Transcript Document 7214064

Cotton Modeling to Assess Climate
Change and Crop Management
V. R. Reddy1 and K. R. Reddy2
1USDA-ARS,
Crop Systems and Global Change Laboratory, BARC-West, Beltsville, MD
20705, USA
2Department
of Plant and Soil Sciences, Mississippi State University, Mississippi State,
MS 39762, USA
December 2005
Why Do We Need Models?
 Provide quantitative description and understanding
of biological problems.
 Help pinpoint knowledge gaps.
 Design critical experiments.
 Synthesize knowledge about different components
of a system.
 Summarize data.
 Transfer research results to users.
Demand for Models
 Farm management (e.g. planting, irrigation, fertilization
and harvest scheduling).
 Resource management (e.g. several Govt. agencies and
private comp. use).
 Policy analysis.
 Production forecasts (e.g. global, regional and local
forecasts).
 Research and development (e.g. research priorities and
guide fund allocations).
 Turning information into knowledge (e.g. information
overflow in every area including agricultural research).
Timeline for Information Flow
Identify knowledge void
Months
Conceptualize the experiment
Months/Years
Months/Years
Implementation
Months
Analyze data
Months
Crop
model/DSS
Months
Years
Scientists
Publication
Months/Years
Ext. Personnel
Industry Reps
Technology transfer
Consultants
Months/Years
Farm decisions
Farmers
SPAR – Database for Modeling
65
Temperature and Crop Development
Species and Genotypic Variability
60
Days to Square
55
50
45
Upland, DP 51
40
35
Pima Cotton
30
25
20
15
15
Upland, DES 119
20
25
Temperature, °C
30
35
SPAR – Database for Modeling
Photosynthesis and Leaf Water Potential
Photosynthesis, mg CO2 m-2 s-1
10
700 µl l-1 CO2
8
350 µl l-1 CO2
6
4
2
0
-1.0
-1.5
-2.0
-2.5
-3.0
Leaf Water Potential, MPa
-3.5
-4.0
GOSSYM: Model Structure
GOSSYM
DATES
CLYMAT
TMPSOL
SOIL
FERT
RAIN
PIX
FRTLIZ
RUNOFF
GRAFLO
ET
CHEM
PREP
UPTAKE
CAPFLO
PNET
NITRIF
GROWTH
ABSCISE
PLTMAP
PMAP
COTPLT
FREQ
RUTGRO
RIMPED
NITRO
MATAL
OUTPUT
For more details on model structure: Hodges et al., 1998
GOSSYM: Model Validation
United States
Greece
Israel
China

Continuous evolution of the model by extensive
testing across diverse environments, soil conditions
and cultural practices.

Information feedback from scientists, farmers and
farm managers.
Climate Change Effects
Atmospheric Carbon Dioxide Enrichment - Yield
Stoneville, MS - Mean of 30 Years
2000
-1
Lint Yield (kg ha )
1800
1600
1400
1200
1000
800
0
200
400
600
800
Atmospheric Carbon Dioxide Concentration (µl l-1)
1000
Climate Change – Cotton Yield
Extreme Events - Cotton Yield
Extreme Years Lint yield
2200
Lint Yield (kg ha-1)
2000
1800
Current + Ambient CO2
Current + Elevated CO2
Future + Elevated CO2
1992
1993
1989
1600
1984
1400
1200
1980
1000
800
Hot Dry
Hot Wet
Cold Dry
Cold Wet
Climate Change Scenario
Normal
Tillage and Erosion Studies

GOSSYM was used to evaluate the effects of
erosion and erosion-related activities on lint yields.

GOSSYM was also used to investigate the effects of
simulated tillage and wheel traffic on growth and
yield.
Insect Damage Assessment

RbWHIMS: Rule-based Wholistic Insect Management
System.

Provides information to the user for determining
pesticide management strategies.

Recommendations include: extent of pest control,timing
of pesticide application/no application and when to
observe the field for future management strategies.
Genetics Improvement Research
 GOSSYM – a tool to predict the effect and
economic benefit of various genetic
combinations.
 Photosynthesis was found to be the limiting
factor in the okra leaf-type cottons which
have more number of bolls/plant and less
lint yield than normal leaf-type cottons.
GOSSYM: Educational Applications

As a tool for learning: principles of crop and soil
management.

As a classroom teaching tool: graphically presents the
changes in plant growth and development.

Educating farm managers to improve their crop
productivity.

Assist crop consultants in the decision making
process.

22 (15 Ph.D and 7 MS) theses on GOSSYM were
accepted since 1979 at MSU.

GOSSYM served as a template to other crop models
(melons, soybean, corn, wheat, rice and potato) at
USDA.
GOSSYM: Model Applications
Field Scale
Pre-season and In-season Decisions

Timely decisions can be taken by predictions with
GOSSYM.

Helps in decision-making regarding leasing of farms.

Estimations before hand – fertilization and irrigation
costs.

GOSSYM – for determining crop termination, nitrogen
application, irrigation management.
Growth Regulator Applications
Simulated vs Observed Plant Height
Simulated Plant Height, cm
175
1:1
n = 162
150
125
100
75
50
25
0
0
25
50
75
100
125
Observed Plant Height, cm
150
175
Growth Regulator Applications
Simulated vs. Observed yield
2.5
1:1
Simulated Yield, t ha-1
n = 37
2.0
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
GOSSYM: Reap Profits
Farmer Plots
GOSSYM Plots
 In another study, GOSSYM plots had a profit of
$100 - $350 ha-1 (McKinion et al., 1989).
 GOSSYM plots had a profit of $80 ha-1 than farmer
plots (Ladewig and Powell, 1992).
GOSSYM
Deficiencies and
Future Development Needs
Deficiencies and Future Needs
Fiber quality?
Nutrients other than
C and N?
GOSSYM
Extreme weather,
Hail? Winds?
Damage due to
UV-B/pests/herbicides?
Modern/transgenic
cottons?
Shall We Discuss!
Thank You