Environmental Plant Physiology Facilities and Tools › class › epp › facilities.pdf ·...

Environmental Plant Physiology Facilities and Tools

K. Raja Reddy [email protected]

mailto:[email protected]

Environmental and Cultural Factors Limiting Potential Yields

Atmospheric Carbon Dioxide Temperature (Extremes) Solar Radiation Water Wind Nutrients (N, P, and K) Others, ozone, UV-B, etc., Growth Regulators (PIX) Facilities

Environmental Plant physiology and Facilities and Tools

Facilities:

Field plots

Free-air carbon dioxide enrichment (FACE) facilities and Temperature-Free-air carbon dioxide enrichment (T-FACE), and Open-top chambers

Indoor plant growth chambers and Greenhouses

Sunlit plant growth chambers

Tools:

Crop simulation models

Crop Responses to Environment - Tools

Field Plots

• Environmental factors co-vary at all times.

• Therefore, cause and effects are difficult to understand and quantify.


Open Top Chambers, USDA-ARS, Auburn, AL

• Some control over certain environmental factors.

• Others such as temperature are not controlled; chamber walls etc..

Crop Responses to Environment - Tools Open Top Chambers, USDA-ARS, Beltsville, MD


• Others such as temperature are not controlled; chamber walls etc..


Free-Air-Carbon Dioxide-Enrichment


Free-Air-Carbon Dioxide Enrichment –Soybean T-FACE with infrared heaters

• Large study area allows for multiple disciplines.

• Some control over certain environmental factors such as CO2 and ozone.

• Others such as temperature earlier are not controlled, but recently added infrared heating.

Crop Responses to Environment - Tools Solardome – Institute of Terrestrial

Ecology, Bangor, UK


• Others such as: temperature controlled to certain degree to the ambient levels; chambers walls etc.

Crop Responses to Environment - Tools Indoor plant growth chambers and

greenhouses


• 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 Plant Process Quantification and Modeling

www.spar.msstate.edu

A 50 ton cooling & Two 5.5 kW heating, air coolant circulating circulation & moisture system condensing system

Mini-rhizotron system for non-destructive root growth and development

SPAR - Soil-Plant-Atmosphere-Research What Can We Control?

Temperatures:10 to 45 oC or 50 to 113 oF

CO2 concentration: Subambient to 1000 ppm

Ultraviolet-B radiation: 0 to three times of ambient UV-B (up to 16 kJ)

Water regimes: Can be manipulated based on measured ET nicely

Fertilization: One or several nutrients can be easily manipulated either alone or in combination

Solar radiation: sunlit (>95% passes through the Plexiglas and reaches plant canopy), no artificial light

SPAR - Soil-Plant-Atmosphere-Research What Can We Measure?

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

Processes:

- Canopy photosynthesis, respiration, and evapotranspiration

- Leaf-level physiological, biochemical and molecular processes

SPAR - Soil-Plant-Atmosphere-Research What can we measure?

Growth and developmental processes:

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.

II. Growth rates:

- Leaf, internode (stem), root, and fruiting structures (square, boll, lint, seed/grain etc.).

SPAR - Measuring Carbon Fluxes Measuring Photosynthesis: Mass-balance approach

During sunlit hours, by maintaining steady or constant CO2 concentration inside the SPAR chamber, we can calculate: Net photosynthesis = Amount of CO2 injected – leak rate

Gross Photosynthesis = Net photosynthesis + Respiration

Suction pump

DACS or Computer

Compressed CO2

Regulators/ Valves/flow meters SPAR

CO2 analyzer

Drying Drying agent agent

SPAR - Measuring Carbon Fluxes

Measuring Respiration:

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]

Regulators/ Valves/flow meters X SPAR

Suction pump

Drying Drying agent agent

DACS or Computer

Compressed CO2

X CO2

analyzer

Time of the Day (Central Standard Time)

