Environmental Plant Physiology
Facilities and Tools
K. Raja Reddy
Krreddy@pss.msstate.edu
Environmental Plant physiology
and Facilities and Tools
9 Facilities:
¾ Field plots
Environmental and Cultural Factors Limiting
Potential Yields
¾Atmospheric Carbon Dioxide
¾Temperature (Extremes)
¾Solar Radiation
¾Water
¾Wind
¾Nutrients (N and K)
¾Others, ozone etc.,
¾Growth Regulators (PIX)
Crop Responses to Environment - Tools
Field Plots
¾ Free-air carbon dioxide enrichment (FACE)
facilities
¾ Indoor plant growth chambers and
Greenhouses
¾ Sunlit plant growth chambers
9 Tools:
• Environmental factors co-vary at all times.
¾ Crop simulation models
Crop Responses to Environment - Tools
• Therefore, cause and effects are difficult to
understand and quantify.
Crop Responses to Environment - Tools
Open Top Chambers, USDA-ARS, Beltsville, MD
Open Top Chambers, USDA-ARS, Auburn, AL
• Some control over
certain environmental
factors.
• Others such as
temperature is not
controlled; chamber
walls etc..
• Some control over certain environmental factors.
• Others such as temperature is not controlled; chamber
walls etc..
1
Crop Responses to Environment - Tools
Crop Responses to Environment - Tools
Free-Air-Carbon Dioxide Enrichment –Soybean
T-FACE with infrared heaters
Free-Air-Carbon Dioxide-Enrichment
• Large study area allows for multiple disciplines.
• Some control over certain environmental factors such as CO2 and ozone.
• Others such as temperature earlier is not controlled, but recently added infrared
heating.
Crop Responses to Environment - Tools
Solardome – Institute of Terrestrial
Ecology, Bangor, UK
Crop Responses to Environment - Tools
Indoor plant growth chambers and
greenhouses
• Some control over certain environmental factors.
• Some control over certain environmental factors.
• Others such as: temperature controlled to certain degree to the ambient
levels; chambers walls etc.
SPAR - Soil-Plant-Atmosphere-Research
Plant Process Quantification and Modeling
www.spar.msstate.edu
• Suitable for certain studies; however, low light levels, poor control
over temperatures, inadequate pot sizes and fertility and irrigation
management.
SPAR - Soil-Plant-Atmosphere-Research
What Can We Control?
9 Temperatures: 10 to 45 °C
9 CO2 concentration: Subambient to 1000 ppm
9 Ultraviolet-B radiation: 0 to three times of ambient
UV-B (up to 16 kJ)
9 Water regimes: Can be manipulated based on
measured ET nicely
A 50 ton cooling &
coolant circulating
system
Two 5.5 kW heating, air
circulation & moisture
condensing system
9 Fertilization: One or several nutrients can be easily
manipulated either alone or in combination
Mini-rhizotron system for
non-destructive root
growth and development
9 Solar radiation: sunlit (>95% passes through the
Plexiglas and reaches plant canopy), no artificial light
2
SPAR - Soil-Plant-Atmosphere-Research
SPAR - Soil-Plant-Atmosphere-Research
What Can We Measure?
What can we measure?
9 Growth and developmental processes:
9 Abiotic conditions:
- Air, canopy and dew-point temperatures
- Solar and ultraviolet-B radiation
- Chamber and outside CO2 concentrations
- Soil water and temperature by depth
- Relative humidity
I. Phenological rates:
- Similar events: Leaf and internode addition
rates, duration rates, etc.
- Dissimilar events: seed to emergence,
emergence to square, square to flower and
flower to open boll.
9 Processes:
- Canopy photosynthesis, respiration, and
evapotranspiration
- Leaf-level physiological, biochemical and molecular
processes
II. Growth rates:
- Leaf, internode (stem), root, and fruiting
structures (square, boll, lint, seed/grain etc.).
