Phosphorus is a vital nutrient for crops, but its excessive use in farming has created a serious environmental problem. When phosphorus escapes from farmlands into rivers and lakes, it fuels harmful algal blooms that destroy aquatic ecosystems.
A significant portion of this pollution comes from colloidal phosphorus (Pcoll), tiny particles that easily wash away with rainwater.
To address this, a 2025 study in the journal Biochar introduced a machine learning (ML) model to predict how effectively biochar—a charcoal-like material made from plant waste—can trap Pcoll in soils.
Understanding Colloidal Phosphorus and Its Impact
Colloidal phosphorus, or Pcoll, refers to phosphorus attached to ultra-fine soil particles between 1 and 1,000 nanometers in size. These particles are so small that they remain suspended in water, making them highly mobile.
When it rains, Pcoll easily washes into nearby water bodies, where it acts as a fertilizer for algae. This process, called eutrophication, leads to oxygen depletion in water, killing fish and other aquatic life.
In the United States alone, cleaning up algal blooms costs over $2.2 billion annually. Farmers often apply more phosphorus fertilizers than crops can absorb, leading to a buildup of “legacy phosphorus” in soils.
Over time, this excess phosphorus escapes as Pcoll, worsening water pollution. Traditional methods to measure Pcoll immobilization are slow and expensive, requiring lab tests. This study aimed to replace these methods with a faster, cheaper solution: machine learning.
Biochar as a Natural Solution
Biochar is made by heating organic materials like crop residues or wood in a low-oxygen process called pyrolysis. The result is a porous, carbon-rich material with a high surface area and chemical reactivity.
When added to soil, biochar acts like a sponge, trapping phosphorus and preventing it from washing away. Its effectiveness depends on several factors, including its oxygen content, phosphorus levels, and how much is applied to the soil.
For example, biochar with high oxygen content has more binding sites for phosphorus, while biochar rich in minerals like calcium can form stable compounds with phosphorus.
However, finding the right type and amount of biochar for different soils has been challenging. This is where machine learning comes into play.
How Machine Learning Predicts Biochar’s Efficiency
The research team collected data from 88 experiments worldwide, focusing on three categories: biochar properties, soil conditions, and application methods. Biochar properties included oxygen content, phosphorus levels, surface area, and pH.
Soil conditions involved texture (clay, silt, sand), pH, and available phosphorus (Olsen-P). Application methods covered biochar dosage (0.08–4% by weight) and pyrolysis temperature (400–600°C).
Using this data, they tested six machine learning algorithms to predict immobilization efficiency (IE-Pcoll), which measures how much Pcoll biochar can trap.
The algorithms included Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN). Among these, the Random Forest (RF) algorithm emerged as the most accurate.
RF works by building multiple decision trees and averaging their predictions, which reduces errors and avoids overfitting. In the training phase, RF achieved an almost perfect score (R² = 0.996), meaning it explained 99.6% of the data’s variability.
During testing, it maintained high accuracy (R² = 0.971), outperforming other models like XGBoost (R² = 0.936) and Neural Networks (R² = 0.964). The key to RF’s success was its ability to handle complex relationships between variables.
For instance, it revealed that biochar’s oxygen content is far more important than soil texture in predicting Pcoll immobilization. This finding surprised researchers, who previously believed soil type played a larger role.
Key Factors Influencing Biochar’s Performance
The study identified five critical factors that determine biochar’s efficiency. First, oxygen content accounted for 49.3% of the impact, as biochar with higher oxygen levels has more chemical binding sites for phosphorus.
Second, soil available phosphorus (Olsen-P) contributed 14.1%, meaning soils with excess phosphorus (over 500 mg/kg) benefit most from biochar.
Third, biochar phosphorus levels played a 12.3% role, as biochar already rich in phosphorus acts as a magnet for additional Pcoll.
