the ace grain flow model: model, results and discussions · 2007. 10. 31. · "higher oil...

The ACE Grain Flow Model: Model, Results and

Discussions

October 31, 2007To the UGPTI Seminar Series

By Dr. William W Wilson and Colleagues DeVuyst, Taylor, Dahl and [email protected]

2

Introduce

• Teaching– Risk analysis (fall)– Ag industrial strategy and marketing (Game

theory)– Commodity trading (444)

3

You know you're living in 2007 when...

• 1. You accidentally enter your password on the microwave.

2. You haven't played Solitaire with real cards in years.

3. You have a list of 15 phone numbers to reach your family of 3.

4. You e-mail the person who works at the desk next to you.

5. Your reason for not staying in touch with friends and family is that theydon't have e-mail addresses.

6. You pull up in your own driveway and use your cell phone to see if anyone ishome to help you carry in the groceries.

7. Every commercial on television has a web site at the bottom of the screen.

8. Leaving the house without your cell phone, which you didn't h ave the first20 or 30 (or 60) years of you r life, is now a cause for panic and you turnaround to go and get it.

10. You get up in the morning and go on line before getting your coffee.

11. You start tilting your head sideways to smile. : )

12 You're reading this and nodding and laughing.

13. Even worse, you know exactly to whom you are going to forward this message.

14. You are too busy to notice there was no #9 on this list.

15. You actually scrolled back up to check that there wasn't#9 on this list

AND NOW YOU ARE LAUGHING at yourself.

4

Analytical Challenge and Background

• Panama Canal Expansion Project and Analysis– Simply expand/update/revise

• Data: Needed to be replicatable/ observable and scientifically defendable

• Focus two extreme scopes of analysis in one• Extreme detail on US intermodal competition• Macro trade and policies

– 50 year projections– Measures of risk

5

Panama Canal

• Status• Picture/map• Impacts

6

Gatun DamGatun DamGatun LocksGatun Locks

Gatun LakeGatun LakeGamboaGamboa

Madden DamMadden Dam

Miraflores LocksMiraflores LocksPedro Miguel LocksPedro Miguel Locks

GAILLARD CUTGAILLARD CUT

7

Post Panamax Ship and Lock Dimensions

Post Panamax Ship and Lock Dimensions

Existing LockPost-Panamax Lock

Chamber length 427m (1,400’)Vessel length 385.8m (1,265’)

Chamber length 305m (1,000’)Vessel length 294.3m (965’)

54.9m (180’)

1.5 - 3m(5’ – 10’)

15.3m(50’)

48.7m (160’)

33.5m (110’)

0.6m(2’)

12m(39.5’)

32m (106’)

8

Paper/reports are as follow

• Available at WWW/nets– Longer-Term Forecasting of Commodity Flows on the

Mississippi River: Application to Grains and World Trade

– Appendix titled Longer-Term Forecasting of Commodity Flows on the Mississippi River: Application to Grains and World Trade: Appendix

– IWR Report 006-NETS-R-12• http://www.nets.iwr.usace.army.mil/docs/LongTermForecastCommodity/06-NETS-R-12.pdf

• NDSU Research Reports forthcoming

9

Overall Approach• Data

• Assumptions

• Model

• Results

10

Model Dimensions and Scope Major components of the model

• Consumption and import demand:– Estimates of consumption were generated based on incomes,

population and the change in income elasticity as countries mature. For the United States, ethanol demand for corn was treated separately from other sources of demand.

• Export supply: For each exporting country and region, export supply is defined as the residual of production and consumption.

• Costs Included:– Production (Variable) costs– Shipping by truck, rail, barge and ocean– Barge delay costs (nonlinear)– Handling costs– Import tariffs

• Model dimensions: The model was defined in GAMS and has – 21,301 variables– 761 restrictions.

