Effect of Intercroping Maize-Soybean on Grain Quality Traits in Kenya
Bibliographic record
Abstract
An experiment was conducted in Kenya to determine the effect of intercropping maize-soybean on soybean grain quality traits. A randomized complete block design replicated three times with seven treatments was used. Data were collected and analysed for soybean protein content, oil content and dry matter while grain size was done for maize and soybean. Data were subjected to ANOVA and means separated using LSD0.05. The results showed that TGX1990-5F variety recorded high protein content (p ≤ 0.05) in sole crop compared to intercropping with 42.96% and 38.4% respectively while SB19 obtained 36.57% and 40.41% of protein between sites for the first season. A bout the second season, TGX1990-5F gave 40.84% of protein compared to 31.98% for GAZELLE in intercrop at both sites. GAZELLE showed higher oil content followed by TGX1990-5F and SB19 was the last in both seasons. TGX1990-5F showed higher dry matter followed by GAZELLE and SB19 was the last in intercrop. In addition, GAZELLE a local variety showed greater size than SB19 in both rainy seasons. Intercropping affected slightly the protein content, oil content and gain size than sole crop but it did not affect the dry matter in both seasons.Thus, TGX1990-5F can be recommended to small-scale farmers for intercropping with maize because it produced higher value of protein and small-scale farmers could use it to fight mal nutrition. GAZELLE variety can be also recommended for oil production. Maize grain size was not affected by intercropping system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".