IMPACT OF PUBLIC AGRICULTURAL R&D INVESTMENTS ON AGRICULTURAL PRODUCTIVITY AND FOOD SECURITY Zuzana...

IMPACT OF PUBLIC AGRICULTURAL R&D

INVESTMENTS ON AGRICULTURAL

PRODUCTIVITY AND FOOD SECURITY

Zuzana Smeets Kristkova, Hans van Meijl and Michiel van Dijk

Paper prepared for presentation at the 19th ICABR Conference, Ravello, June 16 – 19, 2015

Structure of the presentation

Introduction: R&D, Food security and Agricultural sustainability

Methodological approach: Modelling R&D stocks in applied general equilibrium model MAGNET

Results: Projections of R&D stocks, knowledge diffusion and food security towards 2050

Sustainable food and nutrition security is a global challenge

Reaching food security with sustainable agricultural production is one of the largest challenges facing mankind in the next half century:

●population growth, improving living standards in developing countries, competition of food with biofuels increased demand pressures

●limited space for expansion of agricultural land and water resources, migration of rural labour to urban areas limited supply expansion

3

Important role of agricultural biotechnologies

R&D investments in agricultural biotechnologies represent a possible solution for the food security challenge: increasing food availability and food accessibility

Important role in agricultural sustainability: reduction of pesticide use (environment), mitigation of adverse effects of land use change provoked by food-biofuel competition.

4

R&D driven technical change is key for understanding future projections of food security

Yet, most key global economy-wide models disregard R&D as an important technology driver

●Usually, yields or TFP grow according to more or less ad-hoc exogenous assumptions

●Unreliable (contradicting) projections of food prices and demand (food prices either go up or down depending on productivity assumptions)

●Weakens the ability to guide policy makers

5

Aim of this research

Main goal of this research:●To incorporate agricultural R&D investments

into state-of-the art CGE model MAGNET ●To improve insights into the projections of food

security and agricultural sustainability towards 2050

Part of Marie Curie Project METCAFOS – Modelling Endogenous Technical Change in Agriculture for Food Security (April 2014 – April 2016)

Contributes to FOODSECURE project: www.foodsecure.eu

6

http://www.foodsecure.eu/


Introduction: R&D and Food security

Methodological approach: ●Introduction of MAGNET●Concept of modelling public agricultural R&D

investments in MAGNET●Incorporation of international R&D spillovers in

Magnet


CGE model MAGNET has attractive features for modelling food security

Dedicated for analysis of food security (detailed agri-food commodity aggregation, nutrition module, household modelling)

Can also measure sustainability impacts (such as land use, biofuels demand and emissions).

Due to interlinkages between all regions in the world highly equipped for incorporating R&D spillovers and technology transfer.

Being a dynamic global CGE model, takes into account exogenous drivers such as population, diet preferences or limited land supply.

8





Magnet


Introduction of the modelling approach

We focus on public agricultural R&D targeted to major improvements of seeds and varieties in the style of Green revolution

Major assumptions:

●R&D developed in publically funded research institutes – modelled as a specific production sector in the economy

●R&D effects accrue after long lags – requires adoption of specific R&D stock cumulative forms

●R&D raises productivity of land (land-augmenting technical change), which leads to higher output and lower land prices

10

Modelling cumulative RD stocks with vintages in Magnet

Use of gamma distribution for modelling R&D stocks

6 vintage types based on available empirical evidence

Elasticity of land-augmenting TC (aland) to R&D differs per vintage group

Group Typical Regions Max Lag Peak RD elasticity

A USA (Alston et al) 50 24 0.5

BAustralia and New Zealand (Sheng, Hall & Scobie) 35 10 0.4

CEU-15 and other High Income (Thirtle et al.) 25 10 0.4

DEU-12 and Russian Federation (Kristkova et al.) 15 3 0.4

E Latin America (Bervejillo et al.) 25 24 0.3

FAsia Pacific and Africa (Alene, Nin Pratt & Fan) 15 5 0.3

Gamma distribution of 6 vintage types

0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

Weights of knowledge stock distribution - type A - USA

0 3 6 9 12 15 18 21 24 27 30 330.000%1.000%2.000%3.000%4.000%5.000%6.000%7.000%8.000%

Weights of knowledge stock distribution- type B – Australia New Zealand

0 2 4 6 8 10 12 14 16 18 20 22 240.00%1.00%2.00%3.00%4.00%5.00%6.00%7.00%8.00%9.00%

Weights of knowledge stock type C – EU 15 and High Income

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 150.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

Weights of knowledge stock type D –Eastern Europe and Russia





MAGNET


Modelling international R&D spillovers in MAGNET

Agricultural productivity depends not only on domestic R&D, but also on foreign R&D stocks

Factors that influence diffusion of knowledge:

●Similarity of conditions between the two countries – what is the potential of knowledge spill-in?o Production similarity index (correlation coefficient of

agricultural production shares)o Index of agro-ecological conditions (adopted from Pardey

and Pingali, 2010)

●Absorption capacity -how likely will knowledge be diffused?o Education index (region with max year of schooling = 1)o Yield gap index (region with highest yield = 1)



Methodological approach:

● Introduction of MAGNET

●Concept of modelling public agricultural R&D investments in MAGNET

● Incorporation of international R&D spillovers in Magnet


Assumptions for the baseline

Standard exogenous variables:

o Growth of GDP, endowments and population according SSP2 projections

Government expenditures on public agricultural R&D:

o Determined as a fixed share of agricultural GDP in the base year

o Implies that R&D expenditures grow according to agricultural GDP growth (agri VA)

Annual Growth of real R&D investments

-(R&D is a constant share of agri VA)

1960-1970

1970-1980

1980-1990

1990-2000

2000-2010

2010-2020

2020-2030

2030-2040

2040-2050

Historical period Simulation period-2%

0%

2%

4%

6%

8%

10%

12%

14%

Historical and projected growth rates of annual R&D in-vestments

Usa

Brazil

NoAfrica

WeAfrica

SoAfrica

India

EaAfrica

EU16

China

Evolution of knowledge stocks based on gamma distribution

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050Historical Period Simulation period

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000Evolution of R&D stocks Usa

CentrAmer

Brazil

RestSoAmer

NoAfrica

WeAfrica

SoAfrica

India

ReSoAsia

HighIncAsia

SoEaAsia

EaAfrica

EU16

China

Axis Title

1 Canada

2 usa

4 Brazil

6 NoAfrica

7 WeAfrica

10 SoAfrica

11 MiddleEast

12 india

13 ReSoAsia

14 HighIncAsia

15 SoEaAsia

16 EaAfrica

17 EU16

18 EU12

19 China

10.4

10.0

16.0

18.9

15.7

18.3

13.4

11.6

10.5

12.2

14.7

16.2

12.0

10.7

12.1

5.5

7.6

7.1

5.8

3.9

5.3

5.3

1.6

2.9

5.1

4.5

3.2

7.2

5.6

5.2

24.8

12.0

7.8

20.5

64.7

26.8

26.1

46.1

59.5

6.0

18.7

63.8

9.4

14.0

14.3

Growth of RD stocks and RD spillover (geomean 2010-2050)Rdstock Rdspill Rdpot

Comparison of R&D domestic stocks and R&D spillovers

19

3 groups of countries: • Eastern Africa, Western Africa and India – R&D stocks growth exceed R&D spillovers• China, EU-12, South East Asia and South Africa: higher R&D spillover potential but low absorption capacity• USA, EU-16, High income countries and Brazil: these regions can benefit from R&D spillovers

