NH₃ emitted into the atmosphere undergoes intricate chemical reactions to form fine particulate matter PM_2.5. Nitrogen fertilizers are one of the major sources of gaseous ammonia. Recently, research into using biochar to lessen NH₃ emissions from agricultural land has taken center stage and several studies have been executed in that regard. However, biochar’s capacity to reduce emissions of gaseous NH₃ from applied nitrogen fertilizers is affected by both soil and biochar properties. While the effects of soil properties on NH₃ volatilizations have been widely studied, the data concerning the effects of biochar properties on NH₃ volatilizations from the soil are still scanty. It is against this backdrop that this study examined the effects of biochar pH on emissions of NH₃ from the soil amended with varying quantities of nitrogen, as well as the impact on the growth and productivity of Chinese cabbage. To achieve the study objectives, acidic (pH 5.7), neutral (pH 6.7) and alkaline (pH 11.0) biochars were used and each was added to the soil at a rate of 1% (w/w). Nitrogen fertilizers were applied at three rates of 160, 320, 640 kg ha⁻¹. In comparison with the control, the acidic, neutral and alkaline biochar amendments reduced NH₃ emissions by up to 18%, 20% and 15%, respectively. However, only neutral biochar produced higher Chinese cabbage yields than the urea-only amendment and the Chinese cabbage yields increased with the increasing rates of nitrogen applied. Combined applications of neutral biochar and 640 kg/ha of nitrogen are recommended for optimal cabbage yields and low NH₃ emissions.

A quick, accurate and cost-effective method for estimating total soil carbon is necessary for monitoring its levels due to its environmentally and agronomically irreplaceable importance. There are several impediments to both laboratory analysis and spectroscopic sensor technology because the former is both expensive and time-consuming whereas the initial cost of the latter is too high for farmers to afford. RGB photography obtained from digital cameras could be used to quickly and cheaply estimate the total carbon (TC) content of the soil. In this study, we developed models to predict soil TC contents across different cropland types including paddy, upland and orchard fields as well as the TC content of the soil combined from all the aforementioned cropland types on a regional scale. Soil colour measurements were made on samples from the Chungcheongnam-do province of South Korea. The soil TC content ranged from 0.045% to 6.297%. Modelling was performed using multiple linear regression considering the soil moisture levels and illuminance. The best soil TC prediction model came from the upland soil and gave training and validation r² values of 0.536 and 0.591 with RMSE values of 0.712% and 0.441%, respectively. However, the most accurate equation is the one that produces the lowest RMSE value. Hence, although the model for the upland soil was the most stable of all, the paddy soil model which gave training and validation r² values of 0.531 and 0.554 with RMSE values of 0.240% and 0.199%, respectively, was selected as the best soil TC prediction equation of all due to its comparatively high r² value and the lowest RMSE of all equations.

Digital soil mapping (DSM) is a statistical technique that utilizes soil characteristics and environmental factors to create spatial distribution maps representing soil properties. The SCORPAN model, consisting of soil (S), climate (C), organisms (O), relief (R), parent materials (P), age (A) and space (N), describes the environmental factors used in DSM techniques. The objectives of this study were to assess the spatial distribution map of soil carbon stocks in Chungcheong province and predict soil carbon stocks within the 0-30 cm depth using DSM technique. The minimum and maximum predicted carbon stocks were 25.11 ton C ha-1 and 183.55 ton C ha-1 , respectively, with a mean of 46.92 ± 13.66 ton C ha-1. The spatial distribution map of soil carbon stocks revealed higher carbon stock in Chungcheongbuk-do, particularly in Danyang-gun, while lower carbon stocks were observed in the coastal areas of Chungcheongnam-do. The estimated economic value of soil carbon stocks in Chungcheong province was 406.3 billion won, based on the average soil carbon stock, agricultural land area and carbon offset trading price. The validation outcomes of the DSM are summarized as follows: the model achieved a coefficient of determination (R 2) of 0.15, indicating the 15% confidence levels to the validation data. The mean absolute error (MAE) was 20.78, and the root mean square error (RMSE) was 29.51, respectively. The scatter plot between observed and predicted soil carbon stocks revealed that the predicted values were lower than the observed values, indicating a need for improvement in the model's predictive performance. Therefore, the estimated soil carbon stocks and its spatial distribution map in this study can serve as fundamental information for assessing the potential carbon sequestration capacity of agricultural soils and contributing to climate change mitigation and carbon neutrality efforts.