Preceding Crops and Nitrogen Effects on Crop Energy Use Efficiency in Canola and Barley
Bibliographic record
Abstract
Energy use efficiency (EUE) is a key concept which may be used to benchmark best practices in cropping systems through comparison of the impacts of both preceding crops (PCs) and agricultural inputs on crop yield. The EUE is a metric to measure how cultural practices, such as N application and rotational crop use, can influence sustainability in a canola (Brassica napus L.) (C)–barley (Hordeum vulgare L.) (B) rotation. In a 2009 to 2011 PC–C–B rotation study, six PCs (field pea [Pisum sativum L.], lentil [Lens culinaris Medik.], faba bean [Vicia faba L.], canola, wheat [Triticum aestivum L.], and green manure [GRM] legume faba bean) were grown in factorial combination with five N rates (0, 30, 60, 90, and 120 kg ha−1), on field experiments at seven sites across western Canada. When the PC was GRM, the energy output of C or C–B was highest, but insufficient to compensate for lost output during the GRM phase (2009). For all cropping systems, the quadratic response of energy output to optimal N indicated that N applied could be reduced below 120 kg ha−1 without diminishing energy output at some locations. Over the entire 3‐yr crop sequence, legume PCs (lentil or field pea) grown for seed provided the greatest EUE. The GRM improved the yield, and therefore energy output and EUE, of the following crops considerably, but the increased canola and barley energy outputs were not able to alleviate the lost energy output during the preceding crop phase. N fertilizer applied could be reduced without diminishing energy output. Legume preceding crops (lentil or field pea) grown for seed provided the greatest EUE. Legume PC grown for green manure was not able to increase EUE for the entire rotation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".