Rdstock=growth of domestic R&D

Rdspill= growth of R&D stock spilled over

from abroad

Rdpot = growth of R&D stock that can be

potentially absorbed

Growth of R&D driven land-augmenting technical

change

Canada

usa

Brazil

NoAfrica

WeAfrica

SoAfrica

MiddleEast

india

HighIncAsia

EaAfrica

EU16

EU12

China

CentrAmer

RestSoAmer

MiddleEast

ReSoAsia

SoEaAsia

1.2

1.0

0.6

0.6

1.6

0.7

0.7

1.2

0.5

1.5

0.7

0.6

0.8

0.6

0.8

0.7

1.4

0.6

Annual percentage growth of land productivity (2010-2050)

Comparison of R&D driven land–augmenting technical

change in agriculture across baseline scenarios

Canada

usa

Brazil

NoAfrica

WeAfrica

SoAfrica

MiddleEast

india

HighIncAsia

EaAfrica

EU16

EU12

China

CentrAmer

RestSoAmer

MiddleEast

ReSoAsia

SoEaAsia

1.2

1.0

0.6

0.6

1.6

0.7

0.7

1.2

0.5

1.5

0.7

0.6

0.8

0.6

0.8

0.7

1.4

0.6

0.8

0.9

1.1

1.2

1.6

1.6

1.1

1.2

1.1

1.8

0.5

0.8

1.8

1.3

1.3

1.1

1.2

1.3

Annual percentage growth of land productivity (2010-2050)

Baseline alex Baseline Spillover

Evolution of real agricultural prices

22

Canada

usa

CentrAmer

Brazil

RestSoAmer

NoAfrica

WeAfrica

SoAfrica

MiddleEast

india

ReSoAsia

HighIncAsia

SoEaAsia

EaAfrica

EU16

EU12

China

0.3

-0.2

0.5

-0.1

0.3

0.9

2.5

1.6

1.0

2.9

1.0

-0.6

0.7

2.5

0.1

0.2

0.6

-0.1

-0.5

-0.3

-0.7

-0.4

-0.2

0.8

0.0

0.3

2.8

0.9

-0.8

0.1

-0.3

-0.1

-0.2

-0.4

Annual percentage growth rate of real agricultural prices (2010-2050)


Evolution of agricultural price index in time

232007 2010 2020 2030 2040 20500

0.5

1

1.5

2

2.5

3

3.5

4

usa

Brazil

NoAfrica

WeAfrica

SoAfrica

india

EaAfrica

EU16

China

Increasing land pressure is behind growth of agricultural prices

24Canada

USA

CentrAmer

Brazil

RestSoAmer

NoAfrica

WeAfrica

REaEurope

RWeEurope

SoAfrica

MiddleEast

india

ReSoAsia

HighIncAsia

SoEaAsia

EaAfrica

EU16

EU12

China

Oceania

RussiaStan

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Land pressure as a share of land demand to available agricultural land

20502007

Caloric dependency (share of import of calories)

251 Canada

2 usa

3 CentrAmer

4 Brazil

5 RestSoAmer

6 NoAfrica

7 WeAfrica

10 SoAfrica

11 MiddleEast

12 india

13 ReSoAsia

14 HighIncAsia

15 SoEaAsia

16 EaAfrica

17 EU16

18 EU12

19 China

28%

13%

13%

2%

5%

31%

10%

9%

23%

5%

10%

25%

7%

6%

14%

10%

4%

25%

11%

13%

2%

4%

36%

19%

14%

23%

15%

32%

22%

7%

24%

12%

12%

6%

Caloric dependency 2050 vs 2010

2050 2010

Conclusions

Concerning projected R&D growth rates and technology transfer:

● R&D growth rates at the level reached in 2000s, particularly those for China would not be expected any longer

● Regions that might continue with high R&D investment rates are Sub-Saharan African and India. India’s knowledge stocks would gradually reach levels of USA and China.

● International spillovers grow much slower than domestic R&D stocks mainly due to low similarity of production structures and agro-ecological zones between the countries.

● Countries where domestic R&D stocks highly exceed the potential growth of R&D spillovers such as Eastern Africa, Western Africa and India, growth of productivity would mostly rely on domestic R&D policy.

26

Conclusions

Concerning the impact on agricultural productivity

● Endogenous growth rates of lad productivity are lower than the standard exogenous rates and lead to higher price spikes.

● Either R&D public investments in these regions should be strongly boosted, or that our usual assumptions about future growth rates of agricultural production in Sub-Saharan Africa are too optimistic

Concerning food security

● Huge price increases towards 2050, especially in Eastern and Western Africa and India

● Increasing dependence of caloric imports – increasing vulnerability.

27

Conclusions

Limitations and next steps in research:

● Role of private agricultural and non-agricultural R&D: will be included in the next step

● More empirical evidence needed: for modelling R&D spillovers and for understanding R&D driven TC (is it indeed land-augmenting on the aggregate level?)

● Can we assume that the R&D investments will stimulate productivity in the same pace as in the future?

● Use of results for scenario analysis: how much more R&D investments would be needed to avoid price spikes considering also role of biofuel policy?

28

Thank you for your attention

29

Similarity of agricultural production

The more similar are the conditions between region r and s, the more likely knowledge is applicable in region s

Used both by Pardey and Alston

Alston defines index of production structure as a measure of agroecological proximity

The measure of technological spillover potential is defined as a correlation coefficient of commodity shares:

Population growth assumptionsPOP 2007-2010 2010-2020 2020-2030 2030-2040 2040-2050High income1 Canada 3.2 11.0 9.6 7.6 6.82 usa 2.7 8.2 7.5 6.1 5.014 HighIncAs ia 0.6 0.3 -1.6 -3.3 -4.817 EU16 1.5 3.9 2.9 2.4 1.79 RWeEurope 2.5 8.0 7.5 6.3 5.418 EU12 -0.1 -0.7 -1.6 -2.8 -3.120 Oceania 5.4 16.4 13.3 10.5 8.5Latin America3 CentrAmer 3.8 10.9 8.0 5.2 2.84 Brazi l 2.7 8.1 5.7 3.2 0.95 RestSoAmer 3.8 11.0 8.2 5.6 3.2Middle East & North Africa11 MiddleEast 6.7 19.4 14.5 11.3 8.36 NoAfrica 4.7 14.2 10.2 7.3 4.9South and East Asia12 india 4.3 13.3 10.1 7.7 5.319 China 1.5 2.8 0.1 -3.0 -5.713 ReSoAs ia 4.7 16.8 13.7 10.4 7.615 SoEaAs ia 3.5 10.2 7.1 4.2 1.6Sub-Saharan Africa7 WeAfrica 7.9 26.6 23.3 20.1 16.510 SoAfrica 6.9 23.8 20.4 16.3 12.816 EaAfrica 8.0 26.6 21.8 17.3 13.3Eastern Europe & Former SU8 REaEurope -1.3 -4.1 -3.3 -2.8 -2.621 Russ iaStan 1.1 2.9 1.1 0.2 -0.4

