Delineation of irrigation management zones in a Quartzipsamment of the Brazilian semiarid region

Oldoni, Henrique; Bassoi, L. H.

doi:10.1590/s0100-204x2016000900028

Cited by 17 publications

(14 citation statements)

References 35 publications

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“…To exemplify the application of the SDM in the proposal for classification of soil properties, we use estimates of geostatistical parameters presented in the studies by Oldoni and Bassoi (2016) and Guedes et al (2020). The authors used the SPD (or NE) and SDI indexes.…”

Section: Methodsmentioning

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). In addition, these indexes allow the comparison of within-field variability in different research situations such as when comparing the variability of different types of soils, chemical or physical properties, different crops, among others.…”

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: Methodsmentioning

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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“…This number of clusters is commonly used in several works on precision agriculture. It aims to determine the optimal number of management zones (Li et al, 2007;Rodrigues et al, 2015;Oldoni & Bassoi, 2016).…”

Section: Analysis Of Spatial Variabilitymentioning

confidence: 99%

Using multivariate analysis to design management zones

et al. 2020

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Soil chemical and physical attributes are important in any agricultural cropping system, but in precision agriculture they are more relevant due to the possibility of application using different management practices along a production field. However, the correlation between these attributes has been little explored in the delineation of management zones. This work aims to maximize the use of joint spatial variability for soil attributes. Its secondary objectives were 1) reduction of spatial variability dimensionality among all attributes and 2) assessment of agreement between univariate and multivariate management zones. The management zones resulting from the interpolation of attribute values, as well as from the scores of each of the three main components, were delineated using the Fuzzy c-means algorithm. The fuzzy performance and modified partition entropy indexes were used to determine the optimal number of management zones. The Kappa index was used to evaluate the agreement of management zones obtained from attributes with those obtained from principal components. By using principal component analysis, it was possible to reduce the dimensionality of the number of variables that contribute to the joint spatial variability existing in the study area. There was no complete agreement between the uni-and multivariate management zones outlined, which is why further studies on the subject are needed.

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“…The SDI has the potential to evaluate spatial dependence from the semivariograms adjustment, which may be the only index used for such evaluation (Pazini et al, 2015;Buttow et al, 2017;Pelissari et al, 2017), or be used in conjunction with other measures of spatial dependence (Oldoni, Bassoi, 2016).…”

Section: Bulletin Of Geodeticmentioning

confidence: 99%

“…Spatial dependence measures are useful to make classifications and comparisons among different spatial dependence scenarios (different spatial phenomena) (Biondi et al, 1994) without the necessity to take the whole semivariogram. In addition, as an index, they can be used to evaluate the quality of model fit to the semivariogram (Ávila, et al, 2010;Pazini, et al, 2015;Oldoni, Bassoi, 2016;Buttow et al, 2017) and, in consequence, to indicate whether the kriging interpolation will result in good quality maps. Even though the Cubic, Pentaspherical and Wave models have potential applicability in the Environmental Sciences, Geosciences, Soil Sciences and others, there is not a spatial dependence index that considers their specifications.…”

Section: Introductionmentioning

confidence: 99%

Spatial Dependence Index for Cubic, Pentaspherical and Wave Semivariogram Models

et al. 2018

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This study aims to propose a spatial dependence index (and its classification), from the concept of spatial correlation areas, for the Cubic, Pentaspherical and Wave models. The index, called Spatial Dependence Index (SDI), covers the following parameters: the range (a), the nugget effect (C 0 ) and the contribution (C 1 ), beyond considering the maximum distance (MD) between sampled points and the model factor (MF). The proposed index, unlike the most used in the literature, considers the influence of the range parameter to describe the spatial dependence, highlighting the importance of this formulation. The spatial dependence classification, based on the observed asymmetric behavior in the SDI, was performed considering categorizations from the median and the 3rd quartile of the index. We obtain the spatial dependence classification in terms of weak, moderate, and strong, just as it is usually described in literature.

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Delineation of irrigation management zones in a Quartzipsamment of the Brazilian semiarid region

Cited by 17 publications

References 35 publications

Geostatistical-based index for spatial variability in soil properties

Geostatistical-based index for spatial variability in soil properties

Using multivariate analysis to design management zones

Spatial Dependence Index for Cubic, Pentaspherical and Wave Semivariogram Models

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