Skip to main content

Advertisement

SpringerLink
Log in

Machine-learning-based prediction and key factor identification of the organic carbon in riverine floodplain soils with intensive agricultural practices

  • Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Riverine floodplain soils are important reservoirs of organic carbon (OC) in terrestrial ecosystems because of their high biomass productivity and OC input via flood events. Substantial knowledge of the riverine floodplain soil OC distribution and its impacting variables are significant for predicting carbon sequestration and emission. This study aimed at (1) predicting SOC in riverine floodplain soils using the multiple linear regression (MLR), M5P, and random forest (RF) models, (2) comparing the model performances, and (3) revealing the significant variables controlling the spatial dynamic of riverine floodplain soils OC.

Materials and methods

In this study, 4227 topsoil samples (0–20 cm) from the Yangtze riverine floodplain were collected and analyzed for soil organic carbon (SOC) and other properties. We identified the key variables impacting SOC and predicted the SOC spatial distribution based on three predictive models (i.e., MLR, M5P, and RF), measured soil attributes, and land-use data.

Results and discussion

The study results indicated that total soil sulfur and nitrogen concentrations were the most important variables affecting the SOC distribution, followed by soil geochemical variables (e.g., Al2O3, Na2O, CaO, and Fe2O3), and alkalinity conditions. The M5P and RF models showed higher accuracy in predicting SOC compared with the MLR model. Although RF outperformed M5P in SOC prediction, RF was limited in revealing the relationships between SOC and environmental variables, restricting its interpretability. The land-use analysis highlighted that paddy soils were more conducive to maintaining high SOC concentration than upland soils.

Conclusions

We recommend using the RF and M5P models to efficiently predict the SOC distribution in riverine floodplain soils as RF could produce higher prediction accuracy and M5P can detect the splitting process and identify relevant thresholds for the regression. Paddy management is crucial for SOC sequestration and grain production. Therefore, it is recommended as an efficient approach to enhance soil fertility, mitigate climate change, and ensure food security.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Adams TM, Adams SN (2009) The effects of liming and soil pH on carbon and nitrog contained in the soil biomass. J Agric Sci 101:553–558

    Article  Google Scholar 

  • Adhikari K, Hartemink AE, Minasny B, Bou Kheir R, Greve MB, Greve MH (2014) Digital mapping of soil organic carbon contents and stocks in Denmark. PLoS One 9:e105519

    Article  CAS  Google Scholar 

  • An S, Huang Y, Zheng F, Yang J (2008) Aggregate characteristics during natural revegetation on the Loess Plateau. Pedosphere 18:809–816

    Article  CAS  Google Scholar 

  • Andersson S, Nilsson SI, Saetre P (2000) Leaching of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) in mor humus as affected by temperature and pH. Soil Biol Biochem 32:1–10

    Article  CAS  Google Scholar 

  • Bao S (2000) Soil agricultural chemistry analysis. China Agricultural Press (in Chineses), Beijing

    Google Scholar 

  • Behnood A, Behnood V, Modiri Gharehveran M, Alyamac KE (2017) Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm Constr Build Mater 142:99–207

  • Benke KK, Norng S, Robinson NJ, Chia K, Rees DB, Hopley J (2020) Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content. Geoderma 366:114210

    Article  CAS  Google Scholar 

  • Borrelli P, Paustian K, Panagos P, Jones A, Schütt B, Lugato E (2016) Effect of good agricultural and environmental conditions on erosion and soil organic carbon balance: a national case study. Land Use Policy 50:408–421

    Article  Google Scholar 

  • Breiman L (2001) Random Forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Chuai X, Huang X, Lai L, Wang W, Peng J, Zhao R (2013) Land use structure optimization based on carbon storage in several regional terrestrial ecosystems across China. Environ Sci Pol 25:50–61

    Article  Google Scholar 

  • Craft C, Vymazal J, Kröpfelová L (2018) Carbon sequestration and nutrient accumulation in floodplain and depressional wetlands. Ecol Eng 114:137–145

