Nutrient use efficiency and harvest index of cassava decline as fertigation solution concentration increases
Bibliographic record
Abstract
Abstract Response of cassava (Manihot esculenta Crantz) to fertigation as a form of nutrient delivery is unknown. The objectives of this study were to establish a balanced nutrition and to enhance agronomic nutrient use efficiency (ANUE) of cassava under fertigation. This study was conducted in the greenhouse and in the field. In both, the results showed a similar trend. There were six fertigation concentrations and three cassava varieties, selected for their duration of growth in the field. Shoot biomass of the long‐duration variety (Nalumino) was the highest, even though its dry root yield was the lowest (10.18 t ha−1) among the varieties. In contrast, the medium‐duration variety (Kampolombo) produced the highest dry root yield (20.34 t ha−1) and a lower shoot biomass. The highest root yield of the shortest‐duration variety (Mweru) was achieved at 200 mg N, 30 mg P, and 200 mg K L−1 (155.0, 23.3, 155.0 kg N, P, K ha−1), while Nalumino's was at 70 mg N, 7 mg P, and 70 mg K L−1 (54.3, 5.4, 54.3 kg N, P, K ha−1). ANUE and harvest index of these varieties declined as the fertigation concentrations increased. Additionally, the correlation between concentrations of N in the youngest fully expanded leaf (YFEL) blades and dry root yields was the lowest (R2 = 0.5488), whereas P and K were R2 = 0.7237 and R2 = 0.8006, respectively, an indication that nutrient concentrations in the leaf, especially N, cannot easily be used to predict root yield. When cassava reaches nutrient sufficiency, mainly N, its accumulation in the leaf continues without significant increase in the root yield.
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
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) | 0.001 | 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".