Condutividade elétrica aparente do solo como ferramenta para agricultura de precisão em uma área sob Cerrado

• Autor(a) principal: Costa, Marcelo Marques
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: http://locus.ufv.br/handle/123456789/3594
• topic: Teor de água no solo; Zonas de manejo; Variabilidade espacial e temporal; Water content in soil; Management zones; Spatial and temporal variability; CNPQ::CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA

Precision agriculture seeks the management of fields in order to maximize profit and reduce environmental impacts. For this, one must consider the spatial variability of the attributes that define the yield of agricultural crops. The management considering the variability of soil properties provides improvements in the management of inputs, however, a spatial database for this purpose requires a large number of soil samples. To map the soil attributes using direct samples generally is not the option most cost effective. Indirect samplings are preferable, and the apparent soil electrical conductivity (ECa) is a technique with high acceptance in precision agriculture. The objective of this work is to advance the knowledge about this technique by testing it in a Cerrado condition. Therefore, the sampling conditions, the spatial and temporal variability, and the usefulness these information for precision agriculture were evaluated. The results regarding the sampling condition show that when the soil moisture is higher, the ECa correlated most closely with soil attributes and correlated less with soil moisture and was considered to be the best condition for sampling. The spatial variability of ECa generated four classes when used the fuzzy c-means classifier. However, a method of analysis of soil samples within classes combined them in just two, which were distinct for the attributes most correlated with the ECa and the obtained semivariogram showed low discontinuity at the origin. These attributes were the potential of hydrogen (pH), base saturation (V), potential acidity (H + Al), iron (Fe) and full cation exchange capacity (T). The temporal variability of ECa was analyzed by using the principal component technique. The component that best described the temporal variability of ECw explained 18% of the total variability of the data and presented spatial dependence. This component was correlated with altitude, T and electrical conductivity of saturation extract (CEes). In this study the altitude was considered the attribute that most influenced the temporal variability of the ECa. In this study case, the ECa represented a good information for analysis of spatial variability of physical and chemical attributes of soil.