Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Tarr, Alison B.; Moore, Kenneth J.; Bullock, D. G.; Dixon, Philip M.; Burras, C. Lee

doi:10.1007/s11119-005-1385-9

Cited by 34 publications

(18 citation statements)

References 33 publications

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“…These findings are in accordance with previous research indicating that a strong correlation between variables leads to improved results in Co-Kriging [14,37,38]. Having the secondary parameter (pH) more extensively sampled could potentially further improve its prediction accuracy [3,39].…”

Section: Discussionsupporting

confidence: 92%

“…Specifically, in soil science, correlations between multiple soil properties [12] and the prediction of soil heavy metal contents [13] across Europe have been thoroughly studied. In precision agriculture (large scales), different techniques are also used to improve the accuracy of mapping soil variables when using electrical conductivity [14] or when predicting soil parameters such as nitrogen (N) with elevation data as auxiliary information [15]. Geostatistics, remote sensing (e.g., ASTER, Landsat images) and the use of auxiliary maps (Digital Elevation Models, soil maps, land coverage maps, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information

Tziachris¹,

Metaxa²,

Papadopoulos³

et al. 2017

IJGI

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Abstract:In this study, different interpolation techniques are presented, assessed, and compared for the estimation of soil iron (Fe) contents in locations where observations were not available. Initially, 400 soil samples from the Kozani area, which is near Polifitou Lake in northern Greece, were randomly collected from 2013 to 2015 and were analysed in the laboratory to determine the soil Fe concentrations and pH. The soil Fe concentrations were examined for spatial autocorrelation, and semivariograms were used to determine whether pH and Fe exhibited spatial cross correlation. Three interpolation methods, including Ordinary Kriging, Universal Kriging, and Co-Kriging, were applied, and their results were compared with the use of two different cross-validation methods. In the current study, there was evidence of spatial cross correlation of soil Fe and pH for each year, which was subsequently used to improve the interpolation results in locations where there were no measurements. In nearly all cases, Co-Kriging, which takes advantage of the covariance between the two regionalized variables (Fe and pH), outperformed the other interpolation techniques each year.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information

Tziachris¹,

Metaxa²,

Papadopoulos³

et al. 2017

IJGI

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“…In the case of phosphorus, there is a clear need to re- shown previously by Tarr et al, (2005). Serrano et al, (2010) also observed higher soil electrical conductivity in lower areas, where soil moisture levels tend to be naturally higher at the end of spring, in Mediterranean climates.…”

Section: % Below the Reference Value) Differential Vineyard Fertilmentioning

confidence: 60%

Differential vineyard fertilizer management based on nutrient,s spatio-temporal variability

Serrano

Silva

Shahidian

et al. 2017

J. Soil Sci. Plant Nutr.

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Conventionally, vineyard fertilizer management has been based on information from composite soil samples and no account has been taken of the existing spatial variability in soil fertility. This study presents a quantitative analysis of soil phosphorus (P 2 O 5 ) and potassium (K 2 O) content as well as pH carried out in an 80 ha vineyard, during 2011 and 2013 in order to identify their spatial variability and temporal stability. Additionally a quantitative analysis of plant P 2 O 5 and K 2 O content was carried out in 2013 with the objective of evaluating the spatial variability of plant nutrients. In 2013 a contact sensor was used to survey soil apparent electrical conductivity (EC a ) and an active optical sensor was used to measure the plant Normalized Difference Vegetation Index (NDVI). The results showed a low potential for implementing site-specific management of phosphorus fertilizer but an interesting potential for implementing site-specific management of potassium fertilizer and pH correction. The concentration of P 2 O 5 and K 2 O in the plant showed a CV<30%, with adequate values in almost the entire area of the field, in contrast to the concentration of these main macronutrients in the topsoil. These results show that for differential nutrient management of vineyards, plant nutrient concentration is a more stable tool than soil nutrients concentration. The EC a and the NDVI presented weak correlations with soil and plant concentration of, , respectively, P 2 O 5 and K 2 O, which shows that further development of vegetation operational sensors is needed to support decision making in the vineyard fertilization management.

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“…In general, the values of R 2 were satisfactory, since for spatial quantitative models of soil, R 2 values higher than 0.70 are uncommon and values of 0.50 or less are common (Malone et al, 2009); in addition, the statistical results are equivalent to applied studies with different spatialization methods (Tarr et al, 2005;Kumar et al, 2012;Vaysse and Lagacherie, 2015) and considering that the scale adopted was 1 km.…”

Section: Discussionmentioning

confidence: 91%

Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon

Souza

Thomazini

Schaefer

et al. 2018

Rev. Bras. Ciênc. Solo

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Ultisols are the most common soil order in the Brazilian Amazon. The Legal Amazon (LA) has an area of 5 × 10 6 km 2 , with few accessible areas, which restricts studies of soils at a detailed level. The pedological properties can be estimated more efficiently using statistical procedures and machine learning techniques, tools which are capable of recognizing patterns in a large soil database. We analyzed the main chemical and physical properties of the B horizons of the Ultisols of the Brazilian Amazon, as well as the spatial variability of the most explanatory properties of these horizons. Physical and chemical data of 1,068 profiles of the RadamBrasil Project were used. A principal component analysis (PCA) was applied and the most explanatory variables were separated by morphostructural units and climate zones. The technique of machine learning was used for spatialization of the explanatory variables based on predictive covariates. In general, the horizons are thick, clay, with a predominance of negative charges, and low levels of exchangeable cations. The variables retained in the PCA were: sum of bases (SB), Al 3+ , degree of flocculation (Floc), ∆pH, and organic carbon content (C). Areas of greater precipitation have low SB, with higher values in the basement complex (BC) and in areas under the Andean influence. Higher levels of Al 3+ and degrees of flocculation were also associated with greater precipitation. However, the soils are predominantly electronegative, showing a kaolinitic mineralogy. The C contents in general were low, with an increase in more humid zones due to the process of mineralization and illuviation (podzolization), and in the BC due to the protection of C by the aggregation of clay. The use of multivariate analysis allowed a better understanding of the Ultisols' main properties in different morphostructural and climatic domains, and its spatialization facilitated the interpretation of properties and their relationships with environmental characteristics in the Legal Amazon.

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Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate

Cited by 34 publications

References 33 publications

Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information

Spatial Modelling and Prediction Assessment of Soil Iron Using Kriging Interpolation with pH as Auxiliary Information

Differential vineyard fertilizer management based on nutrient,s spatio-temporal variability

Multivariate Analysis and Machine Learning in Properties of Ultisols (Argissolos) of Brazilian Amazon

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