Nutrient Management Strategies on Heterogeneously Fertile Granitic‐Derived Soils in Subhumid Zimbabwe
Bibliographic record
Abstract
Maize (Zea mays L.) is the staple food in southern Africa, but low soil fertility and lack of effective fertilization strategies for variable soil conditions hamper efficient use of nutrient resources. The objective of this study was to establish the influence of soil fertility heterogeneity on maize yield response to manure, liming, and inorganic fertilizers. Three sites, selected to represent three soil fertility domains based on soil organic carbon (SOC) between 3.5 to 8.9 g SOC kg−1 soil, were used during two cropping seasons. Nitrogen, P, K, and S were applied alone (NPKS) or in combinations involving lime, cattle manure, and micronutrients. Grain and stover were analyzed for nutrient uptake, and agronomic efficiencies were computed for N and P. Across sites, maize grain yields increased with increasing SOC. In Year 2, lime was the most important component for increasing maize yields in low SOC soil. For the medium and high SOC soils, treatments with NPKS + manure resulted in the best efficiencies. Although soil pH was low in these fields as well, lime which was applied only in Year 1, did not improve yields either year. Maize yields and nutrient uptake were strongly affected by SOC content, with yields for a site with 3.5 g SOC kg−1 soil significantly lower than the two sites that had >5.3 g SOC kg−1soil. We conclude that farmers must strategically target their limited nutrients resources to fields that are not yet degraded and maintain soil fertility to guarantee returns to fertilizer investments.
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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 | 0.001 |
| 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".