impact of genetically modified maize on smallholder risk in south africa 16 th icabr conference june...
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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.