Digital mapping of soil properties and associated uncertainties in the Llanos Orientales, South America

Ashtekar, Jenette M.; Owens, Phillip R.; Brown, Robert A.; Winzeler, H.; Dorantes, Minerva J.; Libohova, Zamir; DaSilva, Marcio; Castro, Aracely

doi:10.1201/b16500-67

Cited by 9 publications

(10 citation statements)

References 1 publication

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“…The standard deviation of each variable for each soil class was also calculated per soil class based on data obtained from the 39 sampling sites. Both the mean and the standard deviation of the chosen variables per soil class were used as rules for predicting the spatial occurrence of soil classes through ArcSIE, the soil inference function, an ArcGIS extension that has been successfully used for soil mapping [59,[61][62][63][64]. For example, according to the sampling sites, a soil class was found to occur at places where slope values range from 12% to 20% (mean˘standard deviation), with a mean value (typical condition) of 16% coupled with SWI ranging from 2 to 4 and a mean value of 3.…”

Section: Soil Classes Mappingmentioning

confidence: 99%

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

et al. 2016

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Digital terrain models (DTM) have been used in soil mapping worldwide. When using such models, improved predictions are often attained with the input of extra variables provided by the use of proximal sensors, such as magnetometers and portable X-ray fluorescence scanners (pXRF). This work aimed to evaluate the efficiency of such tools for mapping soil classes and properties in tropical conditions. Soils were classified and sampled at 39 locations in a regular-grid design with a 200-m distance between samples. A pXRF and a magnetometer were used in all samples, and DTM values were obtained for every sampling site. Through visual analysis, boxplots were used to identify the best variables for distinguishing soil classes, which were further mapped using fuzzy logic. The map was then validated in the field. An ordinary least square regression model was used to predict sand and clay contents using DTM, pXRF and the magnetometer as predicting variables. Variables obtained with pXRF showed a greater ability for predicting soil classes (overall accuracy of 78% and 0.67 kappa index), as well as for estimating sand and clay contents than those acquired with DTM and the magnetometer. This study showed that pXRF offers additional variables that are key for mapping soils and predicting soil properties at a detailed scale. This would not be possible using only DTM or magnetic susceptibility.

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Section: Soil Classes Mappingmentioning

confidence: 99%

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

et al. 2016

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“…This is an adequate indicator that Geomorphons is able to stratify the landscape in geomorphological units which have correlation with soil types. Possibly, for the tropical conditions evaluated, a number of the GUs could be merged in order to facilitate the understanding of soil distribution along the area of interest, as suggested by Ashtekar et al (2014) in Colombia as a first attempt to model soil properties from Llanos Orientales. For example, ET was predominantly observed in Valley in MCW and in Pit in LCW.…”

Section: Resultsmentioning

confidence: 99%

Geomorphometric tool associated with soil types and properties spatial variability at watersheds under tropical conditions

Silva

Menezes

Mello

et al. 2016

Sci. agric. (Piracicaba, Braz.)

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The application of quantitative methods to digital soil and geomorphological mapping is becoming an increasing trend. One of these methods, Geomorphons, was developed to identify the ten most common landforms based on digital elevation models. This study aimed to make a quantitative assessment of the relationships between Geomorphons units, determined at three spatial resolutions and nine radii, and soil types and properties of two watersheds with different soil-landscape relationships in Brazil to help soil surveying and mapping under tropical conditions. The study was conducted at Lavrinha Creek (LCW) and Marcela Creek (MCW) watersheds, located in the state of Minas Gerais, Brazil. Spatial resolutions of 10, 20 and 30 m were the basis for generating Geomorphons at 9 radii of calculation for the watersheds. They were overlapped to detailed soil maps of the watersheds and a chi-square test was carried out to assess their relationship with soil types. Observation points were compared with the most highly correlated Geomorphons to also assess relationships with soil properties. Geomorphons with resolution of 30-m and radii of 20 and 50 cells, respectively for LCW and MCW, were more highly correlated with the variability of soil types, in accordance with the terrain features of these watersheds. The majority of observation points for each soil type was located in the same Geomorphon unit that was dominant when analyzing soil maps. There was less variability in soil properties between Geomorphon units, which was probably due to the highly weathered-leached stage of soils. Geomorphons can help to improve soil maps in tropical conditions when assessing soil variability due to its high correlation with tropical soil types variability.

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“…Use of the DNDC model requires soil data at each simulated location in the field, but extensive soil testing throughout the field was impractical. Alternatively, we developed detailed soil data using a geomorphometric fuzzy logic soil mapping approach that creates continuous soil property predictions at the resolution of the digital elevation model (Ashtekar et al, 2014). This method used the Geomorphons add-on in Geographic Resources Analysis Support System-geographic information systems, in which all pixels on a 10-m gridded digital elevation model were classified into 1 of 10 possible landforms.…”

Section: Denitrification-decomp Modelingmentioning

confidence: 99%

A Case Study of Environmental Benefits of Sensor-Based Nitrogen Application in Corn

Duval

Anex

et al. 2016

J. Environ. Qual.

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Crop canopy reflectance sensors make it possible to estimate crop N demand and apply appropriate N fertilizer rates at different locations in a field, reducing fertilizer input and associated environmental impacts while maintaining crop yield. Environmental benefits, however, have not been quantified previously. The objective of this study was to estimate the environmental impact of sensor-based N fertilization of corn using model-based environmental Life Cycle Assessment. Nitrogen rate and corn grain yield were measured during a sensor-based, variable N-rate experiment in Lincoln County, MO. Spatially explicit soil properties were derived using a predictive modeling technique based on in-field soil sampling. Soil N 2 O emissions, volatilized NH 3 loss, and soil NO 3 -leaching were predicted at 60 discrete field locations using the DeNitrificationDeComposition (DNDC) model. Life cycle cumulative energy consumption, global warming potential (GWP), acidification potential, and eutrophication potential were estimated using model predictions, experimental data, and life cycle data. In this experiment, variable-rate N management reduced total N fertilizer use by 11% without decreasing grain yield. Precision application of N is predicted to have reduced soil N 2 O emissions by 10%, volatilized NH 3 loss by 23%, and NO 3 -leaching by 16%, which in turn reduced life cycle nonrenewable energy consumption, GWP, acidification potential, and eutrophication potential by 7, 10, 22, and 16%, respectively. Although mean N losses were reduced, the variations in N losses were increased compared with conventional, uniform N application. Crop canopy sensor-based, variable-rate N fertilization was predicted to increase corn grain N use efficiency while simultaneously reducing total life-cycle energy use, GWP, acidification, and eutrophication.

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Digital mapping of soil properties and associated uncertainties in the Llanos Orientales, South America

Cited by 9 publications

References 1 publication

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

Geomorphometric tool associated with soil types and properties spatial variability at watersheds under tropical conditions

A Case Study of Environmental Benefits of Sensor-Based Nitrogen Application in Corn

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