FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891822366> ?p ?o ?g. }

• W2891822366 endingPage "121" @default.
• W2891822366 startingPage "111" @default.
• W2891822366 abstract "Abstract Soil analysis is an important information in agriculture and environmental monitoring. It is usually performed by wet chemical analysis with high cost and chemical products consumption. In the world, it is estimated that 1.5 billion ha is used as agricultural area. If every 5 ha 2 samples (2 depths) were collected, we would have 600 million soil samples for chemical and granulometric analysis. Considering just the analysis of organic matter (OM) by wet combustion method in the laboratory as an example, we would be utilizing about 840 thousand kg of dichromate and ammonium ferrous sulfate and 3 million L of sulfuric acid. The use of these reagents can have a huge ecological consequence if they do not have an adequate final disposal. An alternative methodology such as proximal sensing can be utilized with low environmental impact. Therefore, the objective of this study was to: i) evaluate the analytical quality of soil attributes via different traditional laboratories and sensors, ii) evaluate the prediction of the models using sensors, iii) assess the uncertainties of lime recommendation analyzed by the laboratories. We applied 96 soil samples at two depths collected in Sao Paulo State, Brazil. The determination of 15 soil attributes was performed by four different routine laboratories, and they were predicted by 4 sensors (400–2500 nm). Results indicate that the determination of attributes via chemical analysis with low quality led to high error in spectral models. The great predictive performances of clay, OM, cation exchange capacity (CEC), and pH enable the use of sensors in the evaluation of these attributes. Overall, the criteria for classification of analytical results showed that sand, silt, clay, pH, OM, CEC, and base saturation were the attributes that can be determined by the spectroscopy technique with high-quality outcome. The lime recommendation derived from proximal sensor analysis can be used as an efficient method, since it presented a high correlation with the laboratory result. In this sense, a hybrid laboratory analysis can be developed to optimize analysis with better quality control, which is indicated as a great opportunity in the near future." @default.
• W2891822366 created "2018-09-27" @default.
• W2891822366 creator A5006733842 @default.
• W2891822366 creator A5008685528 @default.
• W2891822366 creator A5056213122 @default.
• W2891822366 creator A5074073344 @default.
• W2891822366 creator A5078213221 @default.
• W2891822366 date "2019-03-01" @default.
• W2891822366 modified "2023-10-12" @default.
• W2891822366 title "Soil analytical quality control by traditional and spectroscopy techniques: Constructing the future of a hybrid laboratory for low environmental impact" @default.
• W2891822366 cites W1148445081 @default.
• W2891822366 cites W1190219959 @default.
• W2891822366 cites W1969839347 @default.
• W2891822366 cites W1973273412 @default.
• W2891822366 cites W1979539465 @default.
• W2891822366 cites W2001470247 @default.
• W2891822366 cites W2001804870 @default.
• W2891822366 cites W2005884775 @default.
• W2891822366 cites W2007909122 @default.
• W2891822366 cites W2015956205 @default.
• W2891822366 cites W2021117478 @default.
• W2891822366 cites W2037112590 @default.
• W2891822366 cites W2045858843 @default.
• W2891822366 cites W2051378084 @default.
• W2891822366 cites W2052903566 @default.
• W2891822366 cites W2054603652 @default.
• W2891822366 cites W2058246635 @default.
• W2891822366 cites W2066070217 @default.
• W2891822366 cites W2069539920 @default.
• W2891822366 cites W2079586129 @default.
• W2891822366 cites W2084370905 @default.
• W2891822366 cites W2084393659 @default.
• W2891822366 cites W2092666033 @default.
• W2891822366 cites W2148393497 @default.
• W2891822366 cites W2157149623 @default.
• W2891822366 cites W2165993842 @default.
• W2891822366 cites W2292439029 @default.
• W2891822366 cites W2501986955 @default.
• W2891822366 cites W2536582757 @default.
• W2891822366 cites W2766217827 @default.
• W2891822366 doi "https://doi.org/10.1016/j.geoderma.2018.09.010" @default.
• W2891822366 hasPublicationYear "2019" @default.
• W2891822366 type Work @default.
• W2891822366 sameAs 2891822366 @default.
• W2891822366 citedByCount "52" @default.
• W2891822366 countsByYear W28918223662019 @default.
• W2891822366 countsByYear W28918223662020 @default.
• W2891822366 countsByYear W28918223662021 @default.
• W2891822366 countsByYear W28918223662022 @default.
• W2891822366 countsByYear W28918223662023 @default.
• W2891822366 crossrefType "journal-article" @default.
• W2891822366 hasAuthorship W2891822366A5006733842 @default.
• W2891822366 hasAuthorship W2891822366A5008685528 @default.
• W2891822366 hasAuthorship W2891822366A5056213122 @default.
• W2891822366 hasAuthorship W2891822366A5074073344 @default.
• W2891822366 hasAuthorship W2891822366A5078213221 @default.
• W2891822366 hasConcept C121332964 @default.
• W2891822366 hasConcept C127413603 @default.
• W2891822366 hasConcept C159390177 @default.
• W2891822366 hasConcept C159750122 @default.
• W2891822366 hasConcept C183696295 @default.
• W2891822366 hasConcept C18903297 @default.
• W2891822366 hasConcept C2776475172 @default.
• W2891822366 hasConcept C2779530757 @default.
• W2891822366 hasConcept C39432304 @default.
• W2891822366 hasConcept C62520636 @default.
• W2891822366 hasConcept C70798638 @default.
• W2891822366 hasConcept C86803240 @default.
• W2891822366 hasConceptScore W2891822366C121332964 @default.
• W2891822366 hasConceptScore W2891822366C127413603 @default.
• W2891822366 hasConceptScore W2891822366C159390177 @default.
• W2891822366 hasConceptScore W2891822366C159750122 @default.
• W2891822366 hasConceptScore W2891822366C183696295 @default.
• W2891822366 hasConceptScore W2891822366C18903297 @default.
• W2891822366 hasConceptScore W2891822366C2776475172 @default.
• W2891822366 hasConceptScore W2891822366C2779530757 @default.
• W2891822366 hasConceptScore W2891822366C39432304 @default.
• W2891822366 hasConceptScore W2891822366C62520636 @default.
• W2891822366 hasConceptScore W2891822366C70798638 @default.
• W2891822366 hasConceptScore W2891822366C86803240 @default.
• W2891822366 hasFunder F4320320997 @default.
• W2891822366 hasFunder F4320321091 @default.
• W2891822366 hasFunder F4320322025 @default.
• W2891822366 hasLocation W28918223661 @default.
• W2891822366 hasOpenAccess W2891822366 @default.
• W2891822366 hasPrimaryLocation W28918223661 @default.
• W2891822366 hasRelatedWork W1550711116 @default.
• W2891822366 hasRelatedWork W2081397537 @default.
• W2891822366 hasRelatedWork W2128687263 @default.
• W2891822366 hasRelatedWork W2152524062 @default.
• W2891822366 hasRelatedWork W2323659846 @default.
• W2891822366 hasRelatedWork W2374958109 @default.
• W2891822366 hasRelatedWork W2377970248 @default.
• W2891822366 hasRelatedWork W2945425323 @default.
• W2891822366 hasRelatedWork W3117270764 @default.
• W2891822366 hasRelatedWork W3146919730 @default.
• W2891822366 hasVolume "337" @default.
• W2891822366 isParatext "false" @default.

• next