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June 5, 2026Open Access

Modelling grain yield under the influence of agrotechnological and climatic factors: a regularised regression analysis

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Authors

AKAnatolii KulykKFKaterina Fokina-MezentsevaOPOksana Piankova

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Overview

Randomized trial models crop yield in a region, suggesting significant impacts of agrotechnological and climatic factors.

Key Points

  • The study aims to model grain yields of cereal and legume crops, focusing on agrotechnological and climatic influences.
  • Utilized regularised regression methods (Ridge, Lasso, ElasticNet) for yield prediction.
  • Applied economic and mathematical analysis with official statistics from 1995–2024.
  • Conducted multicollinearity diagnostics and cross-validation.
  • Regularised regression models outperformed classical OLS regression in yield forecasting accuracy.
  • Ridge regression showed the best predictive performance (R² = 0.606; RMSE = 4.46).
  • Agrotechnological factors like sown area and mineral fertilizers significantly improved yields, while factors like air moisture deficit negatively impacted yield.

Cite This Study

Kulyk et al. (2026) studied this question.

synapsesocial.com/papers/6a22698b763171746d548191https://doi.org/10.22004/ag.econ.401367
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