Soil acidification threatens agricultural productivity across large areas of the world, but predicting how well biochar can restore acidic soils remains difficult. A new review suggests that artificial intelligence could help scientists move from trial-and-error applications toward more precise, mechanism-guided biochar management, provided that researchers improve the quality and biological relevance of the data used to train AI models.
Published in Biochar, the review examines how biochar reduces soil acidity, strengthens soil resistance to future acidification and influences interactions among soil minerals, microorganisms and plants. The authors also assess the rapidly growing use of artificial intelligence and machine learning for predicting biochar performance.
"AI offers a powerful opportunity to connect the complex properties of biochar with soil conditions and biological responses," said corresponding author Ren-kou Xu. "But better predictions will depend on whether the models are built around the mechanisms that actually control soil acidification, rather than simply identifying statistical correlations."
Acidic soils can lose important nutrients such as calcium and magnesium while increasing the toxicity of aluminum and some heavy metals. These changes can restrict root growth, disrupt soil microbial communities and ultimately reduce crop productivity. Biochar offers a promising alternative or complement to conventional lime because it can raise soil pH while also improving nutrient retention, water-holding capacity and soil buffering against renewed acidification.
One of the review's central findings is that researchers should distinguish between organic and inorganic sources of alkalinity in biochar. Current predictive models often treat total alkalinity as a single variable. However, the authors explain that organic functional groups can provide relatively rapid acid neutralization, while inorganic components such as carbonates and silicates may contribute slower but more sustained buffering.
Failing to separate these mechanisms can make AI models less interpretable and limit their ability to predict how different biochars will behave over time or across different soils.
The review also finds that ensemble machine-learning approaches, particularly Random Forest, have become dominant tools in biochar-related predictive research because they can capture nonlinear relationships, handle complex datasets and rank the importance of individual variables. However, the authors emphasize that no single algorithm is universally superior. Model selection should depend on data availability, problem complexity and computational resources.
Looking ahead, the researchers propose combining remote sensing, proximal soil sensors, microbial datasets, chemical imaging and multiple AI models. Such data fusion could allow models to better represent the entire biochar-soil-microbe-plant system rather than relying on isolated laboratory measurements.
"The next generation of AI should not function simply as a black box that predicts whether biochar works," Xu said. "The goal is to develop interpretable models that can explain why a particular biochar works in a particular soil and help identify the most appropriate treatment before it is applied in the field."
The authors call for standardized benchmark datasets, improved reporting of biochar properties, stronger representation of microbial responses and closer collaboration among soil scientists, sensing specialists and AI researchers. Ultimately, they argue that integrating AI with causal inference and established chemical and biological mechanisms could transform AI from a correlation-based prediction tool into a practical platform for precision management of acidified agricultural soils.
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Journal Reference: Guo, L., Li, K. & Xu, Rk. Application and prospect of artificial intelligence in biochar for acidic soil amelioration. Biochar 8, 134 (2026).
https://doi.org/10.1007/s42773-026-00652-6
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About Biochar
Biochar (e-ISSN: 2524-7867) is the first journal dedicated exclusively to biochar research, spanning agronomy, environmental science, and materials science. It publishes original studies on biochar production, processing, and applications—such as bioenergy, environmental remediation, soil enhancement, climate mitigation, water treatment, and sustainability analysis. The journal serves as an innovative and professional platform for global researchers to share advances in this rapidly expanding field.
