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Resumen
Questions: Can herbaceous above‐ground net primary production (ANPP) be estimated from remote sensing when woody and herbaceous plants are intermingled? How does herbaceous ANPP change in space and time in an ecosystem dominated by woody species? What are the main controls of herbaceous ANPP to paddock scale? Location: Native plant communities and buffelgrass roller chopped pastures of the Arid Chaco, western Argentina (28–32° S, 64–67° W; area: 100 000 [ver mas...]
dc.contributor.authorBlanco, Lisandro Javier
dc.contributor.authorParuelo, José María
dc.contributor.authorOesterheld, Martin
dc.contributor.authorBiurrun, Fernando Noe
dc.date.accessioned2018-07-10T14:37:13Z
dc.date.available2018-07-10T14:37:13Z
dc.date.issued2016-07
dc.identifier.issn1100-9233
dc.identifier.issn1654-1103
dc.identifier.otherhttps://doi.org/10.1111/jvs.12398
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/abs/10.1111/jvs.12398
dc.identifier.urihttp://hdl.handle.net/20.500.12123/2749
dc.description.abstractQuestions: Can herbaceous above‐ground net primary production (ANPP) be estimated from remote sensing when woody and herbaceous plants are intermingled? How does herbaceous ANPP change in space and time in an ecosystem dominated by woody species? What are the main controls of herbaceous ANPP to paddock scale? Location: Native plant communities and buffelgrass roller chopped pastures of the Arid Chaco, western Argentina (28–32° S, 64–67° W; area: 100 000 km2). Methods: We decomposed normalized difference vegetation index (NDVI) data from MODIS (pixel size: 250 m × 250 m) into woody (W) and herbaceous (H) components. We calibrated the relationship between field estimates of herbaceous ANPP and the H component of NDVI using linear regression. The regression model fitted was applied to a 10‐yr MODIS database for four paddocks to estimate herbaceous ANPP. We analysed the relationship between herbaceous ANPP and watering point distance and growing season precipitation. Results: The annual integral of NDVI × proportion of the herbaceous component [H/(H + W)] explained 71% and 91% of herbaceous ANPP variation in native plant communities and buffelgrass roller chopped pastures, respectively. The regression model fitted, however, differed (P < 0.05) between the two types of system. The NDVI annual integral explained a higher proportion of herbaceous ANPP variations than the NDVI annual peak or the growing season (December–April) integral. For native plant communities, herbaceous production increased significantly (P < 0.05) with watering point distance, and marginally significantly (P < 0.10) with growing season precipitation. For buffelgrass roller chopped pastures, the herbaceous production increased significantly (P < 0.05) with growing season precipitation. Conclusion; Our model was able to estimate herbaceous ANPP from the decomposition of an NDVI time series that included woody components. Thus, the model provides the basis for more accurate monitoring of spatial and temporal variability of herbaceous ANPP in areas where herbaceous and woody plant components co‐exist. Applying our models, we detected clear spatial and temporal patterns of herbaceous ANPP. The possibility of describing in a spatially explicit way the past 14 yrs of herbaceous ANPP allows designing livestock management strategies and devise alternatives to control degradation processes in the Arid Chaco.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.sourceJournal of Vegetation Science 27 (4) : 716-727 (July 2016)es_AR
dc.subjectTierras de Matorrales_AR
dc.subjectScrublandseng
dc.subjectZona Semiáridaes_AR
dc.subjectSemiarid Zoneseng
dc.subjectProducción Primariaes_AR
dc.subjectPrimary Productioneng
dc.subjectTeledetecciónes_AR
dc.subjectRemote Sensingeng
dc.subjectIndice de Vegetaciónes_AR
dc.subjectVegetation Indexeng
dc.subject.otherMatorraleses_AR
dc.titleSpatial and temporal patterns of herbaceous primary production in semi‐arid shrublands: a remote sensing approaches_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.description.origenEEA La Riojaes_AR
dc.description.filFil: Blanco, Lisandro Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria La Rioja; Argentinaes_AR
dc.description.filFil: Paruelo, José María. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Departamento de Métodos Cuantitativos y Sistemas de Información. Laboratorio de Análisis Regional y Teledetección; Argentinaes_AR
dc.description.filFil: Oesterheld, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Departamento de Métodos Cuantitativos y Sistemas de Información. Laboratorio de Análisis Regional y Teledetección; Argentinaes_AR
dc.description.filFil: Biurrun, Fernando Noe. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria La Rioja; Argentinaes_AR
dc.subtypecientifico


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