Application of zinc, boron, and molybdenum in soil increases lentil productivity, nutrient uptake, and apparent balance
Bibliographic record
Abstract
In severely deficit soil, lentil (Lens culinaris Medic) crop requires micronutrients for increased production. Micronutrient management is, therefore, very important for lentil productivity but mostly ignored. This study was carried out from 2014–2015 to 2016–2017 to understand the effects of zinc (Zn), boron (B), and molybdenum (Mo) on lentil productivity, nodulation, and nutrient uptake and how these elements improve soil micronutrient fertility. The experiment was laid out in randomized complete block design, and the treatments were replicated thrice. Different combinations of Zn, Mo, and B were contrasted with no application of micronutrients. The treatments were Zn alone (Zn), B alone (B), Mo alone (Mo), Zn combined with B (ZnB), Zn with Mo (ZnMo), B with Mo (BMo), and Zn combined with B and Mo (ZnBMo). Doses of Zn, B, and Mo were 3, 2, and 1 kg ha−1, respectively. In this trial, the highest average seed yield (1807 kg ha−1) and yield increment (44%) was obtained in ZnBMo combined application with macronutrients. Single, dual, and combined application of Zn, B, and Mo had significant effects on yield parameters and yield of lentil (P < 0.05). The highest nutrient uptake, maximum nodulation (63.5 plant−1), and the highest protein content (26.6%) in seed were recorded from the treatment receiving all three micronutrients. The increased lentil yield might be associated with increased nodulation and nutrient uptake by the crop under micronutrient-applied treatments. The results suggest that combination of Zn, B, and Mo could be applied for increased lentil production in micronutrient deficit soils.
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 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 | 0.001 | 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".