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resumen

Resumen
The Agricultural Production Systems Simulator (APSIM) is a key tool to identify agricultural managementpractices seeking to simultaneously optimize agronomic productivity and input use efficiencies. The aimsof this study were to validate APSIM for prediction of stover and grain yield of corn in four contrasting soilswith varied N fertilizer applications (156–269 kg N ha−1) and to predict timing and volume from artificialsubsurface drains in continuous [ver mas...]
dc.contributor.authorOjeda, Jonathan Jesus
dc.contributor.authorVolenec, Jeffrey J.
dc.contributor.authorBrouder, Sylvie M.
dc.contributor.authorCaviglia, Octavio
dc.contributor.authorAgnusdei, Monica Graciela
dc.date.accessioned2018-07-20T15:27:58Z
dc.date.available2018-07-20T15:27:58Z
dc.date.issued2018
dc.identifier.issn0378-3774
dc.identifier.otherhttps://doi.org/10.1016/j.agwat.2017.10.010
dc.identifier.urihttp://hdl.handle.net/20.500.12123/2842
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0378377417303293?via%3Dihub
dc.description.abstractThe Agricultural Production Systems Simulator (APSIM) is a key tool to identify agricultural managementpractices seeking to simultaneously optimize agronomic productivity and input use efficiencies. The aimsof this study were to validate APSIM for prediction of stover and grain yield of corn in four contrasting soilswith varied N fertilizer applications (156–269 kg N ha−1) and to predict timing and volume from artificialsubsurface drains in continuous corn and corn-soybean rotations in a silty clay loam soil at West Lafayette,IN. The APSIM validation was carried-out using a long-term dataset of corn stover and grain yields fromthe North Central Region of IN. The CCC (Concordance Correlation Coefficient) and SB (Simulation Bias)were used to statistically evaluate the model performance. The CCC integrates precision through Pearson’scorrelation coefficient and accuracy by bias, and SB indicates the bias of the simulation from the mea-surement. The model demonstrated very good (CCC = 0.96; SB = 0%) and satisfactory (CCC = 0.85; SB = 2%)ability to simulate stover and grain yield, respectively. Grain yield was better predicted in continuouscorn (CCC = 0.73–0.91; SB = 19–21%) than in corn-soybean rotations (CCC = 0.56–0.63; SB = 17–18%), whilestover yield was well predicted in both crop rotations (CCC = 0.85–0.98; SB = 1–17%). The model demon-strated acceptable ability to simulate annual subsurface drainage in both rotations (CCC = 0.63–0.75;SB = 2–37%) with accuracy being lower in the continuous corn system than in corn-soybean rotation sys-tem (CCC = 0.61-0.63; SB = 9–12%). Daily subsurface drainage events were well predicted by APSIM duringlate spring and summer when crop water use was high, but under-predicted during fall, winter and earlyspring when evapotranspiration was low. Occasional flow events occurring in summer when soils werenot saturated were not predicted by APSIM and may represent preferential flow paths currently not rep-resented in the model. APSIM is a promising tool for simulating yield and water losses for corn-basedcropping systems in north central Indiana US.es_AR
dc.formatapplication/pdfeng
dc.language.isoeng
dc.rightsinfo:eu-repo/semantics/restrictedAccesseng
dc.sourceAgricultural water management 195 : 154–171. (2018)eng
dc.subjectMaízes_AR
dc.subjectZea Mayses_AR
dc.subjectRastrojoes_AR
dc.subjectGranoses_AR
dc.subjectDrenaje Subterráneoes_AR
dc.subjectRotación de Cultivoses_AR
dc.subjectRendimiento de Cultivoses_AR
dc.subjectModelos de Simulaciónes_AR
dc.subjectSimulation Modelseng
dc.subjectYieldseng
dc.subjectCrop Rotationeng
dc.subjectSubsurface Drainageeng
dc.subjectGraineng
dc.subjectMaizeeng
dc.subject.otherAPSIMeng
dc.subject.otherIndiana, Estados Unidoses_AR
dc.subject.otherCorn-based Cropping Systemseng
dc.titleModelling stover and grain yields, and subsurface artificial drainagefrom long-term corn rotations using APSIMes_AR
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.description.origenEEA Paranáes_AR
dc.description.filFil: Ojeda, Jonathan Jesus. University of Tasmania. Tasmanian Institute of Agriculture; Australia. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentinaes_AR
dc.description.filFil: Volenec, Jeffrey J. Purdue University. Department of Agronomy; Estados Unidoses_AR
dc.description.filFil: Brouder, Sylvie M. Purdue University. Department of Agronomy; Estados Unidoses_AR
dc.description.filFil: Caviglia, Octavio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Paraná. Grupo Ecología Forestal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentinaes_AR
dc.description.filFil: Agnusdei, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentinaes_AR
dc.subtypecientifico


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