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<p>Crops exhibit diverse growth characteristics influenced by environmental conditions, nutrient inputs, and climate, necessitating adaptive fertilizer management strategies. This study developed crop growth models for green onions (Sinheukgeumjang) and evaluated three fertigation strategies to optimize nutrient application under field conditions. Growth data from a quantitative fertigation control system were fitted using Logistic, Gompertz, and Double Logistic models via the Levenberg–Marquardt algorithm. Model performance was assessed using the coefficient of determination (R²) and Akaike Information Criterion (AIC), and stratified bootstrap resampling quantified parameter uncertainty. Three fertigation regimes were compared: (1) uniform fertigation, distributing nutrients evenly across the growth period; (2) proportional fertigation, allocating fertilizer according to the growth curve slope; and (3) model-informed dynamic fertigation, an enhancement of the proportional approach that integrates real-time soil electrical conductivity (EC) monitoring to adjust nutrient application rates according to both modeled growth and current field conditions. Field implementation in 2024 showed that Model-informed Dynamic Fertigation System (MIDFS) significantly improved plant length and yield compared with the other approaches. MIDFS improved plant growth and yield by approximately 14–15% and 21–28%, respectively, compared to Uniform Fertigation System (UFS) and Proportional Fertigation System (PFS), demonstrating its superior effectiveness over conventional fertigation methods. By aligning nutrient supply with crop demand, this strategy reduced under- and over-fertilization risks and enhanced resource-use efficiency. The study demonstrates that integrating crop growth modeling with uncertainty estimation and real-time sensor data provides a robust framework for dynamic, site-specific fertigation management. This approach supports precision nutrient application and sustainable agricultural production, highlighting the practical value of model-informed, sensor-driven fertigation strategies.</p>

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Growth-model–driven precision fertigation enhances green onion performance in field trials

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Doyun Kim,
  • Yejin Lee,
  • Jwakyung Sung,
  • Jin Hee Park,
  • Tae-Young Heo

摘要

Crops exhibit diverse growth characteristics influenced by environmental conditions, nutrient inputs, and climate, necessitating adaptive fertilizer management strategies. This study developed crop growth models for green onions (Sinheukgeumjang) and evaluated three fertigation strategies to optimize nutrient application under field conditions. Growth data from a quantitative fertigation control system were fitted using Logistic, Gompertz, and Double Logistic models via the Levenberg–Marquardt algorithm. Model performance was assessed using the coefficient of determination (R²) and Akaike Information Criterion (AIC), and stratified bootstrap resampling quantified parameter uncertainty. Three fertigation regimes were compared: (1) uniform fertigation, distributing nutrients evenly across the growth period; (2) proportional fertigation, allocating fertilizer according to the growth curve slope; and (3) model-informed dynamic fertigation, an enhancement of the proportional approach that integrates real-time soil electrical conductivity (EC) monitoring to adjust nutrient application rates according to both modeled growth and current field conditions. Field implementation in 2024 showed that Model-informed Dynamic Fertigation System (MIDFS) significantly improved plant length and yield compared with the other approaches. MIDFS improved plant growth and yield by approximately 14–15% and 21–28%, respectively, compared to Uniform Fertigation System (UFS) and Proportional Fertigation System (PFS), demonstrating its superior effectiveness over conventional fertigation methods. By aligning nutrient supply with crop demand, this strategy reduced under- and over-fertilization risks and enhanced resource-use efficiency. The study demonstrates that integrating crop growth modeling with uncertainty estimation and real-time sensor data provides a robust framework for dynamic, site-specific fertigation management. This approach supports precision nutrient application and sustainable agricultural production, highlighting the practical value of model-informed, sensor-driven fertigation strategies.