What question did this study set out to answer?

The study aims to model grain and legume yields using agro- and meteorological factors with regularized regression methods.

February 13, 2026Open Access

Modeling of Cereal and Legume Yield on the Basis of Agro- And Meteo-Factors Using Regularized Regression Methods

View Full Paper

Ask AI

Bookmark

The study aims to model grain and legume yields using agro- and meteorological factors with regularized regression methods.
Analyzed official statistics from 1995 to 2024.
Selected 11 independent variables reflecting agro-technological and climatic factors.
Used classical and regularized linear regression methods to assess yield.
Performed multicollinearity diagnostics and cross-validation.
Regularized models, especially Ridge regression, yielded higher forecasting accuracy than classical methods.
Ridge regression showed R² = 0.606 and RMSE = 4.46.
Positive yield factors included crop area, rainfall, and fertilizer use.
Negative impacts on yield were linked to moisture saturation deficits and wind speed.

No takes yet. Share an insight, caveat, or question.

Modeling predicts crop yields in the Odessa region, indicating key agricultural and climatic influences.

Kulyk et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark