Predictive sustainability in agriculture: Machine learning analysis of active ingredient restrictions and bans
Rodrigo Garcia Brunini
TL;DR
This paper uses machine learning to analyze why certain agricultural chemicals get banned, aiming to improve sustainability and decision-making in chemical development.
Contribution
The study introduces a machine learning approach to identify key factors influencing bans on agricultural active ingredients.
Findings
Governmental and Non-Governmental Organizations significantly influence active ingredient restrictions.
Codex Alimentarius acts as a regional influencer in banning agricultural chemicals.
Machine learning helps identify key parameters for sustainable chemical development.
Abstract
Developing active ingredients for the global market requires substantial investment, often exceeding 300 million euros. This process takes an average of 12 years from initiation to commercialization. Despite this lengthy timeline, the industry frequently encounters significant restrictions and bans on active ingredients due to stringent international regulations and evolving environmental safety requirements. In this context, the analysis of regulatory lists using advanced machine learning and statistical modeling techniques becomes crucial for identifying the key parameters that influence the restriction and banning of active ingredients. This study aims to provide insights that enhance decision-making processes, thereby contributing to sustainability by reducing unnecessary environmental research and development efforts. The findings indicate that Governmental and Non-Governmental…
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Taxonomy
TopicsGenetically Modified Organisms Research · Insect and Pesticide Research · Organic Food and Agriculture