IFPRI

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Setting Priorities of Regional Agricultural R&D in Africa: Incorporating R&D Spillovers and Economy-

wide Effects

04/08/2023

Michael JohnsonInternational Food Policy Research Institute

ASTI-FARA Conference, Accra, Ghana

December 5th – 7th, 2011

IFPRI

Current Study Objective

1. To review a number of methods that have been applied in the African context and at the regional level (based on 3 past studies).

1. ASARECA in East and Central Africa (2003-2004),

2. CORAF in West and Central Africa (2006-2007), and

3. SADC/FANR in Southern Africa (2010-2011)

2. By comparing and contrasting all 3 studies, this offers an opportunity to:

1. Review the methods used across them

2. Data needs and limitations

3. Review the results and policy implications, and

4. Their translation into policy action within each region.

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IFPRI

Motivations for the 3 studies

Responding to increased demand and recognition for regional agricultural R&D strategy, that... » There are scale economies to be had from regional cooperation given many small

countries (& many that are landlocked) » Given similar resource endowments and constraints, investments in one country

have the potential to generate externalities (spillovers) in neighboring countries, and

» Many cross-cutting issues extend beyond national boundaries» Diverse economies in the region offer great potential to spur growth through

greater trade linkages

Regional cooperation in R&D is already strong - FARA, SROs (ASARECA, CORAF/WECARD, SADC/FANR)

BUT, little empirical work done in helping to identify what the regional priorities should be – especially in a rigorous fashion

IFPRI

Goals across all 3 studies To assess the critical investment and policy alternatives for

regional Ag R&D strategy

Undertake an ex-ante impact evaluation of alternative future growth options and potential welfare benefits from increased Ag R&D investments.

Taking the first important step in empirically investigating alternative entry points for investments needed to achieve regional growth and welfare targets (CAADP, RISDP, MDG) – other analyses would be needed to elaborate specific interventions within each strategic area.

IFPRI

Key Questions Addressed

What investment and policy options, and in which key commodity areas, offer the best potential for accelerating agricultural growth in order to achieve regionally set goals?

Among the key commodity areas, which ones would be most suitable for a regional R&D program based on the degree to which they have greater potential for wider adaptation across countries?

Finally, what kinds of constraints and other complementary or crosscutting issues are important to consider in the context of enhancing productivity growth in the region?

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Context – The Big Picture Challenge

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Perc

ent s

hare

(%)

SADC (w/out SA) / World Ag Exports

SADC (w/out SA) / World Cereal Imports

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Trends in Cereal Yields, 1961-2009

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0

0.5

1

1.5

2

2.519

61

1964

1967

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

2012

2015

2018

MT/ha

SSA (without S. Africa)

South Asia

The Potential is there

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Performance has been mixed across regions...

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0.2

0.4

0.6

0.8

1

1.2

1.4

1961

1964

1967

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

5 per. Mov. Avg. (CORAF)

5 per. Mov. Avg. (ASARECA)

5 per. Mov. Avg. (SADC (without S. Africa))

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.. within regions, and not only agriculture, but overall economies can be quite diverse

Middle Income

Low Income

Low Income & post civil

conflict

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Analytical Framework and Methods

Prices

Spatial Analysis of underlying conditions & potential (DD)

Agricultural Potential / Pop Density

Market Access

Potential welfare effects -- on food security and poverty

outcomes

Rankings among key sub-sectors & activities, implications for future

investments & policies

Regional & economy-wide analysis of future growth scenarios due to R&D

(Economy-wide Multi-Market - EMM)

Analysis of economic returns to regional R&D and spillovers –

commodity-based economic surplus method (e.g. DREAM)Growth IncomePrices

ExpertSurveys &

Consultations

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> 1010-2020-5050-100> 100

persons/sq.km LGP (days)

0-4041-81

82-121122-162163-202203-243244-283284-324325-365

Population density Production potential Market access

Spatial Analysis – mapping out diversity in resource endowments and socio-economic environment (DD) - ASARECA

hours to mkt

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SADC RegionCrop Production Potential (Length of Growing Period)

