Effect of Presprouting Plant Growth Regulators and Natural Materials on Dormancy, Growth, and Yield of Potatoes (Solanum tuberosum L.)
Bibliographic record
Abstract
Irish potatoes are amongst the most highly grown and demanded crops in Kenya for food, industrial starch, and animal feed. Farmers, however, face a serious challenge regarding the timely availability of well-sprouted seed potato tubers due to the physiological seed dormancy period of 2–3 months, thereby reducing production cycles. This study determined the effects of different chitting methods on enhancing the presprouting of different potato varieties in Kenya. Plant growth regulators (PGR) (Gibberellins (GA3), 6-Benzylaminopurine, and Zeatin) and natural materials (grass, banana leaves, and soil) were evaluated for their effects in breaking dormancy and stimulating the growth of sprouts under greenhouse conditions in a complete randomized design (CRD) with three replicates. The evaluation of the presprouted seed in the field was conducted at Egerton University and Kenya Agricultural and Livestock Research Organization (KALRO), Molo, in a split-plot design for two seasons. Data was taken on crop emergence, length, thickness, and colour of sprouts, plant height, tubers per plant, tuber thickness, and tuber yield. Data were subjected to a general linear model to partition the variance component using SAS software version 9.13, and means were separated using the least significant difference ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>p</a:mi> <a:mo>≤</a:mo> <a:mn>0.05</a:mn> </a:math> ). There were significant ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> <c:mo>≤</c:mo> <c:mn>0.05</c:mn> </c:math> ) main effects on the prespouting time, growth, and yield of tubers. The interaction effects due to variety and treatment were also significant ( <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>p</e:mi> <e:mo>≤</e:mo> <e:mn>0.05</e:mn> </e:math> ) for sprout thickness. Natural materials produced the most vigorous sprouts, increased crop emergence, plant height, and superior tuber yield. Natural materials and PGRs increased tuber size for chitted potato seed by 261% and 103%, respectively. Control treatments had a significantly higher frequency of small-sized tubers than natural materials and PGRs, proving the importance of chitting in increasing tuber size and yields. Natural materials increased sprout quality (thickness and length) better than PRGs and control treatments. This study showed that small-holder farmers could adopt the use of readily available soil, grass, and banana leaves while large-scale growers, with access to better facilities, could use PGRs to break tuber dormancy for increased potato tuber yield.
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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 | 0.000 |
| 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".