8. Agricultural Commercialization and Innovation

1

8. Agricultural Commercialization and Innovation

8.1 Rural Markets, Infrastructure and Commercialization

8.2 Agricultural Research and Technology8.3 The Green Revolution8.4 The Gene Revolution

M4902-430 Food and Nutrition SecurityUniversity of Hohenheim

2

8.2 Agricultural Research and Technology

0

1000

2000

3000

2002

kcal

/ ca

pita

and

day

2804kcal

Estimated average requirement

Assumption in this graph: Food quantity in 2050 is the same as in 2002.

2050

2209kcal

Is there enough food available at the global level?

3

Developments over time

100

140

180

220

260

300

1961 1966 1971 1976 1981 1986 1991 1996 2001Year

Inde

x

Arable land

Population

Food Production

Source: FAO (2005).

4

Growth rates of cereal yields have been declining

Source: FAO (2005).

0

1

2

3

4

5

1960s 1970s 1980s 1990s 2000s

Ann

ual g

row

th ra

tes

(%)

RiceWheat

5

The role of agricultural technology for food security

• Increases in food production globally and locally (increases global and local food availability).

• Increases in agricultural and rural incomes. Most of the undernourished derive a great share of their income from agriculture (better access to food).

• Entails positive spillovers to other sectors and contributes to economy-wide growth.

Agricultural technology should not be seen as a silver bullet, but it can contribute to:

6

Public agricultural R&D expenditures1971 1981 1991

Expenditures (million 1985 dollars)

Developed 4,298 5,713 6,941

Developing 2,984 5,503 8,009

SS Africa 699 927 968

Expenditures per capita (1985 dollars)

Developed 6.6 8.0 8.8

Developing 1.2 1.7 2.0

SS Africa 2.7 2.4 1.2

Agr. research intensity (spending relative to agr. GDP, %)

Developed 1.38 1.98 2.39

Developing 0.38 0.50 0.50

SS Africa 0.78 0.86 0.70

Source: ISNAR (1999).

7

Technological progress

Types of technological progress:

Biological/genetic: new seed varieties (land/labor saving)

Mechanical: animal draft technology, tractors, harvesters (labor saving)

Chemical: fertilizers, pesticides (land saving) (intensification or technological progress?)

Managerial: cultural techniques, organization (neutral)

How is it defined?

Increase in total factor productivity (TFP), which leads to an upward shift in the production function.

8

Technology adoptionThe existence of a new technology does not automatically lead to technological progress at the farm level. The technology first has to be adopted by farmers.

Definition: Adoption is defined as the individual farmer’s decision whether or not to take up and use a technology.

0/1 decision (dichotomous choice)?

• Depends on whether technology is divisible or not (e.g., tractor vs. seed or fertilizers).

• Single technology or package of different components (e.g., HYVs plus fertilizers and pesticides).

9

Aggregate adoption profileThe diffusion process of a new technology

Percent of

adopters

t

Early adopters

Takeoff

Saturation( )tbat e

AA ⋅−−+=

1max

10

Factors affecting adoption decision

+++Off-farm income

-+Labor availability++neutral (can be +)Farm size

dependsdependsSoil and water conditions

++Education

++Access to information (extension, media etc.)

---Credit constraint---Risk aversion

Non-divisible technology

Divisible technology

These are just general expectations. Effects may vary by location and over time.

+ means positive and ++ means strongly positive

- means negative and -- means strongly negative effect.

11

Analyzing adoption empirically• Develop a general model A = f(x), where A is adoption, and x a

vector of variables likely to affect the decision.• Specification of x depends on (i) type of technology, (ii) farm

and farmer characteristics, (iii) context (institutions, environment).

• Obtain representative cross-section data of all relevant variables for adopters and non-adopters (assume A as 0/1 dummy).

• Estimate the model using probit or logit techniques. The estimated coefficients indicate whether variables have a significant influence and whether they increase or decrease the probability of adoption.

• If A is not only 0/1, then degree of adoption can be used in a second-step continuous regression.

• Study can be repeated after some time to analyze adoption dynamics.