Natural resources growth assumptionNatres 2007-2010 2010-2020 2020-2030 2030-2040 2040-2050High income1 Canada 0.3 6.9 5.4 5.2 4.52 usa -0.2 7.5 5.8 4.0 3.014 HighIncAs ia 0.2 5.2 4.3 2.8 2.117 EU16 -0.6 4.2 4.1 4.1 4.09 RWeEurope 0.2 6.0 4.4 4.2 4.418 EU12 0.5 7.8 6.9 5.3 3.820 Oceania 1.4 9.6 7.2 5.9 5.5Latin America3 CentrAmer 0.4 9.8 8.8 7.6 6.94 Brazi l 3.2 10.8 8.5 6.0 4.95 RestSoAmer 2.7 13.1 10.1 8.3 7.1Middle East & North Africa11 MiddleEast 2.4 14.7 11.0 8.4 6.66 NoAfrica 3.1 13.6 14.5 11.1 9.1South and East Asia12 india 6.3 22.7 18.8 14.0 11.119 China 8.1 30.8 16.2 7.5 4.213 ReSoAs ia 3.6 16.2 16.7 14.6 13.315 SoEaAs ia 3.5 17.3 14.8 10.9 8.7Sub-Saharan Africa7 WeAfrica 4.5 20.8 21.3 19.1 18.010 SoAfrica 2.3 14.9 13.1 11.3 11.716 EaAfrica 3.7 17.8 19.7 18.4 17.9Eastern Europe & Former SU8 REaEurope -0.1 9.5 9.2 7.4 5.521 Russ iaStan 0.9 12.6 10.2 6.8 4.0

GDP growth assumptionsGDP 2007-2010 2010-2020 2020-2030 2030-2040 2040-2050High income1 Canada 1.0 27.4 21.5 20.6 17.92 usa -0.9 30.1 23.1 16.0 12.214 HighIncAs ia 0.7 20.8 17.2 11.2 8.317 EU16 -2.5 16.7 16.3 16.5 16.29 RWeEurope 0.8 23.9 17.7 16.7 17.718 EU12 2.1 31.1 27.5 21.1 15.020 Oceania 5.6 38.2 28.9 23.7 22.0Latin America3 CentrAmer 1.7 39.3 35.3 30.5 27.74 Brazi l 12.7 43.1 34.2 24.1 19.75 RestSoAmer 10.9 52.5 40.5 33.0 28.2Middle East & North Africa11 MiddleEast 9.4 58.8 43.9 33.7 26.46 NoAfrica 12.4 54.5 58.0 44.3 36.2South and East Asia12 india 25.2 90.8 75.1 56.1 44.619 China 32.2 123.4 64.8 29.9 16.913 ReSoAs ia 14.4 64.6 66.9 58.3 53.015 SoEaAs ia 14.0 69.4 59.2 43.8 34.7Sub-Saharan Africa7 WeAfrica 18.0 83.3 85.0 76.4 72.010 SoAfrica 9.0 59.4 52.2 45.1 46.816 EaAfrica 14.9 71.1 78.7 73.8 71.5Eastern Europe & Former SU8 REaEurope -0.5 38.0 37.0 29.5 22.221 Russ iaStan 3.6 50.6 40.6 27.3 16.2

Agri R&D data sources used for building base data for SAM

Asti Public database (Excel spreadsheet) - most of the developing countries

Asti country papers: RD data for most of Sub-Saharan countries. OECD Database: GERD by sector of performance and field of science:

data for Korea, Taiwan, Singapore, EU-12 OECD members, Russia and Turkey, URL:

Eurostat Database GERD data for Cyprus, Lithuania, Malta, Croatia, Romania, Rest of Europe

UNESCO Database: GERD - Agricultural sciences for: Mongolia, Taiwan, Bolivia, Ecuador, Estonia, Latvia, Ukraine, Kazakhstan, Kyrgyzstan, Armenia, Azerbaijan, Tajikistan, Kuwait, Oman.

Pardey InsTepp Database Summary: Data for USA, Germany, France, Canada, Spain, OECD countries China, India, Brazil and aggregated regions of Asia-Pacific, LA, SSA.

Values were converted from 2005 PPP dollars to 2007 current Dollars

Construction of SAM

Disaggregation of public R&D sector in SAM from public services sector (osg)

The share of public R&D expenditures in the value of output of public services (VO a_osg) is applied to all cost components.

Other com osg RD_pub Other sec osg RD_Pub

Other com

osg +RD_pub

Other sec

osg

RD_pub =

=

+ - =

Inv

RoW

Total

Inv RoW Total

Com

Com

Sectors

Sectors

Factors Reg Hous Tax

Factors

Reg Hous

Tax

Split osg to osg and RD sec

Split osg to osg and RD com

Fixing governmental demand

Fixed governmental demand for public R&D in Magnet

Y

Ypriv Ygov Save

ugflex ugfix

qg rdqgi qgi qgi

qgd i qgm i qgd rd qfd rd

qgs rd qo rd

pm rd

Gov demand for rd is exogenous:Either: qg(i,r) + pg(i,r) = rdpub(r) Or: qg(i,r) + pg(i,r) = gdpagr(r)

Total fixed gov demand = rd demand

Total RD demand = gov demand + fir m demand

RD market price is determined by RD demand and supply

RD supply RD demand

Cobb-Douglas

Empirical evidence

Country Source DistributionMax lag years Own elasticity

USA Alston, Andersen (2011): Gamma, lambda 0.7, delta 0.9 50 0.32 (0.15 + 0.18)

Australia Sheng, Gray, Mullen (2011): gamma 35 0.23

New Zealand As for Australia gamma 35 0.23

UK Thritle, Piesse and Shcimmel. (2008): PDL, Trapezoid, Beta 25 0.1-0.3

Uruguay Bervejillo, Alston, Tumber (2012): Gamma, lambda 0.7, delta 0.9 25 0.565

SSA Alene (2009, 2010): PDL 160.2 with TFP regression,

0.38 with VA/ha

Africa Ninn Pratt and Fan (2009): simulation 10 0.093

China Ninn Pratt and Fan (2009): simulation 10 0.17

Indonesia Ninn Pratt and Fan (2009): simulation 10 0.142

India Fan (2002): PDL 13 0.255

Thailand Suphannachart (2011 RD flows, use of ECM and ADL 7 0.07

Latin AmericaNinn Pratt and Fan (2009): R&D Investment in national and simulation 10 0.103

Czech Republic Kristkova&Ratingergamma distribution, delta=0.8,lambda = 0.4 15 0.2

Example PsIndex for 21 regions

PsIndex1 Canad 2 usa

3 Centr

4 Brazil

5 RestS

6 NoAfri

7 WeAfr

8 REaEu

9 RWeE

10 SoAfri

11 Middl

12 india

13 ReSoA

14 HighIn

15 SoEaA

16 EaAfri

17 EU16

18 EU12

19 China

20 Ocean

21 Russia

1 Canada 0 0.88 0.72 0.72 0.82 0.58 0.54 0.87 0.84 0.72 0.7 0.72 0.64 0.61 0.62 0.74 0.83 0.87 0.6 0.85 0.79

2 usa 0.88 0 0.89 0.68 0.83 0.73 0.81 0.92 0.79 0.9 0.86 0.78 0.62 0.74 0.69 0.87 0.84 0.89 0.75 0.8 0.85

