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Record W1980319298 · doi:10.4141/s01-055

Hay mulching to reduce runoff and soil loss under intensive potato production in northwestern New Brunswick, Canada

2002· article· en· W1980319298 on OpenAlexvenueaboutno aff
H. W. Rees, T. L. Chow, P. J. Loro, Jean‐Michel Lavoie, J. O. Monteith, A. H. Blaauw

Bibliographic record

VenueCanadian Journal of Soil Science · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsnot available
Fundersnot available
KeywordsLoamMulchEnvironmental scienceSurface runoffHayAgronomyErosionSoil conservationPrecipitationSoil waterHydrology (agriculture)AgricultureGeographyBiologySoil scienceGeologyEcology

Abstract

fetched live from OpenAlex

Soil erosion by water associated with potato production in northwestern New Brunswick has been identified as one of the most severe soil degradation problems affecting soil quality in Canada. The objectives of this study were to evaluate the effectiveness of applying various rates of hay mulch following potato (Solanum tuberosum L.) harvest in reducing runoff and soil loss rates under northwestern New Brunswick climatic and soil conditions and to determine the impact of the various hay mulch application rates on potato yield. Wischmeier-like runoff-erosion plots (10 m wide by 30 m long) on a Holmesville gravelly loam soil were used. Annual hay mulch application rates of 0.00, 2.25, 4.50 and 9.00 t ha -1 wet mass were evaluated under continuous up-and-down-slope potato production on 8 and 11% slopes between October 1995 and October 1999. During the study period, annual precipitation was lower than normal. Calculated rainfall erosivities were 102, 66, 73 and 133% of the value typically used for conservation planning in this region (1276 MJ mm ha -1 h -1 ). Seventy-three percent of the average annual erosivity for the 4-yr period was associated with storms occurring in June, July, August and September. Hay mulching at rates of 2.25, 4.50 and 9.00 t ha -1 conserved on average 13, 18 and 28 mm of June to September precipitation, respectively. Mean annual soil losses were reduced to 14, 7 and 2% of the control (5.6 t ha -1 ) by the 2.25, 4.50 and 9.00 t ha -1 treatments, respectively, on the 11% slope and to 43 and 24% of the control (2.0 t ha -1 ) on the 2.25 and 4.50 t ha -1 treatments, respectively, on the 8% slope. Eroded sediment silt, clay and organic matter (OM) contents were 1.6, 1.9 and 2.3 times the content of the surface soil at the experimental site. Mulching at rates as low as 2.25 t ha -1 reduced nutrient losses of NO 3 -N and available P, K, Ca and Mg to 26, 18, 28, 20 and 24% of control, respectively, on the 11% slope, and to 81, 50, 82, 66 and 77% of control, respectively, on the 8% slope. However, levels of nutrient losses from the controls were low to begin with (2.0, 0.4, 2.8, 10.9 and 1.6 kg ha -1 of NO 3 -N, and available P, K, Ca and Mg, respectively). Both total and marketable potato crop yields from all 2.25 and 4.50 t ha -1 treatments were in excess of 5% greater than their controls; however, only the increases in total yields from the 2.25 and 4.50 t ha -1 treatments and marketable yield from the 4.50 t ha -1 treatment on the 8% slope were significantly greater at P< 0.05. Yield of potato on the 9.00 t ha -1 treatment showed a consistent increase in total yield over the 4-yr period, going from 80% of control in 1996 to 127% of control in 1999, indicating a possible improvement in soil productivity. Hay mulching at rates up to 9.00 t ha -1 did not increase the incidence of disease or other defects. Hay mulching was found to be an effective tool for reducing soil loss while maintaining, and in some cases enhancing, potato yield. Key words: Hay mulching, soil loss, water erosion, organic residues, sediment composition, nutrient loss, erosivity

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)0.000
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 score0.146
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.205
Teacher spread0.182 · 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

Citations63
Published2002
Admission routes2
Has abstractyes

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