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Record W3131291035 · doi:10.3390/agronomy11020370

Precision Injection of Dairy Sludge on Crop Yield and N and P Uptake in Juvenile and Mature No-Till Silage Corn

2021· article· en· W3131291035 on OpenAlexaff
Derek Hunt, Shabtai Bittman

Bibliographic record

VenueAgronomy · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsSilageFertilizerAgronomySlurryPhosphorusStarterCropForageNutrientAnimal scienceChemistryBiologyEnvironmental scienceFood scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Starter mineral fertilizer is used by famers to provide phosphorus (P) and nitrogen (N) to emerging corn (Zea mays) plants. Recent studies have shown that dairy slurry can replace mineral fertilizer provided it is precisely positioned close to the corn rows. This 5-year study examined the multi-year effect of precision injected sludge, the thick fraction separated from dairy slurry, on growth and nutrient uptake at the 6-leaf stage and final harvest of no-till corn. The sludge was first injected 15 cm deep and the corn planted < 10 cm from the sludge furrow at least 3 days later. Sludge provided sufficient P for both early growth and full season growth. At final harvest with equivalent total N and P rates (32P 250N treatments), fertilizer and sludge had similar maximum yields (17.9 and 17.4 t ha−1, respectively) and P uptake (26 and 25 kg ha−1, respectively) but fertilizer had higher N uptake than sludge (200 and 162 kg ha−1). N uptake and recovery N use efficiency was greater for sludge than fertilizer based on equivalent min.-N which suggests crop benefits in the sludge other than min-N and P. The study shows that precisely injected dairy sludge can obviate the need for starter mineral fertilizer, and this may help to alleviate P surpluses on dairy farms. This practice also provides a use on dairy farms for the separated solids fraction remaining after the thin fraction is decanted and applied as the primary N source to grass.

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 score0.023
Threshold uncertainty score0.271

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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