Economic impacts of GM crops on smallholders in the ... · PDF fileon smallholders in the...

Economic impacts of GM crops

on smallholders in the Philippines,

Honduras, Colombia and Bolivia

Melinda Smale, Patricia Zambrano,

Jose Yorobe and José Falck-Zepeda

Outline

I. Project goals

II. Highlights of systematic review

III. Selection of case studies/methods

IV. Preliminary findings

V. Conclusions

Goals

Develop good practices for assessing

the impacts of transgenic crops

in developing agriculture

• Conduct systematic review

• Pilot methods in the field

Contribute new evidence

Systematic Review

In the first decade,

Impact on farmers foremost

concern

Bt cotton the most studied

crop-trait combination

China, India and South Africa

most represented countries

Impact Question No.

Farmer 67

Consumer 27

Sector 27

Trade 26

Total 1996-2007 137

1 article may treat more than on question

Crop-trait No.

Cotton (Bt) 63

Maize (Bt) 14

Rice (RR,Bt) 16

Soybeans (RR) 16

Other Crops 22

GE-General 20

Other crops: bananas, potatoes, sweet

potatoes, cassava, wheat, oilseeds,

eggplant, mustard, coarse grains

Farm Impacts—Methods

Generally addressed ex post

Data sources:• farmer survey, trial data, farm records

Farm budget analysis • Stochastic simulation

Econometric models • Production models/input use functions

• Stated /revealed preference models

Farm impacts of Bt cotton

Aspects of

Impact

India China South Africa

(Makhathini

Flats)

Argentina

Adoption Regional disparities

Wide range of

germplasm

Large scale

Wide range of

germplasm

Atypical zone;

Supply-driven

Limited by

transfer fee

Economic

benefits

Generally positive

with exceptions

Generally

positive in

Yellow River

Valley

Generally

positive but

farmers

vulnerable

Limited

magnitude

Reduction in

pesticide use

Highly variable

depending on the

zone

Strong relative

reduction ;

still too high

Limited Evident

Social and

economic

sustainability

Too early to say ;

farmers more

knowledgeable

Since 1999 Institutional

problems ;

subsidized

Yes, but impact

insignificant

General

conclusion

Most

debated

case

Most

successful

case

Least

representative

case

Least

relevant case

Methods challenges

Selection

bias

placement

self-selection

host germplasm

Measurement

bias

farmer recall vs. monitoring of

input use

toxin expression varies by season

and plant part

Estimation

bias

budgets partial

household farm models missing

rare treatment of risk & uncertainty

endogeneity (adoption, input use)

Case Studies

Selection criteria:

Broader geographical representation

Traits not heavily studied

Early phases of adoption

Approaches:

Damage abatement models

Treatment model (IV)

Choice experiments

Stochastic budgeting

Social network analysis

Bt maize in the Philippines

Antonio La Vina, Jose Yorobe Jr., Jessica Dator-

Bercilla, Mary Jean Caleda, Hazel Alfon, and

Loraine Gatlabayan

466 farmers in 16 villages

Isabela Province, Luzon

So. Cotabato Province, Mindanao

Yellow maize for feed


Adopters

• larger farms, more hired labor

• more positive perceptions of

current and future status

• more educated

• wealthier, less risk averse

Controlling statistically for these factors and

other sources of bias, growing Bt maize

significantly increases profits and yields, and

reduces use of insecticides


Seed Attribute Isabela So. Cotabato

Bt

farmers

Non-Bt

farmers

All farmers

Yield loss from

borer

-266 -211 -185

Bt maize 2303 -- -786

Farmer

informant vs.

input agent

-1800 -- -764

Credit or cash

vs. cash

1480 -- 1233

with Ekin Birol


0

.00002

.00004

.00006

.00008

0 10000 20000 30000 40000 50000

Predicted Net Income (P/ha)

Bt Non - Bt

Density

Kernel density of predicted net farm

income for Bt and non-Bt maize growers


Impact on farms using Bt and non-Bt hybrid maize

Variables Bt Non-Bt

Ave. Yield loss (%)

17

31

Ave. Income per ha.(pesos)

24,549

16,664

Ave. Non-Farm Income(pesos/mo)

4,532

1,510

Bt maize in Honduras

Bt maize, Honduras

Arie Sanders, Rogelio Trabanino, Jose Falck-Zepeda

Maize is a subsistence crop

Sample of 114 growers (21% of estimated total)

1. Field trial with isogenic lines

2. On-farm evaluation

◦ Large-scale producers

◦ Small producers (Farmer Field School)

3. Detailed farmer surveys

4. Institutional analysis


Non-Bt hybrid (isogenic) had 2 insecticide

applications and Bt hybrid none

Yield of non-Bt and Bt hybrids the same

HybridsInsecticide

Applications (#)

Productivity

(Kg / Ha)

Pest Control

(US$/ Ha)

DK234 RRYG 0 6,513 156*

DK 234 1.8 6,510 152

In situ, large farmers


Bt maize produced 1,136 kg more per hectare


In situ, smallholders

Bt adopters have higher yields

and lower insecticide costs

Producer

surveyNon-Bt Bt

Farm size (ha) * 19 29

Yield * 4,931 5,909

Owns land (%) 96 95

Mechanized (%) * 35 78

Irrigation (%) * 6 26

Credit (%) * 51 78

No. fertilizer apps * 1.00 0.50

No. insecticide apps * 1.08 0.24

Note: * P < 0.10; **P < 0.05; *** P< 0.01


Bt Cotton in Colombia

Patricia Zambrano,

Luz Amparo Fonseca,

Iván Cardona, and

Eduardo Magalhaes

Farm survey

364 farmers

2007-8 season

18 municipalities in

Tolima, Córdoba and Sucre


Variable Tolima Cordoba Sucre

Non-

Bt

Bt Non-Bt Bt Non-Bt Bt

Farm size ha* 4 8 4 9 3 3

Rent land % 71 76 50 64 3 65

Irrigated area % 37 87 3 6 2 0

Education hh head 4 8 7 9 3 4

Adequate housing % 16 26 4 38 7 12

Cotton % of income 63 41 42 8 52 47


Variable Tolima Coast

Whether farmer adopts Bt + --

Owns harvester +Rents land + --No. applications to control

boll weevil +

Cordoba +Labor cost +

IV estimation – yield

Bt cotton in Colombia

High levels of insecticide use continue

Use of Bt seed largely determined by access of

local associations to services and credit

Lack of information

about Bt seed and

crop management

Poor biosafety

practices in field

trials and refugia

RR Soybeans in Bolivia

Farm survey

124 farmers

l04 local

20 Mennonites

Cuatro Cañadas

Rodrigo Paz

Willy Fernández

Melinda Smale

Patricia Zambrano


Sample


Variable Non-RR RR

No. households * 50 70

Soybean area (ha )* 36 48

Education hh head 5 7

Age hh head 42 43

% hh heads earning income in non-

soybean activities *

22% 37%

RR Soybean in Bolivia

Chemicals Non-RR RR

Fungicide 37.9 37.9

Herbicides 41.5 33.7

Glyphosate 23.4 28.3

Insecticide 21.3 30.5Fertilizer 0.0 4.0

($USD/ha)

Conclusions

Methods

Agronomic/biophysical protocols needed

Random sampling difficult

Weather, conflicts; need panel data

Improve analytical methods

Broaden impact themes

Findings

More endowed farmers adopt first

Institutional context crucial

Strengthen farmer knowledge

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