3 CentrAmer 0.72 0.89 0 0.71 0.8 0.8 0.9 0.88 0.69 0.95 0.87 0.87 0.72 0.91 0.76 0.93 0.88 0.91 0.84 0.66 0.75

4 Brazil 0.72 0.68 0.71 0 0.84 0.34 0.51 0.66 0.68 0.75 0.43 0.7 0.72 0.61 0.68 0.72 0.83 0.81 0.4 0.64 0.48

5 RestSoAmer 0.82 0.83 0.8 0.84 0 0.64 0.73 0.79 0.63 0.83 0.7 0.81 0.74 0.7 0.89 0.84 0.83 0.85 0.61 0.69 0.64

6 NoAfrica 0.58 0.73 0.8 0.34 0.64 0 0.87 0.77 0.44 0.81 0.92 0.75 0.65 0.74 0.65 0.81 0.64 0.67 0.77 0.63 0.79

7 WeAfrica 0.54 0.81 0.9 0.51 0.73 0.87 0 0.79 0.48 0.91 0.9 0.82 0.67 0.78 0.71 0.87 0.74 0.74 0.74 0.56 0.76

8 REaEurope 0.87 0.92 0.88 0.66 0.79 0.77 0.79 0 0.86 0.89 0.9 0.92 0.77 0.77 0.63 0.9 0.9 0.9 0.66 0.89 0.91

9 RWeEurope 0.84 0.79 0.69 0.68 0.63 0.44 0.48 0.86 0 0.66 0.68 0.76 0.59 0.66 0.43 0.65 0.84 0.81 0.55 0.9 0.79

10 SoAfrica 0.72 0.9 0.95 0.75 0.83 0.81 0.91 0.89 0.66 0 0.85 0.86 0.79 0.83 0.75 0.97 0.87 0.86 0.71 0.72 0.78

11 MiddleEast 0.7 0.86 0.87 0.43 0.7 0.92 0.9 0.9 0.68 0.85 0 0.85 0.64 0.8 0.63 0.84 0.78 0.79 0.82 0.74 0.9

12 india 0.72 0.78 0.87 0.7 0.81 0.75 0.82 0.92 0.76 0.86 0.85 0 0.84 0.81 0.69 0.86 0.92 0.88 0.62 0.8 0.82

13 ReSoAsia 0.64 0.62 0.72 0.72 0.74 0.65 0.67 0.77 0.59 0.79 0.64 0.84 0 0.74 0.72 0.85 0.77 0.71 0.45 0.65 0.65

14 HighIncAsia 0.61 0.74 0.91 0.61 0.7 0.74 0.78 0.77 0.66 0.83 0.8 0.81 0.74 0 0.77 0.84 0.81 0.82 0.88 0.58 0.63

15 SoEaAsia 0.62 0.69 0.76 0.68 0.89 0.65 0.71 0.63 0.43 0.75 0.63 0.69 0.72 0.77 0 0.81 0.64 0.68 0.69 0.45 0.46

16 EaAfrica 0.74 0.87 0.93 0.72 0.84 0.81 0.87 0.9 0.65 0.97 0.84 0.86 0.85 0.84 0.81 0 0.83 0.84 0.69 0.71 0.75

17 EU16 0.83 0.84 0.88 0.83 0.83 0.64 0.74 0.9 0.84 0.87 0.78 0.92 0.77 0.81 0.64 0.83 0 0.97 0.66 0.81 0.78

18 EU12 0.87 0.89 0.91 0.81 0.85 0.67 0.74 0.9 0.81 0.86 0.79 0.88 0.71 0.82 0.68 0.84 0.97 0 0.74 0.78 0.77

19 China 0.6 0.75 0.84 0.4 0.61 0.77 0.74 0.66 0.55 0.71 0.82 0.62 0.45 0.88 0.69 0.69 0.66 0.74 0 0.47 0.62

20 Oceania 0.85 0.8 0.66 0.64 0.69 0.63 0.56 0.89 0.9 0.72 0.74 0.8 0.65 0.58 0.45 0.71 0.81 0.78 0.47 0 0.89

21 RussiaStan 0.79 0.85 0.75 0.48 0.64 0.79 0.76 0.91 0.79 0.78 0.9 0.82 0.65 0.63 0.46 0.75 0.78 0.77 0.62 0.89 0

Example GAEZ Index

Obtained from Pardey (2010)GaezIndex

1 Canad 2 usa

3 Centr

4 Brazil

5 RestS

6 NoAfri

7 WeAfr

8 REaEu

9 RWeE

10 SoAfri

11 Middl

12 india

13 ReSoA

14 HighIn

15 SoEaA

16 EaAfri

17 EU16

18 EU12

19 China

20 Ocean

21 Russia

1 Canada 1 1 0.1 0.1 0.1 0.01 0.01 0.64 1 0.01 0.37 0.37 0.37 1 0.37 0.01 1 1 0.37 1 0.372 usa 1 1 0.1 0.1 0.1 0.01 0.01 0.64 1 0.01 0.37 0.37 0.37 1 0.37 0.01 1 1 0.37 1 0.373 CentrAmer 0.1 0.1 1 1 1 0.56 0.56 0.54 0.1 0.56 0.49 0.49 0.49 0.1 0.49 0.56 0.1 0.1 0.49 0.1 0.494 Brazil 0.1 0.1 1 1 1 0.56 0.56 0.54 0.1 0.56 0.49 0.49 0.49 0.1 0.49 0.56 0.1 0.1 0.49 0.1 0.495 RestSoAmer 0.1 0.1 1 1 1 0.56 0.56 0.54 0.1 0.56 0.49 0.49 0.49 0.1 0.49 0.56 0.1 0.1 0.49 0.1 0.496 NoAfrica 0.01 0.01 0.56 0.56 0.56 1 1 0.56 0.01 1 0.23 0.23 0.23 0.01 0.23 1 0.01 0.01 0.23 0.01 0.237 WeAfrica 0.01 0.01 0.56 0.56 0.56 1 1 0.56 0.01 1 0.23 0.23 0.23 0.01 0.23 1 0.01 0.01 0.23 0.01 0.238 REaEurope 0.64 0.64 0.54 0.54 0.54 0.56 0.56 1 0.64 0.56 0.74 0.74 0.74 0.64 0.74 0.56 0.64 0.64 0.74 0.64 0.749 RWeEurope 1 1 0.1 0.1 0.1 0.01 0.01 0.64 1 0.01 0.37 0.37 0.37 1 0.37 0.01 1 1 0.37 1 0.3710 SoAfrica 0.01 0.01 0.56 0.56 0.56 1 1 0.56 0.01 1 0.23 0.23 0.23 0.01 0.23 1 0.01 0.01 0.23 0.01 0.2311 MiddleEast 0.37 0.37 0.49 0.49 0.49 0.23 0.23 0.74 0.37 0.23 1 1 1 0.37 1 0.23 0.37 0.37 1 0.37 112 india 0.37 0.37 0.49 0.49 0.49 0.23 0.23 0.74 0.37 0.23 1 1 1 0.37 1 0.23 0.37 0.37 1 0.37 113 ReSoAsia 0.37 0.37 0.49 0.49 0.49 0.23 0.23 0.74 0.37 0.23 1 1 1 0.37 1 0.23 0.37 0.37 1 0.37 114 HighIncAsia 1 1 0.1 0.1 0.1 0.01 0.01 0.64 1 0.01 0.37 0.37 0.37 1 0.37 0.01 1 1 0.37 1 0.3715 SoEaAsia 0.37 0.37 0.49 0.49 0.49 0.23 0.23 0.74 0.37 0.23 1 1 1 0.37 1 0.23 0.37 0.37 1 0.37 116 EaAfrica 0.01 0.01 0.56 0.56 0.56 1 1 0.56 0.01 1 0.23 0.23 0.23 0.01 0.23 1 0.01 0.01 0.23 0.01 0.2317 EU16 1 1 0.1 0.1 0.1 0.01 0.01 0.64 1 0.01 0.37 0.37 0.37 1 0.37 0.01 1 1 0.37 1 0.3718 EU12 1 1 0.1 0.1 0.1 0.01 0.01 0.64 1 0.01 0.37 0.37 0.37 1 0.37 0.01 1 1 0.37 1 0.3719 China 0.37 0.37 0.49 0.49 0.49 0.23 0.23 0.74 0.37 0.23 1 1 1 0.37 1 0.23 0.37 0.37 1 0.37 120 Oceania 1 1 0.1 0.1 0.1 0.01 0.01 0.64 1 0.01 0.37 0.37 0.37 1 0.37 0.01 1 1 0.37 1 0.3721 RussiaStan 0.37 0.37 0.49 0.49 0.49 0.23 0.23 0.74 0.37 0.23 1 1 1 0.37 1 0.23 0.37 0.37 1 0.37 1