11

Model Details• Production calculations

– Max area potential: • HA=f(trend)

– Base case area subject to max % increase from base period– Max switching between crops:

• 12% (base) -20%– Yields=f(trend)

• Model chooses least cost solution and derives– Area planted to each crop by region– Production– Consumption– Trade– Route and intermodal allocation

12

Objective FunctionW PC S A t Q

t Q t Q

t Q t B Q

t r Q

ci i ci cijjicic

tcij

Rcij

Rcij

wic

tciw

tciw

wic

cipR

cippic

cwp p cwpB

cpq q cpqqpc

= − +

+ +

+ +

+ +

∑∑∑∑∑∑∑∑ ∑∑∑

∑∑∑ ∑∑∑∑∑∑

( )

( )

( )

where i=index for producing regions, j=index for consuming regions,p=index for ports in exporting countries, q=index for ports in importing countries, w=index for river access point on the Mississippi River system,B=barge,R=rail,T=truck,PCci=production cost of crop c in producing region i, Aci=area used to produce crop c in producing region i, t=transportation cost per ton, Q=quantity of grains and oilseed shipped, S=production subsidies in the exporting country;r=import tariffs in the importing country;B=delay costs associated with barge shipments on each of four reaches on the Mississippi river.

13

Restrictions1)

2)

3)

4)

5)

6)

7) 8)

9)

where

y=yield per hectare in producing regions in exporting countries, TA=total arable land in each producing regions in exporting countries, MA=minimum land used for each crop in producing regions in exporting countries, MD=forecasted domestic demand in consuming regions in exporting countries and importdemand in consuming regions in importing countries,PC=handling capacity in each port in both exporting and importing countries, LDw throughput capacity for grains and oilseeds at river access point W, MQp in the minimum quantity of each crop shipped through each port in the U.S.

1)

2)

3)

4)

5)

6)

7) 8)

9)

where

y=yield per hectare in producing regions in exporting countries, TA=total arable land in each producing regions in exporting countries, MA=minimum land used for each crop in producing regions in exporting countries, MD=forecasted domestic demand in consuming regions in exporting countries and importdemand in consuming regions in importing countries,PC=handling capacity in each port in both exporting and importing countries, LDw throughput capacity for grains and oilseeds at river access point W, MQp in the minimum quantity of each crop shipped through each port in the U.S.

5)

6)

7) 8)

Yci Aci jQcij p

Qcip≥ ∑ + ∑

∑ ≤c

Aci TAi

Aci MAci≥∑ + ∑ ≥i

Qcij qQcqj MDcj

∑ ∑ ≤c i

Qcip PCp

∑ ∑ ≤c i

Qciw LDw∑ + ∑ ≥i

Q cipw

Q cwp MQcpR R

∑ = ∑i

Qcip qQcpq

∑ = ∑p

Qcpq jQcqj

14

US Consumption Regions

USSE

USWEST

USSPLAINS

USNE

USECB

USNPLAINS

USPNW

USWCB

USCPLAINS

USDELTADomestic RegionsUSCPLAINSUSDELTAUSECBUSNEUSNPLAINSUSPNWUSSEUSSPLAINSUSWCBUSWEST

15

US Production Regions

USSE

USWEST

USSPLAINS

USNE

USPNW

USCPLAINS

USWNPLAINS

USDELTA

USNPLAINSUSMN

USMI

USOH

USMOW

USMNR

USIowaW

USINRiver

USCPLAINSR

USWiscS

USILNorth

USWiscW

USILSouth

USINNorthUSIowaR

USMOR

Production RegionsUSCPLAINSUSCPLAINSRUSDELTAUSILNorthUSILSouthUSINNorthUSINRiverUSIowaRUSIowaWUSMIUSMNUSMNRUSMORUSMOWUSNEUSNPLAINSUSOHUSPNWUSSEUSSPLAINSUSWESTUSWNPLAINSUSWiscSUSWiscW

16

Consumption

17

Consumption Functions• Changes in consumption as countries’ incomes increase• Econometrics:

– C=f(Y)• C=consumption and Y=income• For each country and commodity using time series data• Use to generate elasticity for each country/commodity

– E=f(Y)• E= Elasticity• Non-linear• Across cross section of time series elasticity estimates• Allow elasticities for each country to change as incomes increase

