Biochar application to soil can reportedly reduce nitrous oxide (N₂O) emissions. Conversely, the aged biochar may not suppress N₂O emissions from soil because its surface functional groups are oxidized over time. This study aimed to determine the effect of aged biochar materials derived from the coconut shell and rice husk at different pyrolysis temperatures (500°C/800°C) on N₂O emissions via denitrification from soil and the surface functional groups of biochar. The results showed that biochar application significantly reduced N₂O emissions from soil treated with both raw material biochars. Under the same pyrolysis and oxidation conditions, the coconut-shell biochar was effective than rice-husk biochar in mitigating N₂O emission. The higher pyrolysis temperature reduced the functional groups on the surface of biochars, resulting in less N₂O emission. However, the oxidation treatment did not affect the C=O functional groups on the surface of 800°C biochars, and therefore N₂O emissions from soil treated with these biochars In contrast, H₂O₂ treatment increased the abundance of C=O functional groups on the surface of the 500°C biochars, resulting in more N₂O emissions. In conclusion, the biochars produced at higher pyrolysis temperatures have long-term effects on the mitigation of denitrification-derived N₂O emissions from farmland soil.

This study aimed to provide knowledge for the use of images from unmanned aerial vehicles (UAVs) for soil sampling. Typically, the diagnosis of agricultural fields requires soil samples from several locations to ensure representativeness. However, mostly the soil properties are spatially heterogeneous. Therefore, depending on the sampling point selection, the results obtained may not accurately reflect the field productivity. To overcome this sampling problem, this study used UAV images as supporting tools for sampling in soil diagnosis and investigated the relationship between the unsupervised classification results of UAV images and soil physicochemical properties in a 1 ha soybean field. The UAV observations were performed shortly after the soybean harvest. The pixel values of the multispectral and thermal infra-red images and a digital surface model were used as features for unsupervised classification by k-means++ clustering. After the smoothing process, the distribution of each cluster in the target crop field was mapped on a geographic information system. Moreover, this was compared to the analytical values of total carbon, total nitrogen, moisture content, particle size distribution, pH (H₂O), and electrical conductivity in 43 soil samples collected from the field to evaluate the feasibility of using aerial images in the selection of sampling points. The distribution of the obtained clusters corresponded to the trends in the soil physicochemical properties within the target field, even though soil analytical values were not learned as features. These results were consistent with the normalized difference vegetation index distribution during soybean growth. Overall, it suggests that unsupervised classification of UAV images can provide useful information for sampling in soil diagnosis.