Education index

EDIN EduIndex

1 Canada 0.93

2 usa 1

3 CentrAmer 0.63

4 Brazil 0.6

5 RestSoAmer 0.67

6 NoAfrica 0.51

7 WeAfrica 0.42

8 REaEurope 0.84

9 RWeEurope 0.94

10 SoAfrica 0.43

11 MiddleEast 0.58

12 india 0.47

13 ReSoAsia 0.41

14 HighIncAsia 0.88

15 SoEaAsia 0.57

16 EaAfrica 0.3

17 EU16 0.84

18 EU12 0.87

19 China 0.57

20 Oceania 0.72

21 RussiaStan 0.83

Yield gap index

Ygindex

1 Canada 2 usa

3 CentrAmer 4 Brazil

5 RestSoAmer

6 NoAfrica

7 WeAfrica

8 REaEurope

9 RWeEurope

10 SoAfrica

11 MiddleEast

12 india

13 ReSoAsia

14 HighIncAsia

15 SoEaAsia

16 EaAfrica

17 EU16

18 EU12

19 China

20 Oceania

21 RussiaStan

1 pdr 0 0.96 0.35 0.32 0.35 1 0.15 0.66 0 0.16 0.03 0.27 0.32 0.19 0.38 0.29 0.65 0.32 0.54 0.7 0.32 wht 0.49 0.48 0.58 0.36 0.48 0.18 0.16 0.54 0.96 0.34 0.3 0.2 0.16 0.52 0.14 0.35 1 0.67 0.5 0.26 0.33 grain 0.46 1 0.27 0.39 0.48 0.3 0.12 0.37 0.56 0.2 0.22 0.12 0.14 0.37 0.34 0.16 0.62 0.44 0.51 0.2 0.24 oils 0.65 0.94 0.89 0.81 0.73 0.33 0.22 0.53 0.94 0.28 0.72 0.34 1 0.41 0.83 0.12 0.75 0.92 0.62 0.43 0.325 sug 0.76 0.83 0.79 1 0.89 0.6 0.15 0.47 1 0.52 0.57 0.8 0.55 0.84 0.64 0.5 0.93 0.68 0.88 0.82 0.446 hort 0.57 0.94 0.42 0.61 0.55 0.47 0.35 0.6 0.92 0.34 0.42 0.23 0.25 1 0.47 0.27 0.63 0.58 0.67 0.58 0.477 crops 0.1 0 0.83 0.98 0.47 0.79 0.19 0.18 0.53 0.5 0.48 0.21 0.43 0.1 0.46 0.16 1 0.25 0.6 0.18 0.188 cattle 0.87 0.85 0.86 0.86 0.84 0.82 0.81 0.84 0.88 0.86 0.77 1 0.66 0.85 0.99 0.9 0.85 0.82 0.86 0.86 0.839 pigpoul 0.81 0.83 0.81 0.88 0.83 0.85 0.79 0.74 0.81 0.84 0.88 0.96 0.75 0.78 1 0.8 0.81 0.78 0.85 0.86 0.8110 milk 0.63 0.63 1 0.68 0.6 0.66 0.6 0.74 0.66 0.65 0.74 0.67 0.86 0.64 0.67 0.63 0.64 0.72 0.9 0.6 0.6120 oagr 0 0.54 0.05 0.63 0.25 0.11 0.22 0.1 0 0.26 0.39 0.18 0.25 0.01 0.18 0.26 0.13 0.21 0.66 1 0.23

Modelling RD spillovers in Magnet

SCENARIO part (3_ScenarioDefinition):

●Calculation of PsIndex (Production Similarity Index) for the starting period as a pair-wise correlation coefficient of production shares

●Upload of GAEZ Index (available in aggregated form)

●Calculation of Absorption index based on Barro&Lee number of schooling years:

● EduIndex(r) = Education(r) / MAXS(rr, DREG, Education(rr));

●Calculation of technology index based on yield gap:

● YGIndex(r,j) = Agriyield2(r,j) / MAXS(rr, DREG, Agriyield2(rr,j));

Modelling RD spillovers

MAGNET model Part (4_MAGNET):

●Spillover coefficients that are updated:

o PsIndex based on current agri prod shares

o Yield gap index: based on aland growth in t-1o YgIndext(j,r) = ((1+alandt-1(j,r)/100)*AggYield(j,r)) /

MAXS(rr, REG, (1+alandt-1 (j,rr)/100)*AggYield(j,rr));

●Spillover coefficients that remain constant over all periods:

o GAEZ index

o Education index

Reconstruction of R&D stocks from 1960 - 2050

R&D expenditures were reconstructed for all 140 regions for 1960 – 2010

Based on the time series, R&D annual flows were converted to R&D vintages as a weighted average of all previous R&D expenditures, where weights are calculated as:

o

o where and

Example matrix of RD vintages0% 0% 1% 1% 3% 4% 5% 6% 6% 7% 7% 7% 7% 6% 6% 5% 5% 4% 4% 3% 3% 2% 2% 2% 1% 1% 1% 1% 1% 0% 0% 0% 0% 0% 0% 0% 0

