Effects of organic and inorganic fertilisation on soil nutrient dynamics in a Savannah region (DR Congo)
Bibliographic record
Abstract
Very few studies have considered the residual effect of inorganic and organic fertilisation on oxisol chemistry and plant productivity while studying the response of maize to fresh fertiliser application. The purpose of the present study was to examine the effects of five treatments of inorganic and organic fertilisers on maize productivity and soil fertility under field conditions in a Savannah region. The trials were conducted from 2009 to 2011 and the treatments included inorganic nitrogen–phosphorus (NP), Entada abyssinica, Tithonia diversifolia, inorganic NP combined with E. abyssinica, and inorganic NP combined with T. diversifolia, and no fertilised plots. The combination of NP with T. diversifolia or E. abyssinica leaves resulted in the highest increase in grain yields during the first crop season.The positive residual effects of fertilisers on maize grain yield were strong and significant in the immediate succeeding season and decreased during the second year following fertilisation. The highest residual effect was observed in plots fertilised with E. abyssinica combined with NP, followed by T. diversifolia combined with NP, and E. abyssinica and T. diversifolia without NP, respectively. The lowest residual effect was observed in plots fertilised with inorganic NP. Analytical results revealed that total nutrients in the soil matrix are not dominantly in forms available for plant uptake. There were significant residual effects for total potassium in the site with low initial nutrient levels. The soil content of bioavailable phosphorus, nickel, zinc, copper, and cobalt was in general below detectable levels.
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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".