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SADC RegionPopulation Density

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Local urban, national & regional

markets

Local Rural

Markets

SADC RegionMarket Access

(Travel time to local rural, urban, national & regional markets)

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CORAF Region - Agricultural suitability

Ideally, this should include climatic and soil information. Due to the lack of good spatial datasets and the heterogeneity of soils in the region, we relied on climatic data: Length of Growing Period

low

medium

high

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CORAF Region - Population density

Thresholds of <30 (low), 30-100 (medium) and >100 (high) persons per km2 were used to identify rural population density regimes of relevance for agriculture

low

medium

high

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CORAF Region - Market access

Access to input & output markets depends on distance, quality and quantity of transport networks, the size of markets, and the nature of the land surface (land cover, topography)

low

medium

high

High: <4 hrs to major seaports or large cities of 500,000+; <2 hrs to towns of 100,000+; <1 hr to

local mkts (10,000+)

Medium: <6 hrs to large cities (500,000+); <4 hrs to towns (100,000+); <2 hrs of local markets (10,000+)

Low: all other areas

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Spatial analysis illustrated potential for spillovers.. Challenge is measuring the spillover potential..

Approach taken, Ad Hoc assumptions for ASARECA – construction of spillover matrix for SADC

For SADC – 3 dimensions used.. » Capture similar production environments across countries using

DD analysis» Estimate yield gaps across countries to proxy knowledge and/or

technology stocks and determine the potential direction of transfer of technologies, and

» Add a capacity for R&D dimension to capture the probability for adoption through successful adaptive research (using evidence of past Rate of Return Studies)

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For ASARECA.. Identifying source of spillover countries (R&D capacities by commodity)

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Ug

and

a Ken

ya

Su

dan

Ken

ya

Ug

and

a

Ken

ya Mad

agas

car

Eth

iop

ia

Tan

zan

ia

Eth

iopi

a

Mad

agas

car

Eth

iopi

a

Bur

und

i

Eth

iopi

a

Tan

zan

ia

Sud

an

Uganda Tanzania

Tanzania

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20

30

40

50

60

cassava coffee cotton maize plantain potato rice sorghum

1st rank 2nd rank 3rd rank

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Spillover potential influenced by development domains - ASARECA

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HH23%

HM17%HL

48%

LH3%

LM2%

LL6%

Irrigated1%

Beans

HH11% HM

8%

HL76%

LH1%

LM1% LL

3%

Cassava

HH15%

HM8%

HL49%

LH2%

LM1%

LL23%

Irrigated2%

Maize

HH8% HM

4%

HL25%

LH1%

LM1%

LL5%

Irrigated56%

RiceLH = “Low Ag Potential” & “High Land Density”

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Potential gross welfare gains from agricultural R&D spillovers in ASARECA

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9

Mill

ion

US$

Additional gains with spillovers

Total regional gains without spillovers

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For CORAF.. Also potentially large

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0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

Cereals Root crops

Pulses & Legumes

Export Crops

2006

NPV

, Bili

on U

S$

Central

Coastal (with Nigeria)

Sahelian

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2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

Cereals Root crops Pulses & Legumes

Export Crops

2006

NPV

, Bili

on U

S$

Central

Coastal (without Nigeria)

Sahelian

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For SADC – construction of spillover matrices

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Final spillover matrices (e.g. Maize)Spillout (columns)and Spillins (rows)

