Enhancing Hot Pepper Agronomic Performance Under Rainfed Conditions Through Organo-Mineral Fertilization in Senegal
Bibliographic record
Abstract
Capsicum (Capsicum spp.), commonly known as pepper, is highly sensitive to climate change. It is an important vegetable and spice crop cultivated worldwide for its di-verse uses, including fresh consumption. In Senegal, it plays a crucial role in both household nutrition and income generation. This study aimed to enhance hot pepper agronomic performance through adaptable, high-yielding technologies in the Tambacounda and Sedhiou regions of eastern and southern Senegal. Five treatments were tested: Absolute Control (T1), Farmer Practice (T2), Microdose fertilizer (T3), Micro-dose + Compost (T4), and Microdose + Compost + Mulching (T5). Trials were conduct-ed in 2025 at the Tambacounda Agriculture Research Center (CRAT) and Ziguinchor Agriculture Research Center, Djibelor, under both rainfed and supplemental irrigation systems, using a randomized complete block design with three replications. Results revealed significant treatment and site differences. At CRAT, T5 (Microdose + Compost + Mulching) achieved the highest yield of 31.6 t/ha, while the lowest was 10.1 t/ha. In Djibelor, overall yields were lower, not exceeding 2 t/ha across treatments. The findings demonstrate that integrated fertilization with compost and mulching substantially improves hot pepper productivity, with outcomes strongly dependent on site-specific ecological conditions. Promoting this practice as a bundled package in Tambacounda and similar agroecological zones could enhance yields and strengthen smallholder farmers’ socio-economic resilience amid changing weather.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; both teacher heads agree on what is shown here.
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".