    Article  Google Scholar 

  • Cui J, Liu C, Li Z, Wang L, Chen X, Ye Z, Fang C (2012) Long-term changes in topsoil chemical properties under centuries of cultivation after reclamation of coastal wetlands in the Yangtze Estuary, China. Soil Tillage Res 123:50–60

    Article  Google Scholar 

  • Davidson EA, Janssens IA (2006) Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440:165–173

    Article  CAS  Google Scholar 

  • Deng X, Zhan Y, Wang F, Ma W, Ren Z, Chen X, Qin F, Long W, Zhu Lv X (2016) Soil organic carbon of an intensively reclaimed region in China: Current status and carbon sequestration potential Sci Total Environ 565:539–546

  • Doetterl S, Stevens A, Six J, Merckx R, Van Oost K, Pinto MC, Casanova-Katny A, Muñoz C, Boudin M, Venegas EZ (2015) Soil carbon storage controlled by interactions between geochemistry and climate. Nat Geosci 8:780–783

    Article  CAS  Google Scholar 

  • Drouin A, Saint-Laurent D, Lavoie L, Ouellet C (2011) High-precision elevation model to evaluate the spatial distribution of soil organic carbon in active floodplains. Wetlands 31:1151–1164

    Article  Google Scholar 

  • Du Laing G, Rinklebe J, Vandecasteele B, Meers E, Tack FMG (2009) Trace metal behaviour in estuarine and riverine floodplain soils and sediments: a review. Sci Total Environ 407:3972–3985

    Article  CAS  Google Scholar 

  • Forkuor G, Hounkpatin OK, Welp G, Thiel M (2017) High resolution mapping of soil properties using remote sensing variables in South-Western Burkina Faso: a comparison of machine learning and multiple linear regression models. PLoS One 12:e0170478

    Article  CAS  Google Scholar 

  • Fornara DA, Tilman D (2008) Plant functional composition influences rates of soil carbon and nitrogen accumulation. J Ecol 96:314–322

    Article  CAS  Google Scholar 

  • Gami SK, Lauren JG, Duxbury JM (2009) Soil organic carbon and nitrogen stocks in Nepal long-term soil fertility experiments. Soil Tillage Res 106:95–103

    Article  Google Scholar 

  • Gan Y, Hamel C, O’Donovan JT, Cutforth H, Zentner RP, Campbell CA, Niu Y, Poppy L (2015) Diversifying crop rotations with pulses enhances system productivity. Sci Rep 5:14625

    Article  CAS  Google Scholar 

  • Giannetta B, Zaccone C, Plaza C, Siebecker MG, Rovira P, Vischetti C, Sparks DL (2019) The role of Fe(III) in soil organic matter stabilization in two size fractions having opposite features. Sci Total Environ 653:667–674

    Article  CAS  Google Scholar 

  • Gillman GP, Sinclair DF, Beech TA (1986) Recovery of organic carbon by the walkley and black procedure in highly weathered soils. Commun Soil Sci Plant Anal 17:885–892

    Article  CAS  Google Scholar 

  • Grant CA, Mahli SS, Karamanos RE (2012) Sulfur management for rapeseed. Field Crop Res 128:119–128

    Article  Google Scholar 

  • Grimm R, Behrens T, Märker M, Elsenbeer H (2008) Soil organic carbon concentrations and stocks on Barro Colorado Island — digital soil mapping using Random Forests analysis. Geoderma 146:102–113

    Article  CAS  Google Scholar 

  • Grybos M, Davranche M, Gruau G, Petitjean P, Pédrot M (2009) Increasing pH drives organic matter solubilization from wetland soils under reducing conditions. Geoderma 154:13–19

    Article  CAS  Google Scholar 

  • Guidi C, Vesterdal L, Gianelle D, Rodeghiero M (2014) Changes in soil organic carbon and nitrogen following forest expansion on grassland in the Southern Alps. For Ecol Manag 328:103–116

