Unveiling the impact of organically activated biochar on physiological, biochemical, and yield attributes of maize under varied field moisture conditions
Bibliographic record
Abstract
Water deficiency in semiarid regions is a limiting factor that affects crop quality and yield. In Punjab, Pakistan, a 27% decline in maize yield was detected over the past two decades just because of water scarcity. Currently, no studies have reported the effects of organically activated biochar (AB) on crop productivity under natural field conditions. For this purpose, a field experiment in a split-split-plot design was conducted with three amendment levels (0, 2, and 4 tonnes ac −1 ), and three maize hybrids (DK-9108, DK-6321, and Sarhaab) under 100%, 70%, and 50% irrigation water (IW) of crop evapotranspiration (ETc). The AB significantly improved the soil’s physical and chemical properties, and maximum improvement was recorded in 4 tonnes ac −1 AB amendment in organic matter (16.6%), total organic carbon (17%), phosphorus (11.43%), and available potassium (29.27%). The 4 tonnes ac −1 AB amendment in soil had a significant impact on total chlorophyll content (0.3–1-fold in DK-6321), carotenoid content (3.9–4.4-fold in Sarhaab), and relative water content (30% and 21% in Sarhaab) under 50% irrigation water (IW) of ETc at V14th and R3 stages, respectively. Moreover, a significant decline in stress markers (proline content and sugar content) was detected at both growth stages in all maize hybrids in AB amended soil. The analysis of plant metabolites indicated increased intensities of phenolics, alkyl esters, and carbohydrates by 2.5–7%, 17–80%, and 40–43% in DK-6321 under 50% IW in 2–4 tonnes ac −1 AB amended soil, respectively. The highest improvement in growth and yield attributes among maize hybrids was detected in the order DK-6321¿DK-9108¿Sarhaab in 2–4 tonnes ac −1 AB amended soil under 70% and 50% IW of ETc, respectively. Hence, this research might help to develop an effective soil amendment to restore degraded soils and improve maize growth under arid climatic conditions.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".