Setting Priorities of Regional Agricultural R&D Investments in Africa: Incorporating R&D Spillovers...
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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.
04/08/2023 – Page 2
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?
IFPRI
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
IFPRI
Trends in Cereal Yields, 1961-2009
04/08/2023 – Page 7
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
IFPRI
Performance has been mixed across regions...
04/08/2023 – Page 8
0
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))
IFPRI
.. within regions, and not only agriculture, but overall economies can be quite diverse
Middle Income
Low Income
Low Income & post civil
conflict
IFPRI
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
IFPRI
> 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
IFPRI
SADC RegionCrop Production Potential (Length of Growing Period)
IFPRI
SADC RegionPopulation Density
IFPRI
Local urban, national & regional
markets
Local Rural
Markets
SADC RegionMarket Access
(Travel time to local rural, urban, national & regional markets)
IFPRI
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
IFPRI
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
IFPRI
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
IFPRI
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)
IFPRI
For ASARECA.. Identifying source of spillover countries (R&D capacities by commodity)
04/08/2023 – Page 19
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
0
10
20
30
40
50
60
cassava coffee cotton maize plantain potato rice sorghum
1st rank 2nd rank 3rd rank
IFPRI
Spillover potential influenced by development domains - ASARECA
04/08/2023 – Page 20
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”
IFPRI
Potential gross welfare gains from agricultural R&D spillovers in ASARECA
04/08/2023 – Page 21
0
1
2
3
4
5
6
7
8
9
Mill
ion
US$
Additional gains with spillovers
Total regional gains without spillovers
IFPRI
For CORAF.. Also potentially large
04/08/2023 – Page 22
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
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 (without Nigeria)
Sahelian
IFPRI
For SADC – construction of spillover matrices
04/08/2023 – Page 23
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
IFPRI
Analysis of alternative future growth scenarios.. Identifying key drivers of growth – with welfare goals in mind
04/08/2023 – Page 25
IFPRI
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
04/08/2023 – Page 28
0
2
4
6
8
10
12
%
Fishery Livestock High-value
O.food Roots Cereals
IFPRI
For CORAF, only few can meet 6% target from closing yield gaps (2006-2015 average)
04/08/2023 – Page 29
0
1
2
3
4
5
6
7
8
Others Livestock O.high valueCotton Cocoa Vegetables & fruitsPulses & oilseeds Roots Cereals
%
Sahel Coastal Central Africa
IFPRI
Measures of the gains from potential spillovers under the ‘targeted’ growth scenario can be quite significant
04/08/2023 – Page 30
IFPRI
Low income countries benefit most from spillovers – esp. for Cereals (SADC)
IFPRI
Beneficiaries of spillovers (or spill-in) by commodity (US$ million, 2009-2015) - SADC
04/08/2023 – Page 32
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
300
400
500
600
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
)
IFPRI
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
IFPRI
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
IFPRI
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
IFPRI
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
04/08/2023 – Page 36
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
04/08/2023 – Page 37
IFPRI
Thank You
04/08/2023 – Page 38