PowerPoint Presentation · model-based dialogue on global prospects for food and agriculture...

06/11/2017

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Building Capacity for Enhancing Bioenergy Sustainability

Through the Use of the GBEP indicators

Measuring “GBEP Indicator 10” with the AGLINK-COSIMO

Model

Introductory Training

09-10 November 2017

Hanoi – Viet Nam

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Dr. Holger Matthey

Economist and Team leaderMedium-term Outlook and Market Analysis

Trade and Markets Division (EST)Food and Agriculture Organization of the United Nations (FAO)

Familiarize participants with

• the structure and general functionality of the Aglink-Cosimo model and,

• its application in assessing the impact of the biofuel sector on domestic agricultural commodity markets.

Design and discuss simulation scenarios that closely reflect their interests and concerns in the development of the bioenergy and agricultural sectors.

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Scope and objectives of training

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Country training programs

• Argentina

• Brazil

• China

• Czech Rep.

• European Commission

• India

• Indonesia

• Japan

• Mexico

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• New Zealand

• Pakistan

• Paraguay

• Poland

• United Kingdom

• South Africa

• South Korea

• Thailand

Workshop agenda

Thursday• Introduction to the workshop and collaboration project

• Introduction to medium-term commodity projections

• Aglink-Cosimo model

• Model-based outlook generation and use

Friday• General scenario analyses with Aglink-Cosimo

• Biofuel applications and exercises

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Introduction to medium-term commodity projections

Session 1

OECD-FAO medium-term projections methodology

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Forward looking analyses

• Forward-looking policy

and market analyses to

support evidence-based

policymaking

• Promote and foster a

model-based dialogue on

global prospects for food

and agriculture between

stakeholders and FAO

• Consensus baseline

• Scenario analyses

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Outlook tool Analysis tool

• Medium-term outlook

model to produce

baseline projections

• Provides guidelines for

decision making

• Control in scenario

work

• Conduct studies on

policy reform

• Timely assessment of

emerging market

issues

• Disaggregating of

complex interactions

Aglink-Cosimo

OECD-FAO Agricultural Outlook

• Produced annually from December to June

• Ten-year consensus projection of global supply,

demand and trade

• Assessment of driving factors in commodity markets

• Theme Chapter – shared messages for FAO-OECD

• Collaborative effort between various organizations

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Going beyond the OECD-FAO Outlook…

• Detailed country outlooks and capacity building for

member countries

• Support to country and regional projects in HQ,

decentralized offices and partner institutions

• Scenario analysis

• Customized input in downstream analyses

Applications of Aglink-Cosimo

• GBEP Tools

• Fish Model

• Fruits, Fibers and Tea Model

• Indirect Land Use Change assessments

• Feed Demand System

• Bilateral Trade Flows

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Questions?

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Introduction to medium-term commodity projections

Session 1

Highlights of the 2017-2026 Agricultural Outlook

Agricultural commodity markets in 2016/17

• Above-average capital investments in recent years

• Record production and abundant stocks of most

commodities in 2016

• Prices of cereals, meats and dairy products

continued to decline from peaks experienced in the

last decade

• Oilseeds, vegetable oils, and sugar provide

exceptions to this trend, with prices rebounding in

2016

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Key macroeconomic assumptions

• Global population growth slow down to 1% p.a., but

strong regional differences remain

• Economic recovery in most industrialized countries,

but growth prospects in large emerging countries

diverge

• Real currency appreciation expected in major

exporters

• Crude oil reference price rising from $44 to $90

Consumption Outlook

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Main Messages

• Global consumption continues to expand and evolve

toward higher value commodities

• Growth will slow down compared to previous decade

• China, India and Sub-Saharan Africa drive global growth

• Convergence in per capita food consumption patterns

remains limited

Consumption by commodity

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Annual growth in demand for key commodity groups, 2007-16 and 2017-26

Global consumption

growth slows down

Total demand mainly

driven by population

growth

Per-capita food

demand limited by:

• Saturation

• Access

For fresh dairy

products, vegetable oil

and sugar strong per-

capita increase

expected

-

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

200

7-16

2017

-26

200

7-16

2017

-26

200

7-16

2017

-26

200

7-16

2017

-26

200

7-16

2017

-26

200

7-16

2017

-26

200

7-16

2017

-26

Cereals Meat Fish Freshdairy

Roots andtubers

Sugar Vegetableoil

%

Due to per capita consumption growth or non-food consumption growth

Due to population growth

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Calorie consumption per capita

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Per capita caloric availability in 2006, 2016 and 2026Increasing trend in caloric availability per capita to continue

• Bridging caloric availability

levels between developing

countries and OECD

• China reaches the overall level

of the OECD caloric availability.• India makes substantial

progress

Cereals remain to be the most

important source of calories across

the world.