    Article  Google Scholar 

  • Guo J, Liu X, Zhang Y, Shen J, Han W, Zhang W, Christie P, Goulding K, Vitousek P, Zhang F (2010) Significant acidification in major Chinese croplands. Science 327:1008–1010

    Article  CAS  Google Scholar 

  • Guo PT, Li MF, Luo W, Tang QF, Liu ZW, Lin ZM (2015) Digital mapping of soil organic matter for rubber plantation at regional scale: an application of random forest plus residuals kriging approach. Geoderma 237–238:49–59

    Article  CAS  Google Scholar 

  • Hassink J (1997) The capacity of soils to preserve organic C and N by their association with clay and silt particles. Plant Soil 191:77–87

    Article  CAS  Google Scholar 

  • Hiltbrunner D, Zimmermann S, Hagedorn F (2013) Afforestation with Norway spruce on a subalpine pasture alters carbon dynamics but only moderately affects soil carbon storage. Biogeochemistry 115:251–266

    Article  CAS  Google Scholar 

  • Hongliang L, Mingsong Z, Binyin L, Ping Z, Longmei L (2019) Spatial prediction of soil properties based on random forest model in Anhui Province. Soils 51:602–608

    Google Scholar 

  • Hossain MS, Hossain A, Sarkar MAR, Jahiruddin M, Teixeira da Silva JA, Hossain MI (2016) Productivity and soil fertility of the rice–wheat system in the High Ganges River Floodplain of Bangladesh is influenced by the inclusion of legumes and manure. Agric Ecosyst Environ 218:40–52

    Article  Google Scholar 

  • IBM Inc. 2009. SPSS for Windows, version 22.0. Chicago, IBM Inc

  • Jelinski NA, Kucharik CJ (2009) Land-use effects on soil carbon and nitrogen on a U.S. Midwestern Floodplain. Soil Sci Soc Am J 73:217–225

    Article  CAS  Google Scholar 

  • Jiménez-Morillo NT, González-Pérez JA, Jordán A, Zavala LM, de la Rosa JM, Jiménez-González MA, González-Vila FJ (2016) Organic matter fractions controlling soil water repellency in sandy soils from the Doñana National Park (Southwestern Spain). Land Degrad Dev 27:1413–1423

    Article  Google Scholar 

  • Kögel-Knabner I, Amelung W, Cao Z, Fiedler S, Frenzel P, Jahn R, Kalbitz K, Kölbl A, Schloter M (2010) Biogeochemistry of paddy soils. Geoderma 157:1–14

    Article  CAS  Google Scholar 

  • Kölbl A, Schad P, Jahn R, Amelung W, Bannert A, Cao Z, Fiedler S, Kalbitz K, Lehndorff E, Müller-Niggemann C (2014) Accelerated soil formation due to paddy management on marshlands (Zhejiang Province, China). Geoderma 228:67–89

    Article  CAS  Google Scholar 

  • Knorr W, Prentice I, House J, Holland E (2005) Long-term sensitivity of soil carbon turnover to warming. Nature 433:298–301

    Article  CAS  Google Scholar 

  • Kukal SS, Rehana R, Benbi DK (2009) Soil organic carbon sequestration in relation to organic and inorganic fertilization in rice–wheat and maize–wheat systems. Soil Tillage Res 102:87–92

    Article  Google Scholar 

  • Lal R (2003) Soil erosion and the global carbon budget. Environ Int 29:437–450

    Article  CAS  Google Scholar 

  • Lal R (2019) Accelerated soil erosion as a source of atmospheric CO2. Soil Tillage Res 188:35–40

    Article  Google Scholar 

  • Lark RM (1999) Soil–landform relationships at within-field scales: an investigation using continuous classification. Geoderma 92(3):141–165

