Assessing Yield Response and Relationship of Soil Boron Fractions with Its Accumulation in Sorghum and Cowpea under Boron Fertilization in Different Soil Series
Bibliographic record
Abstract
Boron (B) is an essential micronutrient in the growth of reproductive plant parts. Its deficiency and/or toxicity are widespread in arid and semi-arid soils with low clay contents. This study was planned to determine the response of sorghum (Sorghum bicolor L., non-leguminous crop) and cowpea (Vigna sinensis L., leguminous crop) to boron (0, 2, 4, and 16 µg g−1) on four distinct soil series from Punjab, Pakistan i.e., Udic Haplustalf (Pindorian region), Typic Torrifluvent (Shahdra region), Halic Camborthid (Khurianwala region), and Udic Haplustalf (Gujranwala region). Overall, there was a significant difference (p < 0.05) in yield between the sorghum (3.8 to 5.5 g pot−1 of 5 kg dry soil) and cowpea (0.2 to 3.2 g pot−1 of 5 kg dry soil) in response to B application. The highest yield was observed in both sorghum and cowpea either in control or at 2 µg g−1 B application in all four soils. Cowpea showed the same yield trend in all four soils (i.e., an increase in yield at 2 µg g−1 B application, followed by a significant decrease at the higher B levels). In contrast, sorghum exhibited greater variability of response on different soils; Udic Haplustalf (Pindorian region) produced the greatest yield at low levels of B application. However, Halic Camborthid produced its lowest yield at that level. Boron concentration in shoots increased with the levels of B application, particularly in sorghum. In cowpea, the plant growth was extremely retarded—and most of the plants died at higher levels of B application even if a lower concentration of B was measured within the shoot. Hot water-extractable B was the most available fraction for cowpea (R2 = 0.96), whereas the easily exchangeable B was most available for sorghum (R2 = 0.90). Overall, these results have implications for micronutrient uptake for both leguminous and non-leguminous crops.
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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 | 0.001 |
| 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 | 0.001 |
| 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".