0 2 4 6 8 10 12 14 16 18 20 22 24Car

bon

Exch

ange

Rat

e, m

g C

O2 m

-2 s

-1

-2

0

2

4

6

PPFD

, µm

ol m

-2 s

-1

0

500

1000

1500

2000

2500Solar Radiation

360 ppm720 ppm

SPAR – Process Quantification and Modeling

Canopy Photosynthesis and Diurnal Trends

SPAR – Process Quantification and Modeling Canopy Photosynthesis and Light Response Curves

PPFD, µmol m-2 s-1

0 500 1000 1500 2000Can

opy

phot

osyn

thes

is, m

g C

O2 m

-2 s

-1

0

1

2

3

4

5

6

7

8

9360 ppm and N-sufficient720 ppm and N-sufficient720 ppm and N-deficient360 ppm and N-deficient

78 DAE and 28 DAT


Cotton Canopy Photosynthesis – N and CO2

Temporal Trends in Photosynthesis Processes

Days after emergence

20 30 40 50 60 70 80 90 100 110 120 130 140

Can

opy

phot

osyn

thes

is, m

g C

O2 m

-2 s

-1

0

2

4

6

8

N-sufficientN-deficient

0

2

4

6

8

10N-sufficientN-deficient

360 ppm

720 ppm

Nitrogen and Photosynthesis Relationships

Leaf N, %1 2 3 4 5

Can

opy

phot

osyn

thes

is(m

g C

O2 m

-2 s

-1)

1

2

3

4

5

6

7

8360 ppm720 ppm

SPAR – Process Quantification and Modeling Cotton Conductance – N and CO2

Days after emergence

20 30 40 50 60 70 80 90 100 110 120 130 140

Can

opy

cond

ucta

nce,

mm

s-1

0

5

10

15

N-sufficientN-deficiennt

Can

opy

cond

ucta

nce,

mm

s-1

0

5

10

15

20N-sufficientN-deficient

360 ppm

720 ppm

Nitrogen and Canopy Conductance Relationship

Temporal Trends in Canopy Conductance

Leaf N, %1 2 3 4 5

Can

opy

Con

duct

ance

(mm

s-1

)0

5

10

15

20

360 ppm720 ppm

Day after emergence

20 40 60 80 100 120 140 160

Cano

py n

et p

hoto

synt

hesis

, g C

O2

m-2

d-1

0

50

100

150

200

1995 ambient Temperature

SPAR – Process Quantification and Modeling Integration of Canopy Net Photosynthesis

Boll Flower opening

SPAR – Process Quantification and Modeling Leaf-level Gas Exchange and Reflectance Measurements

• We can monitor leaf-level gas exchange: Photosynthesis, stomatal conductance,

transpiration, fluorescence, etc.

• We can monitor leaf-level reflectance measurements: Leaf reflectance properties, pigments etc.

• We can also monitor leaf temperatures and leaf water potentials: Leaf temperatures by infrared

thermometers.

Leaf water potential by Pressure bomb.

SPAR – Process Quantification and Modeling Measuring Evapotranspiration (ET)

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.

2. Measuring transpiration:

By sealing the soil surface and around the plant stems, one can accurately measure transpiration.


Measuring Evapotranspiration

Inlet – From Condensate Condenser Coils in ET

the unit Reservoir

Transducer

Solenoid Valves

Outlet - Drain

Time of the Day (Central Standard Time)

0 2 4 6 8 10 12 14 16 18 20 22 24

Can

opy

Wat

er U

se, g

H2O

m-2

s-1

0.0

0.1

0.2

0.3

0.4

0.5

PPFD

, µm

ol m

-2 s

-1

0

500

1000

1500

2000

2500720 µmol CO2 mol-1

360 µmol CO2 mol-1

Solar Radiation

Maize, DAE 36

SPAR – Process Quantification and Modeling Maize - Canopy Evopotranspiration – Diurnal Trends

SPAR - Process Quantification and Modeling Cotton - Leaf and Canopy Transpiration and Leaf Area

H20 CO2

About 250 per sq mm

.

