Estimating Crop Nutritional Status Using Smart Apps to Support Nitrogen Fertilization. A Case Study on Paddy Rice

Area of Science:

• Agricultural Science
• Agronomy
• Plant Nutrition

Background:

• Efficient nitrogen (N) management is vital for sustainable agriculture, but existing methods often face adoption barriers due to cost or usability.
• Smart technologies offer potential solutions for improving N fertilization practices in operational farming contexts.

Purpose of the Study:

• To develop and validate a diagnostic system using smart apps for supporting topdressing N fertilization.
• To establish a resource-efficient methodology for calibrating the system for diverse crop varieties.

Main Methods:

• A diagnostic system was created using smart apps (PocketLAI, PocketN) to estimate the N nutritional index (NNI) in paddy rice.
• Field experiments were conducted to assess the system's accuracy in detecting N stress (NNI < 1) and N surplus (NNI > 1).
• A new calibration method for PocketN was developed and validated across 43 European rice varieties.

Main Results:

• The system accurately identified conditions of N stress and surplus, effectively guiding topdressing N applications.
• The developed calibration methodology proved resource-efficient and successful for adapting the system to different varieties.
• The study demonstrated the system's potential for real-world N management applications.

Conclusions:

• The smart app-based diagnostic system provides a practical and effective tool for optimizing N fertilization in cropping systems.
• The system's adaptability and integration potential with remote sensing technologies offer significant opportunities for precision agriculture.
• This approach enhances economic viability and environmental sustainability in crop production through improved N management.