Enhancing and Extending Enhancing and Extending Field Research Field Research

with Modeling of Agricultural Systemswith Modeling of Agricultural Systems----International CollaborationsInternational Collaborations

L. L. AhujaAhuja , L. Ma, S. , L. Ma, S. AnapalliAnapalli , and J. , and J. KoKo

USDAUSDA--ARS, Agricultural Systems Research ARS, Agricultural Systems Research

and Colorado State Univ., Fort Collins, COand Colorado State Univ., Fort Collins, CO

Need for Quantitative System Need for Quantitative System

Approaches in AgricultureApproaches in Agriculture

��Agriculture in the 21Agriculture in the 21stst century is much more complex century is much more complex due to: environmental concerns, limited water, climate due to: environmental concerns, limited water, climate change: droughts & uncertainty, global competition; change: droughts & uncertainty, global competition; biobio--energyenergy

�� Integrated and quantitative system approaches are Integrated and quantitative system approaches are needed as planning & decision tools for optimal needed as planning & decision tools for optimal management, & to help research develop them. System management, & to help research develop them. System models provide these approaches. models provide these approaches.

��Enhances understandingEnhances understandingof the experimental results & of the experimental results & complex interactions; cause & effect relationscomplex interactions; cause & effect relations

��Enables their synthesis, quantification & Enables their synthesis, quantification & extends extends resultsresultsto longer time periodsto longer time periods

��Helps Helps transfer resultstransfer results, their optimal application to other , their optimal application to other soils/ climates, and soils/ climates, and aids managementaids management

�� Identifies knowledge gapsIdentifies knowledge gapsto focus further research; to focus further research; reduces duplicationreduces duplication

��Good field data help Good field data help improve the modelsimprove the models

Integrating Field Research with System Models Helps Both

CUTTING EDGE CUTTING EDGE

FIELD FIELD

RESEARCHRESEARCH

SIMPLER DECISION SIMPLER DECISION

SUPPORT SYSTEMSSUPPORT SYSTEMS

for Farmers & for Farmers &

Ranchers, Ag Ranchers, Ag

Consultants, and Consultants, and

Action AgenciesAction AgenciesPROCESS MODELS PROCESS MODELS

OF AGRICULTURAL OF AGRICULTURAL

SYSTEMSSYSTEMS

Extend Research & Extend Research &

Applications Applications

THE GUIDING PRINCIPLE & VISIONTHE GUIDING PRINCIPLE & VISION

The CGIAR Science Council The CGIAR Science Council (2005) Research Priorities:(2005) Research Priorities:

•• ““ Modeling and the ability to combine data from Modeling and the ability to combine data from different sources, different sources, ……promises to revolutionize promises to revolutionize understanding of processes affecting management of understanding of processes affecting management of natural resources.natural resources.””

•• ““ Thanks to strategic accumulation of data, tools, and Thanks to strategic accumulation of data, tools, and modeling resources in the coming decade, one can modeling resources in the coming decade, one can expect the development of a more predictive approach expect the development of a more predictive approach to agricultureto agriculture”” . .

•• Policy to focus limited resources on a few Policy to focus limited resources on a few comprehensive field studies and then use models to comprehensive field studies and then use models to extend them to other locations and countries. extend them to other locations and countries.

Agricultural Agricultural SystemsSystemsResearchResearchUnitUnitFort Collins, ColoradoFort Collins, ColoradoU.S.A.U.S.A.

OUR UNITOUR UNIT ’’ S RESEARCH MISSIONS RESEARCH MISSION

Develop whole-system approaches to help optimize resource management & evaluate/ develop sustainable agricultural systems:

• Synthesis of disciplinary knowledge to the whole system level and collaborative research to fill knowledge gaps.

• Computer models of agriculture systems to help research, site-specific management, and create simpler decision aids.

• Decision support technology packages for farmers, ranchers, ag consultants and action agencies for planning and management.