• Derive projections– Use WEFA income and population estimates– Derive consumption as

• C=C+%Change in Y X Elasticity

18

Income Elasticity for Corn

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9Elasticity

0

10

20

30

40

US

Dol

lars

(000

)

19

Grain/Oilseed Production Cost

• Data from Global Insights– By country and crop– Standardized method to derive variable

costs/HA• Combined with estimated yields to derive

costs in $/mt – By crop– By country/region– Projections

20

Corn Cost of Production ($/mt)

19902000

20102020

20302040

20502060

207020

40

60

80

100

120

140

Cos

t US

$/M

T ArgentinaChinaUS IL NorthUS IowaUS C. Plains

21

Soybean Cost of Production ($/mt)

19902000

20102020

20302040

20502060

20700

50

100

150

200

250

Cos

t US

$/M

T ArgentinaBrazil NBrazil SChinaUS IowaUS IL North

22

Ethanol

• Projections and sensitivities– EIA 2005 was the base case – EIA 2006 assumption (Ethanol sensitivity)– Qualified alternative stylized assumptions

• Method– Current known demand: Assumed – New demand:

• Allocated proportionately across states

– DDGs produced• returned to regional feed demand proportionate to its value

23

EIA Corn Ethanol Forecast 2006

According to the EIA:

"Higher oil prices increase the demand for unconventional sources of transportation fuel, such as ethanol and biodiesel, and are projected to stimulate coal-to-liquids (CTL) production in the reference case. . . . The production of alternative liquid fuels is highly sensitive to oil price levels."

0

2000

4000

6000

8000

10000

12000

2000 2005 2010 2015 2020 2025 2030

EIA 2005,USCorn EthanolProduction

EIA 2006,USCorn EthanolProduction

EIA2006,EthanolImports

Energy Information Admnistration (EIA), US DOE, Annual Energy Outlook (AEO)

2006 Outlook

2005 Outlook

Corn ethanol production dramatically up with RFS and higher crude oil price.

US CORN ETHANOL PRODUCTION

2006 Outlook, Imports

Million gallons

PRX_EIAlongterm, PRXrev. 14-Mar-06

24

May 2006 Ethanol Plant Locations

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Current Capacity (mgpy)# 0 - 12# 13 - 36# 37 - 85# 86 - 230# 231 - 1070

Planned Expansion (mgpy)# 0 - 12# 13 - 36# 37 - 85# 86 - 230# 231 - 1070

25

May 2006 Ethanol Plant Locations

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Current Capacity (mgpy)# 0 - 12# 13 - 36# 37 - 85# 86 - 230# 231 - 1070

Planned Expansion (mgpy)# 0 - 12# 13 - 36# 37 - 85# 86 - 230# 231 - 1070

26

Fraley on Yield Technology Potential

• Corn yields double in 25 years, reaching 300b/a in 25 years which was a reasonable goal. – New technology includes traits influencing

• Yields• drought tolerance

– Yields on dryland conditions could increase 8-10%.• fertilizer use• pest resistance. • redesign of corn to increase starch content

– Increase from 2.8 to 3.0 gall/b – With this, he indicated it would be possible to

increase ethanol production to 50 bill gallons, based on a corn crop of 25 bill bushels from 90 million acres in 2030.

27

Monsanto’s GM Corn Pipelinefrom Fraley, July 31, 2006

Increase from 2.8 gall/b; 2.7% increase in ethanol yields (2.87 gall/b)

High fermentable corn

Pre-processing to produce higher value co-products and higher fermentable starch for ethanol. Combined with high-lysine products to improve quality and reduces the amount of DDGs

4 Regulatory submission(3+ years; Prob=.90)

Renessen corn processing system

I Proof of concept(8+ years; Prob=.25)

High yielding corn

8%Under drought stress, hybrids with best performing events show yield advantage; allows expansion of corn areas to more dry regions (western dryland, 10-12 m acres)

II Early Development(5+ years; Prob=.50)

Drought tolerant corn

10%Increase in yield on limited nitrogen environments; 10% target yield increase

1 Proof of Concept (8+ years; Prob=.25)

Nitrogen Utilizing Corn

Yield Impact

ScopePhaseTrait

28

Double-X to single-X hybrids

Expansion of irrigated area, increased N fertilizer rates

Soil testing, balanced NPK fertilization,

conservation tillage

Transgenic (Bt) insect resistance

Reduced N fertilizer & irrigation?

(embodies tremendous technological innovation)USA Corn Yield Trends, 1966-2005

y = 112.4 kg/ha-yr[1.79 bu/ac-yr]

R2= 0.80

2000

4000

6000

8000

10000

12000

1965 1970 1975 1980 1985 1990 1995 2000 2005

YEAR

GR

AIN

YIE

LD (k

g ha

-1)

Integrated pest management

K.G. Cassman, CAST Renewable Energy Agriculture, In Press.

??

29

152.0

0

20

40

60

80

100

120

140

160

180

200

70-71 75-76 80-81 85-86 90-91 95-96 00-01 05-06 10-11 15-16

USDA OfficialEstimate

PRX Forecast

Trend, 1973-2004

Trend, 1990-2004

UNITED STATES CORN YIELD and TRENDPRX_B_Maps_BA, GTB-06-12rev, Jan-03-07

Bushels per acre

PRX has adopted the trendline 1990-2004, which is increasing faster than the longer term 30-year trend.

30

U.S. Corn Yield Trends Over Selected Time Periods

1975 1980 1985 1990 1995 2000 2005 201070

80

90

100

110

120

130

140

150

160

170

bu/a

80-0690-0696-0698-0699-0692-0688-0696-06 oly

31

Modal and Handling rates• Handling • Truck • Barge

– Rate functions– Delay costs

• Rail • Comparisons• Ocean rates• Caution: In many cases, we are splitting <$1/mt

differences among least cost movements!

32

Estimated Relationship Between Distance, Rate/Loaded Mile and Cost/mt

0 500 1000 1500 2000 25000.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

Rat

e ($

/Loa

ded

Mile

)

0

10

20

30

40

50

60

70

Rat

e ($

/MT)

$/Loaded Mile$/MT

33

Barge Rates

• Barge rate functions (instead of barge rate levels)– Critical (essential for solution)– Numerous nil movements on Reaches– Missed values by c/mt

• GAMS implications: Nonlinear programming

34

Reach Definitions

Export RegionsDul/SupECMissouriNOLAPNWRCH1RCH2RCH3RCH4RCH5RCH6TXGulfToledo

35

Barge Rate Functions

0 5 10 15 20Volume of Cummulative Shipments (MMT)

0

5

10

15

20

Bar

ge R

ate

$/M

T RCH 1RCH 2RCH 3RCH 4RCH 5RCH 6

36

Delay Costs and VolumesExisting and Expanded Capacity

• Derived through simulation • Barge capacity-volume relationship was estimated for each lock within the reach. • Model was developed where

– Average wait time = f(volume); and, – Cost = f(wait time)

• Results in hyperbolic function – GAMS had problems solving due to non-linear nature of delay costs– Respecified as a double-log function

• Factors impacting the cost include – value of grain, equipment and labor costs.

• Delay costs for each reach represent the sum of the delay curves at individual locks within the reach.

• Normalized– defined relative to “normal traffic” assumed for other commodities, both upbound and

downstream traffic, and reflect the incremental impact on cost for an assumed change in grain traffic.

