Lentil enhances agroecosystem productivity with increased residual soil water and nitrogen
Bibliographic record
Abstract
Abstract Lentil ( Lens culinaris Medikus) may have a potential to enhance the productivity of agroecosystems in dry areas where water and nutrients are limited. This study quantified soil water, residual soil nitrogen (N), and crop yields in lentil-based systems in comparison with continuous cereal and conventional summerfallow systems. A 3-yr cropping sequence study was conducted for three cycles in Saskatchewan (50.28°N, 107.79°W) from 2007 to 2011. On average, soil retained 187, 196 and 337 mm of water in the 0–1.2 m depth at crop harvest in 2008, 2009 and 2010, respectively. Summerfallow contained the same amounts of water as the cropped treatments at the harvest in 2009 and 2010. However, in 2008, summerfallow contained more soil water than the cropped treatments. The effect of lentil cultivar on soil water conservation varied with years; the cultivars Glamis, Laird and Sedley conserved highest amounts of soil water by the planting time of 2009 and 2010, but no differences were found among cultivars in 2011. Soil available N (NO 3 − + NH 4 + ) at spring planting time was 50.4 kg ha −1 in the preceding lentil treatments, which was 44% higher compared with preceding barley or flax, but was 25% lower compared with preceding summerfallow. Lentil cultivars had a similar amount of soil residual N. Grain production in the 3-yr rotation averaged 6.3 t ha −1 per rotation for the wheat–lentil–durum system and 6.8 t ha −1 for the wheat–cereal–durum monoculture, averaging 36% greater compared with wheat–summerfallow–durum system. The lentil system increased total grain production through the access of residual soil water and biologically fixed N, whereas continuous cereal system relies on inorganic fertilizer input for yield. Summerfallow system relies on ‘mining’ the soil for nutrients. We conclude that the adoption of lentil systems will enhance grain production through the use of residual soil water and available N.
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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".