| Title | Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites |
| Abstract | In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture. |
| Source | Environmental science and pollution research international, pp. 1–12 |
| Keywords | Agricultural soilsgroundwaterPCALDAclassificationmultivariate analysissoil quality |
| Journal | Environmental science and pollution research international |
| Editor | Springer, Berlin, Germania |
| Year | 2016 |
| Type | Articolo in rivista |
| DOI | 10.1007/s11356-016-7944-y |
| Authors | Ielpo P.; Leardi R.; Pappagallo G.; Uricchio V.F. |
| Text | 371342 2016 10.1007/s11356 016 7944 y Scopus 2 s2.0 84992702790 Agricultural soils groundwater PCA LDA classification multivariate analysis soil quality Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites Ielpo P.; Leardi R.; Pappagallo G.; Uricchio V.F. Water Research Institute, National Research Council, Viale de Blasio 5, Bari, 70132, Italy; Institute of Atmospheric Sciences and Climate, National Research Council, s.p. Lecce Monteroni Km 1.2, Lecce, 73100, Italy; Department of Pharmacy, Genoa University, Viale Cembrano 4, Genoa, 16147, Italy In this paper, the results obtained from multivariate statistical techniques such as PCA Principal component analysis and LDA Linear discriminant analysis applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project Improvement of the Regional Agro meteorological Monitoring Network 2004 2007 . LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical nutrients and pesticides inputs in agriculture. Published version http //www.scopus.com/record/display.url eid=2 s2.0 84992702790 origin=inward Articolo in rivista Springer 0944 1344 Environmental science and pollution research international Environmental science and pollution research international Environ. sci. pollut. res. int. Environmental science and pollution research international. Environmental science and pollution research international Print Environmental science and pollution research Print ESPR Print vitofelice.uricchio URICCHIO VITO FELICE pierina.ielpo IELPO PIERINA giuseppe.pappagallo PAPPAGALLO GIUSEPPE |
