Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/17404

Title: Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production
Authors: Nahuel, Peralta
Cicore, Pablo
Marino, Maria
Marques da Silva, JR
Costa, José
Keywords: multivariate techniques
soil properties
geographic information system
lowland soils
spatial variability
Issue Date: 2015
Publisher: Spanish Journal of Agricultural Research
Citation: Nahuel R. Peralta; Pablo L. Cicore; Maria A. Marino; Jose R. Marques da Silva and Jose L. Costa (2015). Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production. SJAR 13(4): 494-515. (ref. SJAR 8032-494/15).
Abstract: The spatial variability in soils used for livestock production (i.e. Natraquoll and Natraqualf) at farm and paddock scale is usually very high. Understanding this spatial variation within a field is the first step for site-specific crop management. For this reason, we evaluated whether apparent electrical conductivity (ECa), a widely used proximal soil sensing technology, is a potential estimator of the edaphic variability in these types of soils. ECa and elevation data were collected in a paddock of 16 ha. Elevation was negatively associated with ECa. Geo-referenced soil samples were collected and analyzed for soil organic matter (OM) content, pH, the saturation extract electrical conductivity (ECext), available phosphorous (P), and anaerobically incubated Nitrogen (Nan). Relationships between soil properties and ECa were analyzed using regression analysis, principal components analysis (PCA), and stepwise regression. Principal components (PC) and the PC-stepwise were used to determine which soil properties have an important influence on ECa. In this experiment elevation was negatively associated with ECa. The data showed that pH, OM, and ECext exhibited a high correlation with ECa (R2=0.76; 0.70 and 0.65, respectively). Whereas P and Nan showed a lower correlation (R2=0.54 and 0.11 respectively). The model resulting from the PC-stepwise regression analysis explained slightly more than 69% of the total variation of the measured ECa, only retaining PC1. Therefore, ECext, pH and OM were considered key latent variables because they substantially influence the relationship between the PC1 and the ECa (loading factors>0.4). Results showed that ECa is associated with the spatial distribution of some important soil properties. Thus, ECa can be used as a support tool to implement site-specific management in soils for livestock use.
URI: http://hdl.handle.net/10174/17404
Type: article
Appears in Collections:ERU - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
ICAAM - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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