SPAR - Measuring Carbon Fluxes
SPAR - Measuring Carbon Fluxes
Measuring Photosynthesis: Mass-balance approach
Measuring Respiration:
During sunlit hours, by maintaining steady or constant
CO2 concentration inside the SPAR chamber, we can
calculate:
During nighttime, by measuring the rise or build up CO2
concentration inside the SPAR chamber, we can calculate,
Respiration rate = [(CO2 Conc., at Time 2 - CO2 Conc., at
Time 1) + leak rate]
Net photosynthesis = Amount of CO2 injected – leak rate
Gross Photosynthesis = Net photosynthesis + Respiration
Regulators/
Valves/flow meters
SPAR
DACS or
Computer
Drying
agent
Drying
agent
Regulators/
Valves/flow meters
X
CO2
analyzer
X
Compressed
CO2
Suction
pump
SPAR
Drying
agent
Drying
agent
CO2
analyzer
DACS or
Computer
SPAR – Process Quantification and Modeling
Canopy Photosynthesis and Diurnal Trends
Canopy Photosynthesis and Light Response Curves
2500
720 ppm
6
Solar Radiation
360 ppm
-1
2000
1500
2
1000
0
500
-2
0
0
2
4
6
8
10 12 14 16 18 20 22 24
Time of the Day (Central Standard Time)
-2
4
Canopy photosynthesis, mg CO2 m-2 s-1
SPAR – Process Quantification and Modeling
PPFD, µmol m s
Carbon Exchange Rate, mg CO2 m-2 s-1
Compressed
CO2
Suction
pump
9
360 ppm and N-sufficient
720 ppm and N-sufficient
720 ppm and N-deficient
360 ppm and N-deficient
8
7
6
5
4
3
2
1
78 DAE and 28 DAT
0
0
500
1000
1500
2000
PPFD, µmol m-2 s-1
3
SPAR – Process Quantification and Modeling
SPAR – Process Quantification and Modeling
Cotton Canopy Photosynthesis – N and CO2
Cotton Conductance – N and CO2
Temporal Trends in Photosynthesis Processes
Temporal Trends in Canopy Conductance
20
8
6
8
0
N-sufficient
N-deficient
720 ppm
8
360 ppm
720 ppm
7
6
5
3
2
1
1
2
4
3
Leaf N, %
4
2
30
40
50
60
70
80
5
5
720 ppm
N-sufficient
N-deficiennt
15
200
5
1
2
3
Leaf N, %
4
5
5
30
40
50
60
70
80
90 100 110 120 130 140
SPAR – Process Quantification and Modeling
Leaf-level Gas Exchange and Reflectance Measurements
•
-2
1995 ambient Temperature
We can monitor leaf-level gas
exchange:
9
150
•
100
•
50
Boll
opening
Flower
60
80
100
120
140
160
Day after emergence
SPAR – Process Quantification and Modeling
Measuring Evapotranspiration (ET)
Photosynthesis, stomatal conductance,
transpiration, fluorescence, etc.
We can monitor leaf-level
reflectance measurements:
9
40
10
Days after emergence
SPAR – Process Quantification and Modeling
Integration of Canopy Net Photosynthesis
0
20
360 ppm
720 ppm
15
0
10
0
20
90 100 110 120 130 140
Days after emergence
-1
20
0
4
6
0
20
Nitrogen and Canopy
Conductance Relationship
10
-1
2
Canopy photosynthesis
(mg CO2 m-2 s-1)
4
Canopy net photosynthesis, g CO2 m d
-2
Canopy photosynthesis, mg CO2 m s
-1
Nitrogen and Photosynthesis
Relationships
360 ppm
N-sufficient
N-deficient
15
Canopy Conductance
(mm s-1)
Canopy conductance, mm s
N-sufficient
N-deficient
Canopy conductance, mm s
360 ppm
-1
10
Leaf reflectance properties, pigments etc.
We can also monitor leaf
temperatures and leaf water
potentials:
9
Leaf temperatures by infrared
thermometers.
9
Leaf water potential by Pressure bomb.
SPAR – Process Quantification and Modeling
Measuring Evapotranspiration
1. Measuring Evapotranspiration (ET):
During day and nighttime periods, by collecting the
condensate (moisture in the air) while passing through
the cooling coils, ET is measured using a set of values,
controllers and pressure transducers every 15 minutes.
Inlet – From
Condenser Coils in
the unit
Condensate
ET
Reservoir
2. Measuring transpiration:
By sealing the soil surface and around the plant stems,
one can accurately measure transpiration.
Solenoid
Valves
Transducer
Outlet - Drain
4
SPAR - Process Quantification and Modeling
Cotton - Leaf and Canopy Transpiration and Leaf Area
SPAR – Process Quantification and Modeling
0.2
1000
500
.
