Optimizing root yield of cassava under fertigation and the masked effect of atmospheric temperature
Bibliographic record
Abstract
BACKGROUND: Fertigation is a rare and an expensive method of fertilizer application to cassava, and hence there is a need to optimize its efficiency for profitability. This study's objective was to optimize root yield of cassava through fertigation using a logistic model. RESULTS: The field treatments were six fertigation concentrations against three cassava varieties, selected according to their maturity period. The logistic model predicted 52%, 116% and 281% benefit of fertigation for the varieties Mweru, Kampolombo and Nalumino, respectively. Furthermore, only half of the amount of fertilizer applied for Mweru was required to achieve twice the root yield of Kampolombo. During the experiment, an unknown importance of atmospheric temperature to cassava and its relationship to fertigation was observed. An elevation of 3.7 °C in atmospheric temperature led to 226%, 364% and 265% increase in root yield of Mweru, Kampolombo and Nalumino, respectively. Conversely, shoot biomass and root yield declined when the average atmospheric temperatures dropped by 3.6 °C. However, the cold temperatures affected the short-growth-duration (Mweru) and medium-growth-duration (Kampolombo) varieties earlier, 22 days after the drop, than the long-growth-duration variety (Nalumino) - 50 days after the drop. CONCLUSION: Fertigation induced resilience of the shoot biomass production to cold which was most pronounced in the root yield of Mweru in response to the highest fertigation concentration. Thus, while fertigation improved cassava's resilience to cold, it only did so effectively for short-growth-duration variety, Mweru. Also, enhanced performance of cassava under increased atmospheric temperature indicated its importance as a climate-smart crop. © 2020 Society of Chemical Industry.
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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 | 0.000 | 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 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".