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dc.contributor.author
Alvarez, Roberto
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dc.contributor.author
Steinbach, Haydee Sara
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dc.date.available
2018-08-15T15:25:04Z
dc.date.issued
2017-09
dc.identifier.citation
Alvarez, Roberto; Steinbach, Haydee Sara; Modeling soil test phosphorus changes under fertilized and unfertilized managements using artificial neural networks; American Society of Agronomy; Agronomy Journal; 109; 5; 9-2017; 2278-2290
dc.identifier.issn
0002-1962
dc.identifier.uri
http://hdl.handle.net/11336/55601
dc.description.abstract
The build-up and maintenance criteria have been introduced for P fertilizer management in the Pampas of Argentina. However, methods for predicting soil test P changes under contrasting fertilizer rates are not available. We performed a meta-analysis using results from 18 local field experiments performed under the most common crop rotations, in which soil test P changes with and without P fertilization and soil P balance were assessed. We assembled 329 soil test P variation data sets corresponding to a period 12 yr and 129 P balance records. The P balance was not a good predictor of annual soil test P changes (R2 = 0.33). In 38% of the cases, the P balance and soil test P changes showed opposite trends. Polynomial regression and artificial neural networks were tested for soil test P modeling. The neural networks performed better than the regressions (R2 = 0.91 vs. 0.83; P < 0.01). The network that yielded the best results used the initial soil test P level, the P fertilization rate and time as inputs. According to the model, unfertilized crops growing in soils with low initial P levels (soil test P = 10 mg kg–1 or lower) were subjected to only small decreases in soil test P levels, whereas greater decreases occurred in soils with initial high P levels. For fertilized crops, the model showed that P-rich soils were less enriched in P than P-poor soils. A simple meta-model was developed for the prediction of soil test P changes under contrasting fertilizer managements.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Society of Agronomy
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Suelos
dc.subject
Fertilización
dc.subject
Fósforo
dc.subject.classification
Agricultura
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dc.subject.classification
Agricultura, Silvicultura y Pesca
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dc.subject.classification
CIENCIAS AGRÍCOLAS
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dc.title
Modeling soil test phosphorus changes under fertilized and unfertilized managements using 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
2018-08-14T18:39:11Z
dc.journal.volume
109
dc.journal.number
5
dc.journal.pagination
2278-2290
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Madison
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: Steinbach, Haydee Sara. Universidad de Buenos Aires. Facultad de Agronomía; Argentina
dc.journal.title
Agronomy Journal
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.2134/agronj2017.01.0014
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://dl.sciencesocieties.org/publications/aj/abstracts/109/5/2278
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