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Record W2999855373 · doi:10.1002/agg2.20010

Economic effect of fall vs. spring plowing of forage on following potato production in Prince Edward Island, Canada

2020· article· en· W2999855373 on OpenAlexaffabout
Mohammad Khakbazan, Judith Nyiraneza, Yefang Jiang, V. Rodd, J. Huang, Bernie J. Zebarth, Keith Fuller, E. P. Smith, Rong‐Zhen Xie

Bibliographic record

VenueAgrosystems Geosciences & Environment · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Prince Edward IslandHealth PEIUniversity of FrederictonBrandon UniversityNova Scotia Department of AgricultureAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPloughTillageAgronomyForageSowingEnvironmental scienceLeaching (pedology)Hordeum vulgareGrazingSoil waterBiologyPoaceaeSoil science

Abstract

fetched live from OpenAlex

Abstract Fall plowing of forage in a typical barley ( Hordeum vulgare L.)–forage–potato ( Solanum tuberosum L.) rotation in Prince Edward Island (PEI), Canada has led to negative environmental impacts, including soil erosion and nitrate leaching to groundwater. Data from five locations in PEI during 2009–2016 were assessed to determine the effects of delaying plow‐down of forage from early and late fall to spring on economic returns and risk of returns trade‐offs for potato producers. Factors related to fall or spring plowing such as soil erosion, nitrate leaching, planting date, effect on weeds, insects and diseases, potato harvest loss, and labor constraints were quantified. Potato yields were the same for fall and spring plowing; however, combined data for the five experiments showed late fall plowing was preferred over spring plowing for risk‐averse or neutral potato growers. Risk neutral farmers would require receiving between CAN$229 and $836 ha −1 yr −1 , depending on yield loss for spring plowing due to delayed seeding, to be indifferent between fall and spring plowing options. Risk‐averse farmers at all levels of risk aversion would require being paid more than $600 ha −1 yr −1 to be indifferent between fall and spring tillage when 4–6% of yield loss for spring plowing due to delayed planting is assumed. Although spring tillage provides reductions in the risk of soil erosion and nitrate leaching, it also affects production risk and uncertainty. Therefore, we recommend farmers plow forage as late as possible in the autumn and replace it with other conservation tillage practices.

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 score0.129
Threshold uncertainty score0.933

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 studies0.0000.000
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 head0.004
GPT teacher head0.175
Teacher spread0.171 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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