• Techniques for more efficient development & maintenance of models & decision tools

More Recent Major Team ProductsMore Recent Major Team Products

•• The Root Zone Water Quality Model (RZWQM) to simulate The Root Zone Water Quality Model (RZWQM) to simulate management effects on water, water quality and crop management effects on water, water quality and crop production. production. Updated to RZWQM2Updated to RZWQM2--DSSAT & RZWQM2DSSAT & RZWQM2 --GIS GIS for precision spatial management & conservation effects assessment--CEAP .

•• GPFARM, a simpler whole farm/ranch decision support system GPFARM, a simpler whole farm/ranch decision support system for strategic planning. for strategic planning.

•• Object Modeling System: Create models from library of standObject Modeling System: Create models from library of stand--alone modulesalone modules

Root Zone Water Quality ModelRoot Zone Water Quality ModelModeling Management Effects on Water, Modeling Management Effects on Water,

Water Quality and Crop productionWater Quality and Crop production

Water Resources Publications, LLC

Distinguishing Features of Distinguishing Features of RZWQMRZWQM

• Agricultural management practices and their integrated effects on water, crop production, and environmental quality ( tillage, irrigation, fertilization, manure application, tile drainage, pesticide application, and crop rotation).

• Macropore/preferential flow.• Water table fluctuation and tile flow.• Chemical transport in runoff/percolation water.

Distinguishing Features Distinguishing Features of RZWQMof RZWQM

•• Detailed carbon/nitrogen dynamics with Detailed carbon/nitrogen dynamics with consideration of microbial populations.consideration of microbial populations.

•• Multiple year simulation for crop rotations with Multiple year simulation for crop rotations with capability of answering capability of answering ““whatwhat--ifif”” scenariosscenarios

•• Detailed cropDetailed crop--specific models from DSSAT specific models from DSSAT packagepackage

RZWQMRZWQM--RZWQM2 ApplicationsRZWQM2 Applications

�Extensively used in U.S & other countries to evaluate water quality/quantity impacts of ag management & develop sustainable systems.

�Adopted by EPA and used by pesticide

industry for pesticide registration

�Used by USGS for NAWQA program�China: water & N could be reduced by 50% w/o reducing corn yield

�Continues to play an important role in our new research projects.

A wholeA whole--farm decision farm decision support system for strategic support system for strategic planning: evaluation of planning: evaluation of alternate cropping system, alternate cropping system, rangerange--livestock systems, livestock systems, and integrated farming and integrated farming options for production, options for production,

GPFARM: A Farm Level DSSGPFARM: A Farm Level DSS

economics, and environmental impactseconomics, and environmental impacts

End UsersEnd Users: Farmers and Ranchers, Consultants, : Farmers and Ranchers, Consultants, Action Agencies, Extension, and ScientistsAction Agencies, Extension, and Scientists

GPFARM Applications

� Several invited presentations to Colorado Conservation Tillage Association & farmers

� MOU with CAWG: GPFARM distributed to 600 members; trained 150 members

� GPFARM-Range model has been extensively used for synthesizing research data from three range research stations in the Great Plains

The Object Modeling System The Object Modeling System (OMS) (OMS)

An Object Modeling System consists of a library of modules which facilitates the assembly of a modeling package, tailored to the problem, data constraints, and scale of application.

Collaborators: ARS, NRCS, USGS, Friedrich-Schiller University, Jena, GermanyHas been adopted by NRCS as a uniform system to deliver conservation technology and is being used to develop a new field to watershed scale model.

Agricultural Systems Research Unit

Model and Field Research a Precursor to Model and Field Research a Precursor to DcisionDcision Support ToolsSupport Tools

The Object Modeling System (OMS) facilitates the development of component-based models which benefits decision support tool development.

CEAP Watershed Compared to SWAT

• The semi-distributed SWAT concept considers distributed information within a sub-basin only statistically but not in terms of location.

• Important processes, e.g., lateral water /nutrient transport and specific management in some parts of a sub-basin cannot be simulated.

The fully distributed CEAP Watershed Modelconcept allows theconsideration of suchprocesses.

