Contrasting impacts of two weed species on lowbush blueberry fertilizer nitrogen uptake in a commercial field
Bibliographic record
Abstract
Numerous studies have speculated that lowbush blueberry (Vaccinium angustifolium) is less efficient than weed species at taking up inorganic nitrogen (N) derived from fertilizers, thus raising questions as to the effectiveness of N fertilization in commercial fields. However, competition for acquiring N as well as specific interactions between blueberry and companion weeds characterized by contrasted functional traits remain poorly documented. Here, we assessed fertilizer-derived N acquisition efficiency and biomass production in lowbush blueberry and two common weed species that have different functional traits-sweet fern (Comptonia peregrina), a N2-fixing shrub, and poverty oat grass (Danthonia spicata), a perennial grass-in a commercial blueberry field in Québec, Canada. In 2015, 15N-labelled ammonium sulfate was applied at a rate of 45 kg ha-1 to 1 m2 field plots containing lowbush blueberry and one of the two weeds present at several different density levels (0 to 25 plants m-2). In 2016, each plot was harvested to determine vegetative biomass and the percentage of fertilizer-derived N recovered (PFNR) in each species. The PFNR was higher in blueberry (24.4 ± 9.3%) than in sweet fern (13.4 ± 2.6%) and poverty oat grass (3.3 ± 2.9%). However, lowbush blueberry required about four times more root biomass than sweet fern and poverty oat grass to uptake an equivalent amount of N from ammonium sulfate. The PFNR in poverty oat grass increased with plant density (from 0.8% to 6.4% at 2-3 and >6 plants m-2, respectively), which resulted in a decrease in blueberry's PFNR (from 26.0 ± 1.4% to 8.6 ± 1.8%) and aboveground vegetative biomass production (from 152 ± 58 to 80 ± 28 g m-2). The increase in biomass production and N content in sweet fern with increasing plant density was not accompanied by an increase in PFNR (29.7 ± 8.4%), suggesting an increasing contribution of atmospherically-derived N. This mechanism (i.e., N sparing) likely explained blueberry's higher biomass production and N concentration in association with sweet fern than with poverty oat grass. Overall, our study confirms lowbush blueberry low efficiency (on a mass basis) at taking up N derived from the fertilizer as compared to weeds and reveals contrasted and complex interactions between blueberry and both weed species. Our results also suggest that the use of herbicides may not be necessary when poverty oat grass is present at a low density (<15 plants of poverty oat grass m-2) and that adding inorganic N fertilizer is counterproductive when this species is present at a high density as it takes up as much fertilizer as lowbush blueberry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".