Blueberry Yield and Soil Mineral Nitrogen Response to Nitrogen Fertilizer and Nitrification Inhibitors under Drip-Fertigation Systems
Bibliographic record
Abstract
In blueberry plantings, nitrification can result in losses of mineral nitrogen (N) through leaching because blueberries prefer ammonium (NH4+) over nitrate (NO3−). The objective of this study was to assess the effects of two rates of N fertilizer, mixed or not with nitrification inhibitors (NI) and applied through two fertigation systems, on berry yield and the concentrations of NH4+-N and NO3−-N along the soil profile. Thus, nine combinations of treatments including two N fertilizer rates (60 and 120 kg N ha−1), two NI (with DCD + Nitra-pyrin or without) and two fertigation application methods (buried and suspended drip lines) and a control (0 kg N ha−1) were tested over three years (2016–2018) in a long-term blueberry planting. Berry yield was on average 47.32 Mg ha−1 in 2016 and 26.86 Mg ha−1 in 2018. In 2017, berry yield varied between 8.60 Mg ha−1 under the control and 11.66 Mg ha−1 with 120 kg N ha−1 applied through suspended drip lines. Low berry yield in 2017 was due to a heavy pruning to rejuvenate the plants. In 2016, the concentration of NH4+-N in the sawdust mulch layer varied between 13.1 and 27.1 mg kg−1 in the spring, 11.4 and 32.1 mg kg−1 in the summer, and 7.9 and 72.9 mg kg−1 in the fall; the concentration of high NH4+-N along the soil profile did not exceed 5 mg kg−1. High concentrations of NH4+-N in the sawdust mulch layer were associated with NI, but did not translate to high berry yields. The concentration of NO3−-N in the soil profile reached 42.6 mg kg−1 in the summer and 39.0 mg kg−1 in the fall and these high concentrations were associated with NI. In 2017 and 2018, there was no effect of NI on NH4+-N concentrations even in the layer of sawdust mulch which was not consistent with the results obtained in 2016. High concentrations of NO3−-N were measured beneath the sawdust mulch layer with treatments including N fertilizer alone or mixed with NI. It is possible that NH4+ retained in the layer of sawdust mulch and not taken up by plant roots was subsequently oxidized to NO3− at the end of the residence time of NI in the soil. Our results showed that high concentrations of NO3−-N along the soil profile occurred mainly during the summer which could be explained by irrigation water driving NO3− leaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".