Recent Examples of Model Recent Examples of Model Application to Enhance and Application to Enhance and

Extend field ResearchExtend field Research

1. Water Quality Studies1. Water Quality Studiesin Tilein Tile--Drained Cropping Systems Drained Cropping Systems

(Nashua, Iowa) (Nashua, Iowa)

•• 36 136 1--acre plots in Nashua, IA under tile drainage (at 120 cm acre plots in Nashua, IA under tile drainage (at 120 cm depth); Variety of data over time from 1978 to 2003depth); Variety of data over time from 1978 to 2003

•• Two crop rotations: continuous corn (CC) and cornTwo crop rotations: continuous corn (CC) and corn--soybean (CS) soybean (CS) rotations rotations

•• Four tillage systems: moldboard plow (MP), ridge till (RT), chiFour tillage systems: moldboard plow (MP), ridge till (RT), chi sel sel plow (CP), and noplow (CP), and no--till (NT)till (NT)

•• FertlizersFertlizers: anhydrous ammonium (AA) from 1977 to 1993; UAN : anhydrous ammonium (AA) from 1977 to 1993; UAN from 1993 to 1998from 1993 to 1998

•• Swine manure from 1998 to 2003: fall & spring applications Swine manure from 1998 to 2003: fall & spring applications

Experimental DesignExperimental Design

1975 1980 1985 1990 1995 2000 2005

Ave

rage

Yie

ld (Kg

ha-1)

0

2000

4000

6000

8000

10000

12000

14000 Measured corn yieldSimulated corn yieldMeasured soybean yieldSimulated soybean yield

1988 1990 1992 1994 1996 1998 2000 2002 2004

Ave

rage

Yea

rly T

ile F

low (cm

)

0

10

20

30

40

50Measured yearly tile flowSimulated yearly tile flow

1988 1990 1992 1994 1996 1998 2000 2002 2004Ave

rage

Yea

rly N

load

(kg

N h

a-1)

0

20

40

60

80

100

Measured yearly N loadSimulated yearly N load

Corn: RMSE=1223, r2=0.52 Soybean: RMSE=626, r2=0.37

RMSE=4.9, r2=0.74

Yearly N load: RMSE=12.6, r2=0.71

Year

1988 1990 1992 1994 1996 1998 2000 2002 2004

Flo

w

Wt C

onc.

(m

g N L

-1)

0

20

40

60

80

100

Measured flow weighted N Conc.Simulated flow weighted N Conc.

Flow weighted N Conc.: RMSE=5.4, r2=0.69

Synthesis of Synthesis of Data:Data:

Average values Average values across across treatmentstreatments

Using Validated Model to Extend Results to Using Validated Model to Extend Results to multiple years and Create multiple years and Create

Simpler Decision ToolsSimpler Decision Tools

Simulated yearly water balance and crop production averaged overSimulated yearly water balance and crop production averaged over 2424--yr for different crop yr for different crop rotation, tillage, and drainage scenarios. Results for cornrotation, tillage, and drainage scenarios. Results for corn --soybean rotation were taken as soybean rotation were taken as

averages from CS and SC phases of the rotation (Ma et al., 2007baverages from CS and SC phases of the rotation (Ma et al., 2007b ).).Scenarios Drain

flow(cm)

Lateral** flow(cm)

Runoff(cm)

ET*** (corn, cm)

Corn yield(kg/ha)

Corn biomass(kg/ha)

ET (soybean, cm)

Soybean yield (kg/ha)

Soybean biomass (kg/ha)

