A comparison of soil maps, kriging and a combined method for spatially predicting bulk density and field capacity of ferralsols in the Havana–Matanzas Plain

Utset, Angel; López, Teresa; Dı́az, Martı́n

doi:10.1016/s0016-7061(99)00055-5

Cited by 39 publications

(17 citation statements)

References 9 publications

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“…LCW showed higher CV and lower values of nugget/sill and nugget effect as well. The opposite situation occurred in the MCW, in disagreement with Utset et al (2000).…”

Section: Ordinary Kriging and Regression Krigingmentioning

confidence: 85%

“…This method might be more appropriate when the complexity of pedogenesis is too large to be captured in a deterministic way (Herbst et al, 2006). Utset et al (2000) reported that soil properties were more precisely estimated for those whose CV was the least. In this work, MCW showed the lowest values of CV and the OK performed better than LCW (higher values of RMSPE), when comparing the same physical properties.…”

Section: Assessment Of Prediction Methodsmentioning

confidence: 99%

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Spatial prediction of soil properties in two contrasting physiographic regions in Brazil

Menezes

Silva

Mello

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods.Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK.In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape.

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“…LCW showed higher CV and lower values of nugget/sill and nugget effect as well. The opposite situation occurred in the MCW, in disagreement with Utset et al (2000).…”

Section: Ordinary Kriging and Regression Krigingmentioning

confidence: 85%

Section: Assessment Of Prediction Methodsmentioning

confidence: 99%

Spatial prediction of soil properties in two contrasting physiographic regions in Brazil

Menezes

Silva

Mello

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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“…Most existing large SWHC maps are based on the use of numerical maps of soils, thus providing averaged soil property values per soil mapping unit (Batjes, 1997;Wosten et al, 1999). This approach is criticized because it does not account for variations of soil properties within soil units, and soil classification criteria cannot correspond to the soil property being mapped (Leenhardt et al, 1994;Voltz et al, 1997;Utset et al, 2000). We have shown here that the use of easy-to-collect information surveyed on numerous plots located at regular spatial intervals allow to overcome these limits, offering new perspectives for large scale digital mapping of soil parameters.…”

Section: Interest Of the Swhc''map And Its Use For Tree Growth Modellingmentioning

confidence: 99%

Mapping soil water holding capacity over large areas to predict potential production of forest stands

et al. 2011

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Ingrid Seynave. Mapping soil water holding capacity over large areas to predict the potential production of forest stands. Geoderma, Elsevier, 2011, 160, pp.355-366. 10 AbstractEcological studies need environmental descriptors to establish the response of species or communities to ecological conditions. Soil water resource is an important factor but is poorly used by plant ecologists because of the lack of accessible data. We explore whether a large number of plots with basic soil information collected within the framework of forest inventories allows the soil water holding capacity (SWHC) to be mapped with enough accuracy to predict tree species growth over large areas. We first compared the performance of available pedotransfer functions (PTFs) and showed significant differences in the prediction quality of SWHC between the PTFs selected.We also showed that the most efficient class PTFs and continuous PTFs compared had similar performance, but there was a significant reduction in efficiency when they were applied to soils different from those used to calibrate them. With a root mean squared error (RMSE) of 0.046 cm 3 cm -3 (n = 227 horizons), we selected the Al Majou class PTFs to predict the SWHC in the soil horizons described in every plot, thus allowing 84% of SWHC variance to be explained in soils free of stone (n = 63 plots). Then, we estimated the soil water holding capacity by integrating the stone content collected at the soil pit scale (SWHC') and both the stone content at the soil pit scale and rock outcrop at the plot scale (SWHC") for the 100.307 forest plots recorded in France within the framework of forest inventories. The SWHC" values were interpolated by kriging to produce a map with 1 km² cell size, with a wider resolution leading to a decrease in map accuracy. The SWHC" given by the map ranged from 0 to 148 mm for a soil down to 1 m depth. The RMSE between map values and plot estimates was 33.9 mm, the best predictions being recorded for soils developed on marl, clay, and hollow silicate rocks, and in flat areas. Finally, the ability of SWHC' and SWHC" to predict 3 height growth for Fagus sylvatica, Picea abies and Quercus petraea is discussed. We show a much better predictive ability for SWHC" compared to SWHC'. The values of SWHC" extracted from the map were significantly related to tree height growth. They explained 10.7% of the height growth index variance for Fagus sylvatica (n = 866), 14.1% for Quercus petraea (n = 877) and 10.3% for Picea abies (n = 2067). The proportions of variance accounted by SWHC" were close to those recorded with SWHC" values estimated from the plots (11.5, 11.7, and 18.6% for Fagus sylvatica, Quercus petraea and Picea abies, respectively). We conclude that SWHC" can be mapped using basic soil parameters collected from plots, the predictive ability of the map and of data derived from the plot being close. Thus, the map could be used just as well for small areas as for large areas, directly or indirectly through water balance indices, to predict forest growth an...

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“…Geostatistics is a group of methods that has been widely used for prediction of variability of soil properties in last two decades (Utset et al 2000). This technique provides prediction of soil properties at unobserved locations.…”

mentioning

confidence: 99%

Soil depth prediction supported by primary terrain attributes: a comparison of methods

Penížek¹,

Borůvka²

2006

PLANT SOIL ENVIRON

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The objective of this study was to investigate the benefits of methods that incorporate terrain attributes as covariates into the prediction of soil depth. Three primary terrain attributes -elevation, slope and aspect -were tested to improve the depth prediction from conventional soil survey dataset. Different methods were compared: 1) ordinary kriging (OK), 2) co-kriging (COK), 3) regression-kriging (REK), and 4) linear regression (RE). The evaluation of predicted results was based on comparison with real validation data. With respect to means, OK and COK provided the best prediction (both 110 cm), RE and REK gave the worst results, their means were significantly lower (79 and 108 cm, respectively) than the mean of real data (111 cm). F-test showed that COK with slope as covariate gave the best result with respect to variances. COK also reproduced best the range of values. The use of auxiliary terrain data improved the prediction of soil depth. However, the improvement was relatively small due to the low correlation of the primary variable with used terrain attributes.

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A comparison of soil maps, kriging and a combined method for spatially predicting bulk density and field capacity of ferralsols in the Havana–Matanzas Plain

Cited by 39 publications

References 9 publications

Spatial prediction of soil properties in two contrasting physiographic regions in Brazil

Spatial prediction of soil properties in two contrasting physiographic regions in Brazil

Mapping soil water holding capacity over large areas to predict potential production of forest stands

Soil depth prediction supported by primary terrain attributes: a comparison of methods

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