ANG BOT DRC LES MAD MAL MOZ NAM SAF SWZ TNZ ZAM ZIMAve spill-in

effect

ANG 1.00 0.00 0.00 0.04 0.23 0.04 0.13 0.30 0.54 0.05 0.11 0.21 0.08 0.25

BOT 0.15 1.00 0.14 0.12 0.40 0.09 0.17 0.45 0.39 0.16 0.14 0.21 0.21 0.25

DRC 0.00 0.00 1.00 0.00 0.16 0.09 0.10 0.06 0.18 0.17 0.00 0.14 0.13 0.10

LES 0.00 0.00 0.00 1.00 0.02 0.02 0.00 0.00 0.01 0.02 0.00 0.01 0.01 0.01

MAD 0.00 0.00 0.00 0.00 1.00 0.05 0.00 0.00 0.12 0.00 0.00 0.00 0.00 0.04

MAL 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.03 0.00 0.00 0.00 0.00 0.01

MOZ 0.00 0.00 0.00 0.00 0.14 0.13 1.00 0.08 0.26 0.16 0.00 0.20 0.00 0.12

NAM 0.00 0.00 0.00 0.00 0.00 0.01 0.00 1.00 0.16 0.00 0.00 0.00 0.00 0.05

SAF 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00

SWZ 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.05 1.00 0.00 0.00 0.00 0.02

TNZ 0.00 0.00 0.00 0.00 0.19 0.29 0.00 0.21 0.43 0.28 1.00 0.19 0.11 0.20

ZAM 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.12 0.00 0.00 1.00 0.00 0.04

ZIM 0.00 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.11 0.20 0.00 0.10 1.00 0.05

Ave spill-out effect

0.00 0.00 0.00 0.00 0.07 0.08 0.02 0.06 0.16 0.10 0.01 0.09 0.03

IFPRI

Potential maize technology spillovers (aggregated at country)

Angola, 0.25

Botswana, 0.25

Tanzania, 0.20

Mozambique, 0.12

CongoDemRep, 0.10

Zimbabwe, 0.05

Namibia, 0.05

Madagascar, 0.04

Zambia, 0.04

Swaziland, 0.02

Malawi, 0.01Lesotho, 0.01

Spillin

SouthAfrica, 0.16

Swaziland, 0.10

Zambia, 0.09

Malawi, 0.08

Madagascar, 0.07

Namibia, 0.06

Zimbabwe, 0.03

Mozambique, 0.02 Tanzania, 0.01

Spillout

Spill-ins (the beneficiaries): This is average effect on productivity in Country

X due to adoption of technologies generated in other SADC countries, and

relative to the productivity effect associated with adoption of country X's

own technologies.

Spill-out (the sources): This is the effect on average productivity in the rest of SADC

region due to adoption of technologies generated in country Y (the source

country), relative to the average own productivity effects in the other countries

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Analysis of alternative future growth scenarios.. Identifying key drivers of growth – with welfare goals in mind

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Not all countries can achieve 6% CAADP target - SADC

Source; The EMM model simulation resultsNotes:(a) 6% of agricultural growth is achieved in 8 countries – under accelerated growth (targeted) (b) But such growth may be unrealistic in some countries where the current growth rate is very low (e.g. Zimbabwe).

IFPRI

For middle incomes countries, almost 50%

of potential growth would come from

Livestock and Fisheries

Key drivers of agricultural growth (2009-2015)

For low incomes countries, almost 60%

of potential growth would come from

Cereals & Grains and Roots

IFPRI

...But varies by country - SADC

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12

%

Fishery Livestock High-value

O.food Roots Cereals

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For CORAF, only few can meet 6% target from closing yield gaps (2006-2015 average)

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8

Others Livestock O.high valueCotton Cocoa Vegetables & fruitsPulses & oilseeds Roots Cereals

%

Sahel Coastal Central Africa

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Measures of the gains from potential spillovers under the ‘targeted’ growth scenario can be quite significant

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Low income countries benefit most from spillovers – esp. for Cereals (SADC)

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Beneficiaries of spillovers (or spill-in) by commodity (US$ million, 2009-2015) - SADC

