Optimizing safe rates of pop-up inorganic starter nitrogen and potassium fertilizers for maize
Bibliographic record
Abstract
Applying starter nitrogen (N) and potassium (K) fertilizers in a pop-up placement directly in the maize seed furrow is a delicate practice due to the direct contact between fertilizers and seeds. This proximity increases the risk of seed damage caused by the salinity of N and K fertilizers and the ammoniacal toxicity of nitrogen fertilizers. This study aims to determine the safe application rates of four commonly used starter fertilizers: monoammonium phosphate (MAP: NH4H2PO4), diammonium phosphate (DAP: (NH4)2HPO4), potassium chloride (KCl), and potassium sulfate (K2SO4) across three soil textures: fine (G1), medium (G2), and coarse (G3). A greenhouse experiment was conducted using a three-factor factorial design (four fertilizer sources, five application rates, and three soil textures) arranged in a randomized complete block design. ANOVA revealed significant effects of fertilizer source, application rate, soil texture, and significant two-way interactions between these factors. Polynomial contrasts of maize germination rates in response to increasing fertilizer doses allowed us to establish the maximum safe rates: i) DAP: 3 kg N ha-¹ in G1, 0.8 kg N ha-¹ in G2 and G3; ii) MAP: 5–7 kg N ha-¹ regardless of soil texture; iii) KCl: 10 kg K ha-¹ in G1, 14 kg K ha-¹ in G2 and G3; K2SO4: >16 kg K ha-¹ regardless of soil texture. The experiment also identified visual signs of toxicity, mainly associated with nitrogen fertilizers. These included delayed and reduced emergence, leaf chlorosis, necrotic roots and seeds, stunted and grooved coleoptiles, and, at high doses, seedling mortality. Other quantitative performance indicators, such as shoot and root biomass, chlorophyll readings, and early vigor, were strongly correlated with germination rates and supported the same conclusions regarding safe fertilizer rates. These findings provide practical recommendations for agronomists and farmers to optimize starter fertilizer management in maize by selecting appropriate application rates and fertilizer sources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".