Analyzing and Predicting the Agronomic Effectiveness of Fertilizers Derived from Food Waste Using Data-Driven Models

Analyzing and Predicting the Agronomic Effectiveness of Fertilizers Derived from Food Waste Using Data-Driven Models

This study evaluates and estimates the agronomic effectiveness of food waste-derived fertilizers by analyzing plant yield and the internal efficiency of nitrogen utilization (IENU) via statistical and machine learning models. A dataset of 448 cases from various food waste treatments gathered from ou...

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Bibliographic Details
Main Authors: Ksawery Kuligowski, Quoc Ba Tran, Chinh Chien Nguyen, Piotr Kaczyński, Izabela Konkol, Lesław Świerczek, Adam Cenian, Xuan Cuong Nguyen
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
kitchen waste
machine learning
nitrogen uptake
predictive analytics
ryegrass growth
Online Access:https://www.mdpi.com/2076-3417/15/11/5999
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