– annualized using procedures in Oak Ridge National Laboratory (2004)

37

10 20 30 40 50 60 70 80Volume (MMT)

-2

0

2

4

6

8

10

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 1

0 10 20 30 40 50 60Volume (MMT)

-5

0

5

10

15

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 2

0 5 10 15 20 25 30Volume (MMT)

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 3

0 10 20 30 40 50Volume (MMT)

-2

0

2

4

6

8

10

12

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 4

0 10 20 30 40 50 60 70 80Volume (MMT)

-5

0

5

10

15

Cha

nge

in R

ate

($/M

T)

Reach 1

Reach 2

Reach 3

Reach 4

Reach 1-4 Existing

0 10 20 30 40 50 60 70 80Volume (MMT)

-5

0

5

10

15

Cha

nge

in R

ate

($/M

T)

Reach 1

Reach 2

Reach 3

Reach 4

Reach 1-4 Expanded

Barge Delay Functions (Grain Volumes Only)

38

Rail rates

• Source– STB confidential/private data set

• Cautions: STB not clear on treatment of– FSC’s– COT/Shuttle payments

• Weighted average 2000-2004– Updated to 2004– careful revisions/modifications.

• Missing rates: If rate is missing– Movement not allowed

• Subtracted $2/mt for shuttle train rebates to PNW

39

Ocean Shipping Rates

• Rates from IGC• Estimated rate functions

– To deal with unreported origins/destinations• Applied to generate ocean shipping matrix

40

Projection Methodology• Demand is projected for each country and region based on income

and population projections from Global Insights;

• Yield and production costs for each producing region are derived;

• Production potential is determined in each country/region subject to the area restriction;

• US modal rates were derived and it was assumed that their spatial relationship was the same during the projection period.

• Ocean shipping costs were projected

• Using these, the model was solved for each year in the projection horizon which was defined in 10 year increments for 50 years.

41

Calibration

• World trade: – Comparing model results to actual exports

suggests these are very similar. • US Port shipments Results are very

comparable to actual shipments. – Export volumes from the US are comparable

by grain type as are interport exports. – Exception is East Coast exports which should

be slightly greater than generated from the model

42

Calibration of ExportsHistorical U.S. Export Shipments (2000-2004)

Total Corn Soybeans WheatEast Coast/Lakes 5,960 1,507 2,049 2,405Gulf 67,774 33,952 19,908 13,915PNW 20,663 6,521 3,749 10,393Internal 4,426 1,878 1,991 557Total 98,823 43,858 27,696 27,269

Base Case - Current CapacityTotal Corn Soybeans Wheat

East Coast/Lakes 2,554 0 0 2,554Gulf 65,215 32,767 19,924 12,524PNW 24,594 9,923 6,101 8,570Internal 8,234 1,005 3,995 3,234Total 100,597 43,695 30,020 26,882

43

Comparison of Actual and Projected Barge Loadings by Reach

Reach Average 2000-2004 Base Case Difference(000 MT) (000 MT) (000 MT)

Reach 1 7,909 7,154 -755Reach 2 10,626 3,781 -6,845Reach 3 7,450 12,235 4,785Reach 4 14,608 21,771 7,163Reach 5 4,169 4,184 15Reach 6 2,317 2,050 -267 Total 47,079 51,175 4,096

44

Shipments from Production Regions to ReachesProduction Export Region Toledo Duluth LA Gulf PNW Texas GulfRegion RCH1 RCH2 RCH3 RCH4 RCH5 RCH6 TOL USD USNO USNW USTUSCP 407 4,900USCPR 2,447USD 863 1,046USIAR 3,781USILN 21,771USILS 1,571USINR 4,184USMN 2,656USMNR 12,235USMOR 3,505USMOW 1,321 340USNP 2,147 14,328 138USOH 758 2,050 1,448USPNW 1,798 2,858USSP 1,608USWNP 4,204

45

Results

• Base case projections• Expansion base case projections• High ethanol

– With/wo China• Sensitivities

46

Base Case Projections: US Exports by Port Area

Base2010

20202030

20402060

0

20

40

60

80

100

120

140

Exp

orts

(MM

T)

US Mex DirUS PNWUS GulfUS EC/Lakes

Total

Base2010

20202030

20402060

0

10

20

30

40

50

60

70

Exp

orts

(MM

T)


Corn

Base 2010 2020 2030 2040 20600

10

20

30

40

Exp

orts

(MM

T)


