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Record W2123431229 · doi:10.1002/jpln.200625130

Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity

2008· article· en· W2123431229 on OpenAlexaboutno aff
Franz Michael Mertens, Stefan Pätzold, Gerhard Welp

Bibliographic record

VenueJournal of Plant Nutrition and Soil Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsSoil sciencePrecision agricultureSpatial variabilityEnvironmental scienceSoil waterLayeringGround truthLoessSoil mapWater contentScale (ratio)Image resolutionSoil testRemote sensingGeologyAgricultureMathematicsEcologyStatisticsGeotechnical engineeringComputer scienceGeomorphology

Abstract

fetched live from OpenAlex

Abstract The site‐specific cultivation as part of the precision‐agriculture concept is more and more introduced into practical farming. However, soil information is often not available in a spatial resolution intrinsically needed for precision farming or other site‐specific soil use and management purposes. One approach to obtain spatially high‐resolution soil data is the non‐invasive measurement of the apparent electrical conductivity (EC a ). In this study, we recorded the EC a on three fields with an EM38 (Geonics, Canada). The EC a data were compared with (1) ground truth data obtained by conventional drilling, (2) traditional soil maps (large scale, ≤1:5,000), (3) the growth and yield of corn. The temporal variability of the EC a due to varying soil moisture and temperature was taken into account by repeated measurements of the same fields and subsequent averaging of the EC a values. Significant correlations (r² = 0.76) were found between the mean weighted clay content (0–1.5 m) and the EC a . Furthermore, in soils with differently textured layers, EC a was used to estimate the thickness of the uppermost loess layer. A comparison of EC a and large‐scale soil maps reveals some pros and cons of EC a measurements. The main advantages of EC a recordings are the high spatial resolution in combination with low efforts. Yet, the EC a signal is no direct measure for a soil type or unit. Depending on the variability of substrates and layering, the EC a pattern can be a precise indicator for the spatial distribution of different soils. A strong conformity of the spatial variability of plant growth (derived from orthophotos and yield maps) and EC a patterns within a field indicates that the EC a signal per se— without conversion to traditional soil parameters—integrates the effects of various soil variables that govern soil fertility. Altogether, EC a surveys can be a powerful tool to facilitate and improve conventional soil mapping.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.223
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations81
Published2008
Admission routes1
Has abstractyes

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