CC-NT-FD* 12.2 13.2 6.8 57.0 7878.3 18426.0 --- --- ---

CC-MP-FD 10.6 11.4 6.8 60.4 7862.2 18384.4 --- --- ---

CC-CP-FD 11.7 12.7 6.8 58.0 7886.9 18445.1 --- --- ---

CC-NT-CD 8.7 15.2 7.4 57.8 7920.3 18433.5 --- --- ---

CC-MP-CD 7.2 13.4 7.3 61.3 7908.0 18382.9 --- --- ---

CC-CP-CD 8.3 14.7 7.4 58.8 7921.7 18433.2 --- --- ---

CS-NT-FD 13.8 15.5 6.9 57.6 7879.7 18410.2 47.9 2971.5 8190.5

CS-MP-FD 12.8 14.4 6.9 58.9 7915.0 18443.1 50.9 3052.2 8544.2

CS-CP-FD 13.5 15.2 6.9 57.9 7915.7 18460.0 48.7 3001.3 8270.7

CS-NT-CD 9.8 18.2 7.6 58.4 7927.3 18428.6 48.4 3024.4 8281.8

CS-MP-CD 8.7 17.1 7.5 59.7 7959.0 18472.6 51.6 3098.3 8638.8

CS-CP-CD 9.5 17.9 7.6 58.8 7958.3 18487.6 49.3 3052.2 8356.0

* CC: continuous corn; CS: corn-soybean rotation; NT: no-till; MP: moldboard plow; CP: chisel plow; FD: free drainage; CD: controlled drainage.** Lateral groundwater flow below the tiles***Evapo-transpiration

Scenarios Flow-weighted N concentration (mg/L)

N loss in Tile

N loss to lateral flow

Net Mineraliza-tion

Denitrification

N uptake in corn biomass

N uptake in soybean biomass

N fixation

∆ inorganic N (1979-2002)

∆ organic N (1979-2002)

CC-NT-FD* 15.0 18.4 19.1 73.3 10.4 224.1 --- --- 85.7 1642.6

CC-MP-FD 17.4 18.4 17.5 70.3 8.6 223.7 --- --- 81.2 1637.6

CC-CP-FD 16.1 18.9 19.2 73.3 9.7 224.3 --- --- 83.2 1639.6

CC-NT-CD 15.3 13.4 21.9 72.2 11.3 224.4 --- --- 86.0 1647.6

CC-MP-CD 18.3 13.1 20.6 69.2 9.4 224.3 --- --- 81.2 1641.6

CC-CP-CD 16.6 13.7 22.0 72.1 10.5 224.7 --- --- 83.2 1644.6

CS-NT-FD 11.8 16.3 18.1 116.5 12.5 225.0 318.8 242.8 49.8 1672.7

CS-MP-FD 13.5 17.3 18.6 118.0 9.5 225.8 333.4 252.0 45.4 1793.6

CS-CP-FD 12.7 17.2 19.0 117.0 10.8 226.0 322.1 246.5 52.3 1679.9

CS-NT-CD 11.7 11.4 21.2 115.8 13.3 225.6 322.4 246.3 49.5 1727.6

CS-MP-CD 13.4 11.7 22.1 117.4 10.1 226.5 336.9 254.9 46.3 1798.6

CS-CP-CD 12.6 11.9 22.2 116.2 11.5 226.8 325.4 249.7 52.0 1684.7

* CC: continuous corn; CS: corn-soybean rotation; NT: no-till; MP: moldboard plow; CP: chisel plow; FD: free drainage; CD: controlled drainage.∆ = change from 1979 to 2002.

Simulated yearly nitrogen balance under different c rop rotation,Simulated yearly nitrogen balance under different c rop rotation, tillage, and drainage scenarios. tillage, and drainage scenarios. Units are in Kg N/ha unless stated otherwise. N app lication rateUnits are in Kg N/ha unless stated otherwise. N app lication rate was 202 kg N/ha for CC and 168 kg was 202 kg N/ha for CC and 168 kg N/ha on corn for CS and SC. Results for cornN/ha on corn for CS and SC. Results for corn --soybean rotation were taken as averages from CS and soybean rotation were taken as averages from CS and SC phases of the rotation (Ma et al., 2007b).SC phases of the rotation (Ma et al., 2007b).

