Cotton Modeling to Assess Climate Cotton Modeling to Assess Climate Change and Crop ManagementChange and Crop Management

December 2005December 2005

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

Provide quantitative description and understandingof biological problems.

Help pinpoint knowledge gaps.

Design critical experiments.

Synthesize knowledge about different componentsof a system.

Summarize data.

Transfer research results to users.

Why Do We Need 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).

Demand for Models

Timeline for Information Flow

Identify knowledge void

Conceptualize the experiment

Implementation

Analyze data

Publication

Technology transfer

Farm decisions

Crop model/DSS

Months

Months

Months/Years

Months

Years

Months/Years

Months/Years

Scientists

Ext. Personnel

Industry Reps

Consultants

Farmers

Months/Years

Months

SPAR – Database for Modeling

Temperature and Crop DevelopmentSpecies and Genotypic Variability

Temperature, °C

15 20 25 30 35

Day

s to

Squ

are

15

20

25

30

35

40

45

50

55

60

65

Upland, DES 119

Pima Cotton

Upland, DP 51

SPAR – Database for Modeling

Photosynthesis and Leaf Water Potential

Leaf Water Potential, MPa

-4.0-3.5-3.0-2.5-2.0-1.5-1.0

Ph

otos

ynth

esis

, mg

CO

2 m

-2 s-1

0

2

4

6

8

10

350 µl l-1 CO2

700 µl l-1 CO2

GOSSYM: Model Structure

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

For more details on model structure: Hodges et al., 1998

GOSSYM: Model Validation

United States Greece

China

Israel

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 - YieldStoneville, MS - Mean of 30 Years

Atmospheric Carbon Dioxide Concentration (µl l-1)

0 200 400 600 800 1000

Lin

t Y

ield

(kg

ha-1

)

800

1000

1200

1400

1600

1800

2000

Climate Change – Cotton YieldExtreme Events - Cotton Yield

Climate Change Scenario

Lin

t Y

ield

(kg

ha

-1)

800

1000

1200

1400

1600

1800

2000

2200Current + Ambient CO2Current + Elevated CO2Future + Elevated CO2

Hot Dry Cold Dry Cold Wet NormalHot Wet

1993

1980

1992

1984

1989

Extreme Years Lint yield

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 ApplicationsGOSSYM: Model ApplicationsField 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

n = 162

Simulated vs Observed Plant Height

Observed Plant Height, cm

0 25 50 75 100 125 150 175

Sim

ulat

ed P

lant

Hei

ght,

cm

0

25

50

75

100

125

150

1751:1

Growth Regulator Applications

n = 371:1

Simulated vs. Observed yield

Observed Yield, t ha-1

0.0 0.5 1.0 1.5 2.0 2.5

Sim

ulat

ed Y

ield

, t h

a-1

0.0

0.5

1.0

1.5

2.0

2.5

GOSSYM: Reap Profits

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 Plots Farmer Plots

GOSSYMGOSSYM

Deficiencies and Future Development Needs

Deficiencies and Future Needs

Fiber quality?Nutrients other than

C and N?

Extreme weather, Hail? Winds?

Modern/transgenic cottons?

Damage due to UV-B/pests/herbicides?

GOSSYM

Thank You

Shall We Discuss!