adoption, yields, profits, efficiency, employment
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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
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50
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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
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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
ield
/ k
g s
eed p
lante
d
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