  • Li Z, Liu C, Dong Y, Chang X, Nie X, Liu L, Xiao H, Lu Y, Zeng G (2017) Response of soil organic carbon and nitrogen stocks to soil erosion and land use types in the Loess hilly–gully region of China. Soil Tillage Res 166:1–9

    Article  Google Scholar 

  • Liang Q, Chen H, Gong Y, Fan M, Yang H, Lal R, Kuzyakov Y (2012) Effects of 15 years of manure and inorganic fertilizers on soil organic carbon fractions in a wheat-maize system in the North China Plain. Nutr Cycl Agroecosyst 92:21–33

    Article  Google Scholar 

  • Lu RK (2000) Methods of Soil Agrochemistry Analysis. Agricultural Science and Technology Press, Beijing (in Chinese)

    Google Scholar 

  • Lujiu L, Sheng SQ, Sun L, Sun Y, Li H, Xu S (2003) Research progress on soil sulfur fertility and crop sulfur nutrition. J Anhui Agric Sci 31:188–190 (in Chinese)

    Google Scholar 

  • Luo J, Xing X, Wu Y, Zhang W, Chen RS (2018) Spatio-temporal analysis on built-up land expansion and population growth in the Yangtze River Delta Region, China: From a coordination perspective. Appl Geogr 96:98–108

    Article  Google Scholar 

  • Mao D, Wang Z, Li L, Miao Z, Ma W, Song C, Ren C, Jia M (2015) Soil organic carbon in the Sanjiang Plain of China: storage, distribution and controlling factors. Biogeosciences 12:1635–1645

    Article  Google Scholar 

  • Margenot AJ, Calderón FJ, Bowles TM, Parikh SJ, Jackson LE (2015) Soil organic matter functional group composition in relation to organic carbon, nitrogen, and phosphorus fractions in organically managed tomato fields. Soil Sci Soc Am J 79:772–782

    Article  CAS  Google Scholar 

  • Matus F, Rumpel C, Neculman R, Panichini M, Mora ML (2014) Soil carbon storage and stabilisation in andic soils: a review. Catena 120:102–110

    Article  CAS  Google Scholar 

  • Mayer S, Kölbl A, Völkel J, Kögel-Knabner I (2019) Organic matter in temperate cultivated floodplain soils: light fractions highly contribute to subsoil organic carbon. Geoderma 337:679–690

    Article  CAS  Google Scholar 

  • McBratney AB, Mendonça Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117:3–52

    Article  Google Scholar 

  • McLatchey GP, Reddy KR (1998) Regulation of organic matter decomposition and nutrient release in a wetland soil. J Environ Qual 27:1268–1274

    Article  CAS  Google Scholar 

  • Mitchell MJ, David MB, Maynard DG, Telang SA (1986) Sulfur constituents in soils and streams of a watershed in the Rocky Mountains of Alberta. Can J for Res 16:315–320

    Article  CAS  Google Scholar 

  • Morellos A, Pantazi X-E, Moshou D, Alexandridis T, Whetton R, Tziotzios G, Wiebensohn J, Bill R, Mouazen AM (2016) Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosyst Eng 152:104–116

    Article  Google Scholar 

  • Oades JM (1984) Soil organic matter and structural stability: mechanisms and implications for management. Plant Soil 76:319–337

    Article  CAS  Google Scholar 

  • Peng S, Tang Q, Zou Y (2009) Current status and challenges of rice production in China. Plant Prod Sci 12:3–8

    Article  Google Scholar 

  • Poeplau C, Don A, Vesterdal L, Leifeld J, Van Wesemael B, Schumacher J, Gensior A (2011) Temporal dynamics of soil organic carbon after land-use change in the temperate zone–carbon response functions as a model approach. Glob Chang Biol 17:2415–2427

    Article  Google Scholar 

  • Post WM, Kwon KC (2000) Soil carbon sequestration and land-use change: processes and potential. Glob Chang Biol 6:317–327