12

Adoption of Bt cotton seeds in ArgentinaProbit analysis (n=289; Pseudo-R2=0.57)

-1.64-0.64Irrigation (dummy)-1.45-0.59Soil (index)1.650.64Climate (index)4.070.04***Bollworm pressure (index)-4.40-1.26***Information constraint (dummy)-3.38-0.99***Credit constraint (dummy)2.270.03**Age (years)3.970.16***Education (years)1.450.00Farm size (ha)-4.99-4.88***Constant

t-statisticCoefficientVariable

Source: Qaim and de Janvry (2003).

13

Impact analysis of technologies

Individual farm level: Compare cost and income effects with and without technology.

With-without or before-after?

With-without can be based on cross section survey but can lead to non-random selection bias.

Before-after takes care of selection bias but requires panel data; time-dependent effects have to be controlled for.

Sometimes, within farm comparisons are possible (with and without on the same farms).

14

Example of Bt cotton in Argentina

Bt plots All non-Bt plots

Non-Bt plots of Bt adopters

1999/2000 season

Number of sprays 2.14 3.74** 4.52**

Insecticide (kg/ha) 1.85 2.43* 4.14**

Yield (kg/ha) 2,032 1,291** 1,537**

2000/2001 season

Number of sprays 2.84 3.70** 5.07**

Insecticide (kg/ha) 2.30 2.35 4.03**

Yield (kg/ha) 2,125 1,285** 1,606**

*, ** Significantly different from Bt plot values at 10% and 5% level, respectively.

Source: Qaim and de Janvry (2005).

15

Example of disease-free (tissue-cultured, TC) banana planting material in KenyaStudy by Qaim (2000)

• Banana is a perennial crop, which is mostly grown by women farmers for home consumption and for sale.

• Usually (pathogen-infected) suckers from old plantations are used for propagation.

• Tissue-culture (TC) is a form of plant propagation carried out in the lab (biotech) to produce pathogen-free seedlings.

• Advantage: Healthier plants and higher yields.

• Technology was introduced in Kenya in late 1990s. Evaluation is based on on-station and on-farm field trials (observed without and assumed with technology comparison).

16

Without With

Plantation establishment

Planting material 55 565

Fertilizer & pesticides 46 82

Labor 71 128

Recurrent annual cost

Fertilizer 25 62

Labor 40 64

Other 33 45

Cost of banana production with and without TC technology (US$/acre)

Source: Qaim (2000).

17

Yield curves with and without technology

0

2

4

6

8

10

12

14

16

18

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

t / a

cre

without TC

with TC

Source: Qaim (2000).

18

Technology-induced changes in cost and income annuities (by farm size)

Small<0.5 acres

Medium0.5-2 acres

Large>2 acres

Cost increase (%) 130 118 92

Yield increase (%) 151 139 103

Net income increase (%) 156 145 106

TFP increase (%) 8 9 6

The annuity is the “average” annual result calculated using annual cost and revenue streams over a 14-year plantation cycle and a discount rate of 10%.

Source: Qaim (2000).

19

Aggregate welfare effects of technology

S1

q1

a

b

c

S0Price

Quantity

Dp0

q0

Small and open economy assumption (tradable good).

cbPS +=Δ

20

Closed economy (or non-tradable)

cbaCS ++=ΔafePS −+=Δ )(

Price

Quantity

S0

D

p0

q0

S1

q1

a b cd

p1

ef

fecbW +++=Δ

21

Semi-subsistence production

dcbCS ++=Δ

abagfePS +−−++=Δ )(

Price

Quantity

S0

Dm

p0

q0

S1

q1

p1

gfedcW ++++=Δ

a b c d

ef

Dh

gx

22

Simulations for banana technologyData needed:

• Banana quantity demanded and supplied (statistics)

• Market price (statistics and/or farm survey)

• Price elasticities of supply and demand (literature)

• Production share of farm size groups i (farm survey)

• Home-consumed shares of groups i (farm survey)

• Technology shift parameter of supply curve (K):

iii AdoptionTFPK ×Δ=Delta TFP is the change in total factor productivity at the farm level (i.e. change in per unit production cost).

