Análise de variabilidade espacial e temporal em um campo de produção de sementes de soja e trigo

Corrêa, Marciabela Fernandes; Gadotti, Gizele Ingrid; Pinheiro, Romário de Mesquita; Nadal, Ariele Paula; Schuch, Luis Osmar Braga; Vergara, Rafael de Oliveira

doi:10.18605/2175-7275/cereus.v13n4p182-194

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Oliveira Junior et al (2020) found classes with low GDE for P and moderate for K and Mat Org. Correia et al (2021) studying the analysis of spatial and temporal variability in a soybean and wheat seed production field also found low GDE for pH and Mat Org attributes. According to Yan et al (2020), several factors, such as the nature of the data, sampling errors, measurement errors, or even micro regionalizations of the variable under analysis, can be the causes of a low GDE.…”

Section: Interpolation Of Soil Nutrients By Geostatisticsmentioning

confidence: 97%

Evaluation of chemical attributes of soils: definition of management zones in silvipastoral system

Filho,

Oliveira,

Oliveira

et al. 2024

Rev. ambiente água

View full text Add to dashboard Cite

Soil chemical analysis is a fundamental to achieve optimal cultivation conditions in a productive silvopastoral system, essential for the planning and management of this activity. The objective of this research is to map the spatial distribution of nutrients in the soil in a 5-year-old silvopastoral system composed of brachiaria-brizantha and jacarandá-da-bahia, and to analyze the effect of tree density and canopy on nutrient levels. The study was conducted at the experimental area of the Capixaba Institute for Research, Technical Assistance, and Rural Extension (INCAPER) in the district of Pacotuba, ES. The delimitation of the work area was done using geographic information systems, to create a regular grid of points to enable systematic soil sampling. A total of 75 disturbed soil samples were collected at depths of 0-0.10 m to obtain quantitative soil nutrient data. High-quality images obtained using a remotely piloted aircraft (RPA) allowed for the overlay of soil nutrient data, pasture data, forest inventory data, and sample plot data to assist in comparative analyses. This enabled identification of the areas with lower canopy development, particularly in the northeast portion. By cross-referencing the spatialized soil nutrient data with the data obtained through RPA, it was possible to correlate them with the lower or higher development of the system. The use of RPA, together with a georeferenced database, proved to be an innovative and powerful tool to support decision-making, allowing for the monitoring of system evolution and serving as a basis for evaluating the relationship between chemical elements and tree canopies. Keywords: geostatistics, precision agriculture, soil nutrients.

show abstract

Section: Interpolation Of Soil Nutrients By Geostatisticsmentioning

confidence: 97%