0
0.0
2
4
6
8
10
12
14
16
18
20
22
24
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
Time of the Day (Central Standard Time)
Leaf Area Basis
y = 36.741 + 0.5098 * X, r ² 0.99
250
200
150
100
100
150
200
250
300
350
400
-2 -1
Transpiration at Ambient [CO2], mg H2O m s
2
0.1
About 250 per sq mm
300
Transpiration at Elevated [CO2], kg H2O m-2 d-1
1500
-1
0.3
0
H20
2000
Solar Radiation
Leaf Area at Elevated [CO2], m plant
0.4
CO2
Maize, DAE 36
PPFD, µmol m-2 s-1
Canopy Water Use, g H2O m-2 s-1
2500
-1
720 µmol CO2 mol
-1
360 µmol CO2 mol
Transpiration at Elevated [CO2], mg H2O m-2 s
-1
Maize - Canopy Evopotranspiration – Diurnal Trends
0.5
0.0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
2
Leaf Area at Ambient [CO2], m plant
1.4
1.6
1.8
2.0
40
Canopy Transpiration
30
20
10
y = 2.7799 + 0.9234 * X, R² 0.83
0
0
10
-1
20
30
40
Traspiration at Ambient [CO2], kg H2O m-2 d-1
SPAR – Process Quantification and Modeling
SPAR – Process Quantification and Modeling
Evapotranpsiration and Two Methods
Condensate and TDR - Potato
Cotton – Determining Potential Developmental Rates
Evapotranspiration
-2 -1
from condensate, mm cm d
10
CO2 and Irrigation Irrigation Treatments
370 µmol mol
8
-1
25%
75%
100%
740 µmol mol
-1
25%
75%
100%
6
4
2
Leaf addition rates on the mainstem and
branches and leaf expansion duration
0
0
Dennis et al. 2007
2
4
6
8
Evapotranspiration from TDR
10
water content, mm cm-2 d-1
SPAR – Process Quantification and Modeling
SPAR – Process Quantification and Modeling
Cotton – Developmental Rates
Cotton – Growth and Developmental Rates
Pictorial Representation
0.07
4-week old cotton seedlings
Rate of Development,1/d-1
0.06
Emergence to Square = -0.1265 + 0.01142*X - 0.0001949*X2; r2 = 0.98
Square to Flower = -0.1148 + 0.00967*X - 0.0001432*X2; r2 = 0.94
Flower to Open Boll = -0.00583 + 0.000995*X; r2 = 0.92
Square to Flower
0.05
0.04
Emergence to square
0.03
0.02
Flower to open boll
0.01
20/12 25/17
30/22
35/27
Day/night Temperature, °C
40/32
0.00
15
20
25
30
35
40
Temperature, °C
5
SPAR - Plant Responses and Modeling
Cotton – Fruit Production Efficiency
SPAR - Plant Responses and Modeling
Cotton – Square and Boll Growth Rates
0.012
0.12
0.11
0.010
0.10
0.009
0.09
0.008
0.08
Boll
0.007
0.07
0.006
0.06
0.005
15
20
25
30
35
Boll Growth Rate, g d-1
Square growth rate, g d-1
0.011
Fruit Production Efficiency
(g kg-1 dry weight)
400
Square
300
200
100
0
24
0.05
40
700 µL L-1
350 µL L-1
26
28
SPAR – Plant Responses and Modeling
Cotton Growth Rate Responses to Water Stress
34
1.2
1.0
Photosynthesis
Environmental Productivity
Indices for Nitrogen
Relative Growth Rates
32
SPAR – Plant Responses and Modeling
Cotton Growth and Developmental Responses to N
1.2
0.8
0.6
0.4
0.2
30
Temperature, °C
Temperature, °C
Stem Elongation
Leaf Area Expansion
1.0
0.8
0.6
Stem Growth
Photosynthesis
Leaf Development
0.4
0.2
Leaf Growth
0.0
-1.0
-1.5
-2.0
-2.5
-3.0
-3.5
0.0
1.00
1.25
Midday Leaf Water Potential, MPa
SPAR – Plant Responses and Modeling
What about Replication?
Environment variables
1.75
2.00
2.25
2.50
SPAR – Plant Process Quantification and Modeling
9 Sunlit, but other abiotic factors can be controllable
nicely.
Plant variables
Variable
Mean and SD
of 12 SPAR
Units
Variable
Mean of 12
SPAR ±
Variance
Range of
variance with
in the SPAR
Tmax, °C
23.0 ± 0.2
Height, cm
54.9 ± 1.7
2.2 – 18.0
Tmin, °C
18.2 ± 0.7
Leaf area, cm2
141 ± 784
1716 -5120
CO2 - day, ppm
700 ± 90
Total weight, g plant-1
16.9 ± 2.1
22.2 – 84.2
CO2 - night CO2
548 ± 52
Yield, g plant-1
12.2 ± 0.99
13.4 – 46.5
Humidity - day, %
58 ± 5
Humidity - night, % 60 ± 4
1.50
Leaf Nitrogen, g m-2 leaf area
9 Not too expensive if the objectives are to quantify
processes and to develop modeling tools.
9 Very well suited for multiple environmental effects on
plants either alone or in combination.
9 Particularly very well suited to address omics
(genomics, metabomolics, proteomics) questions
related environmental controls and responses in crop
and plant science area.
