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Background <p>Genotypic differences in nitrogen use efficiency strongly influence sorghum growth and yield, highlighting the need for precise and reliable prediction of cultivar responses to nitrogen (N) availability. This study investigates the impact of two N treatments on sorghum cultivars, using artificial intelligence (AI) models for prediction.</p> Results <p>A randomized complete block design with two treatments: 0&#xa0;kg N ha<sup>− 1</sup> (0&#xa0;N) and 238&#xa0;kg N ha<sup>− 1</sup> (238&#xa0;N) was used. Six hybrid sorghum cultivars (Gustav, Estyphon, Foehn, Vegga, Aday1 and Beydarı) were evaluated for different traits. Statistical analysis included two-way ANOVA and factorial regression to assess treatment effects. Significant treatment effects were observed. Beydarı and Estyphon exhibited larger stem diameter and leaf area under 238&#xa0;N, while Aday1 had the smallest values under 0&#xa0;N. Gustav showed the highest panicle width, panicle weight, and grain yield under 238&#xa0;N. Stomatal conductance showed an opposite trend, decreasing with N supplementation. Machine learning models, specifically Random Forest (RF) and Light Gradient-Boosting Machine (LightGBM), were used to model the interaction, achieving R<sup>2</sup> values ranging from 0.759 to 0.966 for RF and 0.729 to 0.980 for LightGBM, indicating strong predictive accuracy.</p> Conclusion <p>LightGBM consistently achieved R<sup>2</sup> values greater than 0.92 for key traits, such as stomatal conductance, panicle width, and grain yield, demonstrating its potential to optimize N management. Gustav performed best under high N, whereas cultivar responses to low N were genotype-specific, captured effectively by the machine learning models. These findings highlight the role of AI models in predicting cultivar performance and supporting sustainable agricultural decisions.</p>

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Multivariate analysis and machine learning prediction of Sorghum cultivar traits under nitrogen regulation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月28日

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  • Muhammad Tanveer Altaf,
  • Waqas Liaqat,
  • Mehmet Bedir,
  • Gönül Cömertpay,
  • Seyid Amjad Ali,
  • Muhammad Aasim,
  • Muhammad Azhar Nadeem,
  • Faheem Shehzad Baloch

摘要

Background

Genotypic differences in nitrogen use efficiency strongly influence sorghum growth and yield, highlighting the need for precise and reliable prediction of cultivar responses to nitrogen (N) availability. This study investigates the impact of two N treatments on sorghum cultivars, using artificial intelligence (AI) models for prediction.

Results

A randomized complete block design with two treatments: 0 kg N ha− 1 (0 N) and 238 kg N ha− 1 (238 N) was used. Six hybrid sorghum cultivars (Gustav, Estyphon, Foehn, Vegga, Aday1 and Beydarı) were evaluated for different traits. Statistical analysis included two-way ANOVA and factorial regression to assess treatment effects. Significant treatment effects were observed. Beydarı and Estyphon exhibited larger stem diameter and leaf area under 238 N, while Aday1 had the smallest values under 0 N. Gustav showed the highest panicle width, panicle weight, and grain yield under 238 N. Stomatal conductance showed an opposite trend, decreasing with N supplementation. Machine learning models, specifically Random Forest (RF) and Light Gradient-Boosting Machine (LightGBM), were used to model the interaction, achieving R2 values ranging from 0.759 to 0.966 for RF and 0.729 to 0.980 for LightGBM, indicating strong predictive accuracy.

Conclusion

LightGBM consistently achieved R2 values greater than 0.92 for key traits, such as stomatal conductance, panicle width, and grain yield, demonstrating its potential to optimize N management. Gustav performed best under high N, whereas cultivar responses to low N were genotype-specific, captured effectively by the machine learning models. These findings highlight the role of AI models in predicting cultivar performance and supporting sustainable agricultural decisions.