In a recent study published in Environmental Technology & Innovation, authors Wenqi Jiao et al. developed machine learning models to predict how biocharBiochar is a carbon-rich material created from biomass decomposition in low-oxygen conditions. It has important applications in environmental remediation, soil improvement, agriculture, carbon sequestration, energy storage, and sustainable materials, promoting efficiency and reducing waste in various contexts while addressing climate change challenges. More influences crop yields. Biochar, a soil amendmentA soil amendment is any material added to the soil to enhance its physical or chemical properties, improving its suitability for plant growth. Biochar is considered a soil amendment as it can improve soil structure, water retention, nutrient availability, and microbial activity.   More produced from biomassBiomass is a complex biological organic or non-organic solid product derived from living or recently living organism and available naturally. Various types of wastes such as animal manure, waste paper, sludge and many industrial wastes are also treated as biomass because like natural biomass these More, has shown promise in enhancing crop productivity. However, the effects of biochar application can vary, making it difficult for researchers and farmers to determine the optimal conditions for its use.  

To address this, the authors compiled a dataset of 648 samples from 28 countries and used machine learning to predict crop yield changes in response to biochar application. The analysis involved comparing three machine learning classification models, with a random undersampling method and standardization used to manage data imbalance. The performance of the models was optimized using a grid search method, and key factors affecting crop yields were identified through model interpretation techniques.  

The results indicated that the optimized random forest model demonstrated the best predictive performance, achieving a recall of 0.817 and an accuracy of 0.687 on the testing set. The study identified several key features influencing crop yield predictions. The pHpH is a measure of how acidic or alkaline a substance is. A pH of 7 is neutral, while lower pH values indicate acidity and higher values indicate alkalinity. Biochars are normally alkaline and can influence soil pH, often increasing it, which can be beneficial More of the biochar was found to be the most important, followed by the initial soil pH, the biochar addition rate, and the total nitrogen content of the initial soil.  

This research demonstrates that machine learning models, when combined with imbalanced learning techniques, can effectively predict the positive or negative effects of biochar addition on crop yields. The findings offer valuable insights for developing biochar application strategies, aiming to enhance crop yields and promote biomass recycling.  

Source: Jiao, W., Li, K., Zhou, M., Zhou, N., Chen, Q., Hu, T., & Qi, C. (2025). Determining whether biochar can effectively increase crop yields: A machine learning model development with imbalanced data. Environmental Technology & Innovation, 38, 104154.