dc.creatorFiandino, Santiago Ignacio
dc.creatorPlevich, José Omar
dc.creatorTarico, Juan Carlos
dc.creatorUtello, Marco Jesús
dc.creatorDemaestri, Marcela Alejandra
dc.creatorGyenge, Javier Enrique
dc.date.accessioned2021-12-28T14:22:22Z
dc.date.accessioned2022-10-15T10:44:39Z
dc.date.available2021-12-28T14:22:22Z
dc.date.available2022-10-15T10:44:39Z
dc.date.created2021-12-28T14:22:22Z
dc.date.issued2020-10
dc.identifierFiandino, Santiago Ignacio; Plevich, José Omar; Tarico, Juan Carlos; Utello, Marco Jesús; Demaestri, Marcela Alejandra; et al.; Modeling forest site productivity using climate data and topographic imagery in Pinus elliottii plantations of central Argentina; EDP Sciences; Annals of Forest Science; 77; 4; 10-2020; 1-9
dc.identifier1286-4560
dc.identifierhttp://hdl.handle.net/11336/149325
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4377061
dc.description.abstractContext: Predicting tree growth and yield is a key component to sustainable forest management and depends on accurate measures of site quality. Aims: The aim of this study was to develop both empirical models to predict site index (SI) from biophysical variables and a dynamic model of top height growth for plantations of Pinus elliottii Engelm. in Córdoba, Argentina. Methods: Site productivity described by SI was related to environmental characteristics, including topographic and climatic variables. Separate models were created from only topographic data and the combination of topographic and climate data. Results: Although SI can be adequately predicted through both types of models, the best results were obtained when combining topographic and climate variables (R2 = 0.83, RMSE% = 7.02%, for the best-fitting model). The key factors affecting site productivity were the landscape position and the mean precipitation of the last 5 years before the reference age, both related to the amount of plant-available water in the soils. Furthermore, the top height growth models developed are fairly accurate, considering the proportion of variance explained (R2 = 98%) and the precision of the estimates (RMSE% < 8%). Conclusion: The models developed here are likely to have considerable application in forestry, since they are based on accessible predictor variables, which make them useful for silvicultural and forest management practices, particularly for non-forest areas and for the young or uneven-aged stands.
dc.languageeng
dc.publisherEDP Sciences
dc.relationinfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/10.1007/s13595-020-01006-3
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s13595-020-01006-3
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBIOPHYSICAL FACTORS
dc.subjectCÓRDOBA
dc.subjectDIGITAL ELEVATION MODELS
dc.subjectFOREST PRODUCTIVITY
dc.subjectPREDICTION MODELS
dc.subjectSITE INDEX
dc.titleModeling forest site productivity using climate data and topographic imagery in Pinus elliottii plantations of central Argentina
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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