    Article  Google Scholar 

  • Quinlan JR (1992) Learning with continuous classes. Proceedings the Fifth Australian Joint Conference on Artificial Intelligence, Singapore. World Scientific, pp 343–348

    Google Scholar 

  • R Development Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Austria (ISBN: 3–900051–07–0). http://www.R-project.org

  • Ricker MC, Lockaby BG (2015) Soil organic carbon stocks in a large eutrophic floodplain forest of the Southeastern Atlantic Coastal Plain, USA. Wetlands 35:291–301

    Article  Google Scholar 

  • Rivas Z, Medina HLD, Gutiérrez J, Gutiérrez E (2000) Nitrogen and phosphorus levels in sediments from tropical catatumbo river (Venezuela). Water Air Soil Pollut 117:27–37

    Article  CAS  Google Scholar 

  • Rossel RAV, Behrens T (2010) Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158:46–54

    Article  CAS  Google Scholar 

  • Sarker JR, Singh BP, Dougherty WJ, Fang Y, Badgery W, Hoyle FC, Dalal RC, Cowie AL (2018) Impact of agricultural management practices on the nutrient supply potential of soil organic matter under long-term farming systems. Soil Tillage Res 175:71–81

    Article  Google Scholar 

  • Seeber J, Seeber GUH, Kössler W, Langel R, Scheu S, Meyer E (2005) Abundance and trophic structure of macro-decomposers on alpine pastureland (Central Alps, Tyrol): effects of abandonment of pasturing. Pedobiologia 49:221–228

    Article  Google Scholar 

  • Selige T, Böhner J, Schmidhalter U (2006) High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures. Geoderma 136:235–244

    Article  CAS  Google Scholar 

  • Sihag P, Keshavarzi A, Kumar V (2019a) Comparison of different approaches for modeling of heavy metal estimations. SN Appl Sci 1:780

    Article  CAS  Google Scholar 

  • Sihag P, Mohsenzadeh Karimi S, Angelaki A (2019b) Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity. Appl Water Sci 9:129

    Article  Google Scholar 

  • Six J, Elliott E, Paustian K (2000) Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture. Soil Biol Biochem 32:2099–2103

    Article  CAS  Google Scholar 

  • Stevenson FJ, Cole MA (1986) Cycles of soils: carbon, nitrogen, phosphorus, sulfur, micronutrients. Q Rev Biol 144:4

    Google Scholar 

  • Sorbo B (1987) Sulfate: turbidimetric and nephelometric methods. Methods in Enzymology. Academic, pp 3–6

  • Sutfin NA, Wohl EE, Dwire KA (2016) Banking carbon: a review of organic carbon storage and physical factors influencing retention in floodplains and riparian ecosystems. Earth Surf Process Landf 41:38–60

    Article  Google Scholar 

  • Tang J, Riley WJ (2015) Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nat Clim Chang 5:56–60

    Article  CAS  Google Scholar 

  • Tang Z, Xu W, Zhou G, Bai Y, Li J, Tang X, Chen D, Liu Q, Ma W, Xiong G, He H, He N, Guo Y, Guo Q, Zhu J, Han W, Hu H, Fang J, Xie Z (2018) Patterns of plant carbon, nitrogen, and phosphorus concentration in relation to productivity in China’s terrestrial ecosystems. Proc Natl Acad Sci USA 115:4033–4038

    Article  Google Scholar 

  • The University of Waikato (2018) Waikato environment for knowledge analysis, version 3.8.3, Hamilton, New Zealand

  • Thomson BC, Tisserant E, Plassart P, Uroz S, Griffiths RI, Hannula SE, Buée M, Mougel C, Ranjard L, Van Veen JA, Martin F, Bailey MJ, Lemanceau P (2015) Soil conditions and land use intensification effects on soil microbial communities across a range of European field sites. Soil Biol Biochem 88:403–413

