Value of integrating perennial forage seed crops in annual cropping sequences
Bibliographic record
Abstract
Abstract Cropping sequences integrating perennial forages and annual crops can generate multidimensional agroeconomic and environmental benefits. A 4‐yr cropping sequence study was conducted from 2013 to 2016 to evaluate the relative merits of various cropping sequences. Three forage seed crops (creeping red fescue [ Festuca rubra L.], alsike clover [ Trifolium hybridum L.], and red clover [ Trifolium pratense L.]) and four annual food crops (wheat [ Triticum aestivum L.], canola [ Brassica napus L.], barley [ Hordeum vulgare L.], and pea [ Pisum sativum L.]) were used to generate eight different cropping sequence treatments, which were split into three levels of nitrogen (N): 0, 45, and 90 kg ha −1 . The seed yields of different crops were expressed as canola equivalent yield (CEY) and summed for 4‐yr cropping sequences. Economic benefits for 4‐yr cropping sequences were calculated using the input–output price scenarios of individual years. The highest CEY, gross return, and gross margin over partial variable costs were obtained from creeping red fescue‐based cropping sequences, irrespective of N application rates. Based on CEY and gross margins, the merit order is ranked as creeping red fescue‐based sequences > high diversity annual crop sequences ≈ alsike clover‐based sequence ≈ wheat–canola alternating sequence > continuous canola sequence. The clover‐based sequences had positive effects on the performance of succeeding crops of wheat and canola. Among the annual crop‐based sequences, economic profitability increased with the diversification from continuous canola through wheat–canola alternation to pea–barley–wheat–canola in the sequence. Further study investigating soil health and greenhouse gas emissions parameters is warranted to elucidate multidimensional agroecosystem services of diversified perennial forage seed crop‐based cropping sequences compared with annual crops‐based sequences.
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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".