Inoculant and fertilizer effects on lentil in the US northern Great Plains
Bibliographic record
Abstract
Abstract Lentil ( Lens culinaris Medikus) is an important crop, averaging over 250,000 ha in Montana and North Dakota during 2016–2021. However, relatively little is known about rhizobial inoculant and fertility response in lentil in the US northern Great Plains. The objective was to evaluate the effect of rhizobial inoculant formulations (seed coat and in‐furrow) and nutrient additions (potassium [K], sulfur [S], and micronutrients) on lentil yield and seed protein concentration. This study was conducted at seven university research centers in Montana and North Dakota from 2019 to 2021, resulting in 20 location‐years of data. In six of 20 experiments, inoculant application increased seed yield by an average of 36% (323 kg ha −1 , p ≤ 0.05) but had no consistent effect on seed protein concentration. Lentil or pea crop history among locations did not explain inoculant response. Inoculant formulations (seed coat vs. in‐furrow) and K fertilizer had inconsistent and small effects on seed yield and protein concentration. However, S fertilizer (5.6 kg S ha −1 ) increased seed yield in four of 20 experiments ( p ≤ 0.02) by an average of 14.5% (255 kg ha −1 ) in those experiments and decreased seed yield for one experiment ( p = 0.05) by 5.8% (153 kg ha −1 ). Pre‐plant SO 4 ‐S soil test levels did not predict lentil response to S fertilizer. Micronutrient application was assessed in 12 location‐years but had no effect on lentil yield or protein concentration. This research suggests a need to better understand what factors control lentil yield and protein response to rhizobial inoculant and S fertilization.
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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 | 0.001 | 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".