Integrating quantity–intensity relationships and machine learning to assess potassium dynamics and plant uptake in calcareous soils of India

Key Points

• Potassium dynamics significantly influence plant uptake in calcareous soils, affecting crop yields.
• Key evidence shows machine learning can predict potassium availability with high accuracy and reliability.
• Analysis of soil data integrated quantity-intensity relationships and machine learning techniques for better insights.
• Highlights the need for improved soil management strategies to optimize potassium use in agricultural settings.