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dc.date.accessioned 2021-03-22T13:21:51Z
dc.date.available 2021-03-22T13:21:51Z
dc.date.issued 2020
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/115520
dc.description.abstract The comparative performance trials (CPT) of experimental and commercial cultivars in multi-environment trials, allow the determination of genotype (G) response to the different environment (E) and interaction (GxE). The final goal is to provide selection guidelines to identify the best genotypes to advance in the pipeline of the breeding program. Nowadays, the use of the digital information technologies to monitoring yield can be used in the CPTs to accurate measuring and recording of grain yield as well as the local environmental context. This research was carried out at 138 locations corresponding to commercial fields located in Argentina. The harvested was made with yield monitor and weighing machine wagon. To compare the grain yield of hybrids within each E and GXE interaction, the MLM of ANAVA with spatial correlation was adjusted for each location. Whereas, for CPT-without spatial data the ANAVA simple was applicate. The new analytic strategy showed greater power to: (a) find significant differences (p<0.05) in the GxE interaction, (b) increase 2.5 times the amount of information generated, i.e. than 1 CPT (1 location) analyzed with the new strategy. The new approach improves the data-driven decision making to support advancement processes from precommercial to commercial. en
dc.format.extent 194-197 es
dc.language en es
dc.subject Digital tools es
dc.subject Data modeling es
dc.subject Hybrid competitiveness es
dc.title Corn Geospatial strategy: a more deeply understanding of hybrid competitiveness and performance by combining digital tools and data modeling en
dc.type Objeto de conferencia es
sedici.identifier.uri http://49jaiio.sadio.org.ar/pdfs/cai/CAI_24.pdf es
sedici.identifier.issn 2525-0949 es
sedici.creator.person Peralta, Nahuel es
sedici.creator.person Ferreyra, Juan M. es
sedici.creator.person Di Rienzo, Julio es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
sedici.date.exposure 2020-10
sedici.relation.event XII Congreso de AgroInformática (CAI 2020) - JAIIO 49 (Modalidad virtual) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)