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RESEARCH ARTICLE

Digital mapping of topsoil pH by random forest with residual kriging (RFRK) in a hilly region

Lei Wang https://orcid.org/0000-0002-9844-6136 A , Wei Wu B and Hong-Bin Liu A C
+ Author Affiliations
- Author Affiliations

A College of Resources and Environment, Southwest University, Beibei, Chongqing 400715, China.

B College of Computer and Information Science, Southwest University, Beibei, Chongqing 400715, China.

C Corresponding author. Email: lhbin@swu.edu.cn

Soil Research 57(4) 387-396 https://doi.org/10.1071/SR18319
Submitted: 22 October 2018  Accepted: 1 March 2019   Published: 9 April 2019

Abstract

Soil pH is a vital attribute of soil fertility. The accurate and efficient prediction of soil pH can provide the necessary basic information for agricultural development. In the present study, random forest with residual kriging (RFRK) was used to predict soil pH based on stratum, climate, vegetation and topography in a hilly region. The performance of RFRK was compared with those of the classification and regression tree (CART) and the random forest (RF). Comparative results showed that RFRK provided the best performance. The corresponding values of Lin’s concordance correlation coefficient, coefficient of determination, mean absolute error and root mean square error were as follows: 0.70, 0.51, 0.44 and 0.61 for CART; 0.80, 0.70, 0.34 and 0.48 for RF; and 0.88, 0.80, 0.25 and 0.39 for RFRK. Stratum and average annual temperature were the most important factors affecting the soil pH in the study area. Results indicate that RFRK is a feasible and reliable tool for predicting soil pH in hilly regions.

Additional keywords: classification and regression tree; digital soil mapping; soil pH.


References

Andrade SFD, Mendonça-Santos MDL (2016) Predição da fertilidade do solo do polo agrícola do Rio de Janeiro por meio de modelagem solo x paisagem. Pesquisa Agropecuária Brasileira 51, 1386–1395.
Predição da fertilidade do solo do polo agrícola do Rio de Janeiro por meio de modelagem solo x paisagem.Crossref | GoogleScholarGoogle Scholar | [In Portuguese with an English abstract]

Andriesse JP, Schelhaas RM (1987) A monitoring study on nutrient cycles in soils used for shifting cultivation under various climatic conditions in tropical Asia. III. The effects of land clearing through burning on fertility level. Agriculture, Ecosystems & Environment 19, 311–332.
A monitoring study on nutrient cycles in soils used for shifting cultivation under various climatic conditions in tropical Asia. III. The effects of land clearing through burning on fertility level.Crossref | GoogleScholarGoogle Scholar |

Band LE, Moore ID (1995) Scale: landscape attributes and geographical information systems. Hydrological Processes 9, 401–422.
Scale: landscape attributes and geographical information systems.Crossref | GoogleScholarGoogle Scholar |

Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin 24, 43–69.
A physically based, variable contributing area model of basin hydrology.Crossref | GoogleScholarGoogle Scholar |

Breiman L (2001) Random forests. Machine Learning 45, 5–32.
Random forests.Crossref | GoogleScholarGoogle Scholar |

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) ‘Classification and regression trees.’ (Chapman & Hall/CRC: Boca Raton, FL)

Chen J, Jonsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sensing of Environment 91, 332–344.
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter.Crossref | GoogleScholarGoogle Scholar |

Dharumarajan S, Hegde R, Singhb SK (2017) Spatial prediction of major soil properties using random forest techniques – a case study in semi-arid tropics of South India. Geoderma Regional 10, 154–162.
Spatial prediction of major soil properties using random forest techniques – a case study in semi-arid tropics of South India.Crossref | GoogleScholarGoogle Scholar |

Dokuchaev VV (1883) ‘The Russian chernozem report to the free economic society.’ (Imperial University of St. Petersburg: St. Petersburg) [in Russian]

Fick SE, Hijmans RJ (2017) Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas International Journal of Climatology 37, 4302–4315.
Worldclim 2: New 1-km spatial resolution climate surfaces for global land areasCrossref | GoogleScholarGoogle Scholar |

Filippi P, Cattle SR, Bishop TFA, Odeh IOA, Pringle MJ (2018) Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models. Geoderma 322, 149–162.
Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models.Crossref | GoogleScholarGoogle Scholar |

Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89, 1–45.
Geostatistics in soil science: state-of-the-art and perspectives.Crossref | GoogleScholarGoogle Scholar |

Gray JM, Bishop TFA, Wilford JR (2016) Lithology and soil relationships for soil modelling and mapping. Catena 147, 429–440.
Lithology and soil relationships for soil modelling and mapping.Crossref | GoogleScholarGoogle Scholar |

