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Random forest models (RFM) are useful in predicting the soil carbon (C) contents because RFM predicts soilC with high accuracy under complicated environmental conditions. However, there are very few studies onprediction of soil C using RFM in Korea. Moreover, there is no case study using RFM to predict soil C contentof reclaimed tideland (RTL) soils, which have high C sequestration capacity. Therefore, in this study, theapplicability of RFM was evaluated using published soil properties data, including soil C and soil variables,for RTL soils located in southwestern coastal areas of Korea. In the present study, RFM was built using thedata of 16 variables (e.g., sand, silt, and clay contents, pH, electrical conductivity of saturated soil paste (ECe),and nutrient concentrations) obtained from five RTLs with similar climate, topography, and vegetation. The80% of the total data were trained to build the model, and searched optimal hyper parameters were used toimprove accuracy. The determination coefficient (R2) of the model was 0.67, and the difference betweenmeasured and predicted soil C content was 25.9% on average. However, when the measured values were outof the range of the data trained for building the model or the measured values were close to the minimum ormaximum value, the difference between the predicted and measured values became larger (73.9%). Thecontribution of the independent variables to the prediction of soil C using the model was the greatest (14.9%)for soil NH4+concentrations. Meanwhile, the contribution of ECe, which was highly correlated with soil Ccontent, was not detected, suggesting that the importance of the number and range of training data used tobuild model. Our study shows the possible application of RFM to predict soil C contents of RTL soils inKorea, and further highlights that a large amount of data should be accumulated for high accuracy predictionof soil C using RFM.

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기사명 저자명 페이지 원문 목차
Difference in soil biogeochemical properties of agricultural highland by topographical characteristic and soil management Jung-Hwan Yoon, Kye-Hoon Kim, Jae E. Yang p. 1-12
Assessment of soil enzyme activities in TPH-contaminated soil after soil washing and landfarming application Jin Wook Kim, Young Kyu Hong, Hyuck Soo Kim, Eun Jee Oh, Yong-Ha Park, Sung Chul Kim p. 13-19
Feasibility of adapting soil quality assessment model for estimation of rice productivity in paddy field Youngkyu Hong, Jinwook Kim, Hyucksoo Kim, Junghwan Yoon, Sangphil Lee, Jae E. Yang, Sangho Jeon, Sungchul Kim p. 20-26
Varying nitrogen fertigation for cucumbers grown in greenhouses with soil of optimal or high nutrient status Yang-Min Kim, Chan-Wook Lee, Yo-Sung Song, Ye-Jin Lee p. 27-37
Nutrients runoff and rice growth by soil texture and transplanting time during early maturing rice cultivating Tae-Gu Lee, Myung-Sook Kim, Sang-Ho Jeon, Ha-il Jung, Jung-Hun Ok p. 38-47
Estimation of soil organic matter content using soil organic color chart and soil color meter SPAD 503 Byung-Keun Hyun, Yejin Lee, Cheol-Hyun Ryu, Yuri Cho p. 48-57
Prediction of soil organic carbon contents of rice paddies in south-western coastal area of Korea using random forest models Hyun-Jin Park, Woo-Jung Choi p. 58-70
(A) study on the nutrition components for compost fertilizers in 2020 to 2021 Jae-Hong Shim, Seong-Heon Kim, Yun-Hae Lee, Soon-Ik Kwon, Seong Jin Park p. 71-79
Effects of Protaetia brevitarsis larvae manure application on lettuce growth and soil chemical properties Kyong-Hee Joung, Jong-Won Kim, Seul-Bi Lee, Da-Hyun Jang, Byung-Man Yoo, Sung-Mun Bea, Young-Ho Chang, Young Han Lee, Dong-Cheol Seo p. 80-85
Determination of dissolved organic carbon concentrations using UV-visible absorbance for water samples in a rural watershed, the Republic of Korea Young-Jae Jeong, Su-Jin Lee, Nuri Baek, Hyun-Jin Park, Woo-Jung Choi p. 86-92

참고문헌 (32건) : 자료제공( 네이버학술정보 )

