Effect of minituber size and iron fertilizer on quantitative, qualitative traits and amino acid content of potato ( <i>Solanum tubersum</i> L.)
Bibliographic record
Abstract
This study aims to determine the effects of minituber size and nano-fertilizers on the quantitative and qualitative yield of mini potato tubers produced from healthy tissue culture seedlings. A factorial plot experiment was conducted to examine the effects of minituber sizes, nano-iron oxide, and iron chelate fertilizers on potato growth and yield. The experiment, conducted over two crop years in Iran, used three minituber weight categories (1–3, 3–5, 5–10 g) and seven iron fertilizer treatments: control, soil-applied iron chelate (20 µmol), soil-applied nano-iron oxide (20 µmol), and foliar sprays of 1% and 2% iron chelate or nano-iron oxide. As minituber size increased, plant height, stem count, tuber weight, vegetation index, and iron and protein content increased, while nitrate content decreased. The highest tuber weight plant−1 was achieved with 20 µmol nano-iron oxide soil application (405.23 g) for 1–3 g minitubers. In the 5–10 g minituber category, the highest yields (556.92 g) were observed with 20 µmol nano-iron oxide soil application, followed by 2% nano-iron oxide foliar spray (544.52 g). Smaller minitubers (1–3 g) performed best with nano-iron oxide soil application, while medium-sized (3–5 g) and larger (5–10 g) minitubers benefited most from foliar iron treatments. The study concluded that both minituber size and the type of iron fertilizer application significantly affected potato growth, yield, and quality. These findings underscore the importance of selecting the right minituber size and iron fertilizer treatment to optimize potato production.
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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 | 0.001 | 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, 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".