1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018AUS 1960 0.04 0.41 1.45 3.22 5.5 7.98 10.4 12.4 13.9 14.8 15.2 15 14.5 13.6 12.6 11.4 10.2 8.95 7.78 6.69 5.69 4.8 4.01 3.33 2.74 2.25 1.83 1.48 1.19 0.96 0.76 0.61 0.48 0.38 0.3 0.23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1961 0 0.04 0.44 1.57 3.46 5.92 8.59 11.1 13.3 14.9 15.9 16.3 16.2 15.6 14.7 13.5 12.3 10.9 9.63 8.37 7.19 6.12 5.16 4.31 3.58 2.95 2.42 1.97 1.59 1.28 1.03 0.82 0.65 0.52 0.41 0.32 0.25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1962 0 0 0.04 0.49 1.74 3.86 6.59 9.57 12.4 14.8 16.6 17.7 18.2 18 17.4 16.4 15.1 13.7 12.2 10.7 9.33 8.01 6.82 5.75 4.81 3.99 3.29 2.69 2.19 1.77 1.43 1.15 0.91 0.73 0.58 0.45 0.36 0.28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1963 0 0 0 0.05 0.53 1.87 4.13 7.07 10.3 13.3 15.9 17.8 19 19.5 19.3 18.6 17.5 16.2 14.7 13.1 11.5 9.99 8.59 7.31 6.16 5.15 4.28 3.52 2.89 2.35 1.9 1.53 1.23 0.98 0.78 0.62 0.49 0.38 0.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1964 0 0 0 0 0.05 0.61 2.16 4.78 8.16 11.9 15.4 18.4 20.6 22 22.5 22.3 21.5 20.3 18.7 16.9 15.1 13.3 11.5 9.92 8.44 7.12 5.95 4.94 4.07 3.33 2.71 2.2 1.77 1.42 1.13 0.9 0.71 0.56 0.44 0.35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1965 0 0 0 0 0 0.06 0.65 2.31 5.11 8.74 12.7 16.5 19.6 22 23.5 24.1 23.9 23 21.7 20 18.1 16.2 14.2 12.4 10.6 9.04 7.62 6.37 5.29 4.36 3.57 2.91 2.35 1.89 1.52 1.21 0.96 0.76 0.6 0.47 0.37 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1966 0 0 0 0 0 0 0.06 0.7 2.47 5.46 9.33 13.5 17.6 21 23.5 25.1 25.7 25.5 24.6 23.2 21.4 19.4 17.3 15.2 13.2 11.3 9.65 8.14 6.81 5.65 4.66 3.81 3.1 2.51 2.02 1.62 1.29 1.03 0.81 0.64 0.51 0.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1967 0 0 0 0 0 0 0 0.07 0.8 2.84 6.27 10.7 15.6 20.2 24.1 27 28.8 29.6 29.3 28.2 26.6 24.5 22.2 19.8 17.4 15.2 13 11.1 9.35 7.82 6.49 5.35 4.38 3.56 2.89 2.32 1.86 1.49 1.18 0.94 0.74 0.58 0.45 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1968 0 0 0 0 0 0 0 0 0.08 0.86 3.06 6.76 11.6 16.8 21.8 26 29.1 31.1 31.9 31.6 30.5 28.7 26.4 24 21.4 18.8 16.3 14 12 10.1 8.43 6.99 5.76 4.72 3.84 3.11 2.51 2.01 1.6 1.27 1.01 0.8 0.63 0.49 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1969 0 0 0 0 0 0 0 0 0 0.08 0.87 3.09 6.84 11.7 17 22 26.3 29.4 31.4 32.2 31.9 30.8 29 26.7 24.2 21.6 19 16.5 14.2 12.1 10.2 8.52 7.07 5.83 4.77 3.88 3.14 2.53 2.03 1.62 1.29 1.02 0.8 0.63 0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1970 0 0 0 0 0 0 0 0 0 0 0.08 0.95 3.35 7.42 12.7 18.4 23.9 28.5 32 34.1 34.9 34.6 33.4 31.5 29 26.3 23.5 20.6 17.9 15.4 13.1 11.1 9.25 7.67 6.32 5.18 4.22 3.41 2.75 2.2 1.76 1.4 1.11 0.87 0.69 0.54 0 0 0 0 0 0 0 0 0 0 0 0 0AUS 1971 0 0 0 0 0 0 0 0 0 0 0 0.09 1.03 3.67 8.11 13.9 20.1 26.1 31.2 35 37.3 38.2 37.9 36.5 34.4 31.7 28.8 25.6 22.6 19.6 16.9 14.3 12.1 10.1 8.39 6.92 5.66 4.61 3.73 3.01 2.41 1.92 1.53 1.21 0.95 0.75 0.59 0 0 0 0 0 0 0 0 0 0 0 0AUS 1972 0 0 0 0 0 0 0 0 0 0 0 0 0.1 1.09 3.86 8.53 14.6 21.2 27.4 32.8 36.7 39.2 40.2 39.8 38.4 36.2 33.4 30.2 27 23.7 20.6 17.7 15.1 12.7 10.6 8.82 7.27 5.95 4.85 3.92 3.16 2.53 2.02 1.61 1.27 1 0.79 0.62 0 0 0 0 0 0 0 0 0 0 0AUS 1973 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 1.07 3.81 8.43 14.4 20.9 27.1 32.4 36.3 38.7 39.7 39.4 37.9 35.7 33 29.9 26.6 23.4 20.4 17.5 14.9 12.6 10.5 8.71 7.18 5.88 4.79 3.88 3.12 2.5 2 1.59 1.26 0.99 0.78 0.61 0 0 0 0 0 0 0 0 0 0AUS 1974 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 1.12 3.95 8.75 14.9 21.7 28.1 33.6 37.7 40.2 41.2 40.8 39.4 37.1 34.2 31 27.6 24.3 21.1 18.2 15.5 13 10.9 9.04 7.45 6.1 4.97 4.02 3.24 2.6 2.07 1.65 1.3 1.03 0.81 0.63 0 0 0 0 0 0 0 0 0AUS 1975 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.11 1.22 4.31 9.53 16.3 23.6 30.7 36.6 41.1 43.8 44.9 44.5 42.9 40.4 37.3 33.8 30.1 26.5 23 19.8 16.8 14.2 11.9 9.86 8.12 6.65 5.42 4.38 3.53 2.83 2.26 1.8 1.42 1.12 0.88 0.69 0 0 0 0 0 0 0 0AUS 1976 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.11 1.19 4.23 9.36 16 23.2 30.1 36 40.3 43 44.1 43.7 42.2 39.7 36.6 33.2 29.6 26 22.6 19.4 16.5 14 11.7 9.68 7.98 6.53 5.32 4.31 3.47 2.78 2.22 1.76 1.4 1.1 0.87 0.68 0 0 0 0 0 0 0AUS 1977 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 1.16 4.09 9.06 15.5 22.5 29.1 34.8 39 41.6 42.7 42.3 40.8 38.4 35.4 32.1 28.6 25.2 21.9 18.8 16 13.5 11.3 9.37 7.72 6.32 5.15 4.17 3.36 2.69 2.15 1.71 1.35 1.07 0.84 0.66 0 0 0 0 0 0AUS 1978 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.44 5.09 11.3 19.2 27.9 36.2 43.2 48.5 51.7 53 52.6 50.7 47.7 44 39.9 35.6 31.3 27.2 23.4 19.9 16.8 14 11.6 9.59 7.85 6.39 5.18 4.17 3.34 2.67 2.12 1.68 1.32 1.04 0.82 0 0 0 0 0AUS 1979 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 1.17 4.16 9.21 15.7 22.8 29.6 35.4 39.7 42.3 43.4 43 41.5 39 36 32.6 29.1 25.6 22.3 19.1 16.3 13.7 11.5 9.52 7.85 6.43 5.23 4.24 3.41 2.74 2.18 1.73 1.37 1.08 0.85 0.67 0 0 0 0AUS 1980 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.11 1.24 4.4 9.73 16.6 24.1 31.3 37.4 41.9 44.7 45.8 45.4 43.8 41.2 38 34.5 30.8 27.1 23.5 20.2 17.2 14.5 12.1 10.1 8.29 6.79 5.53 4.48 3.6 2.89 2.31 1.83 1.45 1.14 0.9 0.7 0 0 0AUS 1981 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.31 4.64 10.3 17.5 25.4 33 39.4 44.2 47.1 48.3 47.9 46.2 43.5 40.1 36.3 32.4 28.5 24.8 21.3 18.1 15.3 12.8 10.6 8.74 7.16 5.83 4.72 3.8 3.05 2.43 1.93 1.53 1.21 0.95 0.74 0 0AUS 1982 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.36 4.82 10.7 18.2 26.5 34.3 41 45.9 49 50.2 49.8 48 45.2 41.7 37.8 33.7 29.7 25.8 22.1 18.8 15.9 13.3 11 9.09 7.44 6.06 4.9 3.95 3.17 2.53 2.01 1.59 1.25 0.99 0.77 0AUS 1983 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.42 5.03 11.1 19 27.6 35.8 42.7 47.9 51.1 52.4 51.9 50.1 47.2 43.5 39.4 35.2 30.9 26.9 23.1 19.7 16.6 13.9 11.5 9.48 7.76 6.32 5.12 4.12 3.3 2.64 2.1 1.66 1.31 1.03 0.81AUS 1984 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.11 1.27 4.51 9.98 17.1 24.8 32.1 38.3 43 45.9 47 46.6 44.9 42.3 39 35.4 31.5 27.8 24.1 20.7 17.6 14.9 12.4 10.3 8.51 6.96 5.67 4.59 3.7 2.96 2.37 1.88 1.49 1.17 0.92AUS 1985 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.34 4.74 10.5 17.9 26 33.7 40.2 45.1 48.1 49.3 48.9 47.2 44.4 41 37.1 33.1 29.1 25.3 21.8 18.5 15.6 13.1 10.8 8.93 7.31 5.95 4.82 3.88 3.11 2.48 1.97 1.56 1.23AUS 1986 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.42 5.03 11.1 19 27.6 35.8 42.8 48 51.2 52.5 52 50.1 47.2 43.6 39.5 35.2 31 26.9 23.1 19.7 16.6 13.9 11.5 9.49 7.77 6.33 5.12 4.13 3.31 2.64 2.1 1.66AUS 1987 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.33 4.71 10.4 17.8 25.9 33.6 40.1 44.9 47.9 49.1 48.7 47 44.2 40.8 37 33 29 25.2 21.7 18.4 15.5 13 10.8 8.89 7.28 5.93 4.8 3.86 3.1 2.47 1.97AUS 1988 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.29 4.57 10.1 17.3 25.1 32.5 38.8 43.6 46.5 47.6 47.2 45.5 42.9 39.5 35.8 32 28.1 24.4 21 17.9 15.1 12.6 10.5 8.62 7.06 5.74 4.65 3.75 3 2.4AUS 1989 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.11 1.19 4.21 9.31 15.9 23.1 30 35.8 40.1 42.8 43.9 43.5 41.9 39.5 36.4 33 29.4 25.9 22.5 19.3 16.5 13.9 11.6 9.63 7.94 6.5 5.29 4.28 3.45 2.77AUS 1990 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.11 1.25 4.43 9.8 16.7 24.3 31.5 37.6 42.2 45 46.1 45.7 44.1 41.5 38.3 34.7 31 27.2 23.7 20.3 17.3 14.6 12.2 10.1 8.35 6.84 5.57 4.51 3.63AUS 1991 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.11 1.28 4.52 10 17.1 24.8 32.2 38.4 43.1 46 47.1 46.7 45 42.4 39.1 35.4 31.6 27.8 24.2 20.8 17.7 14.9 12.5 10.3 8.53 6.98 5.68 4.6AUS 1992 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.34 4.76 10.5 18 26.1 33.9 40.5 45.4 48.4 49.6 49.2 47.5 44.7 41.2 37.4 33.3 29.3 25.5 21.9 18.6 15.7 13.1 10.9 8.98 7.36 5.99AUS 1993 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.42 5.02 11.1 19 27.6 35.8 42.7 47.9 51.1 52.4 51.9 50 47.1 43.5 39.4 35.1 30.9 26.9 23.1 19.6 16.6 13.9 11.5 9.47 7.76AUS 1994 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.38 4.9 10.8 18.5 26.9 34.9 41.7 46.7 49.8 51.1 50.6 48.8 46 42.4 38.4 34.3 30.2 26.2 22.5 19.2 16.2 13.5 11.2 9.24AUS 1995 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.35 4.77 10.6 18 26.2 33.9 40.5 45.4 48.5 49.7 49.3 47.5 44.7 41.3 37.4 33.3 29.3 25.5 21.9 18.6 15.7 13.1 10.9AUS 1996 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.37 4.85 10.7 18.3 26.6 34.5 41.2 46.2 49.3 50.5 50.1 48.3 45.5 42 38 33.9 29.8 25.9 22.3 19 16 13.4AUS 1997 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.4 4.97 11 18.8 27.3 35.4 42.2 47.4 50.5 51.8 51.3 49.5 46.6 43 39 34.8 30.6 26.6 22.8 19.4 16.4AUS 1998 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.44 5.09 11.3 19.2 27.9 36.2 43.2 48.5 51.7 53 52.6 50.7 47.7 44 39.9 35.6 31.3 27.2 23.4 19.9AUS 1999 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.48 5.26 11.6 19.9 28.9 37.4 44.7 50.1 53.5 54.8 54.3 52.4 49.3 45.5 41.2 36.8 32.4 28.1 24.2AUS 2000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.14 1.59 5.62 12.4 21.3 30.8 40 47.8 53.6 57.2 58.6 58.1 56 52.7 48.6 44.1 39.3 34.6 30.1AUS 2001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15 1.64 5.81 12.9 22 31.9 41.4 49.4 55.4 59.1 60.6 60.1 57.9 54.5 50.3 45.6 40.7 35.8AUS 2002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.14 1.58 5.58 12.4 21.1 30.6 39.7 47.5 53.2 56.8 58.2 57.7 55.6 52.4 48.3 43.8 39AUS 2003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.51 5.35 11.8 20.2 29.4 38.1 45.5 51 54.4 55.8 55.3 53.3 50.2 46.3 42AUS 2004 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.48 5.24 11.6 19.8 28.8 37.3 44.6 50 53.3 54.6 54.2 52.2 49.2 45.4AUS 2005 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.44 5.1 11.3 19.3 28 36.3 43.4 48.6 51.9 53.2 52.7 50.8 47.9AUS 2006 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.43 5.08 11.2 19.2 27.9 36.2 43.2 48.4 51.7 53 52.5 50.6AUS 2007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.43 5.08 11.2 19.2 27.9 36.2 43.2 48.4 51.7 52.9 52.5AUS 2008 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.13 1.41 5.01 11.1 18.9 27.5 35.6 42.6 47.7 50.9 52.2AUS 2009 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.39 4.94 10.9 18.7 27.1 35.1 42 47 50.2AUS 2010 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12 1.37 4.87 10.8 18.4 26.7 34.6 41.4 46.4