Bulk of additional calories from

sugar and vegetable oil

Dietary preferences persist and

influence nutrition patterns

-

500

1,000

1,500

2,000

2,500

3,000

3,500

200

6

2016

2026

200

6

2016

2026

200

6

2016

2026

200

6

2016

2026

200

6

2016

2026

Sub-SaharanAfrica

India China SoutheastAsia

OECD

kca

l/d

ay/p

erso

n

Cereals Roots and tubers Vegetable oilsMeat Sugar DairyFish Other

Protein consumption per capita

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Protein availability remains diverse

Key difference is the level of animal protein consumption between regions

• low levels in Sub-Saharan Africa, India, and Southeast Asia

In India, main animal protein is coming from the fast expanding fresh dairy product consumption

In Southeast Asia, fish consumption is important

Limited income growth in poor rural and urban households and the slow development of a retail infrastructure for animal protein are seen as the main constraints to animal protein consumption growth

-

20

40

60

80

100

120

200

6

2016

2026

200

6

2016

2026

200

6

2016

2026

200

6

2016

2026

200

6

2016

2026

Sub-SaharanAfrica

India China SoutheastAsia

OECD

g/d

ay/p

erso

n

Cereals Meat Dairy Fish Other

Per capita protein availability in 2006, 2016 and 2026

Production Outlook

Main Messages

• Yield growth will be the main driver of food crop and

feed production

• Growth due to 80% yield and 20% land

• Diverse growth in livestock and fish production

• Intensive and extensive production systems coexist

• Developing countries lead the agricultural production

growth

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Crop production

Yield improvements main driver of crop production growth

Wheat:

– 9% yield growth– 2% net area gain (4 million ha)

mostly in in Russia and India

Maize:

– 11% yield growth, large yield gap remains,

– 2% area growth (4 million ha), all in Africa and Latin America

Rice:

– 12% yield growth, – small net area gain globally (2

million ha), shift into LDC with low yield base

Soybean:

– 9% yield growth, yield gap small and closing,

– 14% area net growth, 16 million ha,

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0

5

10

15

20

25

Wheat Maize Rice Soybean

Gro

wth

20

14

-16

to 2

02

6 (%

)

Area Yield

Relative growth shares of area and yield

75%

0.3

5.3

2.71.53.1

25%

12.0

7.9

6.40

5

10

15

20

25

30

35

40

45

Beef Pork Poultry Sheep Beef Pork Poultry Sheep Total

Developing Developed Increase

Meat production

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Total meat, 13% growth

globally

46% of it in poultry,

becoming leading meat

Pigmeat production

expansion slowed by

increased environmental

regulations and animal

welfare concerns

Beef production expands

in Argentina, China, Brazil, India

Sheepmeat sector global

growth of 22%, faster than

last decade

39 million tons of additional meat produced by 2026

Mt

(c.w

.e/

r.t.

c)

Trade Outlook

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Main Messages

• Growth in trade is closely matched with output

growth in the coming decade

• High concentration in export markets persists

• Growing import demand from developing countries

• Role of trade continues to differ by commodity

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-100

-50

0

50

100

150

2006 2011 2016 2021 2026Bill

ion

co

nst

ant

USD

(20

04-0

6 b

ase)

Regional net trade in agricultural products

Americas Sub-Saharan Africa

Asia Other

North Africa/Middle East

Trade by region

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• The Near East and Africa increasingly dependent on agricultural imports

• Eastern Europe and Central Asia region now established as main exporter, due to high exports of cereals

• Regional net trade flow evolution reflects continuing changes in regional production and consumption patterns

Projections of commodity prices

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Supply and demand

fundamentals keep

international reference

prices in line with inflation

Most commodity group

prices follow similar trends

due to substitutability and

complementarities

Meat prices have historically

followed a somewhat

different path, meat prices

are expected to fall in real

terms to levels similar to

those in the early 2000s

50

100

150

200

250

300

350

400

1990 1995 2000 2005 2010 2015 2020 2025

20

02-

04 =

100

Projected FAO Food Price Indexes - Nominal

Cereals Sugar Vegetable oils

Meat Dairy Total Agriculture

Long term trend of real

commodity prices

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• Prices follow the long run declining trend, with an average price decrease of

about 1.5% per year in real terms

• Prices of agricultural commodities are subject to considerable volatility and may

show large deviations from their long-term trends for an extended period of time

• Prices eventually returned to their long-term trend

0

500

1000

1500

2000

2500

1900 1909 1918 1927 1936 1945 1954 1963 1972 1981 1990 1999 2008 2017 2026

USD

(20

10 b

ase)

Wheat Maize Rice

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Key takeaways and policy

challenges

Consumption Production

• Slowing demand growth • Few sources of demand to replace China

& biofuels• Rising demand for milk, sugar and oils

Policies to combat malnutrition

• Output gains mostly from yield increases• Important sustainability challenges – esp.

in SE Asia• Big yield gaps in poorest countriesInvest in sustainable productivity growth

Trade Prices

• Trade growth to slow• Main cause is weak demand• Export trade to remain concentrated

Need for open and reliable markets

• Real prices at or below current levels• Always a risk of specific price swings!