Leaf Area at Ambient [CO2], m2 plant-1

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Leaf

Are

a at

Ele

vate

d [C

O2],

m2 p

lant

-1

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Transpiration at Ambient [CO2], mg H2O m-2 s-1100 150 200 250 300 350 400

Tran

spira

tion

at E

leva

ted

[CO

2], m

g H

2O m

-2 s

-1

100

150

200

250

300

y = 36.741 + 0.5098 * X, r ² 0.99 Leaf Area Basis

Traspiration at Ambient [CO2], kg H2O m-2 d-10 10 20 30 40

Tran

spira

tion

at E

leva

ted

[CO

2], k

g H

2O m

-2 d

-1

0

10

20

30

40

y = 2.7799 + 0.9234 * X, R² 0.83

Canopy Transpiration

Evapotranspiration from TDR water content, mm cm-2 d-1

0 2 4 6 8 10

Evap

otra

nspi

ratio

n fr

om c

onde

nsat

e, m

m c

m-2

d-1

0

2

4

6

8

10

75% 25%

100% 75% 100%

25%

CO2 and Irrigation Irrigation Treatments

370 mol mol-1 740 mol mol-1

SPAR – Process Quantification and Modeling Evapotranpsiration and Two Methods

Condensate and TDR - Potato

Dennis et al. 2007

SPAR – Process Quantification and Modeling Cotton – Determining Potential Developmental Rates

Leaf addition rates on the mainstem and branches and leaf expansion duration


Cotton – Growth and Developmental Rates

20/12 25/17 30/22 35/27 40/32 Day/night Temperature, °C

4-week old cotton seedlings

Pictorial Representation

Temperature, °C15 20 25 30 35 40

Rat

e of

Dev

elop

men

t,1/d

-1

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

Square to Flower

Emergence to square

Flower to open boll

SPAR – Process Quantification and Modeling Cotton – Developmental Rates

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

SPAR - Plant Responses and Modeling Cotton – Square and Boll Growth Rates

Temperature, °C15 20 25 30 35 40

Squ

are

grow

th ra

te, g

d-1

0.005

0.006

0.007

0.008

0.009

0.010

0.011

0.012

Bol

l Gro

wth

Rat

e, g

d-1

0.05

0.06

0.07

0.08

0.09

0.10

0.11

0.12Square

Boll

SPAR - Plant Responses and Modeling Cotton – Fruit Production Efficiency

Temperature, °C24 26 28 30 32 34

(g k

g-1 dr

y w

eigh

t)

0

100

200

300

400

350 µL L-1700 µL L-1

Frui

t Pro

duct

ion

Effi

cien

cy

SPAR – Plant Responses and Modeling

Cotton Growth Rate Responses to Water Stress

Stem Elongation

Photosynthesis

Midday Leaf Water Potential, MPa-3.5-3.0-2.5-2.0-1.5-1.0

Rel

ativ

e G

row

th R

ates

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Leaf Area Expansion


Cotton Growth and Developmental Responses to N

Leaf Nitrogen, g m-2 leaf area1.00 1.25 1.50 1.75 2.00 2.25 2.50

Envi

ronm

enta

l Pro

duct

ivity

Indi

ces

for N

itrog

en

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Leaf Growth

Leaf Development

Stem Growth

Photosynthesis


An Example – Soybean Seed Germination

Temperature, °C15 20 25 30 35 40

Sow

ing

to e

mer

genc

e, d

4

6

8

10

12

Dai

ly d

evel

opm

enta

l rat

e, d

-1

0.05

0.10

0.15

0.20

0.25AG 5332PR 5333

Days = 13.385 - 3.6309x + 0.3864x2; r² = 0.94Rate = 0.03112 + 0.06763x - 0.006558x2; r² = 0.89

