Scholarlink

<p>Accurate calculation of nitrogen requirements is essential in rice fields utilizing both local manure and mineral fertilizers to mitigate nitrogen deficiencies and yield losses associated with reducing chemical fertilizer use. Traditional approaches often fail to effectively integrate organic and mineral fertilizers or adapt to the complexities of real-farm conditions. To tackle these challenges, this study introduced a novel application of the Nitrogen Fertilizer Optimization Algorithm (NFOA), leveraging remote optical sensors and Sentinel-2 satellite imagery to deliver precise, data-driven nitrogen recommendations for the effective integration of organic fertilization in rice cultivation. Fertilizer prescription maps generated by the NFOA delivered precise nitrogen recommendations tailored for diverse real-farm fields. The algorithm demonstrated strong predictive performance for yield responses to nitrogen application at critical phenological stages, such as panicle initiation and maximum tillering (R<sup>2&#xa0;</sup>= 0.71, <i>p&#xa0;</i>&lt; 0.0001; R<sup>2&#xa0;</sup>= 0.73, <i>p&#xa0;</i>&lt; 0.0001). Key findings demonstrate the model’s ability to optimize nitrogen inputs, achieving up to a 40% reduction in surplus nitrogen while maximizing yields. By promoting a balanced nitrogen input-output equilibrium, the NFOA offers significant environmental and economic benefits, even in the context of the complexities associated with organic fertilization. In conclusion, these findings suggest that the NFOA approach is suitable for calculating nitrogen fertilizer requirements in rice fields using organic fertilization strategies, effectively accommodating the high variability in nutrient content and availability of organic nitrogen to rice crops. However, further refinement is necessary to enhance its predictive accuracy by incorporating advanced spectral indices and accounting for detailed environmental and management factors.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Mid-season variable-rate algorithm for organic and mineral rice fertilization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

查看全文
  • Karen Marti-Jerez,
  • Mar Català-Forner,
  • Núria Tomàs,
  • Gemma Murillo,
  • Carlos Ortiz,
  • Marta S. Lopes

摘要

Accurate calculation of nitrogen requirements is essential in rice fields utilizing both local manure and mineral fertilizers to mitigate nitrogen deficiencies and yield losses associated with reducing chemical fertilizer use. Traditional approaches often fail to effectively integrate organic and mineral fertilizers or adapt to the complexities of real-farm conditions. To tackle these challenges, this study introduced a novel application of the Nitrogen Fertilizer Optimization Algorithm (NFOA), leveraging remote optical sensors and Sentinel-2 satellite imagery to deliver precise, data-driven nitrogen recommendations for the effective integration of organic fertilization in rice cultivation. Fertilizer prescription maps generated by the NFOA delivered precise nitrogen recommendations tailored for diverse real-farm fields. The algorithm demonstrated strong predictive performance for yield responses to nitrogen application at critical phenological stages, such as panicle initiation and maximum tillering (R2 = 0.71, p < 0.0001; R2 = 0.73, p < 0.0001). Key findings demonstrate the model’s ability to optimize nitrogen inputs, achieving up to a 40% reduction in surplus nitrogen while maximizing yields. By promoting a balanced nitrogen input-output equilibrium, the NFOA offers significant environmental and economic benefits, even in the context of the complexities associated with organic fertilization. In conclusion, these findings suggest that the NFOA approach is suitable for calculating nitrogen fertilizer requirements in rice fields using organic fertilization strategies, effectively accommodating the high variability in nutrient content and availability of organic nitrogen to rice crops. However, further refinement is necessary to enhance its predictive accuracy by incorporating advanced spectral indices and accounting for detailed environmental and management factors.