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Record W4213254031 · doi:10.1007/s10705-021-10187-w

Agri-environmental implications of N- and P-based manure application to perennial and annual cropping systems

2022· article· en· W4213254031 on OpenAlexaffabout
Vivekananthan Kokulan, D. V. Ige, O. O. Akinremi

Bibliographic record

VenueNutrient Cycling in Agroecosystems · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCanolaAgronomyManureLeaching (pedology)Environmental sciencePerennial plantCropping systemNutrientFertilizerBiomass (ecology)CropChemistrySoil waterBiology

Abstract

fetched live from OpenAlex

Abstract Continuous manure application based on crop nitrogen (N) requirements could substantially increase field nutrient losses from croplands. Although phosphorus-based (P) manure application is an alternative, crops may suffer from potential microbial P immobilization and fixation of P in soil. A three-year study (2012–2014) was carried out in Manitoba, Canada, to evaluate the agronomic and environmental benefits and tradeoffs between N- and P-based liquid and solid swine manure applications on previously established (2009) annual (ACS) and perennial (PCS) cropping systems. The N-based solid manure produced greater aboveground biomass and grain yields of barley than the unfertilized control in the ACS. The N-based liquid manure produced greater biomass than the control in the PCS. Phosphorus-based treatments produced statistically similar canola oilseed grains as the N-based treatments. Seeding the PCS to canola in 2013 produced greater aboveground biomass yields than the ACS; however, the canola oilseed yields were not significantly different between the two systems. The N-based solid manure application increased Olsen P by approximately 30 mg kg −1 in both cropping systems during the three-year study period. Both N- and P-based liquid manure treatments and the N-based solid manure treatment lost significantly greater nitrate through leaching than the control in 2013, when most leaching losses occurred. Our study also showed that some of the environmental benefits of the perennial cropping system, such as reduced nitrate leaching, could be lost when converted to an annual cropping system.

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 score0.161
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.208
Teacher spread0.203 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

Explore more

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