The importance of modeling the effects of trend and anisotropy on soil fertility maps

Oliveira, Agda Loureiro Gonçalves; Lima, Joaquim Pedro de; Brasco, Thiago Luis; Amaral, Lucas Rios do

doi:10.1016/j.compag.2022.106877

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Using a semivariogram model, the Kriging interpolation method in geostatistical analysis predicts an unsampled value based on the surrounding sample. Oliveira, Lima, Brasco, & Amaral (2022) show that Kriging could provide accurate estimates of soil properties while detecting anisotropy and providing an error in the prediction.…”

Section: Introductionmentioning

confidence: 95%

Spatial Analysis of Soil Available Potassium and Plant Brix Content for Site Specific Nutrient Management in Sugarcane

Wijayanto¹,

Abdilah²,

Purnamasari³

et al. 2022

Agrivita.J. Agr.Sci

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The implementation of SSNM in crop production, for example, paddy and maize, is not new (Li, Zhao, Wang, Sefton, & Triantafilis, 2019;Munnaf, Haesaert, & Mouazen, 2022); however, its application in sugarcane management is still in its infancy, and the integration of technologies into SSNM in sugarcane is a crucial need (Maldaner, 2021). The importance of technologies for sugarcane SSNM has been discussed elsewhere (Li, Zhao, Wang, Sefton, & Triantafilis, 2019;Sanches, Magalhães, & Franco, 2019). From these studies, the most fundamental component and the first step of any SSNM (including SSNM for sugarcane) relates to how to asses variability at the field scale. This implies that the recommendation for soil nutrient management must be conducted not uniformly but at the scale of field variability. The recommendation could be conducted based on the relationship between pertinent soil properties and sugarcane quality that can be observed. Two

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Section: Introductionmentioning

confidence: 95%

Spatial Analysis of Soil Available Potassium and Plant Brix Content for Site Specific Nutrient Management in Sugarcane

Wijayanto¹,

Abdilah²,

Purnamasari³

et al. 2022

Agrivita.J. Agr.Sci

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“…Therefore, it is not recommended in the study of environments, since it evaluates the behavior of variables two to two (Karydis, 2022). Today the technique that presents results closer to the reality of the soil system, is geostatistics, because it takes into account the variability existing in space, enabling the more accurate management of agricultural production systems (Oliveira et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability of the Organic Matter in the Soil in Cassava Cultivation Under Differentiated Management

Souza¹,

Reis²,

Gasparin³

et al. 2022

JAS

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The management of soil organic matter (SOM) is fundamental in agriculture for soil conservation and crop yields. However, in addition the soil being dynamics, it is heterogeneous, therefore, understanding the spatial variability of SOM is essential. The main objective of this study was to evaluate the spatial variability of SOM in a cassava cultivation under different management, seeking to classify its spatial dependence by geostatistics. A filed experiment was conducted out on soil classified as oxisol with four different management systems: irrigation (micro sprinkler, drip and no irrigation), spacing (1.0 × 0.8 m, 1.0 × 1.0 m and 1.0 × 1.5 m), weed control (manual control and no control) and acidity correction (limestone and withouth limestone), totaling 36 experimental plots. To determine the SOM, the wet oxidation method was used, and the semivariograms were generated by the GS+® software. The effect of the different management systems on the spatial variability of the SOM was evaluated at a depth of 0.0-0.2 m. The theoretical semivariogram model that best fitted the study was the Gaussian model, with a well-defined level, also expressing the condition of data stationarity. The spatial dependence was classified as strong and, through the thematic map generated from the kriging, it was possible to observe the variability in the SOM content for different management zones. The use of geostatistics techniques provided important information for understanding the spatial distribution of soil organic matter.

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Exploring 20-year applications of geostatistics in precision agriculture in Brazil: what’s next?

2023

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The importance of modeling the effects of trend and anisotropy on soil fertility maps

Cited by 8 publications

References 32 publications

Spatial Analysis of Soil Available Potassium and Plant Brix Content for Site Specific Nutrient Management in Sugarcane

Spatial Analysis of Soil Available Potassium and Plant Brix Content for Site Specific Nutrient Management in Sugarcane

Spatial Variability of the Organic Matter in the Soil in Cassava Cultivation Under Differentiated Management

Exploring 20-year applications of geostatistics in precision agriculture in Brazil: what’s next?

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