Environmental Factors
Special Topics – Mepiquat Chloride (PIX)
K. Raja Reddy
Krreddy@pss.msstate.edu
Environmental and Cultural Factors
Limiting Potential Yields
¾Atmospheric Carbon Dioxide
¾Temperature (Extremes)
¾Solar Radiation
¾Water
¾Wind
¾Nutrients (N and K)
¾Growth Regulators (PIX)
Developing and Validating a
Model for a Plant Growth
Regulator
Developing and Validating a Model for a Plant
Growth Regulator
We will explore:
• Why do we need models?
• Demand for crop models
• Timeline for information flow
• Developing and validating a plant growth regulator model:
¾ Background information on MC.
¾ Exploring primary effects of MC on cotton
¾ Designing the critical experiment to quantify the responses
¾ Developing a model
¾Validating or testing the model in the real world situations
Why do we need models?
¾ Provide quantitative description and understanding
of biological problems.
¾ Help pinpoint where knowledge is lacking.
¾ Design critical experiments.
¾ Synthesize knowledge about different components of
a system.
¾ Summarize data.
¾ Transfer research results to users.
Demand for Crop Models
¾ Farm management (e.g. irrigation, fertilization and harvest scheduling).
¾ Resource management (e.g. several Govt. agencies and private comp. use)
¾ Policy analysis (e.g. the USDA funds for soil and conservation activities based
on estimates of the USLE, A = R*K*LS*C*P), where A = average annual soil loss, R =
rainfall and runoff factor, K = soil erodibility factor, LS = slope and steepness factor, C
= cropping-management factor, and P = erosion control management factor.
¾ Production forecasts (e.g. global, regional and local forecasts).
¾ Research and development (e.g. research priorities and guide fund
allocations)
¾ Turning information into knowledge (e.g. recently, information
overflow is there virtually in every area including agricultural research).
Timeline for Information Flow
Identify knowledge void
Months
Conceptualize the experiment
Months/Years
Implementation
Months
Analyze data
Years
Scientists
Publication
Crop model/DSS
Months
Months/Years
Industry Reps
Technology transfer
Consultants
Months/Years
Farm decisions
Ext. Personnel
Farmers
Timeline for Information Flow
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
Plant Growth Regulators in Agriculture
Introduction and Background
Control of vegetative growth is a goal of
many cotton growers, particularly in irrigated
and well-fertilized production systems.
Plant Growth Regulators in Agriculture
Introduction and Background
Growth control through stressing the crop (water or
nutrients) has been shown to limit yield potential.
Photosynthesis, Vegetative Growth - Environment
Response to Drought
Photosynthesis, Vegetative Growth - Environment
Response to Fertilization - Nitrogen
120
Photosynthesis
100
80
60
60
40
40
Stem Elongation
20
0
-3.5
-3.0
-2.5
-2.0
-1.5
Midday Leaf Water Potential, MPa
20
0
-1.0
100
Percent of Maximum
80
Photosynthesis
Percent of Maximum
Percent of Maximum
100
80
60
40
20
0
1.00
Leaf Growth
1.25
1.50
1.75
2.00
Leaf Nitrogen, g m-2
2.25
2.50
Plant Growth Regulators in Agriculture
Introduction and Background
¾ Pix or Mepiquat chloride provides the cotton producer a tool
to achieve proper balance between vegetative growth and
reproductive growth without without limiting yield potential.
¾ Mepiquat chloride or PIX contains the active ingredient 1, 1
dimethylepiperidium chloride.
+
N
H3C
CH3
Cl-
Plant Growth Regulators in Agriculture
Introduction and Background
¾ MC is included in the group of chemicals of
inhibitors of the biosynthesis of gibberlic acid.
¾ The chemical is taken up mainly by the green parts
of the plant.
¾ It is systemic in nature, and is translocated both
upwards and downwards through both xylem and
phloem.
¾ It is very stable in the plant tissue, and is lost only
due to, or along with, abscission of plant parts.
Plant Growth Regulators in Agriculture
Introduction and Background
¾Many studies were conducted for the
last two to three decades on MC and its
effects on cotton growth and
development.
Pix was applied at match-head square.
¾These studies have shown that MC
suppresses vegetative growth by
shortening the internodes and thus
affecting plant height, and reducing the
number of nodes and leaf area and
photosynthesis.
Pictures and plant height were taken 4
weeks after treatment.
¾ Yield responses to MC however, were
inconsistent; from increases to no effect
to decreases.
No PIX
All treatments were well-watered and
well-fertilized.
Height, 56 in
16 oz PIX/a
33 in
Plant Growth Regulators in Agriculture
Introduction and Background
¾ These erratic yield responses to MC are probably
due to many reasons.
