Jing Hua China Agricultural University Justus Wesseler Wageningen University Yubin Wang China Agricultural University the 20 th ICABR Conference Ravello

Potential benefits Potential risks GM technology Perceptions Regulations premise Farmers’ decision-making behaviors Asymmetric Information Uncertainty

1 What’s the farmers’ attitudes towards GM technology? 2 3 Is there any relation among farmers’ perceptions, information and willingness to adopt? Is there spatial dependence of farmers’ willingness to adopt the GM crops? 4 What determines affecting farmers’ willingness to adopt?

Spatial Durbin Model Interaction mechanism among perceptions, information and willingness to adopt Farmers’ perceptions and willingness toward GM Crops The different information resources’ influence to the farmers’ willingness to adopt Test the spatial dependence of farmers’ willingness to adopt the GM crops Multiattribute Model Mediation Model Value function

is the GM Crops’ sales price is the crops’ yield is the cost function is the GM Crops’ sales price is the crops’ yield is the cost function of adopting GM technology is the potential benefit to the environment since adopting new technology >0 choose traditional technology adopt GM technology

Trust of regulatory agencies Awareness of GMOs Information Householder’s characteristics Risk Perceptions ( health, environment,ethics) Benefit Perceptions ( production, nutrition, taste, shelf life, pesticide use) Attitude to GMOs Willingnes s to adopt A B C D Determinants of farmers’ willingness to adopt GMOs. Test of Mediating effect

Gansu Wuhan Hebei Beijing The distribution of surveyed samples (east, central, west; 302 random farmers) farmers’ knowledge to biotechnology; farmers’ perceptions towards GM crops; differences in farmers’ willingness to adopt GM crops according to their socio- demographic and household characteristics; farmers’ trust of legislations, government and other propaganda subjects. Main structure of the questionnaire:

Variables Proporti on VariablesProportion gendermale67.2% per capita annual household income below ￥ % female32.8% ￥ ￥ % ageaverage47.7 ￥ ￥ % acreageaverage6.7 ￥ ￥ % family size average4 ￥ ￥ % educatio n primary school15.8% Above ￥ % junior high school 51.7% non-farm income proportion 0-20%20.9% high school27.5%21%-40%11.3% college5.0%41%-60%18.9% 61%-80%19.2% 81%-100%29.7% Table 1 Characteristics of respondents (N=302)

the degree of familiarity towards GM crops knowledge; the number of known GM crops, the benefits and potential risks of GM crops the perceptions of biotechnology knowledge Farmers’ perceptions scores towards GM crops Farmers’ willingness to grow towards GM crops Factor analysis The average score was 4.57 the lowest score was 1.04 the highest score was 7.63 Willingness to growNever grow Do not want to growAs the case More willing to grow Greatly willing to try Proportion13.7%23.3%23.0%29.0%11.0%

Model 1Model 2Model 3Model 4Model 5Model 6 VariablesAttitude Risk Perceptions Householder’s characteristics age-0.013(***)-0.017(***) (***) gender (***) incomeo0.070(***)0.054(***) (***) education0.105(***)0.095(***) (*) Risk perceptions-0.129(***)-0.116(***) Benefit perceptions0.063(***)0.049(***) Trust to regulatory agency0.166(***)0.147(***)0.095(***)-0.261(***) Awareness of GMOs0.146(***)0.045(***)0.027(**)0.146(***) Information0.205(***)0.191(***)0.107(***) （ ** ） Note:“***” significant at 1% level,“**”significant at 5%, “*”significant at 10%. Determinants of farmers’ attitudes to GM plant age, income and education were mediated by risk perceptions trust of regulatory agencies, awareness of GM products and the information of obtaining GMO indirectly influenced farmers’ attitudes to adopt the new technology, and some direct impact on farmers’ behavior, namely paths A and B occur simultaneously

Variables Estimated value Standard value WaldSignificance Willingness to grow (great willing to try as control group) Never grow Don’t want to grow now It depends More willing to grow Independent variables Age Education Non-farm proportion Household members Farm size Benefit expectations Environmental attitude Attitude to GM crops Trust of regulatory agencies Awareness of GM crops Frequency of getting information from media Frequency of discussions with neighbors Information obtained from government Information obtained from academia Information obtained from retailers Information obtained from training Information obtained from neighborhoods and friends Empirical result of farmers’ willingness to adopt GM crops

VariablesTotal effectDirect effectIndirect effect Householder characteristics Age Education Non-farm income proportion Household characteristics Household members Farm size Risk and preference Benefit expectation Environmental attitude Attitude to GM crops Information characteristics Biotechnology training Frequency of obtaining information from media Frequency of discussion with neighborhoods Other variables Trust of regulatory agencies Awareness of GM crops Effect analysis of Spatial Durbin Model the exchange of information between farmers adjacent to each other will affect their willingness to adopt the new technology, confirming the hypothesis of this paper, the neighboring farmer information exchange will be presented spatial dependence, farmers will exhibit similar decision-making behaviors.

1 The farmers’ perceptions increased slightly than 2010; 2 3 The neighborhoods’ information communication had a positive effect on the farmers’ choice behavior while the other information sources such as academia, the government and biotech companies didn’t show significant effects; 4 40% households had a strong willingness to grow while 13.7% chose never to grow and farmers locate in close proximity exhibit similar behaviors. Age, income and education were mediated by risk perceptions trust of regulatory agencies, awareness of GM products and the information of obtaining GMO indirectly influenced farmers’ attitudes to adopt the new technology;

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