Market information and risk management

Southeast Asia:

Prospects and Challenges

Developments in agriculture and fisheries in Southeast Asia

• Extensive structural adjustment

• Changes in fisheries production

• Drivers of production growth in Southeast Asia

• Growing regional participation in world food markets

• Agricultural policies in Southeast Asia: A focus on rice

and self-sufficiency

• Fisheries policies in Southeast Asia: the sustainability

and food security challenge

Southeast Asia - Consumption Food expenditure shares (%)

0

1

2

3

4

5

6

7

8

2001 2006 2011 2016 2021

%

Dairy SugarOther Cereals OtherVeg Oil

0

5

10

15

20

25

30

35

2001 2006 2011 2016 2021

%

Meat/eggs Fish Rice

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Calorie availability

-50

0

50

100

150

200

250

Ch

an

ge

in K

ca

l/d

ay

Other crops Rice Animal

Protein availability

-2

0

2

4

6

8

10

g/p

ers

on

/da

y

Other crops Rice Animal

Southeast Asia - Production Changes in major production activities in Southeast Asia

Increase in production across major production activities, 2016-26

0

5

10

15

20

25

Mill

ion

to

nn

es

Viet Nam Thailand

Philippines Myanmar

Cambodia Laos

Malaysia Indonesia

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Southeast Asia - Trade

Contributions to changes in the trade balance

Changes in net trade balance 2016-26

-3

-1

1

3

5

7

Vegetable oil Rice Meat & eggs Maize Sugar Roots andtubers

Mill

ion

to

nn

es

Philippines Myanmar Laos

Cambodia Thailand Viet Nam

Malaysia Indonesia

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Download the publication from:

http://www.fao.org/publications/oecd

-fao-agricultural-outlook/2017-

2026/en/

Access the full database at:

http://www.agri-outlook.org/data/

Contact us for further information at:

[email protected]

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Aglink-Cosimo model

Session 2

Aglink-Cosimo model structure

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Vision and main objectives

•Provide consensus analyses on the future evolution of international commodity markets.

•Develop increasingly integrated systems that link short, medium and long term projections.

•Publication of the annual “OECD-FAO Agricultural Outlook” in collaboration with the OECD.

•Construct scenarios analyzing emerging market and policy issues using the Aglink-Cosimo model.

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The Outlook: Forecast or Baseline?

• Expectation for the future

• Based on clearly defined assumptions

• A prediction, as of coming events or conditions = Forecast

• A measurement, calculation, or location used as a basis for comparison = Baseline

The Projections team hard at work

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Model

Data

Parameters

Projection Output

EndoExo

Aglink-Cosimo• Partial equilibrium model that provides structural

representations of national and global agricultural

markets where all of the major agricultural sectors are

assumed to be connected

– Non-agricultural markets are not modelled and are treated

exogenously to the model

• Specific assumptions regarding: – Macroeconomic conditions

– Agriculture and trade policy settings

– Weather conditions

– Longer term productivity trends

– International market developments

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• Model, driven by elasticities, technical parameters and policy variables,

• provides representations of national and global agricultural markets where all of the major agricultural sectors are assumed to be connected,

• outlook simulation tool that constructs projections over a ten year period so that all of the main characteristics of the crops and livestock sectors influence the final equilibrium.

Aglink-Cosimo

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Methodological Approach

Key assumptions of the model:

1. World markets for agricultural commodities arecompetitive.

2. Domestically produced and traded commodities areviewed to be perfect substitutes by buyers and sellers.

3. Aglink-Cosimo is a partial equilibrium model for the mainagricultural commodities. Non-agricultural markets arenot modelled and are treated exogenously to the model.

Domestic price (internal market clearing)

Net trade(exports – imports)

World trade balance∑EX = ∑IM

Equilibrium world price

Production

Area/

livestock

Yield

Opening

stocks

Imports

Income

PopulationConsumption

(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

Wheat Beef Skim Milk Powder

Coarse Grains** Sheepmeat Whole Milk Powder

Rice Pigmeat Cheese

Oilseeds** Poultry Butter

Vegetable Oils** Eggs Fresh Dairy Products

Oilseed Meals** Sugar Ethanol

Roots and Tubers

Animal Feed

Cotton Bio-diesel

Fish (Separate Model)

