Agronomic Performance of Collards under Two Intercrops and Varying Nitrogen Application Levels as Assessed Using Land Equivalent Ratios
Bibliographic record
Abstract
Sustainable utilization of the limited land parcels is an important element in resource poor countries whosepopulations depend on agriculture for sustenance. In East Africa, collard vegetable production has been a venturefor many small-scale farmers. Since no approaches are possible in expanding the land resource, improved cropproduction techniques and management promise better yields. We report a potential and sustainable approach forcollard production. We compared the productivity of growing collards in an intercrop with chilli or spring onions,and varying nitrogen levels in each cropping system in terms of land equivalent ratios (LER). Our two - seasondata show that farmers can obtain higher yield in a unit of land when they intercrop collards with spring onionsas indicated by a LER of 1.06, and that growing collards in an intercrop with Chilli leads to a 14% wastage ofland resource compared to growing the crop as a monoculture. Further, we reveal that application of nitrogenousfertilizer might lead to increase in yield but it does not have a statistically significant benefit on the land resourceuse.There were no statistically significant relationship between the yield of collards and their heights in all croppingsystems, but a non-significant(r=-0.29, p=0.164) decline of yield was observed under collard + chilli intercrop.The number of collard leaves was significantly negatively correlated (r=- 0.456, p=0.0249) with yield LER undercollard + chilli intercrop but it did not influence the LER under collard + spring onion intercrop. These findingsdemonstrate the need for proper resource application, and that collard + spring onion intercrop has a potential toimprove collard yield per unit piece of cultivated land without a need for increasing the rate of nitrogenousfertilizer application.
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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 | 0.001 | 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 | 0.001 |
| 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".