Growth, Yield and Seed Nutrient Quality of Soybean Grown in Inland Peatland as Affected by Cow Manure Application
Bibliographic record
Abstract
Soybean yields poorly on peat smallholder farms in Indonesia, leading to a heavy reliance on imported.Soybean productivity on peat faces low soil fertility with limited macro and micronutrients.Cow manure, as an effective alternative of organic ameliorant, is the most important factor in increasing growth and yield to overcome the nutrient content of peat soil.We evaluate the effects of cow manure, an effective alternative of organic ameliorant, and varieties on the growth, yield component, yield, and seed nutritional value of soybean growing in inland peatland.A field experiment was carried out from March to July 2021.The factorial experiment included two factors consisting of cow manure with four treatment dosages, namely 0 t ha -1 , 10 t ha -1 , 20 t ha -1 , and 30 t ha -1 , respectively, and three soybean varieties tested, namely Anjasmoro, Deja 1, and Deja 2, respectively.We determined the growth (plant height and leaves number), yield component (fresh biomass and dry biomass weight), yield (seed dry weight per plot, seed dry weight per plant, weight 100 seeds), and nutritional seed value of soybean.The combination of cow manure and soybean varieties had no significant effect on all variables observed.Application of cow manure at a dose of 30 t ha -1 resulted in enhanced growth, yield component, and yield of soybean grown in peatland but reduced the protein content.Anjasmoro soybeans varieties without cow manure contained the most protein.Moreover, Anjasmoro was an adaptive variety cultivated in peatland compared to Deja 1 and Deja 2. The study emphasizes the need for further research to understand the effects of cow manure on crop yield and nutrient enhancement.
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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 | 0.000 |
| 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) | 0.000 | 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".