Does a Specific Location of Composted Poultry Litter in Soil Influence Nutrient Use Efficiency and Vegetable Production? A Mesocosm Experiment
Bibliographic record
Abstract
Animal wastes may be promoted as an alternative to mineral fertilizers that remain unaffordable to the overwhelming part of smallholder farmers in Sub-saharan Africa. However for an efficient use, mechanisms that underly their impact on crops should be well understood. This study was conducted in mesocosm to evaluate impacts of two ways of composted poultry litter (CPL) addition on growth and nutrient use efficiency by cucumber. It included three treatments with five-bucket replicates each: Control, CPL applied on soil surface (CS) or buried to 10 cm-depth (CB). Dry CPL was added at the rate of 0.5 kg bucket-1. At harvest, root distribution was examined in the 0-5, 5-10 and 10-20 cm depths. Dry biomasses of roots, shoot and fruits were also determined and allowed for calculation of diverse indexes of biomass allocation (root:shoot ratio, root weight ratio, stem weight ratio, leaf weight ratio) and nutrient use efficiency (factor productivity of the compost, partial factor productivity of nutrients, agronomic efficiency of compost, and apparent agronomic efficiency of nutrients). The results showed that application of CPL led to a significant improvement of all considered parameters except for the leaf weight ratio which was higher in the control (44.1±3.3) than in CS (28.1±1.9) and CB 31.2±3.5). Total lateral root length was significantly higher in CS than in CB (113.5±10.7 cm vs. 75.5±9.0 cm). The number of lateral roots per plant in the 0-5 cm soil layer was higher in CS than in CB (5.4 vs. 1 root plant-1); the reverse was observed in 5-10 cm (1.2 vs. 4.4 root plant-1). Both fresh fruit yield and total dry mass were positively correlated to root attributes. These were themselves negatively impacted by soil acidity. All nutrient use efficiency indexes were higher in CS than CB. The CPL improved the agronomic performance of cucumber particularly when applied at soil surface.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".