(Akron, CO)

2. Dryland Copping Systems Studies in the Semi-Arid

Central Great Plains(Akron, CO)

Alternative Crop Rotation Exp.Alternative Crop Rotation Exp.

-23 dryland rotations (beginning 1991)

-Wheat, corn, millets, sunflower. peas

Millet Yield

Measured Grain Yield (kg ha -1)

0 1000 2000 3000 4000 5000

Sim

ulat

ed G

rain

Yie

ld (

kg h

a-1)

0

1000

2000

3000

4000

5000WCM

Corn Yield

Sim

ulat

ed G

rain

Yie

ld (

kg h

a-1)

1000

2000

3000

4000

5000WCMWCF

Wheat Yield

Sim

ulat

ed G

rain

Yie

ld (

kg h

a-1)

1000

2000

3000

4000

5000WCMWF (CT)WF (NT)WCF

Millet Biomass

Measured Biomass (kg ha -1)

0 3000 6000 9000 12000 15000

Sim

ulat

ed B

iom

ass

(kg

ha-1

)

0

3000

6000

9000

12000

15000 WCM

1 to 1 line

average 1 standard deviation

1 to 1 line

average 1 standard deviation

1 to 1 line

circled points are from calibration data set

average 1 standard deviation

1 to 1 line

circled points are from calibration data set

average 1 standard deviation

Wheat Biomass

Sim

ulat

ed B

iom

ass

(kg

ha-1)

0

3000

6000

9000

12000

15000 WCMWF (CT)WF (NT)WCF

average 1 standard deviation

1 to 1 line

Corn Biomass

Sim

ulat

ed B

iom

ass

(kg

ha-1)

0

3000

6000

9000

12000

15000 WCMWCF

average 1 standard deviation

1 to 1 line

SYNTHESIS – Biomass and Yield in 4 Rotations

W F(C T)-F

Soi

l wat

er (

mm

)

100

200

300

400

500 PredictedMeasured

W F(N T)-F

Soi

l wat

er (

mm

)

100

200

300

400

500PredictedMeasured

C T vs. N T - pred icted C TN T

C T vs. N T - m easured CTNT

(a) (c)

(b ) (d)

wheat

1992 1994 1996 1998 2000 2002

wheat wheat wheat wheat

wheat wheat wheat wheat wheat

wheat wheat wheat wheat wheat

1992 1994 1996 1998 2000 2002

wheat wheat wheat wheat wheat

Measured and predicted (RZWQM) total profile (180 cm) soil waterMeasured and predicted (RZWQM) total profile (180 cm) soil water under under wheat fallow (WF) for the beginning fallow data set. (a) convenwheat fallow (WF) for the beginning fallow data set. (a) conventional tillage tional tillage (CT), (b) no(CT), (b) no--till (NT), (till (NT), ( c)comparisonc)comparisonof predicted soil water under WF(CT) of predicted soil water under WF(CT) and WF(NT), (d) comparison of measured soil water under WF(CT) aand WF(NT), (d) comparison of measured soil water under WF(CT) and nd WF(NT) (WF(NT) (SaseendranSaseendranet al., 2005).et al., 2005).

ET - composite

Estimated ET, cm(other losses assumed zero)

0 20 40 60

Sim

ulat

ed E

T, c

m

0

20

40

60

1:1 lineWFWCMWCF

RMSE = 5.35 cm

R2 = 0.80E = 0.92

Wheat-Corn-Fallow (No-Till)F

ract

ion

orga

nic

mat

ter

in 5

cm s

oil l

ayer

0.014

0.016

0.018

0.020

0.022

0.024

0.026

0.028

WCF-WWCF-CWCF-F

WF(CT)-WWF(NT)-W

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Measured (1995)

FOM in the first 5 cm soil layer under W, C, and F phases FOM in the first 5 cm soil layer under W, C, and F phases of WCF, and W phase of WF(CT) and WF(NT) during of WCF, and W phase of WF(CT) and WF(NT) during 1992 to 2002. 1992 to 2002.

