Price

Priority-Setting for Agricultural Biotechnology

in West Africa

USAID/EGATMarch 9, 2005

William A. MastersPurdue University

D S S’ S”Price

Quantity

J (output gain)

I (input change)

Q Q’

K (cost reduction)

Variables and data sources

Market dataP,Q National ag. stats.

Field dataJ Yield change×adoption rateI Input change per unit

Economic parametersK Supply elasticity (=1 to omit)ΔQ Demand elasticity (=0?)

ΔQ

P

The economic gains from new technology are proportional to output before adoption (PxQ) times the probability of cost reduction (“K”)

Figure 1. Economic impact assessment in one picture

Table 1. Concordance and the allocation of R&D investment in Mozambique (1990s)

Share of Agricultural

GDP

Share of research

expenditure

Research intensity

ratio

Cassava 44 15 0.3

Maize 16 12 0.7

Pulses 9 5 0.5

Peanuts 7 5 0.6

Sorghum 6 10 1.6

Rice 4 4 1.0

Cotton 2 15 6.4

Cashew 2 7 3.7

Sweet potato 1 14 14.2 Source: Uaiene, Rafael, 2002. “Priority setting and resource allocation in the National Agronomic Research Institute, Mozambique” (Dec. 2002).

Strategic targeting can be much improved through concordance…

Figure 2. Prevalence of stunting in Sub-

Saharan Africa (latest available,

includes sub-national data)

Source: Redrawn from data compiled by the FAO’s Poverty and Food Security Mapping Project, using the most recent Demographic and Health Survey (DHS) data from ORC Macro, Multiple Indicator Cluster Survey (MICS) data from UNICEF, WHO survey data and national government estimates. Note: Data shown are the percentage of children aged 0-5 whose height for age is at least two standard deviations below the NCHS standard for their age.

Strategic targeting aims for large problems that are being missed by other investors

Fig. 3. Share of food production by crop, 1961-2002

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cal

ori

es (per

cent of to

tal) . All animal products

All other vegetalsPulses+oilcropsBananas+plantainsAll other rootsCassavaAll other cerealsWheatRice (milled equiv.)MilletSorghumMaize

19612002 19612002 19612002Sub-Sah. Africa West Africa Central Africa

Source: Calculated from data in FAOStat (2005), reproduced in Annex 1.

The biggest needs are in cereals,cassava, and oilcrops

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pro

tein

(per

cent of to

tal) .

All animal productsAll other vegetalsPulses+oilcropsBananas+plantainsAll other rootsCassavaAll other cerealsWheatRice (milled equiv.)MilletSorghumMaize

19612002 19612002 19612002Sub-Sah. Africa West Africa Central Africa

Source: Calculated from data in FAOStat (2005), reproduced in Annex 1.

Fig. 3. Share of protein output by crop, 1961-2002

Cereals and oilcrops are especially important for food quality

Figure 5. Average yield of all cereals by region, 1961-2004

0

1

2

3

4

5

1960

1965

1970

1975

1980

1985

1990

1995

2000

Ave

rag

e yi

eld

of

all

cere

als

(mt/

ha)

.

W.Afr.Excl.NigeriaNigeriaCentral AfricaRestOfSub-Sah.Afr.South AsiaRest of the World

Source: Figures 5-10 calculated from FAOStat (2005) data

There are huge catch-up opportunitiesfor Africa to do what Asia did

Figure 6. Average yield of maize by region, 1961-2004

0.5

1.0

1.5

2.0

2.5

1960

1965

1970

1975

1980

1985

1990

1995

2000

Ave

rag

e yi

eld

of

mai

ze (

mt/

ha)

. W.Afr.Excl.Nigeria

Nigeria

Central Africa

RestOfSub-Sah.Afr.

South Asia

The catch-up opportunitiesare large in maize

Figure 7. Average yield of millet by region, 1961-2004

0

1

2

1960

1965

1970

1975

1980

1985

1990

1995

2000

Ave

rag

e yi

eld

of

mill

et (

mt/

ha)

.

W.Afr.Excl.Nigeria

Central Africa

RestOfSub-Sah.Afr.

South Asia

…but catch-up opportunitiesare big in small grains also!

Figure 9. Average yield of cassava by region, 1961-2004

0

5

10

15

20

25

30

1960

1965

1970

1975

1980

1985

1990

1995

2000

Ave

rag

e yi

eld

of

cass

ava

(mt/

ha)


NigeriaCentral AfricaRestOfSub-Sah.Afr.South AsiaRest of the World

There are huge catch-upopportunities in cassava

Figure 10. Average yield of other root crops by region, 1961-2004

0

5

10

15

20

1960

1965

1970

1975

1980

1985

1990

1995

2000

Ave

rag

e yi

eld

of

oth

er r

oo

ts a

nd

tu

ber

s (

mt/

ha)

.

W.Afr.Excl.Nigeria

Nigeria

Central Africa

RestOfSub-Sah.Afr.

South Asia

and also catch-up opportunities in other root crops

Figure 8. Average yield of seed cotton by region, 1961-2004

0

1

2

3

1960

1965

1970

1975

1980

1985

1990

1995

2000

Ave

rag

e yi

eld

of

seed

co

tto

n (

mt/

ha)


NigeriaCentral AfricaRestOfSub-Sah.Afr.South AsiaRest of the World

Africa has already donerelatively well in cotton

Figure 11. Public agricultural R&D per unit of agricultural land, 1971-91 (1985 PPP dollars per hectare)

Africa’s lag is mainly driven by the relatively low level of R&D spending

Figure 12. Agricultural R&D intensityin West and Central Africa, 1971-2001Agricultural R&D Intensity in West and Central Africa, 1971-2001

0

1

10

100

Pub

lic R

&D

(19

93 U

S$/

per

ha o

f ar

able

land

)

.

Cape VerdeSenegalMaliCote d'IvoireMauritaniaGuineaGhanaTogoBurkina FasoNigerNigeria

There is huge variation but no growthin R&D expenditure across the region

Can build on experience of seven Sahel regional workshops (1994-2002) all participants use common spreadsheet methods

• formulas derived directly from graphical model

• using each kind of data in sequence for intermediate results

• with “open architecture” to facilitate adaptation

participants have access to small grants• to implement priority-setting exercises

• to report their results at follow-on workshops

From Priority-Setting to Capacity Building

Results and methods are well-tested across West Africa

Strategic Targeting for Economic Gains

Documents

Price