Performance of Direct-Seeded Upland Rice-Based Intercropping Systems Under Paired Rows in East-West Orientation
Bibliographic record
Abstract
Production potential of rice based intercropping systems with legumes in Uganda is little known. Studies were conducted at Ikulwe Station of the National Agricultural Research Organisation to evaluate upland rice-based intercropping systems under paired-rows in the EW direction. A randomized complete block design with 3 replications was adopted during 2022 and 2023 with 8 pure stand and intercropped treatments. Adjustment from conventional planting to paired rows recorded high rice height and significant 1000 seed weights. In 2022 the 2 pure rice treatments produced significantly more tillers and panicles but legume intercrops reduced numbers of rice tillers and panicles. Intercropping significantly reduced the beans height (48%) and soybean pods (41%) during both years and also reduced the pods filling for beans (66%), groundnuts (36%) and soybeans (18.3%) during 2022. Although paired rice did not influence rice yield during both years, lower mean yield for rice (19.8%), beans (35%), groundnuts (33%) and soybeans (30.5%) were recorded. Lower legume intercrop yields were similarly recorded for beans (70%), groundnuts (73%) and soybeans (62%) during 2023. Partial (p) land equivalent ratios (LER) for intercrops were not significant and less than unity, but the one for rice intercropped with beans increased to more than unity (1.06) during 2023. All combined LER for rice-legumes were more than unity and the pLER of sole crops was 1.0. Rice + beans recorded high area time equivalent ratio during the 2 years while high relative equivalent yield and monetary advantage indices were recorded under rice + soybeans. Rice was more aggressive than other crops and intercropped treatments recorded higher Total Rice Grain yield equivalent (TRGYE) than sole rice. Rice + soybeans (2022) and rice + beans (2023) scored higher TRGY. The findings suggest that farmers can intercrop soybean in rice under paired rows in EW orientation for more benefits.
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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 | 0.001 | 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".