    Article  CAS  Google Scholar 

  • Tong C, Hall CAS, Wang H (2003) Land use change in rice, wheat and maize production in China (1961–1998). Agr Ecosyst Environ 95:523–536

    Article  Google Scholar 

  • Torn MS, Trumbore SE, Chadwick OA, Vitousek PM, Hendricks DM (1997) Mineral control of soil organic carbon storage and turnover. Nature 389:170–173

    Article  CAS  Google Scholar 

  • van Puijenbroek PJTM, Beusen AHW, Bouwman AF (2019) Global nitrogen and phosphorus in urban waste water based on the Shared Socio-economic pathways. J Environ Manag 231:446–456

    Article  CAS  Google Scholar 

  • Viscarra Rossel RA, Brus DJ, Lobsey C, Shi Z, McLachlan G (2016) Baseline estimates of soil organic carbon by proximal sensing: comparing design-based, model-assisted and model-based inference. Geoderma 265:152–163

    Article  CAS  Google Scholar 

  • Vitti C, Stellacci AM, Leogrande R, Mastrangelo M, Cazzato E, Ventrella D (2016) Assessment of organic carbon in soils: a comparison between the Springer-Klee wet digestion and the dry combustion methods in Mediterranean soils (Southern Italy). Catena 137:113–119

    Article  CAS  Google Scholar 

  • Wang X, Piao S, Ciais P, Friedlingstein P, Myneni RB, Cox P, Heimann M, Miller J, Peng S, Wang T (2014) A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506:212–215

    Article  CAS  Google Scholar 

  • Wang X, Zhu B, Hua K, Luo Y, Zhang J, Zhang A (2013) Assessment of soil organic carbon stock in the upper Yangtze River basin. J Mt Sci 10:866–872

    Article  Google Scholar 

  • Watkins D, Nuruddin M, Hosur M, Tcherbi-Narteh A, Jeelani S (2015) Extraction and characterization of lignin from different biomass resources. J Mater Res Technol 4:26–32

    Article  CAS  Google Scholar 

  • Weiss SM, Indurkhya N (1995) Rule-based machine learning methods for functional prediction. J Artif Intell Res 3:383–403

    Article  Google Scholar 

  • Were K, Bui DT, Dick ØB, Singh BR (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape . Ecol Indic 52:394–403

    Article  CAS  Google Scholar 

  • Wheater H, Evans E (2009) Land use, water management and future flood risk. Land Use Policy 26:S251–S264

    Article  Google Scholar 

  • Wiesmeier M, Barthold F, Blank B, Kögel-Knabner I (2011) Digital mapping of soil organic matter stocks using random forest modeling in a semi-arid steppe ecosystem. Plant Soil 340:7–24

    Article  CAS  Google Scholar 

  • Wiesmeier M, Hübner R, Barthold F, Spörlein P, Geuß U, Hangen E, Reischl A, Schilling B, Lützow MV, Kögel-Knabner I (2013) Amount, distribution and driving factors of soil organic carbon and nitrogen in cropland and grassland soils of southeast Germany (Bavaria). Agric Ecosyst Environ 176:39–52

    Article  CAS  Google Scholar 

  • Wiesmeier M, Urbanski L, Hobley E, Lang B, von Lützow M, Marin-Spiotta E, van Wesemael B, Rabot E, Ließ M, Garcia-Franco N, Wollschläger U, Vogel H-J Kögel-Knabner I (2019) Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales. Geoderma 333:149–162

  • Wiseman CLS, Püttmann W (2006) Interactions between mineral phases in the preservation of soil organic matter. Geoderma 134:109–118

    Article  CAS  Google Scholar 

  • Wu G, Kechavarzi C, Li X, Wu S, Pollard SJT, Sui H, Coulon F (2013) Machine learning models for predicting PAHs bioavailability in compost amended soils. Chem Eng J 223:747–754

