduate labor (plant care, data collection) $2,000

Greenhouse supplies and maintenance $2,500

Other equipment and supplies (computers, paper, etc.) $2,500

TOTAL $22,254