Observed seed emergence, d2 4 6 8 10 12

Pred

icte

d se

ed e

mer

genc

e, d

2

4

6

8

10

12

AG 5332Progeny 5333

Y = 1.47294 + 0.7911x; r²=0.91

Model Development

Model Validation

Temperature - Soybean Growth Development

Model Application

Planting

date

Avg. Air

Temp. °F

Days to

emergence

Avg. Air

Temp. °F

Days to

emergence

March 20 55.0 13.0 53.5 14.0

March 30 58.0 12.0 56.0 12.5

April 10 62.0 10.5 60.0 11.0

April 20 65.5 9.0 63.5 10.0

April 30 67.0 9.0 65.0 9.5

May 10 70.0 8.0 68.5 8.5

May 20 73.0 7.0 71.5 7.5

Stoneville, MS Tunica, MS

Dr. Larry Heatherly

at: http://mssoy.org/blog/temperature-and-soybean-emergence/

http://mssoy.org/blog/temperature-and-soybean-emergence

SPAR – Plant Responses and Modeling What about Replication?

Environment variables Plant variables

Variable Mean and SD of 12 SPAR Units

Tmax, °C 23.0 ± 0.2

Tmin, °C 18.2 ± 0.7

CO2 - day, ppm 700 ± 90

CO2 - night CO2 548 ± 52

Humidity - day, % 58 ± 5

Humidity - night, % 60 ± 4

Variable Mean of 12 SPAR ± Variance

Range of variance within the SPAR

Height, cm 54.9 ± 1.7 2.2 – 18.0

2Leaf area, cm 141 ± 784 1716 -5120

Total weight, g plant-1 16.9 ± 2.1 22.2 – 84.2

Yield, g plant-1 12.2 ± 0.99

13.4 – 46.5

Environmental Plant Physiology Research

SPAR – Plant Process Quantification and Modeling

Sunlit, but other abiotic factors can be controllable nicely.

Not too expensive if the objectives are to quantify processes and to develop modeling tools.

Very well suited for multiple environmental effects on plants either alone or in combination.

Particularly very well suited to address omics (genomics, metabomolics, proteomics) questions related environmental controls and responses in crop and plant science area.

Space is limited.

System Simulation Tools The Cotton simulation model, GOSSYM

PMAP

COTPLT

GOSSYM

CLYMAT

SOIL

CHEM

PNET

GROWTH

PLTMAP

OUTPUT

PIX

PREP

RUTGRO

NITRO

MATAL

DATES

TMPSOL FRTLIZ

ET

UPTAKE

CAPFLO

NITRIF

RIMPED

ABSCISE

FREQ

RAIN

FERT

RUNOFF GRAFLO

Program flow

Timeline for Information Flow

Conceptualize the experiment

Implementation

Identify knowledge void Months

Months/Years

Analyze data

Publication

Technology transfer

Farm decisions

Crop model/DSS

Months

Months

Years

Months/Years

Months/Years

Scientists

Ext. Personnel

Industry Reps

Consultants

Farmers

Timeline for Information Flow

Results

Researcher

User

Ext. Personnel

Reports

Reports

Industry Reps.

DSS

Box: 1

Generalize, Enhance

Specify, Refine, Distort

Box: 1

Box: 1 Box: 1

Specify conditions

Box: 1

Box: 1

SPAR and Crop Model Applications

Summary - SPAR Capabilities

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.

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.


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.

Mechanistic plant models and automated, user-friendly expert systems can facilitate selection of the optimum solutions to problems with many variables.


Essentially all of the engineering and computing technologies needed to allow the use of variable and site-specific technologies, such as precision agriculture, are now available.

However, our understanding of the plant ecophysiological responses to the environment as it relates to specific growth and developmental events requires further development.

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.


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.

These relationships include, but should not be limited to, the phenological responses of specific genotypes to temperature and their responses to environmental stresses.

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.

One might also find differences in fruit-shed sensitivity to carbon deficiency caused by imbalance between photosynthesis, fruiting rate, and vegetative growth.


These environment-genotype interactions can be measured and incorporated into a meaningful model.

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.


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 crop-management decision-support systems.

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-Plant-Atmosphere-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.

Environmental Plant Physiology Facilities and Tools › class › epp › facilities.pdf ·...

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