Predicting nitrous oxide emissions from soil planted to sugarcane under various irrigation regimes using machine learning models
Rafael T. BONATO , Lurdineide de A. B. BORGES , Arminda M. CARVALHO , Alexsandra D. OLIVEIRA , Thaís R. SOUSA , Maria L. G. RAMOS , Walter Q. RIBEIRO JUNIOR , Robélio L. MARCHÃO , Fernando A. M. SILVA , Díbio L. BORGES
Front. Agr. Sci. Eng. ›› 2026, Vol. 13 ›› Issue (2) : 25663
Predicting nitrous oxide emissions from soil planted to sugarcane under various irrigation regimes using machine learning models
Nitrous oxide (N2O) is a potent greenhouse gas with about 60% of its emissions are attributed to agricultural activities. Its fluxes are influenced by a range of crop-specific factors, such as nitrogenous fertilizer inputs, soil N availability, tillage practices, temperature, pH and soil moisture. These factors interact in complex, nonlinear ways, creating the need for predictive modeling of N2O emissions to both improve understanding and estimation and identify mitigating strategies. This proposes proposes data-driven machine learning techniques, particularly multilayer perceptron and random forest (RF) algorithms, for estimating soil N2O fluxes in a sugarcane plantation under different irrigation regimes and to contrast machine learning results with conventional analytical methods. The findings indicate that RF modeling achieved a coefficient of determination of 87.4% for N2O emission prediction, and identified ammonium, nitrogen nitrate, soil temperature, and water-filled pore space as the most influential predictors, in that order. The results open new possibilities for integrating machine learning to study N2O fluxes in sugarcane and other major crops. All data and code used in this study are provided openly to support further research.
Brazilian Cerrado / irrigation regimes / machine learning / multilayer perceptron / random forest / sugarcane nitrous oxide emissions
| ● A scalable, data-driven machine learning model for N2O emissions was refined with data from sugarcane crops. | |
| ● A simulation model for forecasting nitrous oxide emissions under various environmental conditions was developed. | |
| ● The efficacy of the random forest model for predicting N2O emissions from irrigated sugarcane fields was determined. | |
| ● The relative impacts of watering regimes, soil types and environmental factors on N2O emissions from sugarcane crops were presented. |
The Author(s) 2025. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)
/
| 〈 |
|
〉 |
PDF
(4498KB)
