PlantProbs.net. Nickel in plants and soil https://plantprobs.net/plant/nutrientImbalances/sodium.html (accessed Apr 28, 2021).
Guodong Liu, E. H. Simonne, and Y. L. Nickel Nutrition in Plants | EDIS. EDis 2011.
Liu, G. D. “A New Essential Mineral Element–Nickel.” Plants Nutr. Fertil. Sci. 2001.
Kabata-Pendias, A.; Mukherjee, A. Trace Elements from Soil to Human; 2007.
Kasprzak, K. S. Nickel advances in modern environmental toxicology. Environ. Toxicol. 11, 145–183 (1987).
Google Scholar
Cempel, M. & Nikel, G. Nickel: A review of its sources and environmental toxicology. Polish J. Environ. Stud. 15, 375–382 (2006).
Google Scholar
Bradl, H. B. Chapter Sources and origins of heavy metals. Interface Sci. Technol. 6, 1–27 (2005).
Google Scholar
Von Burg, R. Nickel and some nickel compounds. J. Appl. Toxicol. 17, 425–431 (1997).
Google Scholar
Freedman, B. & Hutchinson, T. C. Pollutant inputs from the atmosphere and accumulations in soils and vegetation near a nickel–copper smelter at Sudbury, Ontario, Canada. Can. J. Bot. 58(1), 108–132. https://doi.org/10.1139/b80-014 (1980).
Google Scholar
Manyiwa, T. et al. Heavy metals in soil, plants, and associated risk on grazing ruminants in the vicinity of Cu–Ni mine in Selebi-Phikwe, Botswana. Environ. Geochem. Health https://doi.org/10.1007/s10653-021-00918-x (2021).
Google Scholar
Kabata-Pendias. Kabata-Pendias A. 2011. Trace elements in soils and… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Kabata-Pendias+A.+2011.+Trace+elements+in+soils+and+plants.+4th+ed.+New+York+%28NY%29%3A+CRC+Press&btnG= (accessed Nov 24, 2020).
Almås, A., Singh, B., Agricultural, T. S.-N. J. of & 1995, undefined. The impact of nickel industry in Russia on concentrations of heavy metals in agricultural soils and grass in Soer-Varanger, Norway. agris.fao.org.
Nielsen, G. D. et al. Absorption and retention of nickel from drinking water in relation to food intake and nickel sensitivity. Toxicol. Appl. Pharmacol. 154, 67–75 (1999).
Google Scholar
Costa, M. & Klein, C. B. Nickel carcinogenesis, mutation, epigenetics, or selection. Environ. Health Perspect. 107, 2 (1999).
Google Scholar
Agyeman, P. C.; Ahado, S. K.; Borůvka, L.; Biney, J. K. M.; Sarkodie, V. Y. O.; Kebonye, N. M.; Kingsley, J. Trend Analysis of Global Usage of Digital Soil Mapping Models in the Prediction of Potentially Toxic Elements in Soil/Sediments: A Bibliometric Review. Environmental Geochemistry and Health. Springer Science and Business Media B.V. 2020. https://doi.org/10.1007/s10653-020-00742-9.
Minasny, B. & McBratney, A. B. Digital soil mapping: A brief history and some lessons. Geoderma 264, 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017 (2016).
Google Scholar
McBratney, A. B., Mendonça Santos, M. L. & Minasny, B. On digital soil mapping. Geoderma 117(1–2), 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4 (2003).
Google Scholar
Deutsch.C.V. Geostatistical Reservoir Modeling,… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=C.V.+Deutsch%2C+2002%2C+Geostatistical+Reservoir+Modeling%2C+Oxford+University+Press%2C+376+pages.+&btnG= (accessed Apr 28, 2021).
Olea, R. A. Geostatistics for engineers & earth scientists. Stoch. Environ. Res. Risk Assess. 14(3), 207–209. https://doi.org/10.1007/pl00009782 (2000).
Google Scholar
Gumiaux, C., Gapais, D. & Brun, J. P. Geostatistics applied to best-fit interpolation of orientation data. Tectonophysics 376(3–4), 241–259. https://doi.org/10.1016/j.tecto.2003.08.008 (2003).
Google Scholar
Wadoux, A. M. J. C., Minasny, B. & McBratney, A. B. Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth-Sci Rev. https://doi.org/10.1016/j.earscirev.2020.103359 (2020).
Google Scholar
Tan, K. et al. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 382, 120987. https://doi.org/10.1016/j.jhazmat.2019.120987 (2020).
