Pulse-included diverse crop rotations improved the systems economic profitability: evidenced in two 4-year cycles of rotation experiments
Bibliographic record
Abstract
Abstract In the recent past, pulse crops have become increasingly important to agricultural producers as they contribute significantly to the economy. However, the research surrounding the economics of pulse crops is limited. This study determined the net returns and risks of 14 different rotations with various frequencies and sequences of pulse crops and quantified the long-term economic effects. An 8-year field experiment (two 4-year rotation cycles) was carried out at Swift Current, Saskatchewan, and Brooks, Alberta, Canada, during 2010–2019. The crops in the rotation included spring and durum wheat (Triticum aestivum L.) (W), field pea (Pisum sativum L.) (P), chickpea (Cicer arietinum L.) (C), lentil (Lens culinaris Medik) (L), and Oriental mustard (Brassica juncea L.) (M). Net revenue was estimated and a simulation model was used to conduct the risk-return analysis. Net revenue was significantly different among the 14 rotations, where rotations with either high frequencies of lentil or diverse crops generated the highest net income. More diverse rotations such as P-M-L-W or L-C-P-W provided net income that were statistically comparable to the L-L-L-W rotation and were significantly greater than wheat monoculture systems. Risk analysis suggested that neutral or slightly risk averse producers may select rotations with higher frequencies of lentils, whereas more risk averse producers may prefer more diverse rotations. Inclusion of pulses in a rotation as preceding crops had a positive economic impact on the following non-pulse crops and reduced nitrogen cost by 37%, which can lead to a low carbon footprint. Long-term studies with comprehensive datasets are rare and here for the first time we had two full 4-year cycles of experimental data for 14 diverse rotations at three sites, enabling us to make sound conclusions—adopting diverse cropping rotations that include pulses, especially lentil, can reduce economic risks and improve farm profitability.
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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 | 0.000 | 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 | 0.001 |
| 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".