<![CDATA[Climate change has intensified the risk of crop failure and economic losses in the shallot farming sector, necessitating the development of insurance premium models that are adaptive to climate dynamics. This study aims to evaluate existing agricultural insurance premium models for shallots and to develop a new approach that is more responsive, equitable, and sustainable through the integration of data science and statistics. The main innovation of this research lies in the application of a modified Black–Scholes model, incorporating two key variables: rainfall index and shallot production risk. This model serves as the basis for determining insurance premiums in Tasikmalaya Regency. Simulations were conducted using primary data collected through questionnaires distributed to shallot farmers, as well as secondary data on production records and rainfall from 2016 to 2024. The estimation results reveal a positive and significant relationship between rainfall percentiles and insurance premiums, where higher rainfall levels tend to be associated with increased production and insured value, leading to higher premium rates. These findings offer practical insights for insurance companies in designing more accurate and sustainable index-based premium schemes for shallot commodities.]]>
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