Artificial intelligence may soon help farmers make smarter use of biochar, a carbon rich material made from burnt plant waste that has gained popularity as a sustainable soil amendment. A new study has found that AI can accurately predict how biochar influences phosphorus availability in soil, potentially reducing fertilizer waste and improving agricultural productivity.
Phosphorus is an essential nutrient for plant growth, but crops typically absorb only 15 to 20 percent of the fertilizer applied during a growing season. Much of the remaining phosphorus becomes chemically trapped in the soil or washes into rivers and lakes, causing harmful algal blooms and water pollution.
Researchers from Sun Yat-Sen University in China analyzed 534 observations from 32 previous studies to better understand how biochar affects phosphorus behavior in different soil conditions. Using machine learning techniques, they developed predictive models capable of identifying the factors that determine whether biochar releases phosphorus for crops or locks it away.
Among the three AI systems tested the Random Forest model delivered the most accurate results, explaining approximately 91 percent of the variation in phosphorus availability. The model examined 19 different variables, including soil properties and biochar characteristics.
The study found that the temperature used to produce biochar, known as pyrolysis temperature was the most influential factor. Biochar produced at temperatures between 460°C and 482°C showed the strongest ability to improve phosphorus availability in soil. Higher production temperatures tended to reduce phosphorus availability, which could be useful in situations where preventing nutrient runoff is a priority.
Researchers also identified application rate, soil acidity, and existing phosphorus levels as key factors influencing biochar performance. The findings revealed that biochar works most effectively in neutral to slightly alkaline soils, where it can convert phosphorus into forms that plant roots can absorb more easily.
Interestingly, the study challenges the belief that chemically modified biochar is always superior. The researchers found that untreated biochar can perform just as well, or even better, under the right conditions. This could lower production costs and reduce environmental impacts associated with biochar manufacturing.
Experts say the AI based approach could help eliminate much of the guesswork surrounding biochar use. Farmers would be able to predict in advance whether a specific type of biochar is suitable for their fields, allowing for more precise nutrient management and reducing unnecessary fertilizer applications.
The researchers believe the technology could contribute to sustainable agriculture by lowering fertilizer costs, reducing nutrient pollution in waterways, and making better use of agricultural waste materials. While further testing under real field conditions is needed, the study offers a promising step toward data driven farming practices.
The findings were published in the journal Biochar and highlight how artificial intelligence can support environmentally friendly solutions for global food production.