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.
The need for organic soil amendments is increasing in the Republic of Korea against the backdrop of increased soil acidification and nutrient losses. The pyrolysis of biomass produces biochar which not only increases soil productivity but also provides environmental benefits through carbon sequestration. The portion of the brewer’s spent grain (BSG) recycled is by far less than the amount generated, but pyrolysis can help to reverse this trend by turning BSG waste into a valuable soil amendment. The current study, therefore, evaluated the effects of brewer’s spent grain biochar (BBXXX) produced at three different temperatures of 300 °C, 500 °C and 700 °C on the yield and quality characteristics of the leaf lettuce as well as the effects on soil chemical properties through a pot experiment. Each of the BBXXX and BSG were added to the soil at two rates of 2% and 5% by weight. The pH and carbon content of the BBxxx increased with increasing pyrolysis temperatures and the trend was replicated in the soil upon biochar application i.e. the soil pH and carbon content increased alongside temperatures at which biochar was pyrolyzed. On the other hand, however, the soil electrical conductivity (EC) diminished with the increasing pyrolysis temperatures of the biochar applied. With regards to crop growth, the BB500 5% amendment produced the highest marketable yield of the leaf lettuce and while the lettuce grown on the control produced leaf lettuce with the lowest content of nitrate nitrogen, BB500 5% amendment generally produced the highest quality lettuce. The results indicate that BB500 performed agronomically better than the rest of the amendments and is thus recommended as an effective BSG recycling measure.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.