Evaluating Nutrient Removal and Use Efficiency in Pearl Millet (<i>Pennisetum glaucum </i>L.) through Arbuscular Mycorrhizal Fungi Inoculation and Fertilizer Microdosing in Sahelian Sandy Soils
Bibliographic record
Abstract
Pearl millet (Pennisetum glaucum), a staple cereal in the Sahel, faces severe yield limitations due to sandy, nutrient-poor soils and erratic rainfall. Sustainable fertilization strategies that enhance nutrient use efficiency are urgently needed. This study evaluated the combined effects of arbuscular mycorrhizal fungi (AMF) inoculation and fertilizer microdosing on nutrient removal and agronomic efficiency in pearl millet grown in Sahelian sandy soil. A greenhouse experiment with nine treatments combining NPK, urea, cow manure, and AMF was conducted using soil pots arranged in a completely randomized design with five replicates. Nutrient removal (N, P, K, Ca, Mg, Cu, Mn, Fe, Ni) and shoot dry biomass were measured after 12 weeks, and agronomic efficiency (AE) was calculated for N, P, and K. Treatments significantly affected nutrient removal (p < 0.001). AMF alone enhanced micronutrient removal, particularly Mn (296 mg/kg) and Fe (550 mg/kg), while manure + AMF increased macronutrient removal (N ≈ 17 mg/kg, K ≈ 81 mg/kg). Moderate NPK microdosing with AMF achieved the highest AE values (AEN = 6.7 g/g; AEP = 15.8 g/g; AEK = 15.8 g/g). High-input treatments increased nutrient removal but reduced efficiency. This controlled environment study demonstrates that AMF-based microdosing is a promising strategy to improve nutrient efficiency in pearl millet grown on Sahelian sandy soils.
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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".