Google Scholar
Sakizadeh, M., Mirzaei, R. & Ghorbani, H. Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran. Neural Comput. Appl. 28(11), 3229–3238. https://doi.org/10.1007/s00521-016-2231-x (2017).
Google Scholar
Vega, F. A., Matías, J. M., Andrade, M. L., Reigosa, M. J. & Covelo, E. F. Classification and regression trees (CARTs) for modelling the sorption and retention of heavy metals by soil. J. Hazard. Mater. 167(1–3), 615–624. https://doi.org/10.1016/j.jhazmat.2009.01.016 (2009).
Google Scholar
Sun, H. et al. Prediction of distribution of soil cd concentrations in Guangdong Province, China. Huanjing Kexue/Environmental Sci. 38(5), 2111–2124. https://doi.org/10.13227/j.hjkx.201611006 (2017).
Google Scholar
Woodcock, C. E. & Gopal, S. Fuzzy set theory and thematic maps: accuracy assessment and area estimation. Int. J. Geogr. Inf. Sci. 14(2), 153–172. https://doi.org/10.1080/136588100240895 (2000).
Google Scholar
Finke, P. A. Chapter 39 Quality assessment of digital soil maps: producers and users perspectives. Dev. Soil Sci. https://doi.org/10.1016/S0166-2481(06)31039-2 (2006).
Google Scholar
Pontius, R. G. & Cheuk, M. L. A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. Int. J. Geogr. Inf. Sci. 20(1), 1–30. https://doi.org/10.1080/13658810500391024 (2006).
Google Scholar
Grunwald, S. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152(3–4), 195–207. https://doi.org/10.1016/j.geoderma.2009.06.003 (2009).
Google Scholar
Nelson, M. A., Bishop, T. F. A., Triantafilis, J. & Odeh, I. O. A. An error budget for different sources of error in digital soil mapping. Eur. J. Soil Sci. 62, 417–430 (2011).
Google Scholar
McBratney, A. B., Minasny, B. & ViscarraRossel, R. Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis. Geoderma 136, 272–278 (2006).
Google Scholar
Stumpf, F. et al. Uncertainty-guided sampling to improve digital soil maps. CATENA 153, 30–38 (2017).
Google Scholar
Legates, D. R. & McCabe, G. J. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999).
Google Scholar
Sergeev, A. P. et al. High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging. AIP Conf. Proc. 2017, 1836. https://doi.org/10.1063/1.4981963 (2017).
Google Scholar
Subbotina, I. E. et al. Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area. AIP Conf. Proc. https://doi.org/10.1063/1.5045410 (2018).
Google Scholar
Tarasov, D. A., Buevich, A. G., Sergeev, A. P. & Shichkin, A. V. High variation topsoil pollution forecasting in the Russian subarctic: using artificial neural networks combined with residual kriging. Appl. Geochemistry 88, 188–197. https://doi.org/10.1016/j.apgeochem.2017.07.007 (2018).
Google Scholar
Tarasov, D.; Buevich, A.; Shichkin, A.; Subbotina, I.; Tyagunov, A.; Baglaeva, E. Chromium Distribution Forecasting Using Multilayer Perceptron Neural Network and Multilayer Perceptron Residual Kriging. In AIP Conference Proceedings; American Institute of Physics Inc., 2018; Vol. 1978, p 440019. https://doi.org/10.1063/1.5044048.
John, K. et al. Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur. CATENA 206, 2 (2021).
Google Scholar
Gribov, A. & Krivoruchko, K. Empirical Bayesian Kriging Implementation and Usage. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137290 (2020).
Google Scholar
Samsonova, V. P., Blagoveshchenskii, Y. N. & Meshalkina, Y. L. Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands. Eurasian Soil Sci. 50(3), 305–311. https://doi.org/10.1134/S1064229317030103 (2017).
Google Scholar
Fabijańczyk, P., Zawadzki, J. & Magiera, T. Magnetometric assessment of soil contamination in problematic area using empirical bayesian and indicator kriging: a case study in upper Silesia, Poland. Geoderma 308, 69–77. https://doi.org/10.1016/j.geoderma.2017.08.029 (2017).
Google Scholar
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 2, 1–16. https://doi.org/10.1007/s13762-020-03089-x (2021).
Google Scholar
Li, T. et al. Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes. Sci. Total Environ. 628–629, 1446–1459. https://doi.org/10.1016/j.scitotenv.2018.02.163 (2018).
Google Scholar
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.114065 (2020).
