Pulse Crop Adaptation in the Northern Great Plains
Bibliographic record
Abstract
Pulse crops discussed in this review include soybean (Glycine max L.), dry pea (Pisum sativum L.), lentil (Lens culinaris Medik.), dry bean (Phaseolus vulgaris L.) and chickpea (Cicer arietinum L.). Basic maturity requirements, yield relationships with rainfall and temperature, relative yield comparisons, water relationships, water use efficiency (WUE), crop management, tillage systems, and the rotational impact of these crops on productivity were considered. With the exception of soybean, maturity requirements for pulse crops are met in most locations within the northern Great Plains. Yield was more closely related to growing season precipitation than maximum temperature for all pulse crops except dry bean and lentil. The inability to effectively relate weather parameters to dry pea and lentil yield may indicate broad adaptation of these two pulse crops within the northern Great Plains. Correlation analyses showed the productivity of chickpea, dry pea, and lentil to be most closely associated with each other and for dry bean productivity to be most closely associated with that of soybean, effectively grouping pulse crops into their respective cool‐ and warm‐season classifications. Dry pea and chickpea had high WUE values, similar to spring wheat (Triticum aestivum L.). Examination of plant water relations of these crops revealed an ability for chickpea and dry pea to grow at lower relative water contents than spring wheat. Increased wheat grain yield and/or protein following pulse crops under widely different N‐limiting growth conditions indicated a consistent N benefit provided by pulse crops to wheat. Four general research needs were identified. First, comparative adaptation among pulse crops remains poorly understood. Second, best management practices and key production risks remain incompletely characterized. Thirdly, the knowledge of rotational effects of pulse crops in the northern Great Plains remains imprecise and inadequate. Fourth, genetic improvement for early maturity, increased yield, improved harvestability, and disease resistance requires attention. Pulse crops are poised to play a much greater role in diversifying cropping systems in the northern Great Plains but require that these key research areas be addressed so that their production potential can be realized.
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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 | 0.001 |
| 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) | 0.001 | 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".