Aglink – Cosimo Commodities

** Indicates sectors which may be disaggregated

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Norway Switzerland

AGLINK Countries

Australia Canada EU-27 Japan S.-Korea

Mexico N-Zealand USA

Argentina Brazil China Russia

Aglink (OECD) Countries Cosimo (FAO) Countries and Regions

Australia Algeria Nigeria Other LDC Africa

Argentina Bangladesh Pakistan Other LDC Asia

Brazil Chile Paraguay Other LDC Oceania

Canada Colombia Peru Other Western Europe

China Egypt Philippines Other Asia Developed

European Union Ethiopia Saudi Arabia Other Eastern Europe

Japan Ghana South Africa Other Middle East

Mexico Haiti Sudan Other North Africa

New Zealand India Tanzania Other Africa

Norway Indonesia Thailand Other Asia

Russia Iran Turkey Other Oceania

South Korea Israel Ukraine Other Latin America

Switzerland Kazakhstan Uruguay

United States Malaysia Vietnam Mozambique Zambia

Geographical coverage

27 country modules were added to Cosimo

in 2017

• Angola• Burkina Faso• Chad• Democratic Republic of the

Congo• Madagascar• Malawi• Mali• Mauritania• Rwanda• Senegal• Somalia• Uganda

• Myanmar• Cambodia• Lao People's Democratic

Republic• Yemen

• Iraq• Lebanon• Libyan Arab Jamahiriya• Morocco• Tunisia

• Cameroon• Congo• Côte d'Ivoire• Gabon• Kenya• Zimbabwe

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Model

Data

Parameters

Projection Output

EndoExo

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Annual time series for:

Historic (endogenous)• prices• supply (area, yield, animal numbers...)• demand (food, feed, crush...)• trade (exports, imports)

Projection (exogenous)• macroeconomic data (GDP, ex. rate...)• policy variables (tariffs, CAP...)

Data requirements

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• national statistics

• international databases

• surveys, questionnaires

• literature

• calculations

Data sources for endogenous data

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Sources (historic series and projections):• national statistics• international databases• surveys, questionnaires• literature• calculations

Exogenous projections may have to be done by you!

Methods:• trend• time series methods• regression• expert opinion

Data sources for exogenous data

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Model

Data

Parameters

Projection Output

EndoExo

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• are the link between variables

• determine the properties of the model

IDNparms.inp

Parameters

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Introduction to parameters

• types

• function

• sources

Parameters

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Steps to develop and modify the parameter set• example of non-estimation method• based on available estimates

VNMparm.xlsm

Parameters

Parameters

• Strategy for parameter choices

1. Use available estimates.

2. Use systems / appropriate constraints

3. Estimate: research estimation agenda

Model validation by country / by commodity

Emphasis on consultation with experts

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Model

Data

Parameters

Projection Output

EndoExo

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• Produced annually from December to June.

• Ten-year projection of global supply, demand and trade

• Assessment of driving factors in commodity markets

• Theme Chapter – shared messages for FAO-OECD

• Growing scope – fish, cotton, land, fertilizers....

• Collaborative effort between various international organizations and national institutions

OECD-FAO Agricultural Outlook

Uncertainties and Limitations

Related to:– macroeconomic developments

– technology advances (yields, biofuel)

– energy prices,

– weather-related production shocks,

– disease outbreaks,

– agricultural policy developments

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Questions?

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Model based outlook generation

Session 3

Medium term baseline process

Definition of partial equilibrium model

“A partial equilibrium is one which is based on only a restricted range of data, a standard example is price of a single product, the prices of all other products being held fixed during the analysis.”

Source: Definition by George Stigler in Jain, T.R. (2006-07). Microeconomics and Basic Mathematics. New Delhi: VK Publications. p. 28.

Advantages of PE modeling

1. Limited data requirements

2. Disaggregated and detailed sector analysis

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Assumptions of a partial equilibrium model

• The analysis only considers the effects of a given policy action on the market that is directly affected;

• Price and Quantity of a given commodity are taken as endogenous (determined) and all other goods are constant and exogenous to the analysis;

• Markets are perfectly competitive

Assumptions of PE model (2)

• Cost minimization or profit maximization on the production side;

• Utility maximization (given budget constraint) on the consumption side

AGLINK-Cosimo and partial equilibrium modeling (3)

• The functional relationships linking supply and demand to prices are, in most cases, linear in the logarithms of the variables

• Equation coefficients are partial elasticities

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AGLINK-Cosimo and partial equilibrium modeling

SummaryAGLINK-Cosimo is a recursive-dynamic, partial equilibrium, supply-demand model for the main agricultural commodities consisting of behavioural equations which represent responses of economic agents making supply and demand decisions

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Questions?