9 Space is limited.
6
System Simulation Tools
The Cotton simulation
model, GOSSYM
Timeline for Information Flow
Identify knowledge void
GOSSYM
Months
DATES
CLYMAT
Conceptualize the experiment
TMPSOL
SOIL
FERT
RAIN
PIX
FRTLIZ
RUNOFF
Months/Years
GRAFLO
Implementation
ET
CHEM
PREP
Months
UPTAKE
Analyze data
CAPFLO
PNET
Years
NITRIF
GROWTH
ABSCISE
PLTMAP
PMAP
COTPLT
FREQ
RUTGRO
RIMPED
Crop model/DSS
NITRO
MATAL
Months
Scientists
Publication
Months/Years
Industry Reps
Technology transfer
OUTPUT
Consultants
Months/Years
Program flow
Timeline for Information Flow
Ext. Personnel
Farmers
Farm decisions
SPAR and Crop Model Applications
Results
Researcher
DSS
Ext. Personnel
Box: 1
Box: 1
Box: 1
Reports
Specify
conditions
Reports
Box: 1
Industry
Reps.
Box: 1
Box: 1
User
Generalize, Enhance
Specify, Refine, Distort
Summary - SPAR Capabilities
¾ With cotton as an example crop, we have shown how the
SPAR system can be used to generate data needed for
understanding the various facets of growth and
developmental processes and how this understanding can be
used for building process-level models and in learning how
to manage the cotton crop.
¾ Operating a SPAR facility to acquire such data will often be
more economical than the use of field plot experiments
because it allows the scientist to avoid many of the
covarying and confounding factors that occur in field
experiments. Thus, the basic processes can be related more
directly to the environmental variables being studied.
Summary - SPAR Capabilities
¾ As we progress in developing systems for understanding plant
responses to environment, whether in support of global
climatic change research, the application of plants in the
remediation of environmental conditions, or the increased
application of precision agriculture technologies, the need for
diagnostics and management decision aids will become more
urgent.
¾ Mechanistic plant models and automated, user-friendly expert
systems can facilitate selection of the optimum solutions to
problems with many variables.
7
Summary - SPAR Capabilities
¾ Essentially all of the engineering and computing
technologies needed to allow the use of variable and sitespecific technologies, such as precision agriculture, are now
available.
¾ However, our understanding of the plant ecophysiological
responses to the environment as it relates to specific growth
and developmental events requires further development.
¾ Modeling forces the organization of known information and
concepts. Although we may not know enough to develop a
comprehensive model that includes all aspects of plant
growth and development at the landscape or even the plot
scale, modeling some meaningful portions of the system
provides clarity.
Summary - SPAR Capabilities
¾ These environment-genotype interactions can be measured
and incorporated into a meaningful model.
¾ When a model is based on appropriate concepts and
processes, it has predictive capability in new environments
and can be used either alone or with other emerging newer
technologies to disseminate useful plant growth and
development information.
Summary - SPAR Capabilities
¾ For a model to correctly predict plant responses to physical
conditions, the concepts and the response functions must be
appropriately assembled. Critical environment-genotype
relations should be incorporated into the model.
¾ These relationships include, but should not be limited to, the
phenological responses of specific genotypes to temperature
and their responses to environmental stresses.
¾ We would, for example, expect to find quantifiable differences
among genotypes in fruit-shed sensitivity to above-optimum
temperature and to deficiencies of water and/or nutrients.
¾ One might also find differences in fruit-shed sensitivity to
carbon deficiency caused by imbalance between
photosynthesis, fruiting rate, and vegetative growth.
Summary - SPAR Capabilities
¾ In the past, the SPAR facility has been used extensively for
research on only a few species, with a primary purpose of
providing functional parameterizations used in crop simulation
models, which, in turn, are a component of expert cropmanagement decision-support systems.
¾ There are a variety of approaches and facilities to investigate
plant responses to the environment. Among these, the SPAR
facilities are optimized for the measurement of plant and
canopy-level physiological responses to precisely controlled, but
naturally lit, environmental conditions. The data that have been
and will be obtained are unique and particularly instructive for
applied and basic plant biologists.
Facilities
Suggested Reading Material
•
Reddy, K. R., J. J. Read, J. T. Baker, J. M. McKinion, L.
Tarpley, H. F. Hodges and V. R. Reddy. 2001. Soil-PlantAtmosphere-Research (SPAR) facility - a tool for plant research
and modeling. Biotronics 30: 27-50.
•
Reddy, K. R. V. G. Kakani, J. M. McKinion and D. N. Baker.
2002. Applications of a cotton simulation model, GOSSYM, for
crop management, economic and policy decisions. In: L. R.
Ahuja, Liwang Ma and T. A. Howell (Eds.) Agricultural
System Models in Field Research and Technology Transfer,
CRC Press, LLC, Boca Raton, FL, USA. Pp 33-73.
8