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Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models.

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Machine learning optimizes fertilizer use for Brazilian garlic, reducing nitrogen, phosphorus, and potassium application. This approach minimizes costs and environmental impact while maintaining high yields.

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Area of Science:

  • Agricultural Science
  • Agronomy
  • Machine Learning Applications

Background:

  • Brazilian garlic production faces significant yield gaps attributed to suboptimal nutrient management.
  • Machine learning (ML) offers advanced capabilities for analyzing complex yield-influencing factors, reducing reliance on assumptions in nutrient management strategies.

Purpose of the Study:

  • To develop customized fertilizer recommendations for maximizing garlic marketable yield at a local scale using ML.
  • To address nutrient mismanagement in Brazilian garlic cultivation through data-driven insights.

Main Methods:

  • Collected extensive field experimental data (15 N, 24 P, 27 K) from 2015-2017 and grower observational data (61) from 2018-2019 in Santa Catarina, Brazil.
  • Utilized Random Forest (RF), a machine learning algorithm, for model calibration (979 data points) and validation (45 data points).
  • Included features such as cropping system, climate, soil tests, and fertilization for yield prediction.

Main Results:

  • Random Forest achieved high accuracy in predicting marketable yield (R2 = 0.886) with all features, and remained accurate (R2 = 0.882) even after excluding cultivar and climate data.
  • The ML model recommended 200 kg N ha-1 for maximum yield, contrasting with the state recommendation of 300 kg N ha-1, indicating potential over-fertilization.
  • Excessive phosphorus (P) and potassium (K) fertilization was also suggested, with arbuscular mycorrhizal fungi potentially aiding P and K uptake.

Conclusions:

  • ML models can precisely tailor fertilizer recommendations to local conditions, optimizing garlic yield and reducing input costs.
  • Implementing data-driven nutrient management strategies can significantly decrease fertilizer use, lower production expenses, and mitigate environmental impact.
  • This approach supports sustainable intensification of Brazilian garlic production by bridging yield gaps and enhancing technology transfer.