Identifying the factors that determine ecosystem services provision in Pampean agroecosystems (Argentina) using a data-mining approach
Ecosystem services (ES) have become a key concept in the assessment of natural resources, as a way to connect human well-being and ecosystems degradation. However, ES quantification is considered a basic problem because provision varies considerably as a result of land use change and site-specific c...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/93161 |
| Acceso en línea: | http://hdl.handle.net/11336/93161 |
| Access Level: | acceso abierto |
| Palabra clave: | ECOSYSTEM SERVICES CLUSTER CLASSIFICATION TREES LAND USE CROP SEASON PAMPEAN AGROECOSYSTEMS https://purl.org/becyt/ford/4.1 https://purl.org/becyt/ford/4 |
| Sumario: | Ecosystem services (ES) have become a key concept in the assessment of natural resources, as a way to connect human well-being and ecosystems degradation. However, ES quantification is considered a basic problem because provision varies considerably as a result of land use change and site-specific characteristics (i.e. climate, soil, topography, and time). Thus, more detailed studies are needed to assess whether these changes affect ecological variables. We explored the use of environmental and crop management variables in predicting the provision of four ES (soil C balance, soil N balance, N2O emission control and groundwater contamination control) in three agroecosystems located in the Pampa region (Argentina). Data-mining, represented by k-means cluster and classification trees, was used to identify the dependence of ES provision on the variation of both environmental and crop management factors. We used plot level crop management and environmental field information stored in a large database during a 10-year period. The k-means method selected five different clusters. The final configuration showed two contrasting clusters: one with the lowest ES provision, and another one with the highest ES provision. The five clusters were represented in the terminal nodes of the final classification tree. Regarding the predictive power of the variables, crop and year were the most important predictors. Then, differences observed in ES provision resulted from changes in land use (variable “crop”) and crop season (variable “year”). These results are meant to enlighten stakeholders in terms of how to manage Pampean agroecosystems in order to positively influence ES provision. |
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