23

Technology diffusion process(Ex ante estimates based on expert interviews)

0

20

40

60

1999 2002 2005 2008 2011 2014 2017

Perc

ent

Large

Medium

Small

24

Welfare and distribution effects

Aggregate gain in economic surplus: $1.6 million(annual average over 22 year benefit stream)

of which consumers: 43.9%

producers: 56.1%

Benefit distribution among farms:

Small farms (production share 37%): 1.5%

Medium farms (production share 41%): 65.9%

Large farms (production share 22%): 32.6%

25

More optimistic scenario• Lower price for TC seedlings (more lab experience)• Involvement of existing women’s groups to promote

distribution of TC seedlings, extension, and micro-credit.

Such developments would increase productivity effects and speed up adoption, especially among smallholders. Woman-to-woman technology transfer could help to prevent men from taking over the business (and income).

Annual gain in economic surplus: $12.8 million

Small farms (production share 37%): 29.1%

Medium farms (production share 41%): 50.5%

Large farms (production share 22%): 20.4%

26

Internal rate of return (IRR) on R&D investments

91%42%IRR42,4531988969464221105

00

Optimistic benefit (thousand $)

8,58930etc. 252684071927061378359810447014330163202021

Pessimistic benefit (thousand $)Cost (thousand $)Year t

( ) ( )[ ]∑=

−+⋅−=25

11

t

ttt rCBNPV ( ) ( )[ ]∑

=

−+⋅−=25

110

t

ttt IRRCB

NPV: net present value; r: discount rate

27

Ex post and ex ante studiesEx post• looks at results after they occurred• results are observable (straightforward data collection)• important for project evaluations• lessons for future projects can be learned (what went

right? / what went wrong?)

Ex ante• tries to assess results before they occur• data collection is tricky, study has to be build on

assumptions• important for research priority setting (at very early stage)• policy scenarios can highlight institutional constraints and

recommendations for change

28

Lessons learned

• Role of agricultural technologies for food security.• How is technology adoption defined, on what does it

depend, and how can it be analyzed empirically?• How can technology impacts and distribution effects be

analyzed (i) at the farm level, (ii) at the aggregate country or region level?

• Technologies are not good or bad, but they can be more or less appropriate in a particular context.

• The institutional environment is important and can be influenced through suitable policies. “Technological determinism” fails to recognize that.

29

8.3 The Green Revolution• Development and release of high-yielding varieties (HYVs) of

rice & wheat in DCs (combined with other inputs).

• Process started in the 1960s, mainly initiated by public international agricultural research centers (IARCs).

• Most important early centers were: International Rice Research Institute (IRRI), International Center for Wheat and Maize Improvement (CIMMYT).

• Norman Borlaug (at that time chief wheat breeder at CIMMYT) received Nobel Peace Prize for his achievements.

• Today, 16 IARCs, grouped together in Consultative Group on International Agricultural Research (CGIAR, www.cgiar.org).

• Sponsored by partner countries, World Bank, Ford & Rocke-feller Foundations, CGIAR budget is around $340 million p.a.

30

High-yielding varieties (HYVs)

• higher yields than traditional landraces

• dwarf and semi-dwarf (shorter stalks, emphasis on grain)

• shorter duration to maturity (more seasons per year)

• dependent on relatively good water conditions (sufficient rainfall or irrigation)

• more responsive to chemical fertilizers

• more susceptible to pests and diseases, thus more dependent on chemical pesticides (especially in early phase; second generation HYVs were more robust)

Result: Huge increases in global and local food production. Much less hungry people than predicted.

31

Average worldwide cereal yields over time

0.0

1.0

2.0

3.0

4.0

5.0

1960 1980 2004

Yiel

d in

t/ha

RiceWheat

Source: FAO (2005).

32

HYV adoption in the 1970s

Source: Evenson and Gollin (2003).

