IMPACT OF GENETICALLY MODIFIED MAIZE ON SMALLHOLDER RISK IN SOUTH AFRICA 16 th ICABR Conference June 25-27, 2012 Ravello, Italy Greg Regier*, Timothy Dalton, Jeffery Williams Kansas State University Source: Google images

Objective  Is genetically modified (GM) maize a beneficial technology for smallholders in low-income countries?  H 1 : GM maize reduces net returns risk  H 2 : GM maize has higher output  H 3 : 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  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,  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 TypeNYield (kg/ha) Maize Revenue ($/ha) Input Cost ($/ha) Labor Cost ($/ha) Net Returns ($/ha) HlabisaBR Pannar RR GM * Non-GM *234 SimdlangetshaBR Bt Carnia Pannar RR GM Non-GM *-338

H 1a : GM Maize Reduces Risk - Stochastic Dominance

H 1b : GM Maize Reduces Risk - Stochastic Efficiency with Respect to a Function (SERF) RRAC = 2 (moderately risk averse) RRAC = 4 (extremely risk averse) RRAC = 0.37 (slightly risk averse)

H 1b : GM Maize Reduces Risk – SERF RRAC= 2RRAC = 4

H 2 : GM Maize has Higher Output  Maize output = f(labor, fertilizer, herbicide, seed, land, land prep cost, Hlabisa, RR, Bt, assets, experience with herbicide, education)

Production Function Results OLS: LinearOLS: QuadWLS: Quad2SLS: Herbicide2SLS: Labor Coef. Intercept *** *5.3 Labor3.26***2.73* ***-0.4 Fertlizer1.35* ** Herbicide **-26.3 Seed Land993.56*** * * *** Total Cost Land Prep *-15.28** Hlabisa Dummy308.83*** **215.9* RR Dummy217.27***137.45**131.61*332.0***38.9 Bt Dummy N212 Adjusted R- squared ***,**,* indicates significantly different than zero at 1%, 5% and 10% respectively

H 3a : 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)

Cost Function Results OLS - LinearOLS - Quadratic WLS - Quadratic Treatment Effects- Quadratic Coef. Intercept * ** ** ** P(labor)134.59*** P(fertilizer)237.79** ** ** ** P(herbicide)2.91** * P(seed)14.64***79.49*75.83*70.78* Land389.67*** *** *** *** P(land prep)-0.75***9.28*9.68**8.75** Maize Output0.05***0.69***0.61***0.62*** Hlabisa Dummy *** *** *** *** RR Dummy-63.83***-77.67***-69.60*** *** Bt Dummy Inverse Mills Ratio λ 49.77** Adjusted R-squared ***,**,* indicates significantly different than zero at 1%, 5% and 10% respectively

H 3b : GM Maize has Lower Cost - Kernel Density Estimator Total Cost

H 3b : GM Maize has Lower Cost - Kernel Density Estimator Average Cost

Conclusion  H 1 : GM Maize Reduces Risk  SERF RR maize producers must be compensated between $18 and $221 per hectare to switch varieties  H 2 : GM Maize has Higher Output  Production function 8-13% RR maize advantage; N.S.-20% controlling for endogeneity bias  H 3 : 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

Weighted Risk Premiums Relative to BR, Hlabisa

Two-Stage Least Squares (2SLS) Regression  = predicted value of endogenous variable  = parameter of all exogenous variables  = parameter of instrumental variables

Treatment Effects Model  Step 1: Probit Model  Step 2: Include inverse Mills ratio in least squares regression

Histogram of Total Cost

Kernel Density Estimator: Average Cost RR = 0.5, non-RR = % lower costs estimate

Family and Hired Labor (Hours/Hectare) SiteSeed TypeChildMaleFemaleHiredWorkgroup Total HlabisaBR Pannar RR GM **192 Non-GM 18** 153** 177** 68**20 437** SimdlangetshaBR Bt Carnia Pannar RR GM Non-GM 65** 94** 118* ** **,* Indicates significantly higher at 1% and 5% respectively using a one-sided t-test.