Using Validated Model to Explore Using Validated Model to Explore Drought Management StrategiesDrought Management Strategies

Summer Crop Selection in Summer Crop Selection in Rotation with WheatRotation with Wheat

Based on Initial Water Based on Initial Water

Cumulative probabilities Cumulative probabilities of Grain yields of of Grain yields of Corn, Corn, ProsoProsomillet and millet and Canola, and Biomass Canola, and Biomass of Foxtail millet and of Foxtail millet and Triticale planted with Triticale planted with Plant Plant AvailalbleAvailalbleWater at planting Water at planting from 0 to 100%from 0 to 100%

Note:Note: PAW in the soil PAW in the soil profile below 45 cm profile below 45 cm was constrained not to was constrained not to exceed 50% of PAW exceed 50% of PAW at field capacity.at field capacity.

0.0

0.2

0.4

0.6

0.8

1.0

0 1000 2000 3000 4000 5000 6000 7000Yield, kg/ha

cum

ulat

ive

prob

abili

ty

10% PAW

25% PAW

50% PAW

75% PAW

100% PAW

a) Corn

0.0

0.2

0.4

0.6

0.8

1.0

0 1000 2000 3000 4000 5000 6000 7000Yield, kg/ha

cum

ulat

ive

prob

abili

ty

b) Proso millet

0.0

0.2

0.4

0.6

0.8

1.0

0 3000 6000 9000 12000 15000Biomass, kg/ha

cum

ulat

ive

prob

abili

ty

d) Foxtail millet

0.0

0.2

0.4

0.6

0.8

1.0

0 3000 6000 9000 12000 15000Biomass, kg/ha

cum

ulat

ive

prob

abili

ty

e) Triticale

0.0

0.2

0.4

0.6

0.8

1.0

0 1000 2000 3000 4000 5000 6000 7000Yield, kg/ha

cum

ulat

ive

prob

abili

ty

c) Canola

Predicting Peak Standing Crop (rangeland biomass) a t different Predicting Peak Standing Crop (rangeland biomass) a t different levels of initial soil water Contents (SWC) and levels of initial soil water Contents (SWC) and

AprilApril --May precipitationMay precipitation

500

750

1000

1250

1500

1750

2000

2250

130 140 150 160 170 180 190

Apr.-May precipitation, mm

Pea

k st

andi

ng c

rop,

kg

ha-1

SWCi = 0.2SWCi = 0.3SWCi = 0.4

Driest

Normal

Wettest

Limited Irrigation Studies on Corn Limited Irrigation Studies on Corn in the Central Great Plainsin the Central Great Plains

Synthesis of Data: Grain Yield of Corn Synthesis of Data: Grain Yield of Corn

Measured yield, kg ha-1

0 2000 4000 6000 8000 10000 12000 14000

Sim

ulat

ed y

ield

, kg

ha-1

0

2000

4000

6000

8000

10000

12000

14000

1:1 lineIrrigatedRainfed

RMSE(rainfed) = 464 kg ha-1

RMSE(irrigated) = 982 kg ha-1

Using Validated Models to Explore Using Validated Models to Explore

alternative Scenarios for Managing Limited alternative Scenarios for Managing Limited Irrigation WaterIrrigation Water

Rain out experiments with different levels of irrigation Rain out experiments with different levels of irrigation in Cornin Corn

(Seasonal PET=90 cm, irrigated weekly)(Seasonal PET=90 cm, irrigated weekly)

1000

3000

5000

7000

9000

11000

10 20 30 40 50 60 70 80 90 100

Irrigation, cm

Yie

ld, K

g/ha

Uniform 20%V, 80%R 40%v, 60%r

(N not limiting )