0

20

40

60

80

100

Latin America Asia SS Africa

Perc

ent o

f tot

al c

rop

area

WheatRice

33

HYV adoption in the 1990s

0

20

40

60

80

100

Latin America Asia SS Africa

Perc

ent o

f tot

al c

rop

area

WheatRiceRoot cropsProtein crops

Source: Evenson and Gollin (2003).

34

Annual growth rates of food production

Latin AmericaEarly Green Revolution (1961-1980)

Late Green Revolution(1981-2000)

Production 3.083 1.631

Area 1.473 -0.512

Yield 1.587 2.154

HYV contribution 0.463 0.772

Other inputs 1.124 1.382

Source: Evenson and Gollin (2003).

35

Annual growth rates of food production

AsiaEarly Green Revolution (1961-1980)

Late Green Revolution(1981-2000)

Production 3.649 2.107

Area 0.513 0.020

Yield 3.120 2.087

HYV contribution 0.682 0.968

Other inputs 2.439 1.119

Source: Evenson and Gollin (2003).

36

Annual growth rates of food production

Sub-Saharan AfricaEarly Green Revolution (1961-1980)

Late Green Revolution(1981-2000)

Production 1.697 3.189

Area 0.524 2.818

Yield 1.166 0.361

HYV contribution 0.087 0.471

Other inputs 1.069 -0.110

Source: Evenson and Gollin (2003).

37

Did the Green Revolution bypass Africa?

Yes, at least adoption and impacts were much less pronounced than in Asia and Latin America.

Reasons:

• Wheat and rice are less relevant there (improvement in maize, root crops etc. only started later)

• High level of risk entails crop diversification (less focus on one particular crop)

• Less irrigation facilities

• Infrastructure constraints for timely supply of inputs

38

Were the poor disadvantaged?Small farms:• Technology components are divisible, so that they

should be scale neutral.• Still, early studies showed less adoption by smallholders.• HYVs increased market (and sometimes production)

risks, making adoption by poor farmers more difficult.• Institutional constraints are often more severe for small

farms (access to credit etc.)• But, often small farms adopted with a certain time lag.• Farmers in marginal agro-ecological areas were

disadvantaged (HYVs not suitable for them).

Not farm size as such, but institutional and agro-ecological constraints could be observed.

39

…were the poor disadvantaged?Landless rural laborers:• Some claimed: Green Rev. led to reduced employment.• But, it was failed to differentiate between the Green

Revolution and mechanization. HYVs rather increased employment and wage rates.

• Positive income multiplier effects.

Poor food consumers:• Clear benefit through lower prices (real income effect

biggest for the poor).• Small farms are often net consumers of food.

The poor did and do benefit, but agro-ecological and institutional constraints need to be tackled.

40

Poverty in Bangladeshi villages with high and low HYV adoption(but otherwise similar conditions)

Moderate poverty Extreme poverty

Low adoption

High adoption

Low adoption

High adoption

Headcount index 0.47 0.32 0.27 0.15

Relative income gap 0.33 0.26 0.26 0.22

Gini among the poor 0.17 0.14 0.14 0.13

Sen’s poverty index 0.21 0.12 0.10 0.05

Source: Hossain (1988).

41

Were there negative effects for the environment?

• Chemical fertilizers, pesticides, and inappropriate irrigation techniques often cause negative externalities.

• But the big productivity increases on the available land reduced land expansion to ecologically fragile areas.

• Positive environmental effects of reduced poverty.

• Impacts on agro-biodiversityNumber of species grown: was reduced, because rice and wheat were partly substituted for other crops.

Number of varieties within rice and wheat species: was reduced, because relatively few HYVs substituted for many landraces.

42

What were the nutrition effects?

• In terms of calorie consumption, the effects were clearly positive (less PEM).

• In terms of dietary diversity, the results are less clear and might have been negative.

Less species grown on farm.

Relative changes in prices of staple vs. non-staple foods.

• The Green Revolution did not reduce the problem of micronutrient deficiency.

43

Price indices for Bangladesh (1973-75=100)

0

25

50

75

100

125

150

175

200

1973-75 1979-81 1988-90 1994-96

Staple

Non-StaplePlant

Animal& Fish

Conclusion should not be less technology for staple foods, but to breed more explicitly for nutritional quality (biofortification), and to extend the focus also to higher-value products!