Example US RD investments and RD stock

RD investments 1960 – 2010

RD stocks go beyond 2050 – effect of lag

Decline after 2026 because no RD investments after 2010 (in the historical period, not in the simulation period)

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

2011

2014

2017

2020

2023

2026

2029

2032

2035

2038

2041

2044

2047

2050

0

1000

2000

3000

4000

5000

6000

RDRD stock

Historical period Simulation period

Magnet outcome

Assumption of GDP growth

471 Canada

2 usa

14 HighIncAsia

17 EU16

18 EU12

20 Oceania

4 Brazil

5 RestSoAmer

11 MiddleEast

6 NoAfrica

12 india

19 China

13 ReSoAsia

15 SoEaAsia

7 WeAfrica

10 SoAfrica

16 EaAfrica

-20.0 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0

Evolution of GDP growth over period according SSP 2

2040-20502030-20402020-20302010-20202007-2010

Population assumptions

481 Canada

2 usa

14 HighIncAsia

17 EU16

18 EU12

20 Oceania

4 Brazil

5 RestSoAmer

11 MiddleEast

6 NoAfrica

12 india

19 China

13 ReSoAsia

15 SoEaAsia

7 WeAfrica

10 SoAfrica

16 EaAfrica

-10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0

Evolution of Population growth over period according SSP 2

2040-20502030-20402020-20302010-20202007-2010

Model aggregations

25 Production sectors 21 regions 5 periods

REG1 Canada2 usa3 CentrAmer4 Brazil5 RestSoAmer6 NoAfrica7 WeAfrica8 REaEurope9 RWeEurope10 SoAfrica11 MiddleEast12 india13 ReSoAsia14 HighIncAsia15 SoEaAsia16 EaAfrica17 EU1618 EU1219 China20 Oceania21 RussiaStan

PROD_SECT1 pdr2 wht3 grain4 oils5 sug6 hort7 crops8 cattle9 pigpoul10 milk11 cmt12 omt13 dairy14 sugar15 vol16 ofd17 fish18 lowind19 oth_ser20 oagr21 pub_ser22 highind23 rd24 fossilfuel25 CGDSTotal

TRAD_COMM1 pdr2 wht3 grain4 oils5 sug6 hort7 crops8 cattle9 pigpoul10 milk11 cmt12 omt13 dairy14 sugar15 vol16 ofd17 fish18 lowind19 oth_ser20 oagr21 pub_ser22 highind23 rd24 fossilfuel

