Prediction of Corn Zea mays L. Phenology Based on Cardinal Temperature Estimation, Spline Interpolation, and Numerical Analysis

Izzar Suly Nashrudin, Nur Afiiqotul Ula, Imro Atul Khoir

Abstract


Accurate crop phenology prediction is essential for modern agricultural management, irrigation
scheduling, and climate change adaptation. This study develops a numerical-analysis-based
framework to predict maize (Zea mays L.) growth stages using daily meteorological data. The
proposed workflow integrates: (i) the non-linear Wang–Engel formulation to compute daily
thermal units, (ii) cubic spline interpolation for data reconstruction under a missing-data
validation scenario, (iii) Simpson’s 3/8 rule for numerical integration of cumulative thermal
units, (iv) the central difference method to analyze the accumulation-rate dynamics, and
(v) Taylor series expansion for local approximation of the Wang–Engel function around
the optimum temperature. Daily meteorological data were obtained from the Open–Meteo
Historical API for Jakarta, Indonesia in 2025, comprising 348 observation days. Numerical
integration yields a cumulative thermal unit of 112.37 over the first 120 days. Derivative
analysis identifies the maximum accumulation rate of 0.9784 per day at day 44. Using the
adopted thermal thresholds, the model predicts the V3 stage at day 127 and the V6 stage at
day 342.


Keywords


crop phenology; numerical analysis; growing degree days; spline interpolation; simpson integration; indonesian meteorology

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References


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DOI: https://doi.org/10.18860/jrmm.v5i3.39988

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