Diversifying cropping systems enhances productivity, stability, and nitrogen use efficiency
Bibliographic record
Abstract
Abstract Long‐term field experiments are useful for determining cropping system productivity, stability, and resource use efficiency. With 12 yr (2004–2015) of data from five cropping systems on a long‐term experiment (> 30 yr) under semiarid conditions in Saskatchewan, Canada, a systems‐approach was used to compare grain and protein yield, stability, nitrogen (N) dynamics, N fertilizer (FUE G,P ), and available N use efficiency (NUE G,P ) for grain and protein. Annualized grain and protein yields for wheat ( Triticum aestivum L.)‐canola ( Brassica napus L.)‐wheat‐field pea ( Pisum sativum L.; W‐C‐W‐P) were 2244 and 372 kg ha −1 , respectively, 14 to 38% and 33 to 66% higher, respectively, than continuous wheat (ContW), summer fallow‐wheat‐wheat‐wheat (F‐W‐W‐W), F‐W‐W, and lentil ( Lens culinaris Medik) green manure‐wheat‐wheat (GM‐W‐W). Fallow systems were the most stable, but less productive and well‐adapted to low‐yielding conditions, while GM‐W‐W was the least stable and poorly adapted. The ContW had below‐average stability and was better suited to high‐yielding conditions for grain. The W‐C‐W‐P consistently produced above‐average yields, and was best suited for high‐yielding conditions for grain and protein. The ContW and W‐C‐W‐P had the highest NUE G (26.4 g kg −1 ) and NUE P (4.1 g kg −1 ), respectively, with GM‐W‐W having the lowest (18.1 and 2.7 g kg −1 ); FUE was the reverse of NUE. This long‐term study showed that diversified cropping systems that include pulses can more consistently produce higher grain and protein yields, regardless of growing conditions, than most other systems with lower N fertilizer inputs, thereby potentially reducing the negative environmental consequences associated with N fertilizer application.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".