Using multivariate analysis of soil fertility as a tool for forest fertilization planning
2014
Fernández-Moya, Jesús | Alvarado, Alfredo | Morales, Manuel | San Miguel Ayanz, A. | Marchamalo-Sacristán, Miguel
The design of fertilization plans to cover large areas is complex, due to the considerable number of soil samples and soil fertility variables that must be taken into account. Classifying forest stands in groups according to their soil fertility (i.e. in nutrient management areas) can be very helpful to this respect and it is considered to be a first step in what has been called precision forestry. For this paper, we explore the capability of multivariate analyses of topsoil data to be used as tools for evaluating and classifying soil fertility. A case study from a teak (Tectona grandis L.f.) plantation in Costa Rica was used to evaluate and illustrate how to use multivariate analysis with these aims. A topsoil (0–20 cm) database with soil test results assembled by Panamerican Woods Ltd. was used. Different multivariate techniques [Principal Component Analysis, Non-metric Multidimensional Scaling (NMDS), Cluster analysis] were performed and compared. Cluster analysis resulted as an appropriate tool for grouping soil samples into soil fertility classes. Therefore, it is considered as a promising tool which would help to design a fertilization program to meet the specific needs of each group of stands with relatively homogeneous soil fertility properties. NMDS is also a suitable complementary tool to graphically explore the similarities within groups and the differences between them. The application of procedures similar to those being reported may help to optimize the design of nutritional and fertilization plans across large forest plantations, by using multivariate analysis to establish fertilization regimes that are appropriate to groups of stands of more homogeneous soil fertility.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par National Agricultural Library
Découvrez la collection de ce fournisseur de données dans AGRIS