Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production

• Autores: Peralta, Nahuel Raúl; Cicore, Pablo Leandro; Marino, María Alejandra; Marques da Silva, José Rafael; Costa, Jose Luis
• Año de publicación: 2015
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Spanish Journal of Agricultural Research 13 (4) : e1103, 8 pages (2015)
• Materia: Suelo; Propiedades del Suelo; Producción Pecuaria; Ganadería; Sistemas de Información Geográfica; Soil; Soil Properties; Livestock Production; Animal Husbandry; Geographical Information Systems; Propiedades Edáficas
• Institución: Instituto Nacional de Tecnología Agropecuaria

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.
EEA Balcarce
Fil: Peralta, Nahuel Raúl. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina
Fil: Cicore, Pablo Leandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina
Fil: Marino, María A. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina
Fil: Marques da Silva, José Rafael. Centre for Interdisciplinary Development and Research on Environment, Applied Management and Space; Portugal. University of Évora, Escola de Ciências e Tecnologia. Instituto de Ciências Agrárias e Ambientais Mediterrânicas; Portugal. Centro de Inovação em Tecnologias de Informação; Portugal
Fil: Costa, Jose Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina