Logistic Modelling of Silage Maize LAI Dynamics Under Different Irrigation Regimes: A Strategy for Optimizing Crop Yield

Abstract

ABSTRACT The leaf area index (LAI) is a vital parameter in crop eco‐physiology because it substantially influences key processes such as evapotranspiration, light interception, photosynthesis and ultimately seed yield. Together with plant height, the LAI serves as a crucial indicator of crop growth and yield potential. Understanding the dynamic variations in LAI and developing reliable predictive models are essential for advancing research and improving agricultural practices. In this study, we employed a simple, experimentally validated logistic model driven by growing degree days (GDDs) to predict the LAI of fodder maize cultivated under both pulse and continuous irrigation regimes in the dry and semiarid regions of Varamin, Iran. Additionally, we propose a novel logistic equation that permits LAI estimation across different irrigation treatments (60%, 80% and 100% water requirements) independent of GDD and plant height. Evaluations via the R 2 , RMSE and NSE indices demonstrated high accuracy in LAI estimation throughout the entire growth period, with the logistic model consistently outperforming the Gaussian model. Our results highlight the usefulness of LAI modelling for monitoring crop development and devising effective management strategies while emphasizing the importance of integrating advanced modelling techniques into agricultural management, especially in water‐scarce regions. These findings offer promising insights.

Plant phenotyping relevance

LAIという植物形質を推定するロジスティックモデルを開発・検証し、他モデルとの性能比較も行っており、形質推定手法が研究の中心である。

Code and data availability

The paper reports field-measured LAI, plant height, and water consumption data for silage maize under irrigation treatments, modelled with logistic and Gaussian equations in MATLAB. No public dataset, code repository, or model deposit is provided; the Data Availability Statement states the data are available from the对应

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