Soybeans

Base2010

20202030

20402060

0

5

10

15

20

25

30

Exp

orts

(MM

T)US Mex DirUS PNWUS GulfUS EC/Lakes

Wheat

47

Base Case Projections:Barge Reach Volumes

Base 2010 2020 2030 2040 20600

10

20

30

40

50

60

70

Bar

ge V

olum

e (M

MT)

RCH6RCH5RCH4RCH3RCH2RCH1

Total

Base 2010 2020 2030 2040 20600

10

20

30

40

50

Bar

ge V

olum

e (M

MT)


Corn

Base 2010 2020 2030 2040 20600

5

10

15

20

25

Bar

ge V

olum

e (M

MT)


Soybeans

Base 2010 2020 2030 2040 20600

1

2

3

4

Bar

ge V

olum

e (M

MT)


Wheat

48

Delay costs and expansion

• Assume expansion adopted in 2020• Simulate delay costs

– With and with/out changes in other traffic

49

10 20 30 40 50 60 70 80Volume (MMT)

-2

0

2

4

6

8

10

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 1

0 10 20 30 40 50 60Volume (MMT)

-5

0

5

10

15

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 2

0 5 10 15 20 25 30Volume (MMT)

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 3

0 10 20 30 40 50Volume (MMT)

-2

0

2

4

6

8

10

12

Cha

nge

in R

ate

($/M

T)

Current

Actual

Expanded

Reach 4

0 10 20 30 40 50 60 70 80Volume (MMT)

-5

0

5

10

15

Cha

nge

in R

ate

($/M

T)

Reach 1

Reach 2

Reach 3

Reach 4

Reach 1-4 Existing

0 10 20 30 40 50 60 70 80Volume (MMT)

-5

0

5

10

15

Cha

nge

in R

ate

($/M

T)

Reach 1

Reach 2

Reach 3

Reach 4

Reach 1-4 Expanded

Barge Delay Functions (Grain Volumes Only)

50

Change in Barge Volume (Expanded – Current Capacity), 2020

RCH1 RCH2 RCH3 RCH4 RCH5 RCH6 Total-2

-1

0

1

2

3

4

5

Cha

nge

in B

arge

Vol

(MM

T)

WheatSoybeansCorn

• Positive changes in Reach 1, 2 and 4• Negative in Reach 5 and 6• KEY: Interreach competition based on delay costs!

51

Barge Delay CostsDelay Costs: Current Barge Capacity ($/MT Barge Volume (Grain + NonGrain)

Base 2010 2020 2030 2040 2060Reach 1 -0.12 -0.04 -0.02 -0.09 -0.04 -0.14Reach 2 -0.19 0.08 0.14 -0.08 0.03 0.55Reach 3 0.10 0.10 0.10 0.08 0.09 0.10Reach 4 0.45 0.86 1.08 0.93 1.04 0.80

Delay Costs: Expanded Barge Capacity ($/MT Barge Volume (Grain + NonGrain)Base 2010 2020 2030 2040 2060

Reach 1 -0.12 -0.04 -0.46 -0.47 -0.47 -0.47Reach 2 -0.19 0.08 -0.90 -0.94 -0.92 -0.79Reach 3 0.10 0.10 0.10 0.09 0.10 0.10Reach 4 0.45 0.86 0.06 0.00 0.05 -0.03

Delay Costs: Change (Expanded - Current)Base 2010 2020 2030 2040 2060

Reach 1 0.00 0.00 -0.44 -0.39 -0.42 -0.34Reach 2 0.00 0.00 -1.04 -0.86 -0.95 -1.34Reach 3 0.00 0.00 0.00 0.01 0.01 0.00Reach 4 0.00 0.00 -1.01 -0.93 -0.99 -0.83

52

Effect on Delay Costs and Barge Volume of Expansion in Barge Capacity (Reach 4, 2020)

0 10 20 30 40 50 60Volume (MMT)

-1

0

1

2

Cha

nge

in R

ate

($/M

T)

Cur DelayExp DelayCur Cap 2020Price EffectSub. EffectExp Cap 2020

D2

D1

D3

Q1 Q3

53

Summary of Delay Cost Impacts and Expansion

• Effects of expansion on the change in equilibrium between the base case without expansion in 2020 and that with an expansion was also evaluated are:– reduced delay costs of $61 million (about $1.02/mt)– an increase in quantity shipped by barge resulting in a higher barge rate

