Using the ARP-03 for high-resolution mapping of calcic horizons

Priori, Simone; Fantappič, M.; Magini, S.; Costantini, E.

doi:10.2478/v10247-012-0100-0

Cited by 18 publications

(9 citation statements)

References 14 publications

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“…Root counts (RC) below 1.2 m also differed between profiles ( Figure 1C ments), might have constituted an impenetrable layer for roots. ER sensitivity to the presence of hardpan was also reported by other authors (Tabbagh et al, 2000;Priori and Fantappi, 2013). As mentioned, a petrocalcic horizon of high resistivity was also found in trench A4 (ER=56.4 Ohm m); this area however is situated at the bottom of the field where drainage channels converge (at the time of measurements below 1.20 m there was a water content of 22%).…”

Section: Resultssupporting

confidence: 81%

Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

Rossi¹,

Pollice²,

Bitella³

et al. 2015

Ital J Agronomy

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Alfalfa is a highly productive and fertility-building forage crop; its performance, can be highly variable as influenced by within-field soil spatial variability. Characterising the relations between soil and forage-variation is important for optimal management. The aim of this work was to model the relationship between soil electrical resistivity (ER) and plant productivity in an alfalfa (Medicago sativa L.) field in Southern Italy. ER mapping was accomplished by a multi-depth automatic resistivity profiler. Plant productivity was assessed through normalised difference vegetation index (NDVI) at 2 dates. A non-linear relationship between NDVI and deep soil ER was modelled within the framework of generalised additive models. The best model explained 70% of the total variability. Soil profiles at six locations selected along a gradient of ER showed differences related to texture (ranging from clay to sandy-clay loam), gravel content (0 to 55%) and to the presence of a petrocalcic horizon. Our results prove that multi-depth ER can be used to localise permanent soil features that drive plant productivity.

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Section: Resultssupporting

confidence: 81%

Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

Rossi¹,

Pollice²,

Bitella³

et al. 2015

Ital J Agronomy

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“…For fine scale mapping, proximal sensing of the soil's electrical conductivity appears to be an efficient way to map calcic horizons (Priori et al ., ). Priori et al .…”

Section: Mapping Soil Profiles and Pedological Featuresmentioning

confidence: 97%

“…Priori et al . () concluded that the depth to the calcic horizon in a vineyard showed a strong correlation with soil electrical conductivity by combining data from the geoelectrical sensor with a limited number of soil cores.…”

Section: Mapping Soil Profiles and Pedological Featuresmentioning

confidence: 99%

“…For fine scale mapping, proximal sensing of the soil's electrical conductivity appears to be an efficient way to map calcic horizons (Priori et al, 2013). Priori et al (2013) concluded that the depth to the calcic horizon in a vineyard showed a strong correlation with soil electrical conductivity by combining data from the geoelectrical sensor with a limited number of soil cores.…”

Section: Mapping Pedological Featuresmentioning

confidence: 99%

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Pedology and digital soil mapping (DSM)

Minasny

Malone

et al. 2019

European J Soil Science

160

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Pedology focuses on understanding soil genesis in the field and includes soil classification and mapping. Digital soil mapping (DSM) has evolved from traditional soil classification and mapping to the creation and population of spatial soil information systems by using field and laboratory observations coupled with environmental covariates. Pedological knowledge of soil distribution and processes can be useful for digital soil mapping. Conversely, digital soil mapping can bring new insights to pedogenesis, detailed information on vertical and lateral soil variation, and can generate research questions that were not considered in traditional pedology. This review highlights the relevance and synergy of pedology in soil spatial prediction through the expansion of pedological knowledge. We also discuss how DSM can support further advances in pedology through improved representation of spatial soil information. Some major findings of this review are as follows: (a) soil classes can be mapped accurately using DSM, (b) the occurrence and thickness of soil horizons, whole soil profiles and soil parent material can be predicted successfully with DSM techniques, (c) DSM can provide valuable information on pedogenic processes (e.g. addition, removal, transformation and translocation), (d) pedological knowledge can be incorporated into DSM, but DSM can also lead to the discovery of knowledge, and (e) there is the potential to use process‐based soil–landscape evolution modelling in DSM. Based on these findings, the combination of data‐driven and knowledge‐based methods promotes even greater interactions between pedology and DSM. Highlights Demonstrates relevance and synergy of pedology in soil spatial prediction, and links pedology and DSM. Indicates the successful application of DSM in mapping soil classes, profiles, pedological features and processes. Shows how DSM can help in forming new hypotheses and gaining new insights about soil and soil processes. Combination of data‐driven and knowledge‐based methods recommended to promote greater interactions between DSM and pedology.

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“…EMI measurements are also influenced by the gravel content [16], soil depth [17], bulk density [18] and, indirectly, soil water availability [19]. The correlation between ER and soil water retention can be explained by the strong relationships of both variables with soil physical features, like texture, coarse fragments, and porosity [20].…”

mentioning

confidence: 99%

Towards Economic Land Evaluation at the Farm Scale Based on Soil Physical-Hydrological Features and Ecosystem Services

Priori¹,

Barbetti

Meini

et al. 2019

Water

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The economic evaluation of a land parcel is mainly based on the local economy, as well as on the topography, distance to the main streets, distance to the river, and presence of irrigation. Spatial variability of soil features and functionalities are often left behind during economic land evaluation, probably due to a scarce awareness of soil function's economic value. The paper shows an approach for economic land evaluation of irrigated croplands in the Po River plain (Northern Italy), based on spatial variability of soil functions, namely biomass production and carbon sequestration, as well as taking into account the river flood risk. The soil spatial variability was mapped using proximal sensing technology and few calibration points (one every 5 hectares). Biomass production of the main crops of the area, namely maize, soybean, and sorghum, was monitored and mapped for three years (2016, 2017, and 2018) using precision agriculture technologies. The results showed that the available water capacity (AWC) reached the highest correlation with biomass production, additionally, soil texture and cation exchange capacity were significantly correlated. Economic evaluation of the land parcels was computed considering the mean land market value of the area, the site-specific deviations due to the spatial variability of the biomass production by capitalization rate, and carbon sequestration soil functions, applying a natural capital approach by the mean annual value of the carbon market. This site-specific methodology could be applied to many other arable lands. 2 of 21 maps (land use, topography, alluvial and morphological risks, etc.). Rossiter [2] distinguished several land evaluation units, among which there are:

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Using the ARP-03 for high-resolution mapping of calcic horizons

Cited by 18 publications

References 14 publications

Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

Pedology and digital soil mapping (DSM)

Towards Economic Land Evaluation at the Farm Scale Based on Soil Physical-Hydrological Features and Ecosystem Services

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