Managing an annual legume green manure crop for fallow replacement in southwestern Saskatchewan
Bibliographic record
Abstract
Some scientists have suggested that in the Brown soil zone an annual legume green manure crop \n(GM) could be used as a partial-fallow replacement to protect the soil against erosion and \nincrease its N fertility, particularly when combined with a snow trapping technique to replenish \nsoil water used by the legume. We assessed this possibility by comparing yields, N economy, \nwater use efficiency, and economic returns of hard red spring wheat (W) grown in rotation with \nIndianhead black lentil (i.e., GM-W-W) vs. that obtained in a F-W-W system. Further, we \nassessed whether a change in management of the GM crop (i.e., moving to earlier seeding and \nearlier turn-down) was advantageous to the overall performance of this practice. The study was \nconducted over 12 years (1988-99) on a loam soil at Swift Current, SK. (wheat stubble was left \ntall to trap snow, tillage was kept to a minimum, and the wheat was fertilized based on soil tests). \nWhen examined after 6 years, the results suggested that by waiting for full bloom of the legume \n(usually late July or early August) to maximize N2 fixation, soil water was being depleted to the \ndetriment of yields of the following wheat crop. However, the change in management of the GM \ncrop since 1994 has resulted in wheat yields following GM equalling those after fallow. It also \nproduced a significant increase (after one rotation cycle) in grain protein and N yields of aboveground \nparts of wheat in the GM-W-W compared to the F-W-W system, and lead to a gradual \ndecrease in fertilizer N requirements of wheat in the GM system in the last 6 years. These \nsavings in N fertilizer, together with savings in tillage and herbicide costs for weed control on \npartial-fallow vs conventional-fallow areas, and higher revenues from the enhanced grain \nprotein, more than offset the added costs for seed and management of the GM crop. Thus, our \nresults imply that, with proper management and given sufficient time, an annual legume GMcereal \nrotation is a viable option for area producers.
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 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".