| Revista: | Scientia Agricola |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000455515 |
| ISSN: | 0103-9016 |
| Autores: | Coelho, Fabrício Fernandes1 Giasson, Elvio1 Campos, Alcinei Ribeiro1 Tiecher, Tales1 Costa, José Janderson Ferreira1 Coblinski, João Augusto1 |
| Instituciones: | 1Universidade Federal do Rio Grande do Sul, Departamento de Solos, Porto Alegre, Rio Grande do Sul. Brasil |
| Año: | 2021 |
| Volumen: | 78 |
| Número: | 5 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Descriptivo |
| Resumen en inglés | In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for Overall Accuracy. Studies compatible with the Planimetric Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD) averaged between 4 % and 15 % higher accuracy than those of the incompatible group. There seems to be no evidence that increasing the number of variables and samples results in more accurate soil map prediction, but studies using variables related to four soil-forming factors enhanced accuracy. From a density of 0.08 MU km–2 and upwards, it became more difficult for studies to obtain greater accuracy. Artificial neural network classifiers and Decision Tree models seem to be producing more accurate digital soil class maps |
| Disciplinas: | Agrociencias |
| Palabras clave: | Suelos, Edafología, Mapeo digital, Brasil, Redes neuronales artificiales, Revisión bibliográfica |
| Keyword: | Soils, Edaphology, Digital mapping, Brazil, Artificial neural networks, Bibliographic review |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
