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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 our experiments and the existing literature was analyzed. Plant yield and IENU exhibited substantial variability, averaging 2268 ± 3099 kg/ha and 32.3 ± 92.5 kg N/ha, respectively. Ryegrass dominated (73.77%), followed by unspecified grass (10.76%), oats (4.87%), and lettuce (2.02%). Correlation analysis revealed that decomposition duration positively influenced plant yield and IENU (r = 0.42 and 0.44), while temperature and volatile solids had negative correlations. Machine learning models outperformed linear regression in predicting plant yield and IENU, especially after preprocessing to remove missing values and outliers. Random Forest and Cubist models showed strong generalization with high R<sup>2</sup> (0.79–0.83) for plant yield, while Cubist predicted IENU well in testing, with RMSE = 3.83 and R<sup>2</sup> = 0.78. These findings highlight machine learning’s ability to analyze complex datasets, improve agricultural decision-making, and optimize food waste utilization.
ISSN:2076-3417

Similar Items