Soil carbon is an important factor in the process of mitigating climate change and solving greenhouse gas problems. However, the previous technology for soil carbon content analysis required a lot of labor, time, and expensive equipment (i.e. an elemental analyzer). In this study, the disadvantages of previous analysis method were secured by using smartphone images and multiple regression analysis. To predict the soil carbon content, the color variables (e.g., RGB, CIE-L * a * b * , CIE-L * c * h * , and CIE-L * u * v * ), gravimetric water content, and bulk density were used as statistical data. After Pearson's correlation analysis, several variables that had high correlations were removed and then used. In addition, the result of variance inflation factor (VIF) analysis indicated that all variables should not cause multicollinearity problems. The predictive model was classified based on land use, and the predictive model for the entire sample was also derived. The adjusted coefficient of determination (Adj. R 2 ), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used to verify the predictive model. When the verification method was substituted for each predictive model, the reliability of the predictive model classified based on land use was high. Therefore, in order to predict the carbon content in the agricultural soil, it is efficient to assign each prediction model after classifying agricultural land.

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.

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NH3 emitted into the atmosphere undergoes intricate chemical reactions to form fine particulate matter PM2.5. 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.