IMPACT OF GENETICALLY MODIFIED MAIZE ON SMALLHOLDER RISK IN SOUTH AFRICA

16th ICABR ConferenceJune 25-27, 2012

Ravello, Italy

Greg Regier*, Timothy Dalton, Jeffery WilliamsKansas State University

Source: Google images

Objective

Is genetically modified (GM) maize a beneficial technology for smallholders in low-income countries? H1: GM maize reduces net returns risk H2: GM maize has higher output H3: GM maize leads to lower cost

Literature Review

Bt Maize, Philippines Higher yields and net returns; Yorobe and Quicoy 2006; same

results when controlling for selection bias; Mutuc and Yorobe 2007

Yield advantage is smaller controlling for censoring; Mutuc, et al. 2012

Bt Maize, South Africa Higher output, declining as pest pressure decreases, net

returns depends; Gouse, Piesse and Thirtle 2006, Gouse et. al 2006

RR Maize, South Africa Higher output, lower labor use; Gouse, Piesse, and Thirtle 2006

Seed cost cancels gain in yield efficiency; Gouse, Piesse, Thirtle and Poulton 2009

Location: KwaZulu-Natal, South Africa

Background Information

Hlabisa and Simdlangetsha Annual rainfall of 980 mm (38 inches) Marginal land - 13% arable Average maize yield is 1500 kg/ha (24

bu/acre) 39% land ownership by smallholders Labor supply characteristics

Urban migration 26% working age population HIV-positive

Data

212 maize plots (184 households) Plot size 0.49 hectares, farm size1.85

hectares One season, 2009-10 Farmer Characteristics:

Head of household average age of 55 years Pension is top income source for 53%

($168/month) A majority of maize consumed at home High access to credit

Maize Types

Conventional Hybrids Pannar Carnia

GM Hybrids Bt – insect resistant Roundup Ready© (RR) – herbicide tolerant BR “stacked”

Maize Yield, Cost, and Net Returns

* Indicates significantly higher at 5% using a one-sided t-test

Seed Type NYield

(kg/ha)

Maize Revenue

($/ha)Input Cost

($/ha)

Labor Cost

($/ha)

Net Returns($/ha)

Hlabisa BR 15 1910 918 531 143 244

Pannar 15 1788 866 297 335 234

RR 67 1880 910 458 149 304

GM 82 1885 912 471* 148 293

Non-GM 15 1788 866 297 335* 234

Simdlangetsha

BR 20 1347 512 609 186 -283

Bt 18 1351 502 600 251 -349

Carnia 34 1227 463 642 268 -447

Pannar 33 1659 640 549 317 -226

RR 10 1953 737 556 230 -48

GM 48 1475 555 595 219 -259

Non-GM 67 1440 550 596 292* -338

H1a: GM Maize Reduces Risk - Stochastic Dominance

-1500 -1000 -500 0 500 1000 15000

0.2

0.4

0.6

0.8

1

Simdlangetsha

BR Bt CarniaPannar RR

Net Returns ($/hectare)

Pro

ba

bil

ity

-800 -600 -400 -200 0 200 400 600 800 10000

0.2

0.4

0.6

0.8

1

Hlabisa

BR Pannar RR

Net Returns ($/hectare)

Pro

ba

bil

ity

H1b: GM Maize Reduces Risk - Stochastic Efficiency with Respect to a Function (SERF)

-0.00499999999999999 6.07153216591883E-18 0.00500000000000001 0.01-800.00

-600.00

-400.00

-200.00

0.00Series1

Series1

Series1

Series1

Series1

Simdlangetsha, Net Returns ($/hectare)

BR Bt Carnia Pannar RR

ARAC

Ce

rta

inty

Eq

uiv

ale

nt

RRAC = 2 (moderately risk averse)

RRAC = 4 (extremely risk averse)

RRAC = 0.37 (slightly risk averse)

H1b: GM Maize Reduces Risk – SERF

0 0.001 0.002 0.003 0.004 0.005 0.006

-50.00

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

Series1

Series1

Series1

Hlabisa, Net Returns ($/hectare)

BR Pannar RR

ARAC

Ce

rta

inty

Eq

uiv

ale

nt

RRAC= 2 RRAC = 4

H2: GM Maize has Higher Output

Maize output = f(labor, fertilizer, herbicide, seed, land, land prep cost, Hlabisa, RR, Bt, assets, experience with herbicide, education)iKiid

m

ddij

n

jji xDxY

110

Production Function Results

OLS: Linear OLS: Quad WLS: Quad2SLS:

Herbicide2SLS: Labor

  Coef.   Coef.   Coef.   Coef.   Coef.  

Intercept -336.32 *** -167.63 -52.32 -710.6 * 5.3

Labor 3.26 *** 2.73 * 1.77 4.9 *** -0.4

Fertlizer 1.35 * -0.58 -1.19 1.0 2.1 **

Herbicide 0.28 39.45 23.14 128.6 ** -26.3

Seed -26.16 -33.72 5.22 -56.4 -7.0

Land 993.56 *** 1976.90 * 1702.90 * 493.7 1360.9 ***Total Cost Land Prep 1.27 -13.20 * -15.28 ** 2.9 0.1Hlabisa Dummy 308.83 *** 154.65 88.13 371.0 ** 215.9 *

RR Dummy 217.27 *** 137.45 ** 131.61 * 332.0 *** 38.9

Bt Dummy -12.24 -4.90 5.39 -86.4 6.4

N 212     212     212     212     212  Adjusted R-squared 0.45     0.62     0.85     0.17     0.32  ***,**,* indicates significantly different than zero at 1%, 5% and 10% respectively

H3a: GM Maize has Lower Cost

Total Cost = f(maize output, labor price, fertilizer price, herbicide price, seed price, land, land prep price, Hlabisa, RR, Bt, assets, experience with herbicide, education)

i

m

diddij

n

jjii DpyC

110

Cost Function Results

OLS -

Linear

OLS -

Quadratic

WLS -

Quadratic

Treatment

Effects-

Quadratic

  Coef.   Coef.   Coef.   Coef.  

