Effect of Doses of Green Manure from Different Sources on Growth and Yield of Maize in Dryland
Bibliographic record
Abstract
Dryland has low soil fertility. Efforts that can be made to improve soil fertility are fertilizer technologies such as green manure compost. The aim of this study was to determine the type and dose of green manure to increase the growth, production and nutrient uptake of maize in the dryland. The research was conducted in Sidera Village Sigi Biromaru District, Sigi Regency, Central Sulawesi from June to December 2018. The research used a factorial randomized block design. The first factor is the type of green manure consisting of three levels, namely mungbean green manure (K1), peanut green manure (K2) and Centrosema pubescens green manure (K3). The second factor is the dose of green manure which consists of three levels, namely 5 t. ha-1 (D1), 7.5 t. ha-1 (D2) and 10 t. ha-1 (D3). Thus, there are 9 treatment combinations, each treatment consisting of 3 replications so that there are 27 experimental units. Data were analyzed statistically using the F test and if significantly different it was followed by the least significant difference (LSD) Fisher’s test, P-value 0.05. These results showed that the types and dose of green manure increase the growth and yield of maize, namely leaf area, stem diameter, cob length, the weight of 100 dry shelled seeds, and production per hectare of the dry weight of corn shelled. The highest nutrient uptake and maize production were obtained in the application of C. pubescens green manure at a dose of 10 t. ha-1, namely nitrogen uptake of 7.68%, phosphorus of 0.39%, potassium of 0.09% and yield of 6.44 t. ha-1.
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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".