¾ Amounts and timing of application of the chemical
depend on plant size, growth rate, and changing
growing conditions caused by unpredictable weather
after the chemical application.
Developing a Model for Plant Growth
Regulator
Requirements:
1. Quantitative information on the effects
of MC on cotton.
2. A simulation model that can provide
cotton responses to environmental and
management practices.
Designing that Critical Experiment
¾ We need an experiment where other environmental factors
that limit the responses of crop growth should be
eliminated or controlled.
¾ All treatments should be well-watered and well-fertilized.
Also, the radiation and temperatures should be ideal for
cotton growth.
¾ We also need to conduct the experiment where we should
also minimize the competition from within the plant
processes such as supply and demand.
¾ In other words, the experiment should be conducted when
the crop growth is linear and when the sink demand is
minimal.
Designing that Critical Experiment
¾ We need to apply several concentrations of MC to
cover a wide range that can account for even the
multiple applications of MC in the field situations
¾ We need to collect whole plant dry weights regularly to
calculate MC concentration on a tissue dry weight
basis.
¾ We also need to collect the primary cotton responses to
MC more frequently to generate rate parameters as
functions of MC in the tissue.
Mepiquat Chloride - Cotton Growth
Temporal Trends in Plant Height
100
Plant Height, cm
80
60
MC Application Rate, g a.i. ha-1
0
7.65
15.3
30.6
61.2
40
20
0
15
20
25
30
35
Days After Emergence
40
45
Mepiquat Chloride - Cotton Growth
Plant Height - Stem Extension Rate
Stem Elongation Rate, cm d-1
4
y = 3.05 - 58.0 * X2
R2 = 0.87; n = 41
3
2
1
0
0.00
0.01
0.02
0.03
Mepiquat Chloride, mg g-1 dry weight
Mepiquat Chloride - Cotton Growth
Leaf Area Expansion Rate, cm2 d-1
Leaf Area Development
140
y = 106.47 - 848.58 * X ; R2 = 0.95
120
100
80
60
40
0.00
0.01
0.02
0.03
Mepiquat Chloride, mg g-1 dry weight
Photosynthesis, µmol m-2 s-1
Mepiquat Chloride - Cotton Growth
Photosynthetic Rate
45
y = 33.10 - 833.07 * X + 19539.31494 * X2
R2 = 0.86 n = 19
40
35
30
25
20
15
10
0.00
0.01
0.02
0.03
Mepiquat Chloride, mg g-1 dry weight
Model Development
We assumed that MC:
¾ enters the plant through leaves.
¾ systemic in nature and moves freely throughout the
plant (O’Neal., 1988)
¾ will be lost only due to abscission of plant parts or root
death.
¾ no interaction between water deficits and MC, and
cultivars respond similarly to MC (Reddy et al., 1992,
Fernandez et al., 1992, Schott and Schroeder, 1979).
Calculation of MC in the Plant
•
When MC is applied, the simulation model, GOSSYM,
calculates the amount of the chemical intercepted by the plant
canopy based on the method of application.
•
If the chemical is applied as broadcast method, the amount of the
chemical intercepted by the canopy is calculated based on the
light interception algorithms developed by Baker et al. (1978).
•
If the MC is applied as a banded application on the top of the
row, then the intercepted chemical is assumed to be 90% of the
applied chemical.
•
If MC is applied on the crop previously treated with MC, then
the amount of the chemical is added to any of the existing
concentration and the number of applications are updated and
summed.
Calculation of MC in the Plant
The simulation model, GOSSYM, is used to calculate plant dry weight and
weight loss due to abscission of plant parts and root sloughing on a daily basis.
MCLOS = DEADWT * MCCON
MCPLT = MCPLT – MCLOS
IF [DAYNUN.EQ.MCDAY(IMC)] THEN
IF BANDED THEN
MCPLT = MCPLT + (19068 * MCPPA(IMC) * 0.90/POPPLT
ELSE
MCPLT = MCPLT + (19068*MCPPA(IMC) * INT/POPPLT
ENDIF
IMC = IMC + 1
MCCON = MCPLT /PLTWT
Calculation of MC in the Plant
The simulation model, GOSSYM, is used to calculate plant dry weight and weight loss due
to abscission of plant parts and root sloughing on a daily basis.
MCLOS = The amount of MC lost due to abscission of plant parts.
DEADWT = The amount of plant material lost on a daily basis.
DAYNUM = The day of simulation
MCDAY = The date of MC application
IMC = The number of applications of MC during the growing season.
MCPPA = The amount of MC applied in pints per acre.