Google Scholar
Hossain Bhuiyan, M. A., Chandra Karmaker, S., Bodrud-Doza, M., Rakib, M. A. & Saha, B. B. Enrichment, sources and ecological risk mapping of heavy metals in agricultural soils of dhaka district employing SOM PMF and GIS Methods. Chemosphere https://doi.org/10.1016/j.chemosphere.2020.128339 (2021).
Google Scholar
Kebonye, N. M. et al. Self-organizing map artificial neural networks and sequential gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils. J. Geochemical Explor. 222, 106680. https://doi.org/10.1016/j.gexplo.2020.106680 (2021).
Google Scholar
Weather Spark. Average Weather in Frýdek-Místek, Czechia, Year Round – Weather Spark https://weatherspark.com/y/83671/Average-Weather-in-Frýdek-Místek-Czechia-Year-Round (accessed Sep 14, 2020).
Kozák, J. Soil Atlas of the Czech Republic. 2010, 150.
Vacek, O., Vašát, R. & Borůvka, L. Quantifying the pedodiversity-elevation relations. Geoderma 373, 114441. https://doi.org/10.1016/j.geoderma.2020.114441 (2020).
Google Scholar
Krivoruchko, K. Empirical Bayesian Kriging; 2012; Vol. Fall 2012.
Vapnik, V. The nature of statistical learning theory. Technometrics 38(4), 409. https://doi.org/10.2307/1271324 (1995).
Google Scholar
Li, Z., Zhou, M., Xu, L. J., Lin, H. & Pu, H. Training sparse SVM on the core sets of fitting-planes. Neurocomputing 130, 20–27. https://doi.org/10.1016/j.neucom.2013.04.046 (2014).
Google Scholar
Cherkassky, V.; Mulier, F. Learning from Data: Concepts, Theory, and Methods: Second Edition; 2006. https://doi.org/10.1002/9780470140529.
John, K. et al. Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land 9(12), 1–20. https://doi.org/10.3390/land9120487 (2020).
Google Scholar
Vohland, M., Besold, J., Hill, J. & Fründ, H. C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma 166(1), 198–205. https://doi.org/10.1016/j.geoderma.2011.08.001 (2011).
Google Scholar
Fraser, S. J.; Dickson, B. L. A New Method for Data Integration and Integrated Data Interpretation: Self-Organising Maps; 2007.
Melssen, W. J.; Smits, J. R. M.; Buydens, L. M. C.; Kateman, G. Using Artificial Neural Networks for Solving Chemical Problems Part II. Kohonen Self-Organising Feature Maps and Hopfield Networks. Chemometrics and Intelligent Laboratory Systems. Elsevier, Amsterdam, 1, 1994, pp 267–291. https://doi.org/10.1016/0169-7439(93)E0036-4.
Kooistra, L. et al. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal. Chim. Acta 484(2), 189–200. https://doi.org/10.1016/S0003-2670(03)00331-3 (2003).
Google Scholar
Li, L. et al. Methods for estimating leaf nitrogen concentration of winter oilseed rape (Brassica Napus L.) using in situ leaf spectroscopy. Ind. Crops Prod. 91, 194–204. https://doi.org/10.1016/j.indcrop.2016.07.008 (2016).
Google Scholar
Różański, S. Ł, Kwasowski, W., Castejón, J. M. P. & Hardy, A. Heavy metal content and mobility in urban soils of public playgrounds and sport facility areas, Poland. Chemosphere 212, 456–466. https://doi.org/10.1016/j.chemosphere.2018.08.109 (2018).
Google Scholar
Bretzel, F. & Calderisi, M. Metal contamination in urban soils of coastal Tuscany (Italy). Environ. Monit. Assess. 118(1–3), 319–335. https://doi.org/10.1007/s10661-006-1495-5 (2006).
Google Scholar
Jim, C. Y. Urban soil characteristics and limitations for landscape planting in hong kong. Landsc. Urban Plan. 40(4), 235–249. https://doi.org/10.1016/S0169-2046(97)00117-5 (1998).
Google Scholar
Birke, M.; Rauch, U.; Chmieleski, J. Environmental Geochemical Survey of the City of Stassfurt: An Old Mining and Industrial Urban Area in Sachsen-Anhalt, Germany. In Mapping the Chemical Environment of Urban Areas; John Wiley and Sons, 2011; pp 269–306. https://doi.org/10.1002/9780470670071.ch18.
Khodadoust, A. P., Reddy, K. R. & Maturi, K. Removal of nickel and phenanthrene from kaolin soil using different extractants. Environ. Eng. Sci. 21(6), 691–704. https://doi.org/10.1089/ees.2004.21.691 (2004).