1000

3000

5000

7000

9000

11000

10 20 30 40 50 60 70 80 90 100

Irrigation, cm

Yie

ld, K

g/ha

Uniform 20%V, 80%R 40%v, 60%r

(b) Silt Loam

Irrigation, mm 0 100 200 300 400 500 600 700 800

Gra

in yield, k

g ha

-1

2000

4000

6000

8000

10000

12000

20% V, 80% R40% V, 60% R50%V, 50% R

(c) Sandy Loam

Irrigation, mm 0 100 200 300 400 500 600 700 800

Gra

in yield, k

g ha

-1

2000

4000

6000

8000

10000

12000

20% V, 80% R40% V, 60% R50%V, 50% R

(a) Clay Loam

0 100 200 300 400 500 600 700 800

Gra

in yield, k

g ha

-1

2000

4000

6000

8000

10000

12000

20% V, 80% R40% V, 60% R50%V, 50% R

Grain Yield (20:80, 40:60 & 50:50 split Grain Yield (20:80, 40:60 & 50:50 split -- 94 year average)94 year average)

N balance N balance

(20:80 split (20:80 split -- 94 year average)94 year average)

N k

g ha

-1

0

20

40

60

80

100

Clay loamSilt loamSandy loam

(a) Leached N

N k

g ha

-1

100

150

200

250

300

Clay loamSilt loamSandy loam

(b) Uptake N

Irrigation, mm

100 200 300 400 500 600 700

N k

g ha

-1

0

20

40

60

80

100

Clay loamSilt loamSandy loam

(c ) Mineralization(c ) Mineralization

Limited Water Optimization Tool InterfaceLimited Water Optimization Tool Interface

Screen 1 – lowest level of economic input

Screen 3 – greatest economic input detail

Screen 2 – greater economic inputdetail

Production FunctionProduction Functionfor Water Optimizerfor Water Optimizer

Silt loam soil

ET/Irrigation, in0 5 10 15 20 25

Yld

, Bus

hels

0

20

40

60

80

100

120

140

160

Irr vs. Yld (simulated average for 97 yrs)

Yld= 25.4+10.4*ETa-dY=yd+(ym-yd)*(1-(1-dirr/dm)^(1/wue))

Akron

Sandy loam soil

ET/Irrigation, in0 5 10 15 20 25

Yld

, Bus

hels

0

20

40

60

80

100

120

140

160

Irr vs. Yld (simulated average for 97 yrs,)

Yld= 33.5+10.3*ETa-dY=yd+(ym-yd)*(1-(1-dirr/dm)^(1/wue))

Clay loam soil

ET/Irrigation, in

0 5 10 15 20 25

Yld

, Bus

hels

0

20

40

60

80

100

120

140

160

Irr vs. Yld (simulated average for 97 yrs)

Yld= 22.8+9.9*ETa-dY=yd+(ym-yd)*(1-(1-dirr/dm)^(1/wue))

Simulate yield response to irrigation and ETcrop

Simulated corn grain yield Simulated corn grain yield response to initiation of response to initiation of irrigation at different soil irrigation at different soil water depletion levels at water depletion levels at Akron, ColoradoAkron, Colorado

(a) number of irrigations and (a) number of irrigations and irrigation amounts simulated irrigation amounts simulated with the 94with the 94--yr weather record yr weather record in response to initiation of in response to initiation of irrigations at soil water irrigations at soil water depletions until 10 to 90% depletions until 10 to 90% plant available water (PAW), plant available water (PAW), andand

(b) cumulative probabilities (b) cumulative probabilities for corn grain yields simulated for corn grain yields simulated with the 94with the 94––yr weather record yr weather record in response to initiation of in response to initiation of irrigations at soil water irrigations at soil water depletions until 10 to 40% depletions until 10 to 40% PAW. Bars represent one PAW. Bars represent one standard deviation above the standard deviation above the mean.mean.

