Abstract:

To correct the errors caused by fixed leaf area correction coefficients in agricultural meteorological standards from the 1960s, a systematic study was conducted in 2024-2025 at a typical agricultural meteorological experimental site in Zhalant Banner, Inner Mongolia, focusing on 5 major crops: maize, rice, sorghum, soybean, and mung bean. The coordinate paper method was employed to measure the leaf area correction coefficients (K) and leaf area index (LAI) across different growth stages. The results revealed that: (1) K values of crop exhibited significant variations across growth stages, peaking during the maize flowering and silk emergence stage (0.856) and soybean branching stage (0.841). The average K values were maize (0.750), rice (0.739), mung bean (0.735), soybean (0.709), and sorghum (0.683), all higher than traditional empirical values; (2) LAI displayed a unimodal dynamic pattern, with peak values being concentrated in the reproductive growth stage (reaching up to 2.14 during rice heading and flowering), demonstrating distinct crop-specific characteristics; (3) the traditional fixed coefficients were generally underestimated, with a maximum deviation of 17.6%, introducing systematic errors in modern precision agriculture and remote sensing inversion. This study has constructed the first high-precision crop-specific K and LAI dataset for major crops in Xing’an League, providing localized key parameters for regional crop growth models, remote sensing validation, and smart meteorological services. It has advanced the update of agricultural meteorological observation standards toward a ‘dynamic, precise, crop-specific, and growth-stage-specific’ framework, promoting the transition from traditional empirical services to data-driven, model-supported approaches, thereby laying a solid foundation for smart agriculture and climate adaptation management.

Key words: crops, development stage, coordinate paper method, leaf area index, research