PERIODS1 p[1] 2007-20102 p[2] 2010-20203 p[3] 2020-20304 p[4] 2030-20405 p[5] 2040-2050

Modelling cumulative RD stocks with vintages in Magnet Use of gamma distribution for modelling R&D stocks

6 vintage types based on available empirical evidence

Elasticity of land-augmenting TC (aland) to R&D differs per vintage group

Group Typical Regions Max Lag Lambda DeltaElasticity aland to RD Peak

A USA 50 0.7 0.9 0.3 24

B Australia and New Zealand 35 0.7 0.8 0.2 10

C EU-15 and other High Income 25 0.6 0.85 0.2 10

D EU-12 and Russian Federation 15 0.4 0.8 0.2 3

E Latin America 25 0.7 0.9 0.1 24

F Asia Pacific and Africa 15 0.5 0.8 0.1 5

*

Recapitulation - Modelling R&D in Magnet

R&D stock t1

*

*

Growth spill potential

=

=

*

*

Yield Index

Edu Index *

Growth R&D spillover

Growth R&D stock

Aland

R&D spill potential t0

GAEZ Index

Similar. Indext0

R&D stock t0

Similar. Indext1

GAEZ Index

R&D spill potential t1

R&D budget

Real R&Dinv

RDvin t

RDvin t+1

RDvin t+2

RDvin t+3

RDvin t+..

+ =RD stock t0

RD stock t1

Prices of cattle and milk increase the most

52

pdr wht gra in oi l s sug hort crops cattle pigpoul mi lk AGRI_PRIM

Canada -0.2 0.6 0.0 0.3 0.4 0.8 0.3 0.0 -0.2 0.0 0.3

usa -0.7 0.4 -0.1 0.0 0.1 -0.2 0.2 -0.2 -1.0 -0.2 -0.2

CentrAmer -0.4 0.9 1.3 0.9 1.2 0.5 0.8 1.0 -0.8 1.1 0.5

Brazi l -1.3 0.4 0.1 0.1 0.1 -0.1 0.0 0.4 -1.0 0.1 -0.1

RestSoAmer -0.8 0.9 0.6 0.7 0.3 0.1 0.6 1.0 -1.0 0.7 0.3

NoAfrica -0.4 0.9 1.2 1.1 1.7 1.2 1.1 1.8 -0.5 1.3 0.9

WeAfrica -2.4 0.0 3.4 3.1 2.1 2.9 -0.2 4.2 -1.8 2.2 2.5

REaEurope -1.3 0.2 0.1 0.2 -0.4 -0.5 -0.3 -0.3 -0.8 0.3 -0.1

RWeEurope 0.0 -0.1 -0.3 0.0 -0.5 -0.6 -0.1 -0.4 -0.4 -0.5 -0.4

SoAfrica 0.2 0.9 2.2 2.5 1.5 1.4 0.8 3.0 -0.6 1.6 1.6

MiddleEast -0.1 1.3 1.1 1.4 1.7 1.2 1.1 1.5 -0.6 1.4 1.0

india -0.8 1.6 3.3 2.3 3.2 2.7 2.5 2.5 -0.1 5.0 2.9

ReSoAs ia -1.1 0.7 3.1 1.5 3.8 2.0 1.0 3.5 -2.0 4.1 1.0

HighIncAs ia -0.6 0.0 -0.2 0.0 -0.6 -0.8 -0.3 -0.5 -0.9 -0.3 -0.6

SoEaAs ia 0.4 1.1 1.3 1.1 1.2 1.2 1.0 1.7 -0.5 2.0 0.7

EaAfrica -1.5 0.3 3.5 2.7 0.0 2.6 -0.3 4.1 -1.0 3.4 2.5

EU16 -0.1 0.1 0.1 0.1 -0.1 0.2 0.1 -0.1 -0.2 0.3 0.1

EU12 -0.6 0.5 0.4 0.3 0.2 0.0 0.3 0.1 -0.2 0.7 0.2

China 0.8 1.9 1.1 1.0 0.6 1.4 0.9 1.2 -0.2 2.0 0.6

Oceania -0.6 0.8 0.8 0.9 0.9 0.9 0.7 0.9 -0.6 1.0 0.8

Russ iaStan -0.5 0.9 0.9 0.8 0.7 -0.2 0.4 0.4 -0.7 0.4 0.3

Annual percentage growth rates of real agricultural prices (2010 – 2050)

Linking R&D stocks to productivity

Growth of the cumulated R&D stocks obtained from gamma distribution and R&D spillovers are linked to land-augmenting technical change :

● where aland represents land-augmenting technical change parameter, which enters the CES production function

● elasRD is elasticity of aland with respect to R&D growth (values reported in Table 1)

● rdstock and rdspil are growth rates of domestic R&D stocks and R&D spillovers per each region

53

Concept of long R&D lags introduced in MAGNET

Contrary to industrial research, which has a more experimental and short-term character, benefits accrue with considerable delay:

54

It takes 5-10 years before the variety is adopted, due to time spent on experimental trials and regulatory approvals. After, farmers have to learn how to produce it, and consumers have to accept the new product innovation. Therefore, the peak of benefits only comes 15-25 years after the initial investment. Eventually, the variety may become obsolete, as it may be less effective against evolving pests or diseases.” (Alston, 2009)

Historical R&D share in agricultural output

551971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

USA

Australia

New Zealand

UK

China

India

Linear (India)

Indonesia

Brazil

Colombia

Argentina

Ghana

Kenya

Nigeria

South Africa

Average period growth rates of agricultural production

quantity (mean 2010-2050)

Canada

usa

CentrAmer

Brazil

RestSoAmer

NoAfrica

WeAfrica

SoAfrica

MiddleEast

india

ReSoAsia

HighIncAsia

SoEaAsia

EaAfrica

EU16

EU12

China

2.7

1.7

1.7

1.9

2.4

2.1

3.9

2.6

2.3

2.5

2.7

0.8

2.5

3.3

1.5

1.2

2.3

2.2

1.5

2.0

2.0

2.6

2.7

4.2

3.2

2.5

2.5

2.6

0.7

2.6

4.1

1.2

1.2

2.5

Annual percentage growth rate of agricultural production (2010-2050)


Land prices are behind agri price spikes

57Canada

usa

CentrAmer

Brazil

RestSoAmer

NoAfrica

WeAfrica

SoAfrica

MiddleEast

india

ReSoAsia

HighIncAsia

SoEaAsia

EaAfrica

EU16

EU12

China

4.8

3.5

4.5

4.6

4.5

7.8

11.0

8.4

8.0

7.6

7.0

0.5

2.8

11.3

3.4

3.1

4.3

3.9

2.7

3.0

3.2

3.5

5.9

9.9

6.1

6.7

7.4

6.9

-0.3

2.0

9.0

2.9

2.0

0.9

Annual percentage growth rate of land prices (2010-2050)Baseline alex Baseline spillover

Impact of trade balance in agricultural products

58Canada

usa

CentrAmer

Brazil

RestSoAmer

NoAfrica

WeAfrica

SoAfrica

MiddleEast

India

ReSoAsia

HighIncAsia

SoEaAsia

EaAfrica

EU16

EU12

China

-25000 -20000 -15000 -10000 -5000 0 5000 10000 15000 20000

Trade balance in agriculture (value at world prices)

20502007

IMPACT OF PUBLIC AGRICULTURAL R&D INVESTMENTS ON AGRICULTURAL PRODUCTIVITY AND FOOD SECURITY Zuzana...

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