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http://hdl.handle.net/10261/99280
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dc.contributor.author | Pérez-Quezada, Jorge F. | - |
dc.contributor.author | Cavero Campo, José | - |
dc.contributor.author | Williams, Jimmy R. | - |
dc.contributor.author | Roel, Alvaro | - |
dc.contributor.author | Plant, Richard E. | - |
dc.date.accessioned | 2014-07-02T09:42:20Z | - |
dc.date.available | 2014-07-02T09:42:20Z | - |
dc.date.issued | 2003 | - |
dc.identifier | issn: 0001-2351 | - |
dc.identifier.citation | Transactions of the American Society of Agricultural Engineers 46(5): 1365-1374 (2003) | - |
dc.identifier.uri | http://hdl.handle.net/10261/99280 | - |
dc.description.abstract | Soil data were collected from a 30 ha commercial field using a 60 m sampling grid. Monitored yield data were also collected in this field between 1996 and 1999, when it had a wheat-processing tomato-bean-sunflower crop rotation. A comparison between SSURGO-NRCS soil-unit definition and field-measured soil data showed that in this field the former are a good approximation and starting point for precision agriculture studies and management. In a second test, the EPIC model, using the SSURGO database soil type definitions, was found to reproduce the yield variability within this field with reasonable accuracy. The model's performance was not as good when tested with data from soil samples, apparently due to the way EPIC simulates water holding capacity from texture information and the lack of some key variables (not sampled), such as water content at field-capacity (FC), wilting-point (WP), and soil saturated conductivity. A set of runs was performed to simulate the yield at 13 point-locations in the field using FC, WP, and bulk density. Although the accuracy of the simulation did not improve greatly, the model reproduced the yield trend of two of the crops (wheat and sunflower). | - |
dc.description.sponsorship | This research was supported in part by the Fertilizer Research and Education Program of the California Department of Food and Agriculture. | - |
dc.publisher | American Society of Agricultural and Biological Engineers | - |
dc.rights | closedAccess | - |
dc.subject | Precision agriculture | - |
dc.subject | Management zones | - |
dc.subject | Spatial variability | - |
dc.subject | SSURGO data | - |
dc.subject | Temporal variability | - |
dc.subject | EPIC model | - |
dc.title | Simulation of within-field yield variability in a four-crop rotation field using SSURGO Soil-Unit definitions and the Epic Model | - |
dc.type | artículo | - |
dc.identifier.doi | 10.13031/2013.15447 | - |
dc.relation.publisherversion | http://dx.doi.org/10.13031/2013.15447 | - |
dc.date.updated | 2014-07-02T09:42:20Z | - |
dc.description.version | Peer Reviewed | - |
dc.language.rfc3066 | eng | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.openairetype | artículo | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | No Fulltext | - |
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