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COUNTRY MODULE

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Production

Area/

livestock

Yield

Opening

stocks

Imports

Income


(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

Supply

• Crop production:

Area=f(Return/ha, Paymt/ha, Costindex,Area(-1),Rfactor)

Yield=f(Return/t,Paymt/t,Costindex,trend,Rfactor)

Production=Area*Yield

• Oilseed products

OilseedMeal production=mealyield*oilseed crush

Protein meal production=oilseed meal + others…

Oilseedoil production=oilyield*oilseed crush

Veg oil production=oilseed oil + palm oil + others…71

• Meat production:Livestock Inventory=f(Meatprice,Paymt/hd,

Feedpriceindex,Costindex,Inventory(-1),trend,Rfactor)Indigenous meat production=f(Meatprice,Paymt/tn,

feedprice index,costindex,Inventory(-1), production(-1),trend,Rfactor)

• Milk production:Cow Inventory=f(Milkprice,Paymt/hd,

Feedpriceindex,Costindex,Inventory(-1),trend,Rfactor)Cow yield=f(Milkprice,Paymt/tn, feedprice index,costindex,

trend,Rfactor)Milk production= Inventory*yieldMilk products=f(fatprice,proteinprice)

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Supply

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Production

Area/

livestock

Yield

Opening

stocks

Imports

Income


(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

• Food demand

Food demand= f(Ownprice, other prices, pricedeflator, Income/person,trnd,Rfactor)

» Demand constraints apply in log linear relationship

• Crop Feed demand

Feed demand=f(Ownprice, otherprices, nonruminant prod, ruminant prod,trnd, Rfactor)» Fish meal, DDGs are deducted

• Bio-fuel crop feedstock demand Feedstock demand=f(Pricebiofuel, feedstock price, mandate,subsidy,

Rfactor)

• Crop Other use– Other use=f(Price, income,trnd,Rfactor)

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Demand





Production

Area/

livestock

Yield

Opening

stocks

Imports

Income


(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

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• Crops

Stocks= f(Ownprice/(average(ownprice), production, Rfactor)

• Meat and dairy products

Stocks=f(Ownprice, production, Rfactor)

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Stocks





Production

Area/

livestock

Yield

Opening

stocks

Imports

Income


(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

• Exports

Exports= f(producerprice/exportprice, Rfactor)

• Imports

Imports=f(producerprice/importprice, Rfactor)

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Trade

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Production

Area/

livestock

Yield

Opening

stocks

Imports

Income


(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

• Export price

Exportprice= worldprice*(1+exportwedge)*exchangerate

• Import price

Importprice=worldprice*(1+tariff+importwedge)*exchangerate

• Producer price: domestic market clearingProduction+stocks(-1)+imports=consumption+exports+stocks

• Consumer priceconsumerprice=f(producerprice,deflator)

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Prices

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GLOBAL AGLINK-COSIMO MODEL

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Production

Area/

livestock

Yield

Opening

stocks

Imports

Income


(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

How do we carry out the Outlook?1/ Country expertise is taken into account as a starting point

A questionnaire is sent to each collaborator country

2/ The AGLINK-COSIMO model is used to get

a consistent and coherent picture

Collaboration between FAO and OECD Secretariats

3/ Model Outcomes are adjusted through expert opinions

Collaboration between FAO and OECD Secretariats

4/ Final review of projections in OECD commodity groups

Projections (not forecast) are discussed with member/observer countries

Main steps to obtain the baseline

Step 1 : Country standalonesCalibrate country models on questionnaire replies

Step 2 : Merge of country standalone and of COSIMOGet the AGLINK – COSIMO model

Step 3: Computation of the baselineWork on the global model on a commodity base

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85

• All country modules are merged• Models• Data files• Parameter files

• World price endogenized

• Equilibrium solution for all countries and all commodities

Introduction to the global “all linked” model concept





Production

Area/

livestock

Yield

Opening

stocks

Imports

Income


(food, feed,

other use)

Ending

stocks

Exports

Aglink-Cosimo Model

World Prices Market Clearing

• Wheat, Coarse grain, rice, oilseeds, raw sugar, poultry, sheep meat, butter, cheese, skim powder, whole powder, ethanol, biodiesel:– Global exports = Global imports

• Bovine and pig meats:– Pacific market: exports=imports– Atlantic market: exports = imports– Rest of world: exports=imports

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Model closure

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Aglink – Cosimo Outlook Process

Aglink

Cosimo

OECD-FAO World Agricultural Outlook

OECD Questionnaire

Responses

Country Model Development

FAO Experts

CalibratedStand-alone

Models

AdjustedStand-alone

Models

Solve for worldprices

Iterations,exchanges of modifications

OECDCommodity Groups

OECD Experts

FAO Databases

Solve for domestic prices

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Model use

Session 4

Aglink-Cosimo model functionality

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Introduction to Troll-based Modeling

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The Troll Simulation Software

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• Integrated software for econometric, modeling and statistical analysis

• State-of–the-art model simulation engine designed for large systems

• Complete integration of various tasks (calculation, model editing, estimation, simulation...)

• Hundreds of built-in functions and sophisticated modelinglanguage

• Efficient interface to MS-EXCEL

• Available for any platform, MS-WINDOWS or UNIX

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VNM_RI_FO

Country

Coding

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VNM_RI_FO

Commodity

Coding

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Coding

VNM_RI_FO

Activity

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VNM_RI_FO

CountryCommodity

Activity

Coding

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Simplified country model

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Model File

Driver (program)

REGmod.inp

REGsys.inp

Model Definitions

SECTORS

Wheat

Coarse Grains

ACTIVITIES

SUPPLYArea

Yield

DemandFood

Stocks

TradeImports

Exports

Price Domestic

Model Definitions (2)

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DATAEXO

ENDREGexo.csv

REGhist.csv


PARAMETERS

REGparms.inp


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• Simulation exercises with global model– Baseline generation

– create a projection with model, put output in viewer

• Adjustments of other countries– read in new r-factors,

– modify the macro variables

• Parameter modifications – modify parameters,

• Structural changes– exercises to change equation structure

• Model improvement discussion

Practical exercises using the model

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103

Questions?

Friday

Day 1 - Key takeaways

Session 1 Session 2

• Forward looking analysis to support evidence based policymaking

• Aglink-Cosimo – baseline and scenarios• Global agricultural Outlook

Model analysis, consensus projections

• Partial equilibrium model• Exogenous and endogenous data• Parameters connect variables

Partial equilibrium model capabilities

Session 3 Session 4

• Model assumptions• Equation structure• Global model

Specify equations to reflect markets

• Construct a simple model• General scenario analysis• Evaluate the results

Scenario setup is crucial

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106

Questions?

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Basic scenarios

Session 5

General scenario analyses with Aglink-Cosimo

108

• Simulation exercises with global model– Baseline generation

– create a projection with model, put output in viewer

• Adjustments of other countries– read in new r-factors,

– modify the macro variables

• Parameter modifications – modify parameters,

• Structural changes– exercises to change equation structure

• Model improvement discussion

Practical exercises using the model

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109

Aglink–Cosimo Standalones

• Standalone model construction and baseline generation

– how modules are put together, solution process

• Analysis and scenario exercises

– demonstration exercises running these scenarios, compare, discuss one example chosen by the group

VNMys.inp step2.inp Country_Viewer

110

Introduction to the Viewer

Overview over viewer

• Review the results• Projections• Indicators• Ratios

• Introduce revisions• Endogenous• Exogenous

VNMviewer

Baseline projections for agricultural markets

Scenarios involving:

– yields

– areas

– costs

– macroeconomic variables

– domestic support policies

– trade policies

– disease outbreaks

– biofuel policies

Potential application

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112

Biofuel scenarios

Session 6

Biofuel applications and exercises

113

Bioenergy and Food Security

114

Biofuel Prices

BIOFUEL Demand

BIOFUEL Supply

Feedstock A Feedstock B Feedstock C Feedstock D

Crop Prices

Mandatory Blending

Total Energy demand

Price driven Price driven

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115

Ethanol model

Crop Area

Ethanol

Ethanol

Sugar Cane

“Sumol”

Molasses Raw Sugar White Sugar

Feed supply

Coarse grains

FeedFoodEthanol

DDG

Food Feed

Cassava

Second GenerationFood supply

Ethanol Market

116

Biodiesel Market

Crop Area Oilseeds

Meal Oil

Jatropha

Food

Food demandFeed demand

Biodiesel

Biodiesel

Biodiesel model

117

• Biofuels

• Ethanol

• Biodiesel

Country model sections

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118

• Produce an annual medium-term outlook for the biofuel

sector.

• Carry out in-depth analyses of the fundamentals

shaping the biofuel market and their impact on

agricultural markets and food security.

• Assess the impact of stated biofuel policy targets on

commodities markets.

• Evaluate the impact of second generation biofuels.

Biofuels

Objectives:

119

Ethanol:• Molasses

• Sugar cane

• Sugar beet

• Wheat

• Coarse grains

• Roots and Tubers (Cassava)

• Non Agricultural

• Energy crops and residues

(second generation)

Biodiesel:• Vegetable oils

• Jatropha

• Non Agricultural

Feedstocks:

Biofuels

120

• Transportation fuel use and retail prices (gasoline,

diesel)

• Technical conversion factors

• Taxes, tariffs, payments

• Blending requirements

Biofuels

Exogenous data:

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121

• Ethanol and biodiesel domestic production

• Feedstock use

• Ethanol and biodiesel consumption (fuel and other)

• Biofuel trade

• Ethanol and biodiesel domestic and world market

prices

Biofuels

Endogenous data:

122

Total Production = f(price of ethanol/average feedstock price,

policy variables)

Production by feedstock = f(average feedstock price/feedstock

price)

Feedstock demand = f(production by feedstock, conversion

factor)

Byproducts production = f(production by feedstock,

conversion factor)

Biofuels / Production

123

Ethanol/Biodiesel use in low blends (price driven) =

f(ethanol price/gasoline price, per-capita income, population)

Ethanol other use (non fuel) = f(ethanol price, income)

Mandated Ethanol/Biodiesel use = f(mandated blending,

gasoline/diesel use)

Total Ethanol/Biodiesel use = max (mandated, price driven

uses)

+ other use (ethanol)

Trade and domestic prices equations are same as other

commodities

Biofuels / Consumption

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124

Questions?