• About $50 million, or, $0.84/mt.– In total, barge shipping costs including delay costs are reduced by $11

million, or, $0.18/mt. • Other impacts are for

– reduced shipping costs by rail to ports and reaches of about $59 million– increased rail shipments to domestic– slightly greater ocean shipping costs, $10.4 million, due to an increase

in shipping from the US Gulf. • Taken together, the effect of the expansion is to reduce these costs

by $52 million.

54

Summary: Delay Cost Impacts and Expansion

• Effects of expansion on the change in equilibrium between the base case without expansion in 2020 and that with an expansion

-52Total

10.4slightly greater ocean shipping costs (more US Gulf shipments)

59reduced shipping costs by rail to ports and reaches

-.18-11Total

.8450 increase barge shipments (higher barge rates)

1.0261reduced delay costs

$/mt$millionImpact

55

High Ethanol Demand: Exports

Base2010

20202030

20402060

0

100

200

300

400

500

600

Exp

orts

(MM

T)

US Mex DirUS PNWUS GulfUS EC/LakesSouth AfricaMexicoLatin AmericaEuropeChinaCanada WCCanada ECBrazil SBrazil NAustraliaArgentina

Total

Base2010

20202030

20402060

0

50

100

150

200

Exp

orts

(MM

T)


Corn

Base2010

20202030

20402060

0

50

100

150

200

Exp

orts

(MM

T)


Soybeans

Base2010

20202030

20402060

0

50

100

150

200

250

Exp

orts

(MM

T)


Wheat

56

Changes in country exports• Exports from the following countries increase sharply, with the

change from the base to 2020 in ( ): – Argentine corn (16 to 19 mmt)– Europe and Eastern European corn (38 to 48 mmt); – Wheat exports from Australia increase (28 mmt to 32 mmt), Europe

decreases (36 to 32 mmt), US decreases from 14.9 to 13.8; and Canada and Argentina are unchanged.

• Exports from the United States decline from 101 to 78 mmt by 2020– vs. the base case which increased from 101 to 111 mmt. – Gulf exports decrease (76 to 51) – PNW change from 23 to 15 mmt. – Most of the decline is in corn and wheat shipments. – Soybeans decline to 28 mmt for the same reasons described above

57

Change in Barge Reach Volumes High Ethanol Demand – Current Capacity 2020

RCH1 RCH2 RCH3 RCH4 RCH5 RCH6 Total-20

-15

-10

-5

0

5

Cha

nge

in B

arge

Vol

(MM

T)

WheatSoybeansCorn

58

Comparison of Barge Reach Loadings: Base (4 billion), Mid (7.5 billion) and High Ethanol (12 billion) Demand

Base 2010 2020 2030 2040 206010

20

30

40

50

60

70

Bar

ge R

each

Vol

ume

(MM

T)

Base + Forecast Mid Ethanol High Ethanol

Total

Base 2010 2020 2030 2040 20600

10

20

30

40

50

Bar

ge R

each

Vol

ume

(MM

T)


Corn

Base 2010 2020 2030 2040 206014

15

16

17

18

19

20

21

22

Bar

ge R

each

Vol

ume

(MM

T)


Soybeans

Base 2010 2020 2030 2040 20600

1

2

3

4

Bar

ge R

each

Vol

ume

(MM

T)


Wheat

59

Qualified and Stylized Ethanol Impacts and Assumptions

• Above scenario requires that if EIA 2006 demand were to be realize, the model needs to make some extreme assumptions in order to get a solution. In particular, it required

– expanding US acres by 7% reflecting approximately land available in CRP– increasing area available elsewhere in the world

• All these topics are debatable. Most important are those related to– yield increases– the ability to expand area in the United States– demand for non-ethanol corn

• Respecification to evaluate the robustness of the model and the assumptions about these critical variables. Alternative assumptions with respect to three variables for the year 2020.