SOIL WATER DEPLETION LEVEL FOR IRRIGATIONSOIL WATER DEPLETION LEVEL FOR IRRIGATION

Yield, kg ha -10 2000 4000 6000 8000 10000 12000 14000

Cum

. pro

babi

lity

0.0

0.2

0.4

0.6

0.8

1.0

10% PAW20% PAW30% PAW40% PAW50% PAW60% PAW

(b) Cum. probability vs. grain yield

Soil water depletion levels, % plant available wate r (PAW)10 20 30 40 50 60 70 80 90

No.

of i

rrig

atio

ns

0

10

20

30

40

50

60

Irrig

atio

n, m

m

0

100

200

300

400

500

600

No. of irrigationsIrrigation, mm

(a) No. of irrigations and irrigation amount

Late Initiation of irrigation Late Initiation of irrigation -- Comparison between Comparison between soilssoils

Y ie ld , k g h a-10 2 0 0 0 4 0 0 0 6 0 0 0 8 0 0 0 1 0 0 0 0 1 2 0 0 0 1 4 0 0 0

Cum

. pro

babi

lity

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0(b ) S i l t lo a m

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3. Water and N Management for 3. Water and N Management for WheatWheat--Maize DoubleMaize Double--Cropping Cropping

System in ChinaSystem in China

Climate Change Effects on Climate Change Effects on Cropping Systems Cropping Systems

in the Central Great Plains in the Central Great Plains

Wheat Yield in WCF for Projected ScenariosOver the Baseline Experiment Years, 1992 to 2007

Yield (kg/ha)

0 2000 4000 6000

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BL415550693 836

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CO2 (ppm) Temperature (oC)

Precipitation (%) Projected years

Maize Yield in WCF for Projected ScenariosOver the Baseline Experiment Years, 1992 to 2007

Yield (kg/ha)

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CO2 (ppm) Temperature (oC)

Precipitation (%) Projected years

Thank you very much for your Thank you very much for your attention attention

International CollaborationsInternational Collaborations

1.1. Enhancing applications of existing models in Enhancing applications of existing models in field researchfield research

2.2. Easier parameterization of models for field Easier parameterization of models for field scientistsscientists——effective properties of soils/cropseffective properties of soils/crops

3.3. Improving model components, e.g., water Improving model components, e.g., water stress response, N uptake, management stress response, N uptake, management effects, effects, --------

International Collaborations International Collaborations Contd.Contd.

4. Developing next-generation, stand-alone modular process components

5. Sharing model components and databases

6. Possibly developing a common modular modeling framework, with parameterization, visualization, and analysis tools

ASAASA--SSSASSSA--CSSA Initiatives to CSSA Initiatives to Advance Models and ApplicationsAdvance Models and Applications

A. A new book series on Advances in Ag Systems Modeling:

1. Response of crops to limited water2. Introducing system models in field research 3. Root-soil interactions4. Quantifying soil structure effects5. Quantifying soil carbon changes in cropping systems

ASAASA--SSSASSSA--CSSA Initiatives to CSSA Initiatives to Advance Models and ApplicationsAdvance Models and Applications

B. Ad-hoc Committee on Modules, Models, andDatabases:

1. E-publish well-documented code for process modules, after having them peer reviewed for quality of science, documentation and meta data, industry-standard code structure, and pre-published validation.

2. E-publish the models built from peer-reviewed modules, after peer review of the model interfaces and pre-published validations.

AdAd--hoc Committee Contd.hoc Committee Contd.

3. E-publish experimental databases needed for modules and models after peer review for quality of measurements, spatial representation, and completeness for calibration/validation

4. Maintain a library of peer-reviewed modules, models, and databases on the CSA website or website of a contractor or collaborator

Purpose of the Proposed ActivitiesPurpose of the Proposed Activities

• Advance science through synthesis and quantification of processes and interactions in the form of code, which scientistscan use to build future models.

• Promote sharing of modules, models, and databases, which will reduce duplication and encourage the best science in the modulescontributed by the experts for each process.

• E-publishing and web storage of modules, models, and databases will provide publishing credit to the contributors, an essentialmotivation for time consuming tasks of using standard code structure and documentation.

• Important Note:The above proposed activities are not supposed to interfere with or impede further development and applications of existing models. Rather, they are meant to help future enhancement of these models by providing peer-reviewed components.

The Tri-Societies welcome your feedback on these initiativesAnd future collaborations