Intercept

-

138.92 * -2947.07 **

-

2839.8

6 ** -2352.97 **

P(labor) 134.59 *** 436.95 290.77 31.99

P(fertilizer) 237.79 ** 6558.10 **

6598.8

1 ** 5410.83 **

P(herbicide) 2.91 ** -41.96 -39.81 -44.59 *

P(seed) 14.64 *** 79.49 * 75.83 * 70.78 *

Land 389.67 *** 1630.07 ***

1547.0

3 *** 1558.06 ***

P(land prep) -0.75 *** 9.28 * 9.68 ** 8.75 **

Maize Output 0.05 *** 0.69 *** 0.61 *** 0.62 ***

Hlabisa Dummy

-

168.77 *** -187.69 *** -170.97 *** -149.05 ***

RR Dummy -63.83 *** -77.67 *** -69.60 *** -162.31 ***

Bt Dummy 6.57 4.51 2.90 7.75

Inverse Mills Ratio

λ 49.77 **

Adjusted R-

squared  0.84      0.88     0.91    ***,**,* indicates significantly different than zero at 1%, 5% and 10% respectively

H3b: GM Maize has Lower Cost - Kernel Density Estimator

200

400

600

800

Tota

l Cost

(U

SD

)

0 500 1000 1500 2000Output (Kilograms)

RR RR local linearnon-RR non-RR local linear

Total Cost

H3b: GM Maize has Lower Cost - Kernel Density Estimator

01

23

Ave

rage C

ost

(U

SD

)

0 500 1000 1500 2000Output (Kilograms)

RR RR local linearnon-RR non-RR local linear

Average Cost

Conclusion

H1: GM Maize Reduces Risk SERF

RR maize producers must be compensated between $18 and $221 per hectare to switch varieties

H2: GM Maize has Higher Output Production function

8-13% RR maize advantage; N.S.-20% controlling for endogeneity bias

H3: GM Maize leads to Lower Cost Cost function

18-23% lower costs for RR maize; 33% controlling for selection bias

Nonparametric regression At least 17% lower costs for RR maize

THANK YOU!

Acknowledgements: Bill and Melinda Gates Foundation by the provision of data under the Global Development Grant OPP 53076, “Measuring the Ex-Ante Impact of Water Efficient Maize for Africa.” Assistance from Marnus Gouse in the understanding of data.

Source: Google images

Future Research

More advanced techniques to control for selection bias

Control for censoring Tradeoff between no-till and intercropping Labor supply

Constrained or not Effect on GM maize adoption by country

Impact over several years in multiple regions

Weighted Risk Premiums Relative to BR, Simdlangetsha

-0.00499999999999999 6.07153216591883E-18 0.00500000000000001 0.01

(600.00)

(500.00)

(400.00)

(300.00)

(200.00)

(100.00)

- Series1

Series1

Series1

Series1

Series1

BR Bt Carnia Pannar RR

ARAC

Ris

k P

rem

ium

Weighted Risk Premiums Relative to BR, Hlabisa

-0.00399999999999999 0.00100000000000001 0.00600000000000001

(80.00)

(70.00)

(60.00)

(50.00)

(40.00)

(30.00)

(20.00)

(10.00)

-

10.00

20.00

Series1

Series1

Series1

BR Pannar RR

ARAC

Ris

k P

rem

ium

Two-Stage Least Squares (2SLS) Regression

= predicted value of endogenous variable

= parameter of all exogenous variables = parameter of instrumental variables

i

m

lillij

n

jjKi zxx

110ˆ

Kix̂j

l

Treatment Effects Model

Step 1: Probit Model

Step 2: Include inverse Mills ratio in least squares regression

)(

)(ˆi

ii a

a

iij

n

jji wRR

1

*

Histogram of Total Cost

Kernel Density Estimator: Average Cost

01

23

Ave

rage C

ost

(U

SD

)

0 500 1000 1500 2000Output (Kilograms)

RR RR local linearnon-RR non-RR local linear

RR = 0.5, non-RR = 0.617% lower costs estimate

Family and Hired Labor (Hours/Hectare)

SiteSeed Type Child Male Female Hired

Workgroup

Total

Hlabisa BR 2 37 62 39 47 187

Pannar 18 153 177 68 20 437

RR 2 41 52 22 76 194  GM 2 41 54 25 71** 192

Non-GM 18** 153** 177** 68** 20 437**

Simdlangetsha

BR 21 47 58 87 28 242

Bt 42 70 122 39 54 327

Carnia 55 93 115 42 45 350

Pannar 75 96 121 59 62 414

RR 48 77 103 39 33 300  GM 35 62 91 59 39 286

Non-GM 65** 94** 118* 50 53 381****,* Indicates significantly higher at 1% and 5% respectively using a one-sided t-test.