Inoculation of Trichoderma harzianum on Zea mays its effect on the addition of nitrogen fertilizer at 50%
Bibliographic record
Abstract
The crop Zea mays (maize) requires nitrogen fertilizer (NF) usually as NH4NO3 (ammonium nitrate), which applied in excess causes loss of productivity in soil. An alternative to reduce and optimize the dose of NF in crop Z. mays is to inoculate it with Trichoderma harzianum. The main objective was to analyze the effect of three doses of T. harzianum in Z. mays at 50% of NF. This experiment was performed in a greenhouse under an experimental design of random blocks, with 5 treatment and 5 replicates, the re-sponse variables used were: phenology: seedling height (SH) and root length (RL), and biomass: aerial and radical fresh/dry weight (AFW/ADW)/(RFW/RDW) at seedlings and flowering stage, the experimental data were analyzed by Tukey 0.05%. The results showed a positive effect of the specific density of all viable structures of T. harzianum in Z. mays since was observed 92% of seed germination, numerical value statistical difference to the 81% in Z. mays without inoculum and NF at 100% or relative control (RC). At seedling Z. mays with T. harzianum 40 g/100 g seeds registered an ADW of 0.32 g and a RDW of 0.25 g, these values were statistical different to the 0.21 g of ADW, and 0.19 g of RDW in Z. mays without inoculum and fed with NF at 100% or RC. The above mentioned suggests that T. harzi-anum transform seed and root exudates in plant growth promoting substances (PGPS), optimizing the use of NH4NO3 and allowing its reduction until 50% without causing a nutritional deficit on normal Z. mays growth.
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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) | 0.001 | 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 | 0.001 |
| 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".