44

What would have been without the Green Revolution?

-13.3 to -14.4Per capita calorie consumption, DCs

6.1 to 7.9Proportion of children malnourished, DCs

27 to 30Food imports by DCs

35 to 66Crop prices, all countries (world market)

-15.9 to -18.6Crop production, DCs

4.4 to 6.9Crop production, ICs

2.8 to 4.9Cropped area, DCs

2.8 to 4.9Cropped area, ICs

-19.5 to -23.5Crop yields, developing countries (DCs)

2.4 to 4.8Crop yields, industrialized countries (ICs)

Multi-market model simulations (% change to base case)

Source: Evenson and Gollin (2003).

45

8.4 The Gene Revolution

Agricultural output growth (food availability)

Suitable for small farms (income generation)

More nutritious staple foods (vitamins and minerals)

Environmental and health benefits

Environmental and health risks

High-tech is inappropriate for small farms

Increasing disparities

Multinationals exploit small farms (dependencies)

Pro and contra arguments in the public debate

46

Definition of biotechnologyBiotechnology includes all processes where living organisms or parts thereof are used for human purposes.

Modern biotechnology:(i) cell and tissue culture(ii) marker-assisted breeding (molecular markers)(iii) genetic engineering (genetically modified (GM) crops).

Agronomic traits• Resistance to pests, diseases, drought, salinity, coldness etc.• Increase yield potentialsOutput traits• Improve nutrient composition (vitamins etc.)• Produce new substances (e.g., “pharming”)

GM breeding objectives

47

Global area under GM crops

Source: James (2006).

0

20

40

60

80

100

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Mill

ion

ha

Total

Industrialized countries

Developing countries

48

What type of technologies (2006)?1. Insect-resistant Bt cotton

2. Herbicide-tolerant soybeans

• India 3.8 m ha (2002)• China 3.5 m ha (1997)• South Africa 0.1 m ha (1997)• Mexico <0.1 m ha (1996)• Argentina <0.1 m ha (1998)

• Argentina 16 m ha (1997)• Brazil 12 m ha (2003)• Paraguay 1.8 m ha (2004)

49

Insect-resistant Bt cottonGene from the soil bacterium Bacillus thuringiensis (Bt) makes the plant resistant to the cotton bollworm.

• Technology developed by US company Monsanto.• Since 1997/98 commercialized in several countries:

USA, China, Argentina, South Africa, Mexico.• In India, Bt cotton was approved in 2002/03.

50

Adoption of Bt cotton in India

Source: Qaim et al. (2006).

0

0.5

1

1.5

2

2.5

3

3.5

4

2002 2003 2004 2005 2006

Mio

. Hek

tar

6.6 ha 5.7 ha 6.2 ha 5.6 ha

Average farm size of adopting farmers:

4.5 ha

51

Bt cotton in India

Bt cotton Conventional cotton Change

Insecticide (kg/ha) 5.1 10.3 -50%

-$45

+$56

Insecticide cost (US$/ha) 64.7 109.5

Seed cost (US$/ha) 80.9 25.2

Survey of 375 cotton farms in four states (2002/03)

Source: Qaim et al. (2006).

Yield (kg/ha) 1,628 1,213 +34%

Gross revenue (US$/ha) 706.8 533.0 +$174

Total cost (US$/ha) 434.4 371.7 +$63

Net revenue (US$/ha) 272.4 161.3 +$111

52

How can yield effects be explained?

( ) ( )ZGXFYield ⋅=

Yield and damage control function

with ( ) ( )1,0=⋅G

( ) ( )InsBtG

⋅−⋅−+=⋅

γβμexp11

(logistic)

53

Estimated damage control function

0

20

40

60

80

100

0 1.6 3.2 4.8 6.4 8 9.6 11.2 12.8 14.4

Insecticide use (kg/acre)

Dam

age

cont

rol (

%) With Bt

Without Bt

Source: Qaim und Zilberman (2003).

