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.

Ammonium ions (NH4+) are commonly found in contaminated water and are a contributing factor to water eutrophication. Carbonized rice husk, derived from various biomass sources, possesses a porous structure, and its characteristics are influenced by the feedstock and pyrolysis conditions. Hence, this study aimed to investigate the applicability of carbonized rice husk as an absorbent for NH4+ removal. The adsorption kinetics were analyzed using the Pseudo-first-order and Pseudo-second-order models, while the adsorption characteristics were assessed using the Langmuir and Freundlich isotherms. The adsorption rate of NH4+ by carbonized rice husk increased until 240 min and then gradually approached equilibrium state. Notably, the highest NH4+ adsorption rate was observed in pH 7.1 carbonized rice husk 36.045 mg/g∙min. Moreover, the NH4+ adsorption capacity exhibited an increase with increasing concentration and quantity of the solution. The pH of the carbonized rice husk was found to influence the NH4+ adsorption process, with higher pH values corresponding to increased NH4+ adsorption rates. The NH4+ sorption rate carbonized rice husk was higher in pH 11.0 at 31.440 mg/g compared to pH 6.1 (7.642 mg/g) and pH 7.1 (10.761 mg/g). These findings highlight the impact of pyrolysis conditions on the adsorption characteristics of carbonized rice husk.

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BACKGROUND: Biochar has ability to reduce N loss, increase crop yield, and sequestrate carbon in the soil However, there is still limited study concerning the interactive effects of various biochars on NH 3 loss and plant growth. This study, therefore, was conducted to investigate the NH 4 + adsorption characteristics of biochar derived from rice and maize residues. METHODS AND RESULTS: By-products were pyrolyzed under oxygen-limited conditions at 300-700°C for 1 hour and used for experiment of NH 4 + adsorption in aqueous solution. The adsorption characteristics of biochar were studied using Langmuir isotherm. Biochar yield and hydrogen content decreased with increasing pyrolysis temperatures, whereas pH, EC, and total carbon content increased. The biochar pyrolyzed at lower temperatures was more efficient at NH 4 + adsorption than those produced at higher temperatures. In addition, the R L values, indicating equilibrium coefficient were between 0 and 1, confirming that the result was suitable for Langmuir isotherm. CONCLUSION:The maize stalk biochar pyrolyzed at 300°C was the most efficient to adsorb NH 4 + from the aqueous solution. Furthermore, the adsorption results of this experiment were lower than those of other prior studies, which were ascribed to different experimental conditions such as ingredients, and pyrolysis conditions.