Effectiveness of Nitrification Inhibition through Addition of Local Litter to Corn Plants in Andisols
Bibliographic record
Abstract
Nitrification is the oxidation process of NH4+ to NO3- microbially. The nitrification process can produce compounds in the form of NO3-, N2O, or NO which can cause environmental pollution through water, soil, or air, thus harming living things. The research was conducted to find ways to inhibit or control nitrification effectively and sustainably. The experiment was carried out in a plastic greenhouse located in Plesungan, Karanganyar until the maximum vegetative planting period of corn around May-June using a completely randomized design three replications. Andisols soil media was taken from Tegalrejo Village, Tengaran District, Semarang Regency 07º25'28.3 '' LS and 110º31 '35.7 '' BT with an altitude of 760 meters above sea level. There are 5 treatments, one control, four are natural inhibitor treatments in the form of litter addition. The litter used was Gliricidia maculate, Albizzia falcataria, Senna siamea, and Tithonia diversifolia. Statistical analysis showed that treatment just significantly affect NO2- concentrations (potential nitrification), not significantly affect NH4+, NO3- concentrations, and efficiency utilization of N. However, measurement results in the laboratory and field showed that the addition of local litter could inhibit nitrification, which was demonstrated through the efficiency of N utilization. Tithonia diversifolia because it has the highest average N utilization efficiency of 25.79%, 58.71% more efficient than control treatments. Also followed by plant growth results showed that the root’s dry weight and canopy’s dry weight positively correlated with NH4+ concentrations and efficient utilization of N, also canopy’s dry weight negatively correlated with NO2- concentrations (potential nitrification). The highest results occurred in the Tithonia diversifolia treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".