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Angola, 293.3

Angola, 26.6 Angola, 24.3

Congo, DR, 100.1

Congo, DR, 52.8Congo, DR, 29.3

Madagascar, 122.2Madagascar, 47.4

Mozambique, 167.5

Mozambique, 109.7

Mozambique, 253.8

Mozambique, 63Mozambique, 18.2

Tanzania, 207.3

Tanzania, 117

Tanzania, 175.6

Tanzania, 55.2Tanzania, 59.1

Zimbabwe, 391.9

Zimbabwe, 55.3 Zimbabwe, 28.2

0

100

200

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700

800

900

1,000

1,100

1,200

Maize Rice Cattle Cassava Sorghum Beans

Cum

ulati

ve, U

S m

illio

n (2

009 -

2015

)

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Developing criteria for ranking priorities using scoring method (e.g. from results of analysis)

Commodity or Sector

Contribution to Ag growth*

Degree of regional spillover potential**

Contribution to poverty reduction

Contribution to intra-regional trade

Gender considerations

Total Weighted Score

 Final rank

Weights 0.5 0.5 0.0 0.0 0.0 1

Maize 25.6 39.1       32.4 1

Cassava 14.5 8.4       11.5 2

Rice 8.9 13.6       11.3 3

Cattle 5 9.4       7.2 4

Fruit & Veg 10.2 -       5.5 5

Beans 3.2 5.9       4.5 6

Fisheries 7.3 -       4.1 7

Sorghum 1.6 6.1       3.8 8

Wheat 4.3 2.3       3.3 9

Potato 3.5 2.5       3 10

Chicken 2.9 2.5       2.7 11

Milk & Egg 4.2 -       2.5 12

Groundnuts 2.3 2.6       2.4 13

Pigs 2.1 1.1       1.6 14

Sheep & Goats 1.6 1.5       1.5 15

Cotton 1.2 1.3       1.2 16

Sugar 1.1 0.3       0.7 17

Millet 0.5 0.8       0.7 18

Total 100.00 100.00 100.00 100.00 100.00 100.00

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Key Findings across all 3 studies

Each region has lots of similarities across countries, and therefore development domains, that can be exploited for targeting R&D and technology development

There are also clear differences (areas of comparative advantage), which offer opportunities for specialization

Smaller and lower income countries stand to benefit more from focusing attention on adaptive R&D

Final rankings according to commodities that offer the greatest potential for driving sector growth and greater technology spillovers – emphasize food staples (crops and livestock) – either though seeds, farming practices, or NRM

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Implications

There also needs to be strong national agricultural research systems for regional efforts to succeed.

Regional efforts have a lot to gain from taking advantage of strong NARS -- as a source for existing knowledge and expertise – especially when they have a high potential for adaptation in neighboring countries.

Analysis only partial, » Other criteria may also matter (e.g. poverty reduction, contribution

intra-regional trade, climate change, gender)» Other analyses still needed to elaborate specific interventions within

each strategic area identified

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Summary Conclusion

Increasing productivity growth where there is high potential for growth, spillovers, and demand

Strengthening links with markets:» Domestic urban markets and intra-regional trade linkages

(regional common markets – inputs and outputs)» Focusing on entire value chain (e.g. links with agro-industry)

Exploiting opportunities for greater cooperation in R&D (incentives)

Strengthening NARs and their capacities for adaptive research and extension (including impact assessment and priority setting)

Translating evidence into strategies, policies, and programs through bottom up – and following through with implementation

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IFPRI

Key Lessons Improving methods as integration is not always easy – but

strengthening use of simpler models and approaches (e.g. DREAM)

Second, the availability of sufficient and quality data: » Agricultural and socioeconomic data at lower administrative units» Research resource capacities, time frames, and expenditures by commodity or

research discipline and type (especially unit costs)» Yield performance data by technology and location (actual and on-farm trials,

expert opinion)» Information on past observations of adoption, diffusion, time frames, and simple

returns to agricultural investments (ROR)

On the policy front, in order to enjoy the benefits from greater cross-country cooperation in R&D, » Finding cost-effective ways to collaborate based on cross-country similarities

and comparative advantage» Dominance by donors – maybe changing if linked closer to RECs and CAADP

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IFPRI

Thank You

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