Biochar Can Enhance Potassium Fertilization Efficiency and Economic Feasibility of Maize Cultivation
Bibliographic record
Abstract
Field experiments were conducted to study the effect of biochar on potassium fertilizer leaching and uptake, efficiency and effectiveness of K fertilization, and economic viability of farming maize. Thirty tons ha-1 of biochar prepared from organic waste was applied to an Inceptisol. The experiment was arranged in a randomized block design with 7 treatments, namely control (without biochar and KCl), K1 (200 kg ha-1 KCl), BK0 (biochar, without KCl), BK1/4 (biochar + 50 kg ha-1 KCl), BK 1/2 (biochar + 100 kg ha-1 KCl), BK 3/4 (biochar + 150 kg ha-1 KCl), and BK1 (biochar + 200 kg ha-1 KCl) and three replicates for each treatment. The results suggest that biochar could replace and reduce KCl fertilizer. Biochar application increased the availability of nutrients by 69-89% for K+, 61-70% for Ca++, 39-53% for N total, 179-208% for P, and 14-184% for K.The results showed that the sole application of biochar increased maize production (6.24 Mg ha-1) by 14% compared sole application of KCl fertilizer (5.45 Mg ha-1). In contrast, dual application of biochar and 75% lower dosage of KCl fertilizer application increased maize production by 29%. Application of biochar and KCl fertilizer at the rate of 50 kg ha-1 resulted in the highest relative agronomic effectiveness (137%) and K fertilizer efficiency (18%). This application rate was also superior both technically and economically as assessed in terms of production (7.02 Mg ha-1), value of sales (revenue; IDR 19,305 million ha-1), income (IDR 8,663 million ha-1), and economic feasibility (R/C, 1.8).
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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) | 0.000 | 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 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".