INT = The fraction of MC intercepted by the crop.
POPPLT = The number of plats per acre.
MCPLT = The amount of MC in the plant tissue.
PLTWT = The plant dry weight
MCCON = The concentration of MC in mg per gram dry weight.
19068 = The factor that converts pints per acre of 4.2%-strength MC into mg per acre of
active ingredient.
Calculation of Reduction Factors
The next step in the model is to calculate MC
reduction factors
Based on the literature and from the experiments
specifically designed to generate a quantitative database,
the primary effects of MC are on:
¾ Stem elongation
¾ Leaf area expansion
¾ Photosynthesis
Mepiquat Chloride - Cotton Growth
Plant Height - Stem Extension Rate
Stem Elongation Rate, cm d-1
4
y = 3.05 - 58.0 * X
R2 = 0.87; n = 41
3
2
1
0
0.00
0.01
0.02
0.03
Mepiquat Chloride, mg g-1 dry weight
Mepiquat Chloride - Cotton Growth
Calculating reduction factor for stem extension rate
Reduction Factor
1.2
y = 1 - 18.619 * MCCON
1.0
0.8
0.6
0.4
0.2
0.00
0.01
0.02
0.03
-1
Mepiquat Chloride, mg of MC (a.i.) g dry weight
Calculation of Reduction Factors
Based on our database, we now can calculate MC reduction factors
assuming that potential stem elongation rate, leaf expansion rate
and photosynthesis rate are equal to one:
MCDZ = 1- 18.618 * MCCON
MCDA = 1- 7.774 * MCCON
MCDPN = 1- 25.167 * MCCON + 590.286 * MCCON2
MCDZ = The reduction factor for the rate of stem elongation.
MCDA = The reduction factor for the rate of leaf area expansion.
MCDPN = The reduction factor for the rate of photosynthesis.
These reduction factors are used to decrease those parameters in
various subroutines in the simulation model.
Validating the MC Model
9 We have incorporated these algorithms into the cotton
simulation model, GOSSYM.
9 The simulation model is mechanistic in nature and does a
fairly good job in predicting cotton performance across a
wide range of environmental (soil and weather) and
management practices (nitrogen, water, PGR’s and crop
termination chemicals).
9 The data sets used to compare comprised of a wide range
of environmental conditions, a variety of cultural
practices and diverse genetic resources.
9 The data sets include a total of 50 cropping systems from
1989 to 1991.
GOSSYM
DATES
CLYMAT
TMPSOL
SOIL
FERT
RAIN
PIX
FRTLIZ
RUNOFF
GRAFLO
ET
CHEM
PREP
UPTAKE
CAPFLO
PNET
NITRIF
GROWTH
ABSCISE
PLTMAP
PMAP
COTPLT
FREQ
OUTPUT
RUTGRO
NITRO
MATAL
RIMPED
Carbohydrate Stress
Light Interception
Turgor
Solar Radiation
Photosynthesis
C Supply
C Stress
Temperature
Age
Turgor
Growth Potential of
Roots, Stems, Leaves
Squares and Bolls
C Demand
Nitrogen Stress
Root Density
Temperature
Soil N and Water
N Uptake
Plus Plant N
Reserves
N Supply
N Stress
Temperature
Age
Turgor
Growth Potential of
Roots, Stems, Leaves
Squares and Bolls
N Demand
Nutritional Stress
Delay
Morphogenesis
Nitrogen
Stress
Nutritional
Stress
Carbohydrate
Stress
Determine Actual
Organ Growth
Fruit and Leaf
Abscission
Model Validation
GOSSYM Model Predictions - Plant Height
Simulated vs. Observed
Simulated Plant Height, cm
175
2
n = 162, Slope = 0.98; r = 0.97
150
1:1
125
100
75
50
25
0
0
25
50
75
100
125
Observed Plant Height, cm
150
175
Simulated Mainstem Nodes, no. plant-1
GOSSYM Model Predictions - Mainstem Nodes
Simulated vs. Observed
25
n = 162, Slope = 1.007; r2 = 0.99
1:1
20
15
10
5
0
0
5
10
15
20
Observed Mainstem Nodes, no plant-1
25
GOSSYM Model Predictions - Lint Yields
Simulated vs. Observed
Simulated Yield, t ha-1
2.5
2.0
n = 37, Slope = 1.006; r2 = 0.96
1:1
1.5
1.0
0.5
0.0
0.0
0.5
1.0
1.5
Observed Yield, t ha-1
2.0
2.5
Management Questions ???
Planting
Fertilization
Pix
Irrigation
Pesticide Application
Growth Stage Determination
Crop Termination
Management Answer . . . Simulation Models