Estimation of soil penetration resistance with standardized moisture using modeling by artificial neural networks
| Autor: | Mariele Monique Honorato Fernandes, Rafael Scabello Bertonha, Anderson Prates Coelho, Carolina Fernandes, Carlos Eduardo Angeli Furlani, Renata Fernandes de Queiroz, Matheus Flavio da Silva |
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| Přispěvatelé: | Universidade Estadual Paulista (Unesp) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Nonlinear models
010504 meteorology & atmospheric sciences Artificial neural network Soil test Moisture Soil science 04 agricultural and veterinary sciences Penetration (firestop) 01 natural sciences Models accuracy 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Environmental science Soil horizon Surface layer Linear models Soil moisture Water content 0105 earth and related environmental sciences Earth-Surface Processes |
| Zdroj: | Scopus Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP |
| Popis: | Made available in DSpace on 2020-12-12T02:34:36Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) One of the most used evaluations to monitor soil compaction is based on soil penetration resistance (SPR). However, since SPR is influenced by soil moisture, this evaluation performed in the field may often lead to incorrect interpretations. This study aimed to evaluate the accuracy of models in the estimation of soil penetration resistance with standardized moisture (SPRlab) based on soil penetration resistance measured in the field (SPRfield) and on soil moisture (U) and indicate the best soil layer and best model for that. Samplings were carried out in the years 2016 (72 points – 24 in each layer) and 2017 (270 points – 90 in each layer) in three soil layers (0.00–0.10 m, 0.10–0.20 m and 0.20–0.30 m). Samples collected in 2017 were used to calibrate the models and samples collected in 2016 were used to validate them. The models used were obtained by multiple linear and nonlinear regressions and artificial neural networks (ANNs). Models were calibrated with all sampled layers and stratified per layer. In the latter case, the samples were separated into two parts, one with the surface layer (0.00–0.10 m) and another with subsurface layers (0.10–0.20 m and 0.20–0.30 m). SPRlab can be estimated with high accuracy from SPRfield and U measured in the field. We recommend the use of ANN models (MLP or RBF) and soil samples collected from the 0.10–0.30 m layer for the monitoring of soil penetration resistance. São Paulo State University (Unesp) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900 Jaboticabal São Paulo State University (Unesp) School of Agricultural and Veterinarian Sciences, Via de Acesso Prof. Paulo Donato Castellane s/n, 14884-900 Jaboticabal |
| Databáze: | OpenAIRE |
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