Long-term assessment of management of an annual legume green manure crop for fallow replacement in the Brown soil zone
Bibliographic record
Abstract
In the Brown soil zone of western Canada summerfallowing (F) is traditionally used to reduce the water deficit associated with cereal production, but frequent use of this practice results in soil degradation and reduces the N-supplying capacity of soils. Some scientists suggest that an annual legume green manure crop (LGM) could be used as a partial-fallow replacement to protect the soil against erosion and increase its N fertility, particularly when combined with a snow-trapping technique to replenish soil water used by the legume. We assessed this possibility by comparing yields, N economy, water use efficiency, and economic returns for hard red spring wheat (W) (Triticum aestivum L.) grown in rotation with Indianhead black lentil (Lens culinaris Medikus) green manure (i.e., LGM-W-W) vs. that obtained in a traditional F-W-W system. Further, we assessed whether a change in manage ment of the LGM crop (i.e., moving to earlier seeding and earlier turn-down) was advantageous to the overall performance of this practice. The study was conducted over 12 yr (1988–99) on a medium-textured Orthic Brown Chernozem at Swift Current, Saskatchewan. Wheat stubble was left tall to trap snow, tillage was kept to a minimum, and the wheat was fertilized based on NO 3 soil tests. When we examined results after 6 yr, we concluded that by waiting until full bloom to turn down the legume (usually late July or early August) so as to maximize N 2 fixation, soil water was being depleted to the detriment of yields of the following wheat crop. The change in management of the LGM crop since 1993 resulted in wheat yields following LGM equaling those after fallow (due to improved water use efficiency), a gradual and significant increase over time in grain protein and in N yield of aboveground plant biomass of wheat in the LGM-W-W compared to the F-W-W system, plus a gradual decrease in fertilizer N requirements of wheat in the LGM system accompanying an improvement in the N supplying power of the soil. These savings in N fertilizer, together with savings in tillage and herbicide costs for weed control on partial-fallow vs. conventional-fallow areas, and higher revenues from the enhanced grain protein, more than offset the added costs for seed and management of the LGM crop. Thus, our results imply that, if producers seed the LGM in April and turn it down in early July, an annual LGM-cereal rotation is a viable option in the semiarid Canadian prairies; however, one negative consequence of adopting this management strategy is the possibility of enhancing NO 3 leaching. Key words: Nitrogen yields, grain protein, green fallow, summerfallow substitute, economic returns, NO 3 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 | 0.001 | 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 | 0.001 | 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".