Guo PT, Wu W, Sheng QK, Li MF, Liu HB, Wang ZY (2013) Prediction of soil organic matter using artificial neural network and topographic indicators in hilly areas. Nutrient Cycling in Agroecosystems 95, 333–344.
Prediction of soil organic matter using artificial neural network and topographic indicators in hilly areas.Crossref | GoogleScholarGoogle 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.
Digital mapping of soil organic matter for rubber plantation at regional scale: an application of random forest plus residuals kriging approach.Crossref | GoogleScholarGoogle Scholar |

Heggelund LR, Diez-Ortiz M, Lofts S, Lahive E, Jurkschat K, Wojnarowicz J, Cedergreen N, Spurgeon D, Svendsen C (2014) Soil pH effects on the comparative toxicity of dissolved zinc, non-nano and nano ZnO to the earthworm Eisenia fetida. Nanotoxicology 8, 559–572.
Soil pH effects on the comparative toxicity of dissolved zinc, non-nano and nano ZnO to the earthworm Eisenia fetida.Crossref | GoogleScholarGoogle Scholar | 23739012PubMed |

Helyar KR, Cregan PD, Godyn DL (1990) Soil acidity in New South Wales – current pH values and estimates of acidification rates. Australian Journal of Soil Research 28, 523–537.
Soil acidity in New South Wales – current pH values and estimates of acidification rates.Crossref | GoogleScholarGoogle Scholar |

Hengl T, Heuvelink GBM, Stein A (2004) A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma 120, 75–93.
A generic framework for spatial prediction of soil variables based on regression-kriging.Crossref | GoogleScholarGoogle Scholar |

Jenny H (1941) ‘Factors of soil formation.’ (McGraw-Hill: New York, NY)

Jenny H (1980) ‘The soil resources.’ (Springer-Verlag: New York, NY)

Kassai P, Sisák I (2018) The role of geology in the spatial prediction of soil properties in the watershed of Lake Balaton, Hungary. Geologia Croatica 71, 29–39.
The role of geology in the spatial prediction of soil properties in the watershed of Lake Balaton, Hungary.Crossref | GoogleScholarGoogle Scholar |

Kim Y, Park NW (2016) Spatial disaggregation of coarse scale satellite-based precipitation data using machine learning model and residual kriging. Journal of Climate Research 11, 183–195.
Spatial disaggregation of coarse scale satellite-based precipitation data using machine learning model and residual kriging.Crossref | GoogleScholarGoogle Scholar | [in Korean with English abstract]

Kumar S, Lal R, Liu D (2012) A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma 189–190, 627–634.
A geographically weighted regression kriging approach for mapping soil organic carbon stock.Crossref | GoogleScholarGoogle Scholar |

Lark RM (1999) Soil–landform relationships at within-field scales: an investigation using continuous classification. Geoderma 92, 141–165.
Soil–landform relationships at within-field scales: an investigation using continuous classification.Crossref | GoogleScholarGoogle Scholar |

Lauber CL, Hamady M, Knight R, Fierer N (2009) Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Applied and Environmental Microbiology 75, 5111–5120.
Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale.Crossref | GoogleScholarGoogle Scholar | 19502440PubMed |

Li J, Heap AD, Potter P, Daniell JJ (2011) Application of machine learning methods to spatial interpolation of environmental variables. Environmental Modelling & Software 26, 1647–1659.
Application of machine learning methods to spatial interpolation of environmental variables.Crossref | GoogleScholarGoogle Scholar |

Li ZW, Wang JH, Tang H, Huang CQ, Yang F, Chen BR, Wang X, Xin XP, Ge Y (2016) Predicting grassland leaf area index in the meadow steppes of northern China: a comparative study of regression approaches and hybrid geostatistical methods. Remote Sensing 8, 632
Predicting grassland leaf area index in the meadow steppes of northern China: a comparative study of regression approaches and hybrid geostatistical methods.Crossref | GoogleScholarGoogle Scholar |

Ließ M, Glaser B, Huwe B (2012) Uncertainty in the spatial prediction of soil texture. Geoderma 170, 70–79.
Uncertainty in the spatial prediction of soil texture.Crossref | GoogleScholarGoogle Scholar |

Lin LIK (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268.
A concordance correlation coefficient to evaluate reproducibility.Crossref | GoogleScholarGoogle Scholar |

Lin LIK (2000) A note on the concordance correlation coefficient. Biometrics 56, 324–325.

Liu ZP, Shao MA, Wang YQ (2013) Large-scale spatial interpolation of soil pH across the loess plateau, China. Environmental Earth Sciences 69, 2731–2741.
Large-scale spatial interpolation of soil pH across the loess plateau, China.Crossref | GoogleScholarGoogle Scholar |

Liu Y, Cao G, Zhao N, Mulligan K, Ye X (2018) Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach. Environmental Pollution 235, 272–282.
Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach.Crossref | GoogleScholarGoogle Scholar | 29291527PubMed |

McBratney AB, Odeh IOA, Bishop TFA, Dunbar MS, Shatar TM (2000) An overview of pedometric techniques for use in soil survey. Geoderma 97, 293–327.
An overview of pedometric techniques for use in soil survey.Crossref | GoogleScholarGoogle Scholar |

McBratney AB, Santos MLM, Minasny B (2003) On digital soil mapping. Geofísica Internacional 117, 3–52.

Mosleh Z, Salehi MH, Jafari A, Borujeni IE, Mehnatkesh A (2016) The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environmental Monitoring and Assessment 188, 195
The effectiveness of digital soil mapping to predict soil properties over low-relief areas.Crossref | GoogleScholarGoogle Scholar | 26920129PubMed |

Nielsen DR, Bouma J (Eds) (1985) ‘Soil spatial variability: proceedings of a workshop of the ISSS and the SSSA, Las Vegas, USA, 30 November–1 December 1984.’ (Pudoc: Wageningen, The Netherlands)

Ou Y, Rousseau AN, Wang L, Yan B (2017) Spatio-temporal patterns of soil organic carbon and pH in relation to environmental factors—a case study of the black soil region of northeastern China. Agriculture, Ecosystems & Environment 245, 22–31.
Spatio-temporal patterns of soil organic carbon and pH in relation to environmental factors—a case study of the black soil region of northeastern China.Crossref | GoogleScholarGoogle Scholar |

Pahlavan-Rad MR, Akbarimoghaddam A (2018) Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). Catena 160, 275–281.
Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran).Crossref | GoogleScholarGoogle Scholar |

Schwamberger EC, Sims JL (1991) Effects of soil pH, nitrogen source, phosphorus, and molybdenum on early growth and mineral nutrition of burley tobacco. Communications in Soil Science and Plant Analysis 22, 641–657.
Effects of soil pH, nitrogen source, phosphorus, and molybdenum on early growth and mineral nutrition of burley tobacco.Crossref | GoogleScholarGoogle Scholar |

Smith JL, Halvorson JJ, Bolton H (2002) Soil properties and microbial activity across a 500 m elevation gradient in a semi-arid environment. Soil Biology & Biochemistry 34, 1749–1757.
Soil properties and microbial activity across a 500 m elevation gradient in a semi-arid environment.Crossref | GoogleScholarGoogle Scholar |

Szatmári G, Pásztor L (2019) Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms. Geoderma 337, 1329–1340.
Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms.Crossref | GoogleScholarGoogle Scholar |

Tan X, Guo PT, Wu W, Li MF, Liu HB (2017) Prediction of soil properties by using geographically weighted regression at a regional scale. Soil Research 55, 318–331.
Prediction of soil properties by using geographically weighted regression at a regional scale.Crossref | GoogleScholarGoogle Scholar |

Tu C, He T, Lu X, Luo Y, Smith P (2018) Extent to which pH and topographic factors control soil organic carbon level in dry farming cropland soils of the mountainous region of southwest China. Catena 163, 204–209.
Extent to which pH and topographic factors control soil organic carbon level in dry farming cropland soils of the mountainous region of southwest China.Crossref | GoogleScholarGoogle Scholar |

Tziachris P, Aschonitis V, Chatzistathis T, Papadopoulou M (2019) Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters. Catena 174, 206–216.
Assessment of spatial hybrid methods for predicting soil organic matter using DEM derivatives and soil parameters.Crossref | GoogleScholarGoogle Scholar |

Vaysse K, Lagacherie P (2015) Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France). Geoderma Regional 4, 20–30.
Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France).Crossref | GoogleScholarGoogle Scholar |

Viscarra Rossel RA, Webster R, Kidd D (2014) Mapping gamma radiation and its uncertainty from weathering products in a Tasmanian landscape with a proximal sensor and random forest kriging. Earth Surface Processes and Landforms 39, 735–748.
Mapping gamma radiation and its uncertainty from weathering products in a Tasmanian landscape with a proximal sensor and random forest kriging.Crossref | GoogleScholarGoogle Scholar |

Wu W, Li AD, He XH, Ma R, Liu HB, Lv JK (2018) A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China. Computers and Electronics in Agriculture 144, 86–93.
A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China.Crossref | GoogleScholarGoogle Scholar |

Xi CF, Zhu KG, Zhou MZ, Du GH, Li XR, Zhang SY, Yang BQ, Hou CQ, Tang JC, Zhou CH (1998) ‘Soils of China.’ (Chinese Agriculture Press: Beijing) [in Chinese]

Ye X, Li H, Ma Y, Wu L (2014) The bioaccumulation of Cd in rice grains in paddy soils as affected and predicted by soil properties. Journal of Soils and Sediments 14, 1407–1416.
The bioaccumulation of Cd in rice grains in paddy soils as affected and predicted by soil properties.Crossref | GoogleScholarGoogle Scholar |

Zhu AX, Qi F, Moore A, Burt JE (2010) Prediction of soil properties using fuzzy membership values. Geoderma 158, 199–206.
Prediction of soil properties using fuzzy membership values.Crossref | GoogleScholarGoogle Scholar |