Spatial Dependence Degree and Sampling Neighborhood Influence on Interpolation Process for Fertilizer Prescription Maps

Amaral, Lucas Rios do; Justina, Diego Della

doi:10.1590/1809-4430-eng.agric.v39nep85-95/2019

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Some researchers use metrics together to better assess spatial variability and optimize decision-making: Taylor et al (2007) using the NE and the MCD; Souza et al (2008) using the NE and the integral scale J2; Oldoni and Bassoi (2016) using SPD and SDI; Amaral and Della Justina (2019) and Guedes et al (2020) using the NE and SDI. These findings show the need for further studies to propose and evaluate the performance of the indexes, mainly for the SDM that is being proposed.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies have highlighted the importance of measuring the spatial variability of agricultural and soil properties using geostatistical indexes Oliveira, 2014, 2016;Appel Neto et al, 2018;Santos et al, 2018;Amaral and Della Justina, 2019;Leroux and Tisseyre, 2019). Such indexes are useful to assess the quality of the model fit on the semivariogram (Pazini et al, 2015;Oldoni and Bassoi, 2016;Büttow et al, 2017) and, consequently, to indicate whether kriging interpolation results in good quality maps (Appel Neto et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Geostatistical-based index for spatial variability in soil properties

Neto¹,

Seidel²,

Oliveira³

2020

Revista Brasileira De Ciência Do Solo

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The assessment of spatial variability of environmental variables such as soil properties is important for site-specific management. A geostatistical index that allows quantifying and characterizing the structure of spatial variability is fundamental in this context. Thus, this study aimed to develop a new spatial dependency index, called the Spatial Dependence Measure (SDM) for the spherical, exponential, Gaussian, cubic, pentaspherical, and wave semivariogram models; and comparing it with some of the indexes available in the literature. The SDM is also dimensionless, in the same way as the Spatial Dependence Index (SDI), also considering more parameters of the semivariogram, when compared to the Spatial Dependence Degree (SPD) and Relative Nugget Effect (NE) indexes. In a simulation data study, it is observed that the SDI and SDM indexes showed an advantage over the SPD (or NE). To exemplify the application of the SDM in the proposal for the classification of soil properties, we used estimates of geostatistical parameters presented in the two studies. The results indicate that the SDM can be a measure that, analyzed together with the SDI, can help to improve the description of the spatial variability structure. Thus, this study expands the number of geostatisitcal-based measures and increases the power of decision on the description of the degree of spatial variability of agricultural and soil attributes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geostatistical-based index for spatial variability in soil properties

Neto¹,

Seidel²,

Oliveira³

2020

Revista Brasileira De Ciência Do Solo

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“…For all tested parameters to configure the model in this study, the neighborhood is the most important as in other studies (e.g., [36,38,60]). The sampling neighborhood can explain the general trend induced due to different sample densities among the study area by defining the maximum search area and the number of neighbors included.…”

Section: Geostatistical Modeling Strategiesmentioning

confidence: 99%

“…Geostatistical interpolation techniques are often applied in environmental analyses on, e.g., atmospheric [32][33][34], terrestrial [35][36][37], freshwater [38], and marine ecosystems [39][40][41], to generate a spatial surface prediction of a target variable from point measurements [42]. In this context, geostatistical interpolation algorithms, such as Kriging, are probabilistic methods relying on variogram models that account for the spatial structure of measured values at given locations and their overall spatial arrangement, as well as the prediction location [43].…”

Section: Introductionmentioning

confidence: 99%

“…The provision of a full parameter list and configuration that defines the model strategy allows a reproducible interpolation of a target variable. Few studies related to precipitation and soil prediction inform about the model strategy, focusing on certain parameters configuration, such as algorithm used, amount of samples, and surrounding search with a detailed description on the data [34,[36][37][38]40]. Since software packages allow simple and straightforward analyses of randomly collected data sets without previous testing of quality standards and with default modeling configuration, more often, thematic maps get published without quality check and error estimation.…”

Section: Introductionmentioning

confidence: 99%

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Default versus Configured-Geostatistical Modeling of Suspended Particulate Matter in Potter Cove, West Antarctic Peninsula

Neder

Sahade

Abele

et al. 2020

Fluids

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The glacier retreat observed during the last decades at Potter Cove (PC) causes an increasing amount of suspended particulate matter (SPM) in the water column, which has a high impact on sessile filter feeder’ species at PC located at the West Antarctic Peninsula. SPM presents a highly-fluctuating dynamic pattern on a daily, monthly, seasonal, and interannual basis. Geostatistical interpolation techniques are widely used by default to generate reliable spatial information and thereby to improve the ecological understanding of environmental variables, which is often fundamental for guiding decision-makers and scientists. In this study, we compared the results of default and configured settings of three geostatistical algorithms (Simple Kriging, Ordinary Kriging, and Empirical Bayesian) and developed a performance index. In order to interpolate SPM data from the summer season 2010/2011 at PC, the best performance was obtained with Empirical Bayesian Kriging (standard mean = −0.001 and root mean square standardized = 0.995). It showed an excellent performance (performance index = 0.004), improving both evaluation parameters when radio and neighborhood were configured. About 69% of the models showed improved standard means when configured compared to the default settings following a here proposed guideline.

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