Eight Years of GM Adoption by

Smallholders in KwaZulu Natal Marnus Gouse and Johann Kirsten

Department of Agricultural Economics, Extension and Rural Development, University of Pretoria

Jenifer PiesseDepartment of Management, King’s College London and University of Stellenbosch

Colin Thirtle Centre for Environmental Policy, Imperial College London,

University of Pretoriaand University of Stellenbosch

16th ICABR ConferenceRavello 13-27 June 2008

Adoption, Yields, Profits, Efficiency, Employment

Results for 8 years show the rapid changes & transition

Single area or year results do not persist

To advise policy we need to see how new seeds use develops as farmer’s learn and adapt

Herbicide tolerant is winning out and reduces employment by over 50% - private benefit but social cost? Need to know the output change

Percentage of total South African maize area using GM

This shows 70% is GM and Bt is the favourite with about 45%. But this is 96% commercial farmers. Smallholders grow 16.2% of the acreage and produce only 4.2% of the output. How different are they from commercial farmers?

0%10%

20%30%

40%50%

60%70%

80%90%

100%

2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10

Total Bt Maize % Total RR Maize %

Total Stacked Maize % Total GM area %

Number of surveys in Hlabisa and maize plots used in analysis

SeasonFarmerssurveyed

Useable plots

Bt plots

HT plots

BR plots

Conventional plots

2001/02 59 116 58 0 0 58

2002/03 67 78 31 0 0 47

2003/04 135 188 64 2 0 122

2004/05 78 68 17 3 0 48

2005/06 121 125 39 22 0 64

2006/07 87 94 21 35 0 38

2007/08 102 97 12 38 19 28

2009/10 96 95 0 65 14 16

58 58

65 16

Yields of conventional, Bt and HT maize, 8 seasons 2001/02 - 2009/10

0

50

100

150

200

250

300

2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2009/10

kg g

rain

/ kg

see

d

0

100

200

300

400

500

600

700

800

mm

rain

Conv Bt HT Rainfall Sept-March Rainfall Sept-Dec

Yield comparison for Conventional, Bt, HT and BR maize (kg grain / hectare)

Season Conventional Bt kg HT kg BR kg

2005/06 number 61 37 21

Mean 440 537 481

% of Conv 22 % 9 %

2006/07 number 38 22 35

Mean 451 470 875

% of Conv 4 % 94 %2007/08 number 28 12 38 19

Mean 1869 2261 2062 2263

% of Conv 21 % 10 % 21 %2009/10 number 16 0 65 16

Mean 1707 1880 1910

% of Conv 10 % 12 %

Seed types with highest profit or lowest loss for the four seasons

2005/06 2006/07 2007/08 2009/10

All expenditures HT HT Bt HT

Without family labour Conv. HT Bt Conv.

Only direct expenditures

Bt HT Bt Conv.

Data - VariablesDistrict – Hlabisa (Simdlangentsha – Dumbe 2006-7) • Output – kgs of maize• Land – hectares • Family Labour• Hired Labour• Seed cost• Fertilizer cost• Herbicide cost• Land preparation dummy• Area/farmer/soil quality dummy

Stochastic Frontier

• The general form of the production frontier is

• The Vi’s are independently and identically distributed random error terms and uncorrelated with the regressors, and the Ui’s are non-negative random variables associated with the technical inefficiency of the firm.

) N(0, V and N(0, U with

U - V = where + x + = Y

2V

2U

iiiiij

ji

~|)|~

Hypothesis Tests

(1) Functional Form

Log-LikelihoodsLLR Test

DoF

15

Critical value at

5%

Outcome

Parameter Restrictions

H0: CDH1:

TranslogStatistic

H0: All jk = 0 -421.32 -414.37 13.9 15 25Accept H0 - CD

is adequate(2) Frontier

TestsLLR test

Parameter Restrictions: H0: γ = δi = 0

Gamma t stat Statistic DoFCritical Value

Outcome

Restrictions:H0: γ = 0 0.859 19.08 80.55 8 14.85

Reject H0 -

frontier not OLS(3) Inefficiency

ModelH0: δ=0 H1: δ≠0

-449.86 -421.32 57.08 8 14.85Reject H0 – the δi

belong in the frontier

Frontier Model - Elasticities

Production Frontier

Variable Coefficient Std. Error t - stat Confidence

Dependent variable Output Sum of elasticities = 0.872

Land 0.128 0.096 1.33 90%

Family Labour 0.068 0.052 1.31 90%

Hired Labour 0.038 0.022 1.74 95%

Seed Expenditure 0.545 0.075 7.3 99%

Fertiliser Expenditure 0.093 0.062 1.49 97.5%

Constant 2.99 0.536 5.57 99%

Explaining Inefficiency

Coefficient Std. Error t - stat Confidence

Bt Seed 0.485879 0.244882 1.98 99%

Ht or Stacked Gene -0.72487 0.288971 -2.51 99%

Land prep by hoe 0.606268 0.240819 2.52 99%

Education of Head 0.035076 0.054172 0.65

Female head -0.23942 0.175965 -1.36

Household size 0.055114 0.028358 1.94 95%

No intercrop -0.37071 0.183592 -2.02 99%

Area & skill dummy -0.64139 0.277299 -2.31 99%

Constant 0.193324 0.587458 0.33

Annual averages of efficiencies by seed type

Year Seed obs Mean St dev min max % of conv

2005 conv 61 0.38 0.18 0.07 0.78 100.00%

2005 Ht 21 0.36 0.16 0.12 0.69 93.62%

2005 Bt 38 0.34 0.15 0.04 0.80 88.59%

2006 Ht 35 0.42 0.19 0.10 0.72 129.17%

2006 conv 38 0.33 0.19 0.05 0.74 100.00%

2006 Bt 22 0.29 0.16 0.04 0.73 67.56%

2007 Br 19 0.83 0.05 0.73 0.92 115.54%

2007 Ht 38 0.73 0.11 0.43 0.90 101.14%

2007 Conv 28 0.72 0.13 0.35 0.88 100.00%

2007 Bt 12 0.66 0.22 0.07 0.86 91.71%

2009 Br 15 0.78 0.09 0.53 0.85 135.84%

2009 Ht 67 0.68 0.13 0.23 0.82 118.88%

2009 Conv 16 0.57 0.18 0.22 0.79 100.00%

Ranking of Seed Varieties by District and Method, 2006/7

MethodYields Gross Margins Efficiency Levels

District/Rank 1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd

Full Sample HT Bt Con HT Bt Con HT Con Bt

Hlabisa HT Con Bt HT Con Bt HT Con Bt

Simdlangent. Bt Con HT Con Bt HT Bt Con HT

Dumbe Bt Con Bt Con Con Bt

Land preparation method indication by Hlabisa farmers

Land preparation method2005/06 2006/07 2007/08 2009/10

Hired tractor 30 % 22 % 10 % 0 %

Own oxen drawn plough 41 % 44 % 21 % 9 %

Hired oxen team 12 % 20 % 60 % 7 %

Hand and hoe with no herbicide

2 % 7 % 1 % 0 %

Hand and hoe with herbicide 15 % 4 % 8 % 84 %

Family labour per hectare by seed type (7 hour man-days)

Farming activities Conventional Bt HT BR

2005/06 Total days 48.85 62.48 38.68

% of conventional 127.9 79.2

2006/07 Total days 54.55 38.77 38.62

% of conventional 71.7 70.8

2007/08 Total days 50.03 52.34 25.91 30.52

% of conventional 104.6 51.8 61.0

2009/10 Total days 20.46 9.23 10.02

% of conventional 45.1 49.0

Conclusions• First 2 years–Bt looked fine–then more arid, no gain• 2006 HT stops erosion, high yields, 10% less work• By 2009, HT & BR are adopted, but 50% less work• Change prep & planting methods–learning by doing• Like tractors in Asia in 1960s? Not in KZN perhaps• Need to know the substitution & output effects of TC• If land is not the constraint output could double and

employment increase – if not employment falls hard• Small samples give any answer you want as they vary so

much over time and space.• How many areas and years for sound policy advice?• So commercial maize is Bt – smallholders is HT – why?

DfID study - BIASES, ENDOWMENTS & IMPACTS • The distributional impact of biased technological

change depends both on the factor saving (or using) biases and the factor endowments in the economy.

• If a labour saving technology is introduced in a land scarce/labour abundant economy labour incomes will fall and poverty will increase.

• But labour for planting is the constraint in much of SSA. Economic development with unlimited supplies of land – Bent Hansen

• If land is poor but plentiful, planting area and output could double and labour demand for all other tasks increase substantially. Can we guess for Malawi?

Rockefeller supported studies of Bt maize amongst smallholders

2001/02 – relatively high stalk borer infestation

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kg y

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eed p

lante

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All sites NorthernHighveld

SouthernHighveld

Hlabisa Venda Mqanduli Flagstaff

Own Conventional isoline Bt

32%

2002/03 – lower pressure = 16% in KZN (Hlabisa and Simdlangentsha)2003/04 – no borers = found no benefit