54

Impact variability

Maharashtra Karnataka Tamil Nadu Andhra Pradesh

Insecticide use -46% -62% -78% -34%

Yield +32% +73% +43% -3%

Per-ha gain $92 $270 $247 -$69

Effects of Bt cotton in four states of India (2002/03)

Factors influencing Bt cotton impacts:

• Local bollworm pressure

• Crop management practices

• Local suitability of varieties into which Bt is incorporated

Source: Qaim et al. (2006).

Yield effect = (Bt gene effect) + (variety effect)

55

Yield function with Bt as dummy

0.380

0.275***Bunny (dummy)

-8.120***

-0.332**

0.062**

1.634

1.057**

1.931***

0.463***

Model (3)

0.362

-7.370**

-0.326**Bt-Andhra Pr.

0.061***

1.386

1.020**

1.930***

0.348***

Model (2)

0.355R2

-7.633***Constant

0.063***Irrigation

1.495Labor

1.015**Insecticide

1.969***Fertilizer

0.237***Bt (dummy)

Model (1)

Translog Specification (not all variables shown)

56

Technology adoption as a learning process

2002/03 2003/04 2004/05 2005/06

Number of adopters 113 108 165 251

Disadopters after the season 51 26 18 n.a.

Disadopters, who re-adopted later on 38 14 n.a. n.a.

Sample of 375 farmers observed over several years

Source: Qaim (2005).

57

Bt cotton impacts:International evidence

India China South Africa

Argen-tina Mexico

Insecticide -50% -65% -33% -47% -77%

Yield +34% +10% +22% +33% +9%

Per-ha gain $111 $470 $18 $23 $295

Sources: Qaim et al. (2006); Pray et al. (2002); Thirtle et al. (2003); Traxler et al. (2003).

China: Benefit Small farms (<1ha): +$401/haby farm size Larger farms (>1ha): +$293/ha

58

16%79%42%6%34%Company benefit share

84%21%58%94%66%Farmer benefit share

$58$87$13$32$56Seed price difference

MexicoArgen-tina

South AfricaChinaIndia

Adoption 42% 66% 85% 10% 50%

Sources: Qaim et al. (2006); Pray et al. (2002); Thirtle et al. (2003); Qaim et al. (2003);Traxler et al. (2003).

Distribution of Bt cotton benefits between farmers and biotech companies

59

Demand for Bt cotton seeds in Argentina

0

100

200

300

400

500

0 20 40 60 80 100 120

Seed price (US$/ha)

Are

a (t

hous

and

ha)

0

2

4

6

8

10

Prof

it (m

US$

)

Total

Small farms

Large farms

Source: Qaim and de Janvry (2003).

Profit curve for Monsanto

60

Predicted effects of Bt cotton in Argentina

Large farms(n = 115)

Small farms(n = 173)

Insecticide reduction (kg/ha) 2.6 1.4

Yield gain (kg/ha) 295 447

Yield gain (%) 18.7 41.4

Total gross benefit (US$/ha) 82.4 97.2

Source: Qaim and de Janvry (2005).

61

Herbicide-tolerant (HT) soybeans in Argentina• Also called Roundup Ready soybeans, because they are

tolerant against the herbicide Roundup (glyphosate).• Developed by Monsanto and commercialized in Argentina in

1997 (in the US in 1996).• Unlike in the US, HT soybean technology is not patented in

Argentina.

Argentina 1997 2004

HT soybean area (m ha) 0.4 14.5

% official seeds 100 35

Technology premium (%) 150 30

62

Agronomic effects of HT soybeans

Source: Qaim and Traxler (2005).

Survey of 118 plots in three provinces of Argentina.

ChangeHerbicide use (l/ha) +108%

of which in Toxicity class I to III -94%

Toxicity class IV +248%

Machinery time (h/ha) -20%

Fuel (l/ha) -18%

Share of farmers using zero-tillage +91%

Whether herbicide tolerance is suitable for small farms is an open question, since weeds are often controlled manually.

