How do roll timing and seeding rate affect lentil yields?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Lentil ( Lens culinaris Medik.) production has increased exponentially in Montana in the last two decades. However, there are important gaps in knowledge on best management practices for lentil. Agronomic recommendations are based on performance of old cultivars outside of the area for seeding rate, and on anecdotal evidence for proper roll timing, particularly since the widespread adoption of no‐till farming. Replicated field experiments were conducted at three sites during the 2019, 2020, and 2021 growing seasons in Montana to determine the impacts of roll timing and seeding rate on lentil yield and identify best practices. Overall, rolling at emergence and at the 10‐leaf stage decreased yields by 5% and 8%, respectively, but rolling just after planting or at the early vegetative stage (two‐ to four‐leaf stage) did not decrease yields. Higher yields were achieved at higher seeding rates, with yields increasing between 6 and 52 lb ac −1 for each additional plant established per square foot, but emergence rates were variable and relatively low, so a higher seeding rate may be necessary to achieve plant densities above 12 plants ft −2 in this region. In five out of nine site years, the largest partial economic returns were achieved with 22.5 or 30 live seeds ft −2 seeding rate, corresponding to achieved plant densities of 12 to 16 plants ft −2 . It was generally economical to increase seeding rate from 15 to 22.5 live seeds ft −2 , thus increasing average achieved plant density from 8 to 13 plants ft −2 , except when seed costs were high (>$0.45 lb −1 ) combined with low market prices (< $0.20 lb −1 ).
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.
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 | 0.002 | 0.001 |
| 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 it