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genetic modification and yield risk in corn: parametric and non-parametric analysis. Elizabeth Nolan University of sydney Paulo santos Monash university. Bt corn. First genetically modified traits approved at end of 1996 Introduced commercially for 1997
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genetic modification and yield risk in corn: parametric and non-parametric analysis Elizabeth Nolan University of sydney Paulo santos Monash university
Bt corn • First genetically modified traits approved at end of 1996 • Introduced commercially for 1997 • In 2012, corn hybrids with at least one GM trait were planted in over 88% of the crop area in the United States • Soil bacterium Bacillus thuringiensis is toxic to lepidopterous insects • European Corn Borer (CB) (1996) • Corn Rootworm (RW) (2002) • allows for an almost complete control of the European corn borer and corn rootworm • Superior to that of previously used techniques • Previous damage control technologies: • up to 80% against first generation corn borer • 67% against second generation borer • 63% in the case of corn rootworm.
Pesticides and risk • complete evaluation of the impact of new technologies requires explicit recognition of risk • Debate about how pesticides affect risk • Risk reducing (Feder) • Risk increasing if output uncertainty is the dominant cause of randomness (Pannell (1991) and Horowitz and Lichtenberg (1993)) • The impact of GM traits is, therefore, an empirical question
Objectives • Identify effect of GM traits on production risk 1. flexible moments approach of Antle to analyse higher moments • variance of a distribution does not distinguish between upside risk and downside risk • parametric 2. stochastic dominance • Can compare distributions of yields originated by different technologies • rank them in terms of desirability under minimal assumptions regarding decision makers’ preferences • non-parametric • Complementary methods • parametric approach allows quantification of the contribution of different input factors to differences in yield distributions , but requires distributional assumptions. • Stochastic dominance does not require distributional assumptions, not possible to quantify the influence of specific inputs on yield distributions.
Data • Results of experimental field trials of corn hybrids submitted by corn breeders to the Agricultural Extension Services of ten universities from 1997-2009 • Illinois, Indiana, Iowa, Kansas, Minnesota, Missouri, Nebraska, Ohio, South Dakota and Wisconsin • Range of locations important for results • Choice of period • Observations • 147,790 individual trials • 8,423 hybrids • 339 locations • 430 companies
Advantages of data • Advantages of experimental data • standardised trials • avoid problems of identification • but recognise that experimental yields higher than on farm yields • Provide • details of agronomic practices • information about the traits present in each hybrid • Information on weather conditions • wide variety of production conditions • over period since introduction of GM traits up to 2009 • Spatial variability compensates for relatively short temporal range
Independent variables • GM traits (combinations) • Site details and agronomic practices • plant density • soil type • cultivation type (conventional versus minimum or no till) • previous crop • early or late trial • irrigated or dryland • nitrogen application in lbs/ac • Weather conditions • monthly rainfall April to September • average minimum and maximum temperatures April to September • Year by location (CRD) interaction terms
Empirical method (parametric) • Large unbalanced panel dataset • Individual corn hybrids are the cross sectional elements • Use both fixed effects and the Hausman-Taylor random effects specification to estimate a linear production function • Square (cube) residuals to obtain variance (skewness) • Regress variance and skewness on the individual inputs (including combinations of GM traits) to find marginal variance and skewness for each input.
Results of parametric analysis • Marginal variance for most GM traits (and their combinations) is positive • presence of GM leads to an increase in variance • HTO only weakly statistically significant • RWO not statistically significant • Most of the GM trait combinations have a statistically significant negative effect on skewness • Increase in downside risk. • RWO and HTO and RWHT not statistically significant effect.
Stochastic dominance • GM traits also have an important impact on mean yield • possible that decision makers are willing to trade the increase in variability and downside risk (which they may dislike) • with the increase in mean (which they may like) and still be better-off • Use stochastic dominance to take into account these simultaneous changes.
Ranking new technologies • If a new technology, for example GM traits, is superior according to first order stochastic dominance (FOD) criterion • will be selected by any risk-averse or risk neutral firm • will be chosen by farmers who always prefer higher expected return to lower • If the new technology is superior according to the second order stochastic dominance (SOD) criterion • will be selected by those farmers who prefer higher return to lower • and are also strictly risk averse
SD for sub groups of GM hybrids • Divide data into subsets • analyse for each trait within specific CDFs for unconditional yield for the sub sets • CBHT hybrids first order dominate conventional hybrids • produce more than conventional hybrids under all conditions • effect of increased mean yield more than compensates for the increase in variance and downside risk in terms of producers’ welfare • CBO, CBRW, and CBRWHT hybrids • No dominant strategy
Results • Most combination of traits lead to increased variance • exception is rootworm resistance by itself, which has a negative coefficient, but is not statistically significant • Downside risk increases with the presence of CBO, CBHT, CBRW, and CBRWHT • RWO and HTO, by themselves and in combination have no statistically significant effect on downside risk • However, CBHT first order dominates conventional for the period 1999-2009
Conclusion • Pest resistance traits appear to lead to an increased variability of yield • Farmers may be more concerned about downside risk • our results show that in most trait combinations downside risk is increased for GM hybrids • Results differ from those of other recent studies • Data include observations from more marginal corn producing states
Hausman Taylor • Fits random effects model • Some covariates correlated with the unobserved effects • But not with idiosyncratic error • Estimate yit = x′1itβ1 + x′2itβ2 + z′1iγ1 + θi+ μit where x1it is a matrix of variables that are time varying and uncorrelated with θi,, x2itis a matrix of variables that are time varying and are correlated withθi andz1iis a matrix of variables that are time invariant and uncorrelated with θi (in this case, the various combinations of GM traits) • Hausman and Taylor show that we can use x1it, z1i , x2it - x2i and x1i as instrumental variables
Stochastic dominance for subsets of traits and combinations of traits by period
Production function • When the disturbance h(X)ε enters the production function in an additive way • Allows for the possibility of increasing, decreasing or constant marginal risk (Just and Pope 1978). • Can express function as: yit = f(Xit) + uit = f(Xit) + h(Zit) εit • where • yit is output • we assume that E(εit) = 0, var(εit) = 1. • f(Xit) is the deterministic component of production (representing the conditional mean of production) as a function of the independent variables and • uit is the stochastic component (representing its conditional variance), which can be rewritten as a function of input use h(Zit).
Description of probability distributions • conditional mean and variance functions are not sufficient for a description of a stochastic production function. • risk not only equivalent to output variance (uit2) • Antle (1983) proposed the flexible moments approach • shows that consistent estimates of all central moments can be obtained econometrically without imposing arbitrary restriction on the moments of the distribution.