Espacialização em alta resolução de atributos da acidez de Latossolo por meio de sensoriamento em tempo real

Corassa, Geomar Mateus; Amado, Telmo Jorge Carneiro; Tabaldi, Fabiano Maurício; Schwalbert, Raíssa; Reimche, Geovane Boschmann; Nora, Douglas Dalla; Alba, Paulo José; Hörbe, Tiago de Andrade Neves

doi:10.1590/s0100-204x2016000900030

Cited by 13 publications

(12 citation statements)

References 15 publications

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“…Corassa et al [5] analyzed the correlation of soil electrical conductivity by on-the-go soil sensor, and observed that this process of determining real-time soil properties requires specific care, such as calibration for each type of soil and crop. However, when there is a high spatial resolution, i.e., a high number of sampling points, associated with oriented or traditional sampling, the use of sensor can lead to satisfactory results and savings in the application of inputs.…”

Section: Resultsmentioning

confidence: 99%

Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation

Ferraz

Barbosa²,

Reynaldo³

et al. 2019

DYNA

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This study aimed to characterize the spatial variability of pH in soils of two farms in the state of Paraná, Brazil, based on two different sampling methods used in precision agriculture, by means of geostatistical analyzes. The first method of sampling the pH grid consisted in the collection of soil samples by the traditional method (1 point / ha). The second method of pH determination was by on-the-go soil sensor (200 points / ha). The spherical model was better suited to most semivariograms, regardless of the sampling method. After adjusting the semivariograms for soil pH determination methods, thematic maps were made using normal kriging. The best spatial distribution of pH was obtained where the attribute was sampled by the on-the-go sensor. The number of pH samples collected and the sampling method influenced the visual representation of pH variability.

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Section: Resultsmentioning

confidence: 99%

Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation

Ferraz

Barbosa²,

Reynaldo³

et al. 2019

DYNA

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“…The success of management using MZ depends on the relationship of the variable to be mapped to those that will be managed. Studies have shown a correlation between the apparent soil electrical conductivity (CEa) and chemical and physical attributes of the soil (MORAL et al, 2010;RODRÍGUEZ-PÉREZ et al, 2011;CORASSA et al, 2016) that influence the crop yield. The measurement of ECa is faster and cheaper than traditional soil sampling and laboratory analysis (BOTTEGA et al, 2017) which makes it a potential variable in MZ delimitation.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the relationship between ECa and soil attributes (MORAL et al, 2010;RODRÍGUEZ-PÉREZ et al, 2011;CORASSA et al, 2016) and its potential as a limiting factor for MZ and considering the scarcity of studies on the relationship of ECa with soil compaction, the objective of this work was to evaluate the use of management zones, delimited on the basis of the mapping of the ECa spatial variability, as a factor of differentiation of soil compaction levels.…”

Section: Introductionmentioning

confidence: 99%

Characterization of the soil compaction based on the mapping of the apparent electrical conductivity

Bottega¹,

Sari²,

Oliveira³

et al. 2021

Reveng

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Based on the measurement of soil penetration resistance (PR), it is possible to identify compacted soil layers, where root growth may be harmed, affecting crop development and yield. The objective of this work was to analyze the use of management zones (MZ), delimited on the basis of mapping of the spatial variability of the soil apparent electrical conductivity (ECa), in the differentiation of soil compaction levels. The work was carried out in a 25.8-ha no-tillage area, cultivated under a center pivot. The ECa was measured under two soil moisture conditions (13.7 and 16.45%), using the Terram® equipment. Soil penetration resistance (PR) was measured using the SoloStar PLG5500 penetrograph. Based on the spatial variability ECa mapping, management zones (2, 3, and 4 zones) were delimited. The mean PR values ??of each MZ were compared by the t-test of means. It was possible to differentiate mean values ??of penetration resistance (PR), which vary from 0.9 to 2.10 MPa, from the characterization of management classes generated on the basis of the ECa spatial variability. The highest stratification of PR values ??was obtained as a function of sampling directed at delimited management zones when the soil had lower moisture content (13.7%). The highest mean PR values ??were obtained for the split of the ECa map into at least three classes. It was identified that for the study area there is no need to perform any mechanical decompaction operation.

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“…The spatial variability of crop yield under the notillage system (NTS) is influenced by several factors, especially soil chemical attributes (Rodrigues et al, 2012(Rodrigues et al, , 2013Santi et al, 2012). Knowledge of these limiting factors is essential for commercial crop planning (Rodrigues et al, 2012;Santi et al, 2012) and for site-specific management (Rodrigues et al, 2013;Corassa et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Soil chemical attributes restricting grain yield in Oxisols under no-tillage system

Corassa

Santi

Silva

et al. 2018

Pesq. agropec. bras.

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The objective of this work was to identify soil chemical attributes restricting grain yield in Oxisols under the no-tillage system, using directed soil sampling. High, medium, and low yield zones were defined in two agricultural fields using historical yield data of several crops. The yield zones were defined based on the harvest maps of the following crops: corn in 2008/2009, white oat in 2009, and corn in 2012/2013 in field I, with 117.70 ha; and corn in 2009/2010, soybean in 2010/2011, and wheat in 2012 in field II, with 107.30 ha. Soil sampling points were georeferenced in each yield zone, where samples were collected at eight soil depths, spaced 0.05 m apart, totaling 80 variables. Low yields were associated with low cation exchange capacity, low phosphorus and organic matter contents, and high clay content. In both studied fields, the highest organic matter content in the subsurface layers was the main indicator of high yield. Soil sampling considering yield zones is an efficient strategy to identify chemical attributes restricting grain yield and also allows guiding more precise site-specific interventions.

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Espacialização em alta resolução de atributos da acidez de Latossolo por meio de sensoriamento em tempo real

Cited by 13 publications

References 15 publications

Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation

Spatial variability of soil pH sampled by two methodologies used in precision agriculture in farms under crop rotation

Characterization of the soil compaction based on the mapping of the apparent electrical conductivity

Soil chemical attributes restricting grain yield in Oxisols under no-tillage system

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