Intermediate Maturing Soybean Produce Multiple Benefits at 1:2 Maize:Soybean Planting Density
Bibliographic record
Abstract
A study was conducted to identify the most suitable intercropping arrangement in smallholder farms in Western Kenya. Biomass and N (nitrogen)-accumulation, N2 fixation and grain yield of maize and soybeans grown as intercrops at three planting densities were assessed. The study was conducted in four seasons. Three soybean varieties, Namsoy 4m, SC Squire and TGx1987-18F, were used in the experiment. Maize: soybean planting densities 1:1 (D1), 1:2 (D2), 1:3 (D3) as well as sole soybean (SS) and sole maize (SM) were tested. Higher biomass, N-accumulation, and N-fixed in the order 3.8 Mg ha-1, 260 kg ha-1 and 161 kg ha-1 respectively, were recorded in D3 with long maturing variety TGx1987-18F. Conversely, higher soybean grain yield < 2.4 Mg ha-1 was achieved by intermediate maturing SC Squire in D3. The highest maize yield in the intercrop was obtained in D1. N balance calculations indicated that planting TGx1987-18F resulted in an addition of 6 to 67 kg N ha-1, while SC Squire and Namsoy 4 m removed 3 to 89 kg N ha-1 when soybean grain was removed from the field. The differences in N balances between the intercrops depended on the N-fixed and the amount of N in harvested soybean and maize grain. Greater land equivalent ratio < 1.75 were obtained with SC Squire and Namsoy 4m in D2. We concluded that intermediate maturing soybean have multiple benefits for farmers in Western Kenya at 1:2 maize: soybean planting density provided that the practice is accompanied with good soil and crop management practices.
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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 | 0.000 | 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".