Fourth, the application rate (5.6% impact) showed that applying 2% biochar by weight reduced Pcoll loss by 30–50%. Finally, surface area (4.7% impact) revealed that biochar with a surface area over 5 m²/g trapped 20% more Pcoll than low-surface-area versions.
Interestingly, soil texture (e.g., clay or sand content) had minimal influence. This suggests that biochar’s inherent properties matter more than the soil it’s applied to.
Simplifying the Model for Real-World Use
Initially, the model used 18 variables, but many were redundant. Using statistical techniques like Pearson correlation and hierarchical clustering, the team narrowed it down to 11 key factors.
For example, sand content was removed because it had little impact and was strongly correlated with silt content. Similarly, soil total carbon was excluded because it overlapped with other variables like biochar’s oxygen content.
The simplified model not only performed better (R² = 0.973) but also became faster and easier to use. Farmers no longer need to test every possible variable—just the most important ones.
A Practical Tool for Farmers: The Biochar GUI
To make the model accessible, the researchers built a graphical user interface (GUI). Farmers input basic data like biochar oxygen content, soil pH, and application rate, and the tool predicts how much Pcoll loss they can reduce.
For instance, if a farmer applies biochar with 15% oxygen content at a 2% dose to slightly acidic soil (pH 6.5), the GUI might predict a 65–70% reduction in Pcoll runoff.
The team tested the GUI with new data and found errors below 20%, which is acceptable for environmental applications. This tool could save farmers time and money by reducing the need for repeated lab tests.
How Biochar Traps Phosphorus: The Science Explained
Biochar immobilizes Pcoll through four main mechanisms. First, surface adsorption occurs when the porous structure of biochar physically traps Pcoll particles.
Second, chemical bonding happens as oxygen-rich groups on biochar’s surface form strong bonds with phosphorus. Third, mineral precipitation involves minerals in biochar ash, like calcium, reacting with phosphorus to form stable compounds.
Fourth, soil aggregation binds soil particles into clumps, preventing Pcoll from washing away. Additionally, biochar reduces the mobility of other colloids, such as iron and aluminum, which often carry phosphorus. By trapping these colloids, biochar indirectly lowers Pcoll loss.
Environmental and Agricultural Benefits
The study highlights several benefits of using biochar. First, reducing Pcoll loss by 50% could prevent 10–15 algal blooms yearly in regions like the U.S. Midwest.
Second, soils treated with biochar retain 30% more phosphorus, cutting fertilizer needs by 20–25%. Third, biochar locks carbon in soil for centuries, fighting climate change—one ton of biochar can store three tons of CO₂. Finally, biochar enhances water retention and microbial activity, boosting crop yields by 10–15%.
While promising, the model has limitations. It relies on data from 88 experiments, mostly from temperate regions. More research is needed in arid and tropical climates.
Long-term effects of biochar, such as its durability over decades, also remain unclear. Future studies could explore the role of soil microbes and iron oxides, which influence Pcoll dynamics but weren’t included in this model.
Conclusion: A Sustainable Future for Farming
This study shows how machine learning can turn complex science into practical tools for farmers. By identifying the optimal biochar type and application rate, the model helps reduce phosphorus pollution while improving soil health.
Key takeaways include: biochar’s oxygen content is the most critical factor for trapping phosphorus, the Random Forest model offers unmatched accuracy (R² = 0.971), and a user-friendly GUI makes the technology accessible to non-experts.
As agriculture faces growing pressure to adopt eco-friendly practices, tools like this bridge the gap between research and real-world action. By tailoring biochar use to local conditions, farmers can protect waterways, save money on fertilizers, and contribute to a healthier planet.
Reference: Eltohamy, K.M., Alashram, M.G., ElManawy, A.I. et al. Machine learning-assisted model for predicting biochar efficiency in colloidal phosphorus immobilisation in agricultural soils. Biochar 7, 57 (2025). https://doi.org/10.1007/s42773-025-00442-6
