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Record W2998323690 · doi:10.1371/journal.pone.0226619

Lowbush blueberry fruit yield and growth response to inorganic and organic N-fertilization when competing with two common weed species

2019· article· en· W2998323690 on OpenAlexafffund
Charles Marty, Josée-Anne Lévesque, Robert L. Bradley, Jean Lafond, Maxime C. Paré

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsAgriculture and Agri-Food CanadaUniversité de SherbrookeUniversité du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologies
KeywordsVacciniumWeedHerbaceous plantBiologyAgronomyFertilizerHuman fertilizationEricaceaeHorticultureVegetative reproductionBotany

Abstract

fetched live from OpenAlex

Inorganic N fertilizers are commonly used in commercial blueberry fields; however, this form of N can favor increased weed species' growth, which can ultimately reduce the benefits of fertilization. We hypothesized that chipped ramial wood (CRW) compost is an effective alternative organic fertilizer for blueberry plants when weeds are present, as ericaceous shrub species are generally more efficient in utilizing organic N than herbaceous weed species. In this study, we measured the growth, fruit yield, and foliar N response of lowbush blueberry (Vaccinium angustifolium Aiton) to an application of 45 kg N ha-1 in the form of organic (CRW) or inorganic N (ammonium sulfate) in two areas of a commercial field colonized by either poverty oat grass (Danthonia spicata (L.) Beauv.) or sweet fern (Comptonia peregrina (L.) Coult.). We also assessed the impact of the fertilization treatments on litter decomposition rates. Contrary to our hypothesis, we found no significant increase in blueberry fruit yield or growth using CRW. By contrast, inorganic N-fertilization increased fruit yield by 70%. The effect was higher in the area colonized by D. spicata (+83%) than by C. peregrina (+45%). Blueberry fruit yield was on average twice higher in the area of the field having D. spicata than C. peregrina, suggesting a stronger competition with the latter. However, the increase in D. spicata density from 0-1 to >25 plants m-2 reduced fruit production by three-fold and strongly impacted vegetative growth in both fertilized and unfertilized plots. The impact of increased C. peregrina density was comparatively much lower, especially on vegetative growth, which was much higher in the area having C. peregrina. These patterns are likely due to a lower competition for N uptake with C. peregrina as this species can derive N from the atmosphere. Interestingly, the higher fruit yield in the area colonized by D. spicata occurred even in plots where the weeds were nearly absent (density of 0-1 plant m-2), revealing the influence of unidentified variables on blueberry fruit yield. We hypothesized that this difference resulted from over-optimal foliar N concentrations in the area colonized by C. peregrina as suggested by the significantly higher foliar N concentrations and by the negative correlation between foliar N concentrations and fruit yields in this area. The possibility of an influence of C. peregrina on flowering and pollination success, as well as of unidentified local site conditions is discussed. The tested N-fertilization treatments did not affect foliar N concentrations or litter decomposition rates. Overall, our results show that ammonium sulfate is very effective at increasing fruit yields but that both fruit yields and the efficiency of the N-fertilization treatment are decreased by increased D. spicata density, especially above 25 plants m-2. Although CRW did not significantly enhance fruit yields in the short term, this fertilizer may have a long-term beneficial effect.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.959

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)0.0010.000

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 head0.047
GPT teacher head0.214
Teacher spread0.167 · 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

Citations19
Published2019
Admission routes2
Has abstractyes

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