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dc.contributor.author
Alvarez, Roberto
dc.contributor.author
de Paepe, Josefina
dc.contributor.author
Gimenez, Analía
dc.contributor.author
Recondo, Verónica
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Pagnanini, Federico
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Mendoza, Maria Rosa
dc.contributor.author
Caride, Constanza
dc.contributor.author
Ramil, Denis
dc.contributor.author
Facio, Facundo
dc.contributor.author
Berhongaray, Gonzalo
dc.date.available
2021-09-08T17:43:59Z
dc.date.issued
2020-03
dc.identifier.citation
Alvarez, Roberto; de Paepe, Josefina; Gimenez, Analía; Recondo, Verónica; Pagnanini, Federico; et al.; Using a nitrogen mineralization index will improve soil productivity rating by artificial neural networks; Taylor & Francis; Archives of Agronomy and Soil Science; 66; 4; 3-2020; 517-531
dc.identifier.issn
1476-3567
dc.identifier.uri
http://hdl.handle.net/11336/139920
dc.description.abstract
In the Pampas, nitrogen fertilization rates are low and soil organic matter impacts crop yield. Wheat (Triticum aestivum L.) yield was related to total soil nitrogen (total N) and to nitrogen mineralization potential (mineralized N) to determine whether the effects of organic matter may be attributed to its capacity to act as a nitrogen source or to the improvement of the soil physical condition. Data of 386 sites from throughout the region comprised in a recent soil survey were used, in which climate and soil properties to 1 m depth were determined. Artificial neural networks were applied for total N and mineralized N estimation using climate and soil variables as inputs (R2 = 0.59–0.70). The models allowed estimating total N and mineralizable N at county scale and related them to statistical yield information. Neural networks were also used for yield prediction. The best productivity model fitted (R2 = 0.85) showed that wheat yield could be predicted by rainfall, the photothermal quotient, and mineralized N. The soil organic matter effect on crop yield seems to be mainly related to its nitrogen mineralization capacity. Using mineralized N as predictor would be a valuable tool for rating soil productivity.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Taylor & Francis
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ARTIFICIAL NEURAL NETWORKS
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SOIL NITROGEN MINERALIZATION
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SOIL ORGANIC MATTER
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SOIL PRODUCTIVITY
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WHEAT YIELD
dc.subject.classification
Ciencias del Suelo
dc.subject.classification
Agricultura, Silvicultura y Pesca
dc.subject.classification
CIENCIAS AGRÍCOLAS
dc.title
Using a nitrogen mineralization index will improve soil productivity rating by artificial neural networks
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-09-07T14:54:40Z
dc.journal.volume
66
dc.journal.number
4
dc.journal.pagination
517-531
dc.journal.pais
Reino Unido
dc.description.fil
Fil: Alvarez, Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.fil
Fil: de Paepe, Josefina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Gimenez, Analía. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.fil
Fil: Recondo, Verónica. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.fil
Fil: Pagnanini, Federico. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.fil
Fil: Mendoza, Maria Rosa. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.fil
Fil: Caride, Constanza. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.fil
Fil: Ramil, Denis. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.description.fil
Fil: Facio, Facundo. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Berhongaray, Gonzalo. Universidad Nacional del Litoral. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Archives of Agronomy and Soil Science
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/abs/10.1080/03650340.2019.1626984?journalCode=gags20
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/03650340.2019.1626984
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