Soil quality under two different management schemes in coffee plantations of southern Colombia.

 

Αποθηκεύτηκε σε:
Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφείς: Valbuena-Calderón, Oscar Eduardo, Rodríguez-Pérez, Wilson, Suárez-Salazar, Juan Carlos
Μορφή: artículo original
Κατάσταση:Versión publicada
Ημερομηνία έκδοσης:2016
Περιγραφή:The aim of this work was to develop an additive soil quality index (ASQI) in agrofostery managements of coffee (Coffea arabica L.). The study took place under two intense and traditional management schemes, in nine farms (32 lots) in the south of Colombia, during 2013. A separation of means analysis was held through the LSD Fisher test (P<0,05) to each of the physical and chemical variables of the soil. The variables that showed differences between the schemes were submitted to a main components analysis to select the minimum data set (MDS) of the components that explained the most variability and the redundancy was verified within the indicators, based on the correlation. The ASQI was obtained from the total sum of soil quality index (SQI) of all the indicators, taking into account that the higher the score of the ASQI, the higher the quality of the soil within the study system. The selected physical variables were the content of sand and clay; while the chemical variables were: organic carbon (OC), P, CA, Mg, total bases (TB) and Ca/Mg. The best ASQI was obtained from traditional management, because the value of the selected variables matched in a bigger proportion with the quality objective identified for the ASQI quanti cation; in this case the crop yield, based on limit values for coffee plantations. 
Χώρα:Portal de Revistas UCR
Ίδρυμα:Universidad de Costa Rica
Repositorio:Portal de Revistas UCR
Γλώσσα:Español
OAI Identifier:oai:archivo.portal.ucr.ac.cr:article/21092
Διαθέσιμο Online:https://archivo.revistas.ucr.ac.cr/index.php/agromeso/article/view/21092
Λέξη-Κλειδί :additive soil quality index
minimum data set
principal component analysis.
índice calidad de suelo aditivo
conjunto mínimo de datos
análisis de componentes principales.