Machine learning-based assessment of the impact of biochar amendment on plant productivity in salt-affected soils
Year: 2025
Type: conference-paper
Abstract: Soil salinization is one of the major environmental problems facing the world at present, and its negative impact on agricultural production and ecological balance is increasingly prominent. In this study, the BP neural network algorithm was applied to build a prediction model of plant productivity in salt-affected soils improved by biochar, and the internal mechanism of biochar application affecting plant growth in salt-affected soils was deeply revealed. The results showed that the nitrogen content of biochar (SHAP = 0.08) had the most significant positive effect on vegetation productivity. The pH value of biochar (SHAP = 0.06) and the amount of biochar applied (SHAP = 0.06) showed a certain negative effect. This study not only provides a solid theoretical basis for the biochar restoration of salt-affected soils, but also provides important technical support for the sustainable management practice of salt-affected soils, and has important scientific value and practical significance for promoting the ecological restoration of salt-affected soils.
Source: E3S Web of Conferences
Authors: Zhenxuan Mao, Kun Chen, Qiang Liu, Mengjiao Xu
Institution: Ocean University of China
Language: English
Subfield: Soil Science
Domain: Life Sciences
SDGs: Life on land, Zero hunger
Open Access status: diamond
OpenAlex ID: W4410448229