63

Economic effects of HT soybeans

Herbicide cost -44%

Machinery and labor cost -14%

Seed cost +24%

Total production cost -10%

Per-ha income gain $23

Argentina

Farmers 90%

Consumers 1%

Companies 9%

Effects on production costs and farmers’ benefit

Benefit distribution (in%)

Source: Qaim and Traxler (2005).

USA

21%

22%

57%

64

Environmental risks of GM crops

Resistance development to Bt toxin in pest populations:

• Through natural genetic variation, there are always some resistant bollworms in any population.

• If only resistant individuals survive and mate with each other, then rapid buildup of resistance.

• Strategy: non-Bt refuge areas to decrease selection pressure.

• Theoretically appealing, but does it work practically?

• Biological simulations over 15-year period with detailed meteorological and entomological data from Argentina.

• Does frequency of resistant individuals increase rapidly over time?

65

Resistance simulations, Argentina

0

0.02

0.04

0.06

0.08

0.1

0 3 6 9 12 15

Year

Freq

uenc

y of

resi

stan

t bol

lwor

ms

0

0.2

0.4

0.6

0.8

1

Freq

uenc

y of

resi

stan

ce fo

r 0%

refu

ge

0% Refuge

20% Refuge

5% Refuge

Source: Qaim and de Janvry (2005).

66

Loss of agro-biodiversity?Country Technology Area (ha) No. of

varietiesUSA RR soy 26 m 1,200

Bt corn 9.6 m 750Bt cotton 2.3 m 19

Argentina RR soy 16 m 61Bt corn 1.6 m 21

Bt cotton 25,000 2China Bt cotton 3.5 m 53South Africa Bt corn 0.4 m 11

Bt cotton 0.1 m 3

Source: Qaim (2005).

67

Out-crossing of transgenes and suppression of natural vegetation?

• Out-crossing can occur when there are natural relatives of GM plants (depends on location, center of diversity? maize in Mexico, soybean in China, rice in SE Asia)

• Whether or not natural vegetation is suppressed depends on the fitness advantage of the transgene (herbicide tolerance, insect resistance, drought tolerance).

Risk can only be assessed from case to case. So far, “invasion” has not been observed.

68

Effects on ecosystems and non-target species• GM crops do interfere with natural equilibria, just like any

other agricultural tool.• Assessment depends on reference system.• Hitherto studies: GM crops are less harmful than

conventional but more harmful than organic agriculture.• British studies (species diversity within fields is lower for

herbicide-tolerant crops; birds would be harmed if there were no weeds as breeding places for insects).

Reference system has to be defined properly; long-term effects need to be monitored carefully.

69

Institutional risks

• There is the risk that biotechnology developments will bypass small farmers in developing countries, who stand to benefit the most.

• This is not because biotech in general is not suitable for small farms, but because specific technologies for the poor might not be developed.

• How can LDCs be reached?

70

Distribution of GM crop area by…

Crop

Source: James (2004).

Canola6%

Soybean60%

Maize23%

Cotton11%

Herbicide tolerance

(HT)72%

HT/Bt stacked

9%

Insect resistance

(Bt)19%

Trait

71

Private sector dominates GM crop research• Almost all GM crops available thus far have been

commercialized by multinational companies.• 85% of global R&D expenditures in agricultural biotech

are by private sector.• Less than 5% of global investments in DCs.• The annual biotech budget of the CGIAR is $25 million

(as compared to $1 billion of Monsanto alone).

Private companies develop technologies for big lucrative markets, not for niche markets dominated by poor farmers and consumers.

More public research on GM crops, targeted to the poor, is needed.

72

The Gene Revolution: conclusions• GM technology is not a panacea for food security.

• But it could boost food production and rural incomes in DCs in a sustainable way.

• Not every single GM technology might be appropriate for smallholder farmers.

• There are environmental risks, that have to be assessed case-by-case. So far, risks seem to be manageable. But biosafety and long-term monitoring are essential.

• Institutional risks are acute. No indications of exploitation. But will suitable technologies be developed on a larger scale?

• More public investments into R&D and capacity building are needed. This presupposes better acceptance (better science-based information, less emotions & half-truths).