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Record W2807988399 · doi:10.2134/jeq2017.07.0292

Long‐term Cropping Effects on Partitioning of Water Flow and Nitrate Loss between Surface Runoff and Tile Drainage

2018· article· en· W2807988399 on OpenAlexaff
Alex Woodley, C. F. Drury, W. D. Reynolds, C. S. Tan, X.M. Yang, T. O. Oloya

Bibliographic record

VenueJournal of Environmental Quality · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsTile drainageLoamSurface runoffNitrateFertilizerAgronomyEnvironmental scienceCrop rotationDrainageSurface waterSoil waterHydrology (agriculture)ChemistrySoil scienceEnvironmental engineeringCropBiologyEcologyGeology

Abstract

fetched live from OpenAlex

Surface runoff and tile drainage are the main pathways for water movement and entry of agricultural nitrate into water resources. The objective of this 5‐yr study was to characterize the partitioning of water flow and nitrate loss between these pathways for a humid‐temperate Brookston clay loam soil under 54 to 59 yr of consistent cropping and fertilization. Cropping treatments included monoculture corn ( Zea mays L., MC), continuous bluegrass ( Poa pratensis L.) sod (CS), and a corn–oat–alfalfa ( Medicago sativa L.)–alfalfa rotation (RC–RO–RA1–RA2). Fertilization treatments included annual fertilizer addition (F) and no fertilizer addition (NF). Tile drainage and surface runoff occurred primarily during the nongrowing season (November–April), and they were highly correlated with the mean saturated hydraulic conductivity of the near‐surface soil profile. Tile drainage accounted for 69 to 90% of cumulative water flow and 79 to 96% of cumulative nitrate loss from fertilized rotation and CS, whereas surface runoff accounted for the majority of the nitrate losses in MC (i.e., 75–93% of water flow and 65–96% of nitrate loss). Cumulative nitrate losses were highest in the RC‐F (152 kg N ha −1 ), RC‐NF (101 kg N ha −1 ), RA2‐F (121 kg N ha −1 ), and RA2‐NF (75 kg N ha −1 ) plots, and these high losses are attributed to N mineralization from the plowed alfalfa and fertilization (if applicable). Fertilization increased cumulative nitrate loss in tile drainage from all treatments, whereas no fertilization increased cumulative nitrate loss in surface runoff from the rotation. Cropping system and fertilization on clay loam soil changed how water flow and nitrate loss were partitioned between tile drainage and surface runoff. Core Ideas Water partitioning is highly correlated with mean saturated hydraulic conductivity. Tile drainage was the main pathway of water flow and nitrate loss in rotation plots. Surface runoff was the main pathway of water flow and nitrate loss in monoculture corn. Fertilization increased nitrate loss and water flow through tile drainage. Average nitrate loss was 36% greater in the rotation plots versus monoculture corn.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.027
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.247
Teacher spread0.236 · 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 teacher head, 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

Citations31
Published2018
Admission routes1
Has abstractyes

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