Minimizing Lentil Harvest Loss through Improved Agronomic Practices in Sustainable Agro-Systems
Bibliographic record
Abstract
Lentils are one of the most common legume crops used to diversify the cereal-oilseed cropping system in semi-arid environments. Lentils are a major source of protein and fiber for human consumption worldwide. However, the morphological characteristics of lentil plants—such as a short stem and low pod positioning—and complicated combine harvesting methods often result in yield loss. This also increases the susceptibility of a lentil crop to disease and render it less competitive against weeds. As a result, producers have resorted to using pesticides in order to mitigate the effects of weeds and disease. As a consequence, there have been undesirable negative environmental impacts on sustainable agroecosystems. Although land rolling, stubble management, and pesticide usage are common agronomic practices used to increase lentil yield and mitigate the issues associated with its morphology, their comprehensive effects on lentil growth and harvest loss are still not fully understood. In this study, we examined the impact of stubble management, the timing of land rolling, and the application of common fungicides and herbicides on lentil growth and yield. We found that stubble management and the timing of rolling modified lentil morphological structures, and thus impacted lentil yield and seed loss. These results were influenced by environmental factors, such as precipitation during the growing season. Although the results did not show significant interaction between fungicide application and lentil growth and yield, herbicide applications, stubble management, and the timing of rolling, along with common pesticide application strategies tested in our study, showed effects that were dependent on environmental conditions. Based on our results, we concluded that stubble management and the timing of rolling, combined with pesticide applications, can affect lentil seed loss and yield by modifying plant morphology. This was largely influenced by environmental conditions such as precipitation.
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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 | 0.001 |
| 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".