참고문헌 목록에 대한 테이블로 번호, 참고문헌, 국회도서관 소장유무로 구성되어 있습니다.
번호 참고문헌 국회도서관 소장유무
1 Ahn, B.K., D.Y. Ko, H.J. Choi, and H.G. Chon. 2020. Distribution of water-soluble nutrients and physico-chemical properties of upland and orchard field soils in Jeonbuk province. Korea J. Soil Sci. Fert. 53:375-381. 미소장
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3 Calle, J.L.P., M. Ferreiro-Gonzalez, A. Ruiz-Rodriguez, G.F. Barbero, J.A. Alvarez, M. Palma, and J. Ayuso. 2021. A methodology based on FT-IR data combined with random forest model to generate spectralprints for the characterization of high-quality vinegars. Foods 10:1141. 미소장
4 Chaplot, V., M. Bernoux, C. Walter, P. Curmi, and U. Herpin. 2001. Soil carbon storage prediction in temperate hydromorphic soils using a morphologic index and digital elevation model. Soil Sci. 166:48-60. 미소장
5 Florinsky, I.V., R.G. Eilers, G. Manning, and L. Fuller. 2002. Prediction of soil properties by digital terrain modelling. Environ. Modell. Software 17:295-311. 미소장
6 Franklin, J. 2005. The elements of statistical learning: data mining, inference and prediction. Math. Intell. 27:83-85. 미소장
7 Grimm, R., T. Behrens, M. Märker, and H. Elsenbeer. 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island—Digital soil mapping using random forests analysis. Geoderma 146:102-113. 미소장
8 Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Firedman. 2009. The elements of statistical leargnin: data mining, inference, and prediction. Springer, New York, USA. 미소장
9 Hwang, H.Y., S.H. Kim, M.S. Kim, D.W. Le, J.E. Rim, J.H. Shim, and S.J. Park. 2019. Soil organic carbon fractions and stocks as affected by organic fertilizers in rice paddy soil. Korean J. Soil Sci. Fert. 52:520-529. 미소장
10 Jeong, G.Y. 2018. Spatial prediction and economic evaluation of soil carbon stocks using digital soil mapping in an agricultural landscape. Geogr. J. Korea 52:389-401. 미소장
11 Karhu, K., A.I. Gärdenäs, J. Heikkinen, P. Vanhala, M. Tuomi, and J. Liski. 2012. Impacts of organic amendements on carbon stocks of an agricultural soil—Comparison of model-simulations to measurements. Geoderma 189:606-616. 미소장
12 Kim, H.J., S.K. Kim, S.W. Kim, K.J. Kwak, and O.D. Kwon. 2021. Changes in chemical properties of orchard soils in Jeonnam province between 2002 and 2018. Korean J. Soil Sci. Fert. 54:1-9. 미소장
13 Kim, J. and S. Grunwald. 2016. Assessment of carbon stocks in the topsoil using random forest and remote sensing images. J. Environ. Qual. 45:1910-1918. 미소장
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15 Lal, R. 2008. Carbon sequestration. Philos. Trans. R. Soc. B: Biol. Sci. 363:815-830. 미소장
16 Lee, J.H., J.H. Im, K.M. Kim, and J. Heo. 2015. Change analysis of aboveground forest carbon stocks according to the land cover change using multi-temporal Landsat TM images and machine learning algorithms. J. Korean Assoc. Geogr. Inf. Stud. 18:81-99. 미소장
17 Lee, Y.H., M.S. Kim, S.J. Park, H.Y. Hwang, and S.H. Kim. 2020. Assessment of soil organic carbon fractions and stocks under different farming practice in a single maize cropping system. Korean J. Soil Sci. Fert. 53:626-634. 미소장
18 Lim, S.S., H.I. Yang, H.J. Park, S.I. Park, B.S. Seo, K.S. Lee, S.H. Lee, S.M. Lee, H.Y. Kim, and J.H. Ryu. 2020. Land-use management for sustainable rice production and carbon sequestration in reclaimed coastal tideland soils of South Korea: A review. Soil Sci. Plant Nutr. 66:60-75. 미소장
19 Nabiollahi, K., S. Eskandari, R. Taghizadeh-Mehrjardi, R. Kerry, and J. Triantafilis. 2019. Assessing soil organic carbon stocks under land-use change scenarios using random forest models. Carbon Manag. 10:63-77. 미소장
20 Nguyen, T.T., T.D. Pham, C.T. Nguyen, J. Delfos, R. Archibald, K.B. Dang, N.B. Hoang, W. Guo, and H.H. Ngo. 2022. A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion. Sci. Total Environ. 804:150187. 미소장
21 Park, H.J., B.S. Seo, Y.J. Jeong, H.I. Yang, S.I. Park, N.R. Baek, J.H. Kwak, and W.J. Choi. 2022. Soil salinity, fertility and carbon content, and rice yield of salt-affected paddy with different cultivation period in southwestern coastal area of South Korea. Soil Sci. Plant Nutr. https://doi.org/10.1080/00380768.2021.1967082. 미소장
22 Powers, J.S. and W.H. Schlesinger. 2002. Relationships among soil carbon distributions and biophysical factors at nested spatial scales in rain forests of northeastern Costa Rica. Geoderma 109:165-190. 미소장
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25 Sothe, C., A. Gonsamo, J. Arabian, and J. Snider. 2022. Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations. Geoderma 405:115402. 미소장
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28 Wang, B., J.M. Gray, C.M. Waters, M.R. Anwar, S.E. Orgill, A.L. Cowie, P. Feng, and D.L. Liu. 2022. Modelling and mapping soil organic carbon stocks under future climate change in south-eastern Australia. Geoderma 405:115442. 미소장
29 Wiesmeier, M., L. Urbanski, E. Hobley, B. Lang, M. von Lützow, E. Marin-Spiotta, B. van Wesemael, E. Rabot, M. Ließ, and N. Garcia-Franco. 2019. Soil organic carbon storage as a key function of soils-A review of drivers and indicators at various scales. Geoderma 333:149-162. 미소장
30 Yao, D., X. Zhan, and C.K. Kwoh. 2019. An improvved random forest-based computational model for predicting novel miRNA-disease associations. BMC Bioinformatics 20:1-14. 미소장
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