New plant breeding techniques (NPBTs) are seen as promising and innovative tools to achieve food security and food safety. Biotechnological innovations have great potential to address sustainable food development, and they are expected in the near future to play a critical role in feeding a growing population without exerting added pressure on the environment. There is, however, a considerable debate as to how these new techniques should be regulated and whether some or all of them should fall within the scope of EU legislation on genetically modified organisms (GMOs), despite the product obtained being free from genes foreign to the species. In the EU, the adoption of these methods does not rely only on the scientific community but requires social acceptance and a political process that leads to an improved regulatory framework. In this paper, we present the results of an online survey carried out in Italy with 700 randomly selected participants on consumer attitudes towards food obtained by NPBTs. By applying the decision tree machine learning algorithm J48 to our dataset, we identified significant attributes to predict the main drivers of purchasing such products. A classification model accuracy assessment has also been developed to evaluate the overall performance of the classifier. The result of the model highlighted the role of consumers’ self-perceived knowledge and their trust in the European approval process for NPBT, as well as the need for a detailed label. Our findings may support decision makers and underpin the development of NPBT products in the market.

Exploring Consumers’ Attitudes towards Food Products Derived by New Plant Breeding Techniques

Gabriella Vindigni
;
Iuri Peri;
2022

Abstract

New plant breeding techniques (NPBTs) are seen as promising and innovative tools to achieve food security and food safety. Biotechnological innovations have great potential to address sustainable food development, and they are expected in the near future to play a critical role in feeding a growing population without exerting added pressure on the environment. There is, however, a considerable debate as to how these new techniques should be regulated and whether some or all of them should fall within the scope of EU legislation on genetically modified organisms (GMOs), despite the product obtained being free from genes foreign to the species. In the EU, the adoption of these methods does not rely only on the scientific community but requires social acceptance and a political process that leads to an improved regulatory framework. In this paper, we present the results of an online survey carried out in Italy with 700 randomly selected participants on consumer attitudes towards food obtained by NPBTs. By applying the decision tree machine learning algorithm J48 to our dataset, we identified significant attributes to predict the main drivers of purchasing such products. A classification model accuracy assessment has also been developed to evaluate the overall performance of the classifier. The result of the model highlighted the role of consumers’ self-perceived knowledge and their trust in the European approval process for NPBT, as well as the need for a detailed label. Our findings may support decision makers and underpin the development of NPBT products in the market.
agricultural biotechnology, new plant breeding technique, NPBT; consumers’ attitude, food safety, machine learning, data mining
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/531380
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