Scenario 1

Impact of ethanol production on domestic cassava price from 2009 to 2016

• Ex- post 2009-2016

• Assess the impact of domestic fuel blending mandate and ethanol production from cassava on the domestic cassava market between 2009 and 2016.

• The ethanol blending mandate intends to generate fuel use, but the envisaged 5% of total gasoline use have never been reached.

• It is assumed that no fuel consumption of ethanol occurs without a mandate.

• Scenario 1 removes the blending requirement and subsequently all ethanol production for fuel use.

Scenario 1: Setup

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0

50

100

150

200

250

2009 2010 2011 2012 2013 2014 2015 2016

Thn

ou

san

d C

ub

ic M

eter

s

Domestic Ethanol Market

Production Total Use Other Use Fuel Use Net Trade

Scenario 1: Background


Ethanol Gasoline Consumption

Blendingratio

Total Use Other Use Fuel use

Thousand cubic meters2009 86 81 5 5,501 0.09%2010 89 84 5 6,123 0.08%2011 96 86 10 6,003 0.17%2012 104 89 15 5,973 0.25%2013 122 91 31 6,179 0.50%2014 123 82 41 6,848 0.60%2015 163 97 67 7,255 0.92%2016 176 100 77 7,757 0.99%

The current blending mandate is 5% of gasoline volume sold.


0

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2009 2010 2011 2012 2013 2014 2015 2016

Thn

ou

san

d C

ub

ic M

eter

s

Ethanol production by feedstocks

Cassava Molasses Sugar cane Other

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0

200

400

600

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2009 2011 2013 2015

Tho

usa

nd

To

nn

es

Molasses

Feed Use

Use for Ethanol Production

0

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800

1000

1200

1400

2009 2011 2013 2015

Tho

usa

nd

To

nn

es

Roots and Tubers

Industrial UseFood UseFeed UseUse for Ethanol Production

0

5000

10000

15000

20000

25000

2009 2011 2013 2015

Tho

usa

nd

To

nn

es

Sugar Cane

Sugar Production

Use for Ethanol Production

Use of ethanol feedstuffs

Scenario 1: Results

0

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2009 2010 2011 2012 2013 2014 2015 2016

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usa

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es

Tho

usa

nd

To

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es

Roots and Tubers market impact

Ethanol Production Baseline Ethanol Production ScenarioFood Use Baseline Food Use ScenarioNet Trade Baseline Net Trade Scenario

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

-0.50%

-0.45%

-0.40%

-0.35%

-0.30%

-0.25%

-0.20%

-0.15%

-0.10%

-0.05%

0.00%

2009 2010 2011 2012 2013 2014 2015 2016

Price Impact

Cassava Molasses

Scenario 1: Results

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• Cassava industry is export oriented and domestic price depends on global market.

• Ethanol production for biofuel only accounted for about 5% of domestic use in 2009, however the share increase to reach around 43% in 2016, still accounting for only 6-7% of production.

• Domestic price appeared only marginally elevated, which hardly impacted food or feed use.

• Molasses price drops significantly, but sugarcane price and production would be only slightly affected.

Scenario 1: Implications

134

Policy applications

Session 7

Biofuel applications and exercises

Impact of the ethanol blending ratio increase to 3.5% in 2018 and further

gradual raise to 5% by 2023

• 2017 – 2026

• Assess the impact of the introduction of a E5 blending mandate by the Ministry of Industry and Trade (MOIT) in January 2018 on the domestic and international markets for cassava and other relevant agricultural commodities.

Scenario 2

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• Two different paths are considered:

– Scenario 2.a assumes the enforcement of a blending ratio of 3.5% from 2018 throughout the projection period.

– Scenario 2.b assumes the introduction of a 3.5% blending ratio in 2018, its gradual increase to 5% over the following five years, and for it to remain at this level to 2026.


