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Abstract
In recent years, the use of forage crop sequences (FCS) has been increased as a main component into the animal rations of the Argentinian pasture-based livestock systems. However, it is unclear how year-by-year rainfall variability and interactions with soil properties affect FCS dry matter (DM) yield in these environments. Biophysical crop models, such as Agricultural Production Systems Simulator (APSIM), are tools that enable the evaluation of crop [ver mas...]
dc.contributor.authorOjeda, Jonathan Jesus
dc.contributor.authorPembleton, Keith G.
dc.contributor.authorCaviglia, Octavio
dc.contributor.authorIslam, Md. Rafiqul
dc.contributor.authorAgnusdei, Monica Graciela
dc.contributor.authorGarcia, Sergio Carlos
dc.coverage.spatialPampa (general region)
dc.date.accessioned2018-09-18T15:10:38Z
dc.date.available2018-09-18T15:10:38Z
dc.date.issued2018-01
dc.identifier.issn1161-0301
dc.identifier.otherhttps://doi.org/10.1016/j.eja.2017.10.004
dc.identifier.urihttp://hdl.handle.net/20.500.12123/3389
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1161030117301508?via%3Dihub
dc.description.abstractIn recent years, the use of forage crop sequences (FCS) has been increased as a main component into the animal rations of the Argentinian pasture-based livestock systems. However, it is unclear how year-by-year rainfall variability and interactions with soil properties affect FCS dry matter (DM) yield in these environments. Biophysical crop models, such as Agricultural Production Systems Simulator (APSIM), are tools that enable the evaluation of crop yield variability across a wide of environments. The objective of this study was to evaluate the APSIM ability to predict forage DM yield and water productivity (WP) of multiple continuous FCS. Thirteen continuous FCS, including winter and summer crops, were simulated by APSIM during two/three growing seasons in five locations across the Argentinian Pampas. Our modelling approach was based on the simulation of multiple continuous FCS, in which crop DM yields depend on the performance of the previous crop in the same sequence and the final soil variables of the previous crop are the initial conditions for the next crop. Overall, APSIM was able to accurately simulate FCS DM yield (0.93 and 3.2 Mg ha−1 for concordance correlation coefficient [CCC] and root mean square error [RMSE] respectively). On the other hand, the model predictions were better for annual (CCC = 0.94; RMSE = 0.4 g m−2 mm−1) than for seasonal WP (CCC = 0.71; RMSE = 1.9 g m−2 mm−1), i.e. at the crop level. The model performance to predict WP was associated with better estimations of the soil water dynamics over the long-term, i.e. at the FCS level, rather than the short-term, i.e. at the crop level. The ability of APSIM to predict WP decreased as seasonal WP values increased, i.e. for low water inputs. For seasonal water inputs, <200 mm, the model tended to under-predict WP, which was directly associated with crop DM yield under-predictions for frequently harvested crops. Even though APSIM showed some weaknesses in predicting seasonal DM yield and WP, i.e. at the crop level, it appears as a potential tool for further research on complementary forage crops based on multiple continuous FCS in the Argentinian livestock systems.eng
dc.formatapplication/pdfeng
dc.language.isoeng
dc.publisherElsevieres_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesseng
dc.sourceEuropean journal of agronomy 92 :84-96. (January 2018)eng
dc.subjectForrajeses_AR
dc.subjectForageeng
dc.subjectCultivo Secuenciales_AR
dc.subjectSequential Croppingeng
dc.subjectMaízes_AR
dc.subjectZea Mayses_AR
dc.subjectMaizeeng
dc.subject.otherRegión Pampenaes_AR
dc.subject.otherAPSIMeng
dc.titleModelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampaseng
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.description.origenEEA Paranáes_AR
dc.description.filFil: Ojeda, Jonathan Jesus. Universidad Nacional de Entre Ríos. Facultad de Ciencias Agropecuarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentinaes_AR
dc.description.filFil: Pembleton, Keith G. University of Southern Queensland. School of Agriculture. Computational and Environmental Sciences and Institute for Agriculture and the Environment; Australiaes_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: Islam, Md. Rafiqul. University of Sydney. Faculty of Veterinary Science. Dairy Science Group; Australiaes_AR
dc.description.filFil: Agnusdei, Monica Graciela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentinaes_AR
dc.description.filFil: Garcia, Sergio Carlos. University of Sydney. Faculty of Veterinary Science. Dairy Science Group; Australiaes_AR
dc.subtypecientifico


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