    Article  CAS  Google Scholar 

  • Xu X, Hu H, Tan Y, Yang G, Zhu P, Jiang B (2019) Quantifying the impacts of climate variability and human interventions on crop production and food security in the Yangtze River Basin, China, 1990–2015. Sci Total Environ 665:379–389

    Article  CAS  Google Scholar 

  • Yu X, Ding S, Zou Y, Xue Z, Lyu X, Wang G (2018) Review of rapid transformation of floodplain wetlands in Northeast China: roles of human development and global environmental change. Chin Geogr Sci 28:654–664

    Article  Google Scholar 

  • Zehetner F, Lair GJ, Gerzabek MH (2009) Rapid carbon accretion and organic matter pool stabilization in riverine floodplain soils. Glob Biogeochem Cycles 23(4), GB4004

  • Zhan Y, Luo Y, Deng X, Zhang K, Zhang M, Grieneisen ML, Di B (2018) Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal Kriging model. Environ Sci Technol 52:4180–4189

    Article  CAS  Google Scholar 

  • Zhang G-L, Liu F, Song X-D (2017a) Recent progress and future prospect of digital soil mapping: a review. J Integr Agric 16:2871–2885

    Article  Google Scholar 

  • Zhang H, Wu P, Fan M, Zheng S, Wu J, Yang X, Zhang M, Yin A, Gao C (2018) Dynamics and driving factors of the organic carbon fractions in agricultural land reclaimed from coastal wetlands in eastern China. Ecol Indic 89:639–647

    Article  CAS  Google Scholar 

  • Zhang H, Wu P, Yin A, Yang X, Zhang M, Gao C (2017b) Prediction of soil organic carbon in an intensively managed reclamation zone of eastern China: a comparison of multiple linear regressions and the random forest model. Sci Total Environ 592:704–713

    Article  CAS  Google Scholar 

  • Zhang H, Wu P, Yin A, Yang X, Zhang X, Zhang M, Gao C (2016) Organic carbon and total nitrogen dynamics of reclaimed soils following intensive agricultural use in eastern China. Agric Ecosyst Environ 235:193–203

    Article  CAS  Google Scholar 

  • Zhao Y, Wang M, Hu S, Zhang X, Ouyang Z, Zhang G, Huang B, Zhao S, Wu J, Xie D, Zhu B, Yu D, Pan X, Xu S, Shi X (2018) Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proc Natl Acad Sci USA 115:4045–4050

    Article  CAS  Google Scholar 

  • Zhou P, Pan G, Li L, Zhang X (2009) SOC enhancement in major types of paddy soils in a long-term agro-ecosystem experiment in South China. V. Relationship between carbon input and soil carbon sequestration. Sci Agric Sin 42:4260–4268 (In Chinese with English abstract)

    CAS  Google Scholar 

Download references

Acknowledgements

This study was funded by the China Geological Survey (1212010310305), the National Natural Science Foundation of China (41907064), and the Open Fund of Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Natural Resource (2019CZEPK05).

Author information

Authors and Affiliations

  1. School of Geography and Ocean Science, Nanjing University, Nanjing, 210046, China

    Jie Chen, Huan Zhang, Manman Fan & Chao Gao

  2. School of Chemistry and Life Science, Nanjing University Jinling College, Nanjing, 210089, China

    Jie Chen

  3. School of Marine Science and Engineering, Nanjing Normal University, Nanjing, 210023, China

    Huan Zhang

  4. Anhui Geological Survey, Hefei, 230022, China

    Furong Chen

Authors
  1. Jie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manman Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Furong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huan Zhang.

Additional information

Responsible editor: Jun Zhou

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 854 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Zhang, H., Fan, M. et al. Machine-learning-based prediction and key factor identification of the organic carbon in riverine floodplain soils with intensive agricultural practices. J Soils Sediments 21, 2896–2907 (2021). https://doi.org/10.1007/s11368-021-02987-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11368-021-02987-y

Keywords

Search

Navigation