– yields were defined as 169 vs 161 in our base case– area harvested as defined as 79.2 versus 73-74 in our base case – ethanol conversion 3 gallons per bushes, versus 2.8

60

Comparison of Exports by Port Area: Revised 2020

2010 Revised 2020 20200

50

100

150

Exp

orts

(MM

T)


Total

2010 Revised 2020 20200

10

20

30

40

50

60

70

80

90

Exp

orts

(MM

T)


Corn

2010 Revised 2020 20200

10

20

30

40

Exp

orts

(MM

T)


Soybeans

2010 Revised 2020 20200

5

10

15

20

25

30

Exp

orts

(MM

T)US Mex DirUS PNWUS GulfUS EC/Lakes

Wheat

61

Comparison of Barge Reach Loadings: Revised 2020

2010 Revised 2020 20200

10

20

30

40

50

60

70

80

Bar

ge V

olum

e (M

MT)


Total

2010 Revised 2020 20200

10

20

30

40

50

60

Bar

ge V

olum

e (M

MT)


Corn

2010 Revised 2020 20200

5

10

15

20

Bar

ge V

olum

e (M

MT)


Soybeans

2010 Revised 2020 20200

1

2

3

4

Bar

ge V

olum

e (M

MT)


Wheat

62

Longer-term Demand Elasticity for Barge Shipments

• Derived arc elasticities for the total system are – The derived arc elasticities

for the total system are -.23 and -.22 for 20% and 40% increases in barge rates.

• Elasticities vary by reach. For a 20% increase in rates, elasticities for shipments on Reach 2, 3, and 4 are -0.67, -0.28 and -0.16 respectively.

• Substantial inter-Reach substitution as barge rates change.

Base+20%

+40%+60%

+80%+100%

+150%+200%

0

10

20

30

40

50

60

Barg

e R

each

Vol

ume

(MM

T)


63

Barge Reach Volumes and US Rail Capacity, 2004

• Assuming rail capacity (measured as loadings) at the equivalent of 131 mmt up to 201 mmt

• Increases in rail capacity – inverse impact on barge

shipments. – barge demand increases

substantially as rail capacity is reduced.

– Increases in rail capacity, holding rates and everything else constant, reduce equilibrium barge shipments.

131 MMT141 MMT

151 MMT161 MMT

171 MMT181 MMT

191 MMT201 MMT

0

10

20

30

40

50

60

Barg

e R

each

Vol

ume

(MM

T)


64

Summary 2

• Results are highly dependent on assumptions as below, in rank order– Ethanol– Yield growth rates– CRP– Rail capacity– Rail-barge substitution

65

Extensions: Model Specifications

• Given the size and complexity of this model, there obviously a number of alternative approaches.

• In rank order, those that would likely have the greatest impact on the results would be – estimate an elasticity of substitution amongst modes,

estimate them and include in the model (data exists to do this, and would allow less extreme shifting amongst modes in response to critical variables);

– incorporating explicit supply and demand functions for underlying commodities (but, this is not inconsequential due to the disaggregated specification of the model).

66

Extensions: Seasonality and Congestion

• Model: revolves around the annualization process in supply and demands, delay costs and rate relationships.

• Pressures in the world shipping market mean the seasonal features of demand are becoming increasingly more important. This is particularly true

– given the growth in production and exports from southern hemisphere countries.– It appears that the demands for capacity expansion are for escalated shipments

in an increasingly concentrated shipping season (i.e., following US harvest buy prior to commencing of new-crop Southern hemisphere grains).

• Demands for dealing with seasonality will escalate and mechanisms to allocate priorities for shipments within a more compressed window will escalate

• Alternatives to expanding capacity to facilitate seasonal peakedness in shipping demands

– explore alternative peak pricing, congestion pricing and/or allocation and priority mechanisms.

– Other high fixed cost industries (railroads for shipping grains and other commodities; airlines, cell phones etc) have benefited from these mechanisms

the ace grain flow model: model, results and discussions · 2007. 10. 31. · "higher oil...

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