Global Potential Effects Analysis of Biochar on Crop Yields and Soil Quality

Biochar has been widely used for soil improvement, but uncertain results persist due to diverse biochar characteristics, soil properties, and crop responses. Therefore, the effects of biochar on crop yields and soil quality were evaluated using effect sizes from 1011 paired data points from field trials, based on a global meta-analysis method. The results indicated that biochar with a higher total phosphorus concentration (≥1.0%), total carbon concentration (≥70%), specific surface area (≥50 m2 g^-1), and biochar application rates of 10–30 t ha^-1 are optimal for improving crop yields. Biochar made from manure (effect size, 42%) exceeded that made from ligneous (22%) or cereal (12%) material. Porous, acidic, or young soil types are optimal for biochar application, while sandy and clay soils are preferred over loam soil. Soils with lower available nitrogen (<80 mg kg^-1), phosphorus (<10 mg kg^-1), potassium (<120 mg kg^-1), pH (<4.5), and cation exchange capacity (<10 cmol kg^-1) were more effective. The effect of biochar on yield is higher for cash crops (oil plants: 37%, vegetables: 28%) compared to food crops (legumes: 26%, maize: 20%, wheat: 12%), with no significant effect observed on rice. Finally, biochar increases crop yields by improving soil quality through enhanced levels of soil organic carbon, total nitrogen, ammonium-nitrogen, nitrate-nitrogen, and soil pH while reducing soil bulk density. Our research enhances understanding of the relationships between biochar, soil, and crops, aiding researchers, manufacturers, and farmers in making informed decisions regarding biochar selection, planting locations, and crop choices. However, it remains unclear to what extent machine learning can accurately predict crop yield or SOC when biochar is applied to soil. In our study, Random Forest (RF) and Multilayer Perceptron Neural Network (MLP- NN) models were employed to predict crop yield and SOC with 297 paired data from field trials. The results indicated that the RF model (test R² = 0.83) did not differ significantly from the MLP- NN model (test R² = 0.84) in predicting crop yield. However, the RF model (test R² = 0.87) performs significantly better than the MLP- NN model (test R² = 0.53) in predicting SOC. The most influential features for crop yield were found to be the biochar application rate (15%), initial SOC (13%), biochar pH (10%), and biochar TP (10%). In contrast, the variation of SOC was primarily influenced by latitude (26%), biochar application rate (22%), initial SOC (15%), and biochar pH (13%). Furthermore, both crop yield and SOC variation were influenced by multiple factors, not solely one, and their impacts were not necessarily linear. This study suggests that the optimization of biochar pH and phosphorus content, along with the regulation of its application rate in sandy or clay- rich soils, can simultaneously enhance both crop yield and SOC. In the future, we hope to develop a decision support system with prediction, different scenarios, and consultation capabilities based on geospatial location.