Nutrient‐charged biochars increased nutrient‐use efficiency in a cotton–maize rotation in Burkina Faso
Bibliographic record
Abstract
Abstract Soil nutrient depletion, low crop nutrient‐use efficiency, and limited access to fertilizers are serious issues leading to poor yield in agroecosystems of Burkina Faso. Blending biochars with organic or inorganic nutrients could slow down nutrient release, which can enhance fertilizer‐use efficiency. This study investigates whether charging biochar with nutrients and Acacia gum ( Acacia senegal L.) coating can improve nutrient‐use efficiency in a cotton ( Gossypium hirsutum L.)–maize ( Zea mays L.) rotation system using low application rates (<5 t ha −1 ). The experiment was conducted in western Burkina Faso during three cropping seasons (2018–2020) with five treatments: T0, control; T1, conventional practice with compost and nitrogen (N), phosphorus (P), and potassium (K) application; T2, microdose (planting hole application) of NPK; T3. nutrient‐charged biochar activated with dissolved NPK; T4, nutrient‐charged biochar activated with dissolved NPK and coated with Acacia gum. Biochar‐based fertilizers significantly increased soil organic C content, as well as bioavailable P and calcium (Ca), without significantly improving crop yields, compared to conventional practice and microdose of NPK. When nutrient‐charged biochar was coated with Acacia gum, N‐use efficiency (NUE; N recovered in grain/N fertilized) was improved compared to uncoated nutrient‐charged biochar, although the difference was not significant. Despite no significant effects on crop yields during three cropping seasons, the use of coated biochar‐based fertilizer may be a technically effective solution to improve NUE in the long term in agroecosystems characterized by nutrient‐impoverished soils under a constraining South‐Sudanese climate.
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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 | 0.001 | 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".