0

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Tho

usa

nd

Cu

bic

Met

ers

Ethanol Production

Scenario 2.a Scenario 2.b Baseline

Scenario 2: Results

Scenario 2: Results

0

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900

Baseline Scenario 2a& 2b

Baseline Scenario 2a Scenario 2b Baseline Scenario 2a Scenario 2b

2018 2023 2026

Tho

usa

nd

Cu

bic

Met

ers

Ethanol Market balance

Domestic Supply Total Demand Biofuels Use Other Use

Net Trade Exports Imports

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Scenario 2: Results

• Domestic ethanol production increases 75% in 2018, 98% and 154% in 2023 for Scenarios 2.a & 2.b respectively, and 95% and 154% in 2026 for Scenarios 2.a & 2.a

• Biofuels become the predominant use of ethanol in all the scenarios

• Other use remains unchanged

• Exports would decrease and imports would increase, however net trade would remain positive for all the scenarios

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Roots and Tubers Market

Scenario 2.a Net Trade Scenario 2.b Net Trade Baseline Net Trade

Scenario 2.a Demand Scenario 2.b Demand Baseline Demand

Scenario 2.a Domestic Supply Scenario 2.b Domestic Supply Baseline Domestic Supply



-1000

-500

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2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026

Tho

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Molasses Market

Scenario 2.a Net Trade Scenario 2.b Net Trade Baseline Net Trade

Scenario 2.a Domestic Supply Scenario 2.b Domestic Supply Baseline Domestic Supply

Scenario 2.a Demand Scenario 2.b Demand Baseline Demand

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Scenario 2: Results

Change market balance in 2018 with respect to the baselineCassava Molasses

Domestic Supply 0% 2%Total Demand 34% 35%

Food Use 0% -Feed Use 0% -5%

Biofuels Use 76% 75%Other Use -1% -5%Net Trade -13% 422%

Stocks 32% 17%Producer Price 4% 32%

Scenario 2: Results

Changes in 2023 Scenario 2.a Changes in 2023 Scenario 2.bCassava Molasses Cassava Molasses

Domestic Supply 0% 3% 1% 2%Total Demand 48% 45% 76% 72%

Food Use 0% -1%Feed Use 0% -6% 0% -8%

Biofuels Use 103% 92% 160% 145%Other Use 0% -7% -1% -9%Net Trade -9% 836% -16% 1387%

Stocks 49% 43% 76% 67%Producer Price 2% 45% 4% 58%

Scenario 2: Results

Changes in 2026 Scenario 2.a Changes in 2026 Scenario 2.bCassava Molasses Cassava Molasses

Domestic Supply 0% 3% 1% 3%Total Demand 50% 44% 80% 72%

Food Use 0% 0%Feed Use 0% -7% 0% -9%

Biofuels Use 102% 86% 163% 140%Other Use 0% -8% -1% -10%Net Trade -10% 1215% -16% 2077%

Stocks 50% 42% 81% 70%Producer Price 2% 53% 4% 68%

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Scenario 2: Results

0100200300400500600700800900

Bas

elin

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Scen

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2a

& 2

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Scen

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2a

Scen

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2b

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Scen

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Scen

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2018 2023 2026

Tho

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Vietnam ethanol domestic production by feedstock

Cassava Molasses Sugar Cane Others

0%

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40%

60%

80%

100%

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& 2

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2018 2023 2026

Vietnam ethanol domestic production by feedstock: shares

Cassava Molasses Sugar Cane Others

Scenario 2: Results

0%

1%

2%

3%

4%

5%

6%

2018 2019 2020 2021 2022 2023 2024 2025 2026

World Prices Changes

Scenario 2.a Molasses Scenario 2.a Roots and Tubers

Scenario 2.b Molasses Scenario 2.b Roots and Tubers


• The additional ethanol demand is expected to be supplied domestically, however the production would be partially based on imported feedstocks.

• The price increase would not significantly stimulate the cassava domestic production, as the market remains dominated by exports.

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Scenarios are based on assumptions related to:

• Market developments;

• Domestic policies;

• Trade policies;

• Weather conditions

Uncertainties concerning Scenarios

Summary

• Aglink-Cosimo system generates baseline projections and simulation analysis of policy alternatives

• Complex integration of biofuel sectors in agriculture depends on reliable policy and economic data and parameters

• Scenarios allow to trace out interactions and interdependencies across sectors and countries

150

Areas for potential cooperation

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151

• Data review

• Country module development

• Scenario work

Discussion for future co-operation

• Benefits– Participate in annual world commodity projection– Access to data, models, FAO staff – Participate in analysis projects– Access to capacity building – training– Tap global expertise

• Costs– Depends on commitment– 2-6 staff months– Provide data on markets and policies– Advice as to policy issues etc.– Input into projection– country questionnaire

Co-operators Program

• Annual Cycle – from November to April/May

• Questionnaire on historical data and projection judgements

• Participation in baseline process

• Meetings – OECD/FAO

• Model maintenance - research

Time requirements

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154

Questions?

Thank you

Visit our website:

www.agri-outlook.org

Thank you

Visit our website:

www.agri-outlook.org

http://www.agri-outlook.org/

http://www.agri-outlook.org/

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