Adding random points to sampling grids to improve the quality of soil fertility maps

Baio, Fábio Henrique Rojo; Alixame, Danieli; Neves, Danilo Carvalho; Teodoro, Larissa Pereira Ribeiro; Júnior, Carlos Antonio da Silva; Shiratsuchi, L. S.; Oliveira, Job Teixeira de; Teodoro, Paulo Eduardo

doi:10.1007/s11119-023-10031-x

Precision Agric

2023

DOI: 10.1007/s11119-023-10031-x

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Adding random points to sampling grids to improve the quality of soil fertility maps

Fábio Henrique Rojo Baio

Danieli Alixame

Danilo Carvalho Neves

et al.

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Cited by 2 publications

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Correlations between soil properties and spectral index (healthy vegetation) in soybean crops

Roque,

Teixeira de Oliveira,

Baio

et al. 2023

Agron. Colomb.

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Precision agricultural technologies, such as the use of spatial variability of soil properties, have been extensively studied for soybean cultivation. The objective of this study was to analyze the spatial variability of soil properties cultivated with soybean and to correlate the healthy vegetation (HV) spectral index with the bands B8A (classifying vegetation - 865 nm), B11 (measuring the moisture content of soil and vegetation - 1610 nm), B02 blue (useful for soil and vegetation discrimination - 490 nm). A sampling grid was installed for data collection in an area of 2,126.02 ha, with 270 regular points and 98 random points, totaling 368 points. For the soil, the contents of P (resin), K+, Ca2+, Mg2+, H+, Al3+, pH values, sum of bases (SB), cation exchange capacity (CEC), and base saturation were determined at a depth of 0.0 to 0.20 m. Most of the soil properties had exponential and spherical dependence. Clay percentages and Ca, Mg, and P contents had positive spatial correlation with the healthy vegetation spectral index (HV) while no spatial correlation was observed for pH, B, K, silt, sand, S, H+Al, Al, SB, and CEC. The sensor image used in this study in relation to HV showed good application for observing the spatial variability of the soil properties and soybean yield.

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Correlations between soil properties and spectral index (healthy vegetation) in soybean crops

Roque,

Teixeira de Oliveira,

Baio

et al. 2023

Agron. Colomb.

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High-resolution banana row maps for the characterization of spatial variability in the field

Silva,

Barros,

Silva

et al. 2024

Revista Brasileira De Ciência Do Solo

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Precision agriculture (PA) practices in banana production chains have received limited attention. Based on the literature, the investigation of spatial and temporal variability in banana orchards should be customized according to the characteristics of the crop. This study aimed to develop and evaluate methods for mapping the spatial variability in soil properties at row- and clump-resolutions in a banana orchard, and to generate row and clump maps with high-spatial-resolution soil property information. A banana orchard was investigated, and georeferenced soil sampling was conducted with calibration and validation points. Methods for reconstructing banana rows and clumps were proposed, called Methods 1 and 2 and Alternative Methods 1 and 2. Surface and line maps at row- and clump-resolutions for soil chemical and physical properties were generated using ordinary kriging and Voronoi polygons. Subsequently, the discrepancies between the data obtained from the validation points and the predictions devised from the surfaces generated by the proposed approaches were calculated, and the RMSE was used as a performance parameter. Methods 1 and 2 were appropriate and reliable approaches for site-specific management and allow for specific and optimized crop management in banana cultivation, offering greater accuracy in cultivation operations such as fertilization.

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Adding random points to sampling grids to improve the quality of soil fertility maps

Cited by 2 publications

References 18 publications

Correlations between soil properties and spectral index (healthy vegetation) in soybean crops

Correlations between soil properties and spectral index (healthy vegetation) in soybean crops

High-resolution banana row maps for the characterization of spatial variability in the field

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