Google Scholar
Jakovljevic, M.; Kostic, N.; Antic-Mladenovic, S. The Availability of Base Elements (Ca, Mg, Na, K) in Some Important Soil Types in Serbia; 2003. https://doi.org/10.2298/zmspn0304011j.
Orzechowski, M.; Smolczynski, S. IN SOILS DEVELOPED FROM THE HOLOCENE DEPOSITS IN NORTH-EASTERN POLAND*; -, 2007; Vol. 15.
Pongrac, P. et al. Mineral element composition of cabbage as affected by soil type and phosphorus and zinc fertilisation. Plant Soil 434(1–2), 151–165. https://doi.org/10.1007/s11104-018-3628-3 (2019).
Google Scholar
Kingston, G.; Anink, M. C.; Clift, B. M.; Beattie, R. N. Potassium Management for Sugarcane on Base Saturated Soils in Northern New South Wales; 2009; Vol. 31.
Santo, L. T., Nakahata, M. H., & Schell, V. P. Santo LT, Nakahata MH, Ito GP and Schell VP (2000)…. – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Santo+LT%2C+Nakahata+MH%2C+Ito+GP+and+Schell+VP+%282000%29.+Calcium+and+liming+trials+from+1994+to+1998+at+HC%26S.+Technical+supplement+to+Agronomy+Report+83%2C+Hawaiian+Agricultural+Research+Centre. (accessed May 16, 2021).
Burgos, P., Madejón, E., Pérez-de-Mora, A. & Cabrera, F. Horizontal and vertical variability of soil properties in a trace element contaminated area. Int. J. Appl. Earth Obs. Geoinf. 10(1), 11–25. https://doi.org/10.1016/j.jag.2007.04.001 (2008).
Google Scholar
Olinic, T. & Olinic, E. The effect of quicklime stabilization on soil properties. Agric. Agric. Sci. Procedia 10, 444–451. https://doi.org/10.1016/j.aaspro.2016.09.013 (2016).
Google Scholar
Madaras, M.; Lipavský, J. Interannual Dynamics of Available Potassium in a Long-Term Fertilization Experiment; 2009; Vol. 55. https://doi.org/10.17221/34/2009-pse.
Madaras, M., Koubova, M. & Lipavský, J. Stabilization of available potassium across soil and climatic conditions of the Czech Republic. Arch. Agron. Soil Sci. 56(4), 433–449. https://doi.org/10.1080/03650341003605750 (2010).
Google Scholar
Pulkrabová, J. et al. Is the long-term application of sewage sludge turning soil into a sink for organic pollutants?: Evidence from field studies in the Czech Republic. J. Soils Sedim. 19(5), 2445–2458. https://doi.org/10.1007/s11368-019-02265-y (2019).
Google Scholar
Asare, M. O., Horák, J., Šmejda, L., Janovský, M. & Hejcman, M. A medieval hillfort as an island of extraordinary fertile archaeological dark earth soil in the Czech Republic. Eur. J. Soil Sci. 72(1), 98–113. https://doi.org/10.1111/ejss.12965 (2021).
Google Scholar
Zádorová, T. et al. Identification of Neolithic to Modern Erosion-Sedimentation Phases Using Geochemical Approach in a Loess Covered Sub-Catchment of South Moravia Czech Republic. Geoderma 195–196, 56–69. https://doi.org/10.1016/j.geoderma.2012.11.012 (2013).
Google Scholar
Tlustoš, P. et al. Nutrient status of soil and winter wheat (Triticum Aestivum L.) in response to long-term farmyard manure application under different climatic and soil physicochemical conditions in the Czech Republic. Arch. Agron. Soil Sci. 64(1), 70–83. https://doi.org/10.1080/03650340.2017.1331297 (2018).
Google Scholar
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. 260, 2 (2020).
Yan, P., Peng, H., Yan, L. & Lin, K. Spatial variability of soil physical properties based on GIS and geo-statistical methods in the red beds of the Nanxiong Basin, China. Polish J. Environ. Stud. 28, 2961–2972 (2019).
Google Scholar
Beguin, J., Fuglstad, G. A., Mansuy, N. & Paré, D. Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches. Geoderma 306, 195–205 (2017).
Google Scholar
Adhikary, P. P., Dash, C. J., Bej, R. & Chandrasekharan, H. Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India. Environ. Monit. Assess. 176, 663–676 (2011).
Google Scholar
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 18, 3327–3342 (2021).
Google Scholar
Eldeiry, A. A. & Garcia, L. A. Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing. Soil Sci